Technologies doi: 10.3390/technologies14100640

Authors: Shuping Xue Yuanyuan Wang Danyang He Shuwei Zhang Lei Jia Yun Yang

The multilayer composite structure of waste toothpaste packaging hinders its effective recovery through conventional sorting and recycling processes. In this study, a flexible single-electrode triboelectric sensor based on waste toothpaste tubes (WTT sensor) was fabricated by functionally repurposing and integrating different components of toothpaste packaging waste. The aluminum–plastic composite film and aluminum foil recovered from discarded toothpaste tubes constituted the core triboelectric functional unit, with the aluminum foil also serving as the conductive electrode, while cardboard from the outer toothpaste box was used to construct an elastic spacer. At an excitation frequency of 1 Hz, increasing the applied force from 10 to 300 N raised the open-circuit voltage from approximately 15 to 40 V and the short-circuit current from approximately 0.1 to 0.3 μA. The output voltage exhibited a piecewise linear relationship with the applied force, with sensitivities of  0.391 VN−1 below 50 N and 0.024 VN−1 above 50 N. The output signal remained relatively stable over 1000 consecutive contact–separation cycles. The WTT sensor was attached to the heel region of an athletic shoe to acquire distinct electrical signals corresponding to five types of human motion: tiptoe standing, jumping, squatting, walking, and running. Subsequently, 15 features encompassing time-domain, peak-related, and frequency-domain characteristics were extracted and used in conjunction with an optimized ExtraTrees model for motion pattern recognition. To minimize the risk of data leakage, Complete motion recordings were first assigned exclusively to either the training or test set at a ratio of 7:3; all action samples segmented from a given recording were kept in the same subset to avoid data leakage. The optimized model achieved an overall classification accuracy of 96.18% on the independent test set. In addition, the WTT sensor was used as a self-powered input unit to wirelessly control multiple movements of a remote-controlled car. This work provides a simple and feasible upcycling strategy for converting difficult-to-recycle toothpaste packaging waste into value-added flexible smart sensing devices.

Technologies doi: 10.3390/technologies14100639

Authors: Hafiz Abdul Wajid Ali Tufail Aabid A. Mir

Transparent and efficient water management is becoming a necessity for smart cities worldwide. Recently, technological advancements in water system infrastructure are helping address modern challenges for sustainable water management, particularly in densely populated cities experiencing rapid urbanization. Smart water metering (SWM) systems are increasingly deployed because they generate detailed consumption data and enable early identification of system-wide water losses. However, privacy and security risks, including unauthorized ownership, data manipulation, and tampering, can create mistrust between utilities and citizens, especially when combined with issues such as system-wide water loss, unhealthy physical water infrastructure, and billing inaccuracies. This study introduces a conceptual framework that explores how non-fungible tokens (NFTs) might contribute to more transparent and trustworthy SWM operations. In this approach, each meter is assigned a unique NFT-based digital identity, allowing installation records, maintenance logs, ownership status, and usage history to be represented in a structured and traceable way. The framework is compared with existing identity and asset-representation mechanisms, including PKI, DIDs, and non-tokenized blockchain registries. A simple data flow model illustrates how NFTs could function as digital identities for physical meters, and the role of digital water governance in authorizing token issuance is discussed. These ideas are conceptual rather than operational; they outline how meter-related metadata could become more auditable and interoperable across stakeholders. This paper also proposes a phased pilot pathway as a future validation plan, together with an illustrative evaluation framework to guide pilot design, along with potential security challenges and conceptual mitigation strategies. Moreover, the framework highlights how NFT-enabled identities and smart contracts may support improvements in transparency, accountability, and user trust in SWM systems. It is intended as a foundation for future empirical evaluation in smart-city contexts, not as evidence of a deployed or validated solution.

Technologies doi: 10.3390/technologies14100638

Authors: Anawin Khametong Chanon Warisarn Simon J. Greaves Pornchai Supnithi

This research presents a non-uniform track configuration in which the main track is twice as wide as its adjacent tracks, enabling high-quality soft information and enhancing adjacent-track detection via low-density parity-check (LDPC)-based turbo decoding. We then propose an iterative joint a priori probability (JAPP) detection method for turbo decoding in non-uniform track systems, in which highly-reliable information from the wider track is fed back to improve the reliability of adjacent track detection in the branch metric calculation step. Moreover, to maximize robustness against inter-track interference (ITI), the reader offset is also optimized to achieve a better bit-error rate (BER) performance. The simulation results demonstrate that non-uniform iterative JAPP configurations significantly outperform conventional uniform configurations, even at higher areal densities, achieving substantial BER improvements at an optimal reader offset of 20%.

Technologies doi: 10.3390/technologies14100637

Authors: Emmanuel Ahatsi Oludolapo Olanrewaju

Telecommunication base transceiver stations (BTS) require power-source switching and thermal-management logic that remains predictable and checkable under grid disturbances, generator operation and thermal excursions. Existing microcontroller-based BTS site-management implementations, including the ATmega32-based MBSMECS architecture that motivates this study, provide the basis for the present formal analysis, describe this logic operationally as narrative relay behaviour rather than as an explicit verifiable control model. This paper reformulates the switching and thermal-control logic of that implemented system as a two-layer formal control framework: a finite-state model that represents grid-supply, generator-start-permissive, generator-supply, restoration, transfer and shutdown conditions as explicit states and transition conditions and a Boolean interlock layer that expresses three-phase grid availability, the generator-start permissive and source exclusivity as logical expressions. A symbolic lumped-parameter thermal model with hysteresis switching is developed to represent the thermostat-driven air-conditioner allocation logic and is treated as a discrete-event process coupled to, rather than separate from, the power-switching automaton. Component-level sizing calculations from the original design are retained as supporting engineering-consistency checks rather than as the paper’s central contribution. The framework is exercised through eleven structured operating and fault scenarios and summarized in a nine-property formal verification table that distinguishes properties satisfied by construction (source exclusivity, generator lockout, thermal-threshold activation) from properties the source design leaves unresolved (partial-phase-loss continuity, thermostat-interrupt exit behaviour, and alarm/thermal concurrency). No new experimental or simulated hardware data are introduced; all quantitative illustrations are explicitly labelled as hypothetical. The contribution is a transparent and reusable formal representation of the switching and thermal-control logic, distinct from the underlying hardware implementation that supports design review, verification and future extension of this class of BTS control system.

Technologies doi: 10.3390/technologies14100636

Authors: Jamal Boussouf Ismail Lamaakal Ibrahim Ouahbi Khalid El Makkaoui Mounia Zaydi Belhassen Zouari

Backdoor attacks pose a serious threat to small language models (SLMs) because compromised models can behave normally on benign inputs while producing attacker-specified outputs when a hidden trigger is activated. Existing defenses commonly require model retraining, operate only before deployment, rely on input-level signals, or incur excessive inference overhead. This paper presents AEGIS (Activation Evaluation and Guardrail for Inference Security), a non-invasive runtime framework for detecting backdoor-induced anomalies in transformer representations. AEGIS dynamically monitors equidistant internal layers, mean-pools and concatenates their hidden states, and compresses the resulting high-dimensional representation into a 64-dimensional latent space. A hybrid compression mechanism uses zero-shot principal component analysis for predominantly linear text representations and a lightweight autoencoder for nonlinear or multimodal representations. Detection is then performed entirely on the GPU using cosine distance from a centroid calibrated on clean data, without modifying or retraining the protected model. We evaluate AEGIS across Mistral-7B, Qwen2.5-7B, a 4-bit QLoRA-backdoored Qwen2.5-1.5B model, and a BadNets-backdoored Vision Transformer, covering simulated latent and PEFT-style attacks, genuine fine-tuned backdoors, FP16 inference, and 4-bit NF4 quantization. Across the main optimized shield-overhead experiments, AEGIS achieves AUROC values between 0.95 and 1.00, true-positive rates between 86% and 100%, and prefill-stage detection latency between 0.47 and 2.40 ms. In the dedicated T4 deployment stress tests, which measure the full guardrail overhead under edge and real QLoRA settings, latency ranges from 5.20 to 7.77 ms with combined model-plus-guardrail memory between 2.59 and 4.02 GB. AEGIS obtains AUROC = 1.00 and 100% true-positive rate against the genuinely fine-tuned QLoRA backdoor, while achieving AUROC = 0.997 against the fine-tuned visual BadNets attack. Under 4-bit quantization, it retains an AUROC = 0.95 with a 4.02 GB memory footprint. These results show that a compact GPU-native implementation of activation-space monitoring can provide effective, retraining-free backdoor detection for real-time transformer inference.

Technologies doi: 10.3390/technologies14100635

Authors: Hamza Reguieg Essaid Sabir Mohamed El Kamili

Federated learning must accommodate statistical heterogeneity, costly client participation, and distinct fairness objectives for model performance and participation frequency. We introduce Fair Bayesian Stackelberg Federated Learning (FBS-FL), in which a server maintains beliefs over private client cost types and selects stochastic inclusion probabilities and incentives under an expected client-inclusion constraint. The framework combines an analytical cost-threshold response model with a scalable PPO–EXP3 behavioral implementation using approximate belief tracking. We evaluate FBS-FL over five seeds on CIFAR-10, FEMNIST, and Shakespeare. FBS-FL obtains Jain indices from 0.91 to 0.93 and KL divergences to uniform participation from 0.07 to 0.09, compared with 0.81 to 0.86 and 0.16 to 0.22 for the strongest baseline values on the same participation metrics. The evaluation jointly examines participation fairness, mean accuracy, and worst-client performance. The results indicate improved participation balance with small accuracy variation under the reported protocol.

Technologies doi: 10.3390/technologies14100634

Authors: Andres Valle-Gonzalez Maryam Carrillo-Reales Adalberto Ospino-Castro Diego Restrepo-Leal Carlos Robles-Algarín

High-resolution atmospheric modeling over topographically complex coastal zones requires multi-source spatial data fusion to bridge the scale gap between satellite-assimilated global reanalysis and localized boundary layer dynamics. This study presents a spatial data fusion framework coupling convection-permitting Weather Research and Forecasting (WRF) simulations (3 km grid spacing) with machine learning calibration and GIS multi-criteria decision analysis across continuous computational grids (24,871 cells). Focusing on a high-resolution seasonal case study during the peak Caribbean Low-Level Jet (CLLJ) and Trade Winds regime along the Colombian Caribbean coast, predictive algorithms were benchmarked across day-grouped and Leave-One-Station-Out (LOSO) spatial transfer protocols against classical meteorological baselines. Support Vector Regression (SVR) with an RBF kernel achieved superior residual calibration (R2=0.9523, RMSE=0.4601 m/s) under day-grouped cross-validation, and an average Leave-One-Station-Out (LOSO) spatial transfer RMSE of 1.1030 m/s under blind geographic transfer to unmonitored stations, corresponding to a >76% RMSE reduction relative to raw, uncalibrated WRF output in that same spatial-transfer setting. Chronological forward-forecasting tests further showed that this seasonally augmented kernel model does not extrapolate reliably to calendar months absent from training (R2=−0.09), so a reduced 8-variable configuration is recommended for genuine forward forecasting into unseen seasons. Scalar calibrations were transferred to horizontal wind vectors preserving simulated flow direction, and vertically scaled to 80 m hub height using WRF prognostic shear. Calibrated fields were integrated into a Boolean GIS-MCDA model incorporating wind power density, slope limits, environmental/indigenous reserves, and infrastructure proximity. The framework delineated an 8.9% high-feasibility macro-corridor (2214 cells; ≈19,926 km2) in northern La Guajira, while substantially reducing uncalibrated mesoscale overestimation in the sheltered wake of the Sierra Nevada de Santa Marta. The methodology provides a robust screening-level tool for coastal renewable resource assessment.

Technologies doi: 10.3390/technologies14100633

Authors: Fei Chen Bing Guo Yan Shen Jian Xu Mingjie Zhao Xin Chen Junnan Li

Temporal knowledge graph forecasting aims to predict future missing facts from continuously evolving relational data in dynamic information systems. Such forecasting tasks are increasingly important for intelligent applications involving complex interactions among entities, events, and evolving environments. Existing approaches typically employ unified architectures that process all queries through the same representation pipeline, despite substantial variations in structural connectivity, historical availability, and semantic information among different queries. Such a homogeneous modeling strategy may limit the ability to effectively handle diverse forecasting scenarios, including highly connected entities, recurrent temporal patterns, and cold-start cases with insufficient historical evidence. To address this challenge, we propose a Heterogeneous Modality Mixture-of-Experts (H-MoE) framework that adaptively selects specialized reasoning pathways according to query characteristics. Unlike conventional mixture-of-experts architectures with homogeneous subnetworks, our framework incorporates three complementary experts with distinct inductive biases: a time-aware graph neural network for structural relational reasoning, a temporal transformer for historical sequence modeling, and a semantic representation adapter based on pretrained language models for knowledge transfer in sparse scenarios. A query-aware gating network is introduced to dynamically allocate computational resources among different experts according to the available structural, temporal, and semantic evidence. Furthermore, we employ entropy-based routing regularization to encourage confident expert selection while maintaining balanced expert utilization, together with a representation diversity constraint to promote complementary feature learning among heterogeneous reasoning pathways. Experiments on three benchmark temporal knowledge graph datasets indicate that the proposed framework can improve forecasting performance under the evaluation setting used in this study. The largest observed gains occur for queries with limited historical evidence. These findings support heterogeneous expert specialization as a useful design direction, while the scope of the conclusions remains limited to the tested ICEWS benchmarks and implementation.

Technologies doi: 10.3390/technologies14100631

Authors: Jorge Ortíz-Marín Guillermo Adolfo Anaya-Ruiz Gilberto Alejandro Herrejón-Pintor Vicente Venegas-Rebollar Edgar L. Moreno-Goytia

Current applications in photovoltaic power generation and energy storage require isolated, efficient, and reliable DC-DC converter topologies. However, the design of the nanocrystalline-core medium-frequency transformer (MFT), as a key element of these isolated topologies, often overlooks the impact of specific operating conditions, such as non-sinusoidal input signals and variations in total harmonic distortion (THD). This work introduces an innovative design method customized for a high-efficiency Central-Tapped medium-frequency transformer (CT-MFT) integrated into a SiC-based central-tapped phase-shift full-bridge (CT-PSFB) converter. Unlike earlier proposals, this work considers key parameters in the transformer design, including non-sinusoidal excitation waveforms and changes in THD during converter operation. After the CT-MFT is designed, the CT-MFT/CT-PSFB structure is simulated and validated using a tailor-made prototype. The experimental results confirm the structure’s effectiveness: the CT-MFT achieves 99.65% efficiency, and the CT-PSFB achieves 94.7% average efficiency, with THD up to 22.5% and 97.66% peak efficiency.

Technologies doi: 10.3390/technologies14100632

Authors: Dandan Wang Yanping Lu Hongyan Liu Jingzheng Dong

Carbon emission prediction of coastal port-industrial zones integrates industrial production, maritime logistics and spatial environmental governance. Existing models fail to simultaneously capture multi-scale temporal fluctuations and spatial correlations of industrial units under meteorological and policy interference. This paper constructs an interpretable dual-branch framework combining Ensemble Empirical Mode Decomposition–XGBoost (EEMD-XGBoost) and a Temporal Graph Neural Network (T-GNN). The EEMD-XGBoost branch extracts multi-scale temporal features from non-stationary emission sequences, while the T-GNN models spatiotemporal dependencies of industrial sub-units; weighted fusion integrates the two branches, and the framework outputs interpretable indicators including feature importance, spatial contribution and temporal attention weights to analyze emission driving factors. Two 2023 datasets are adopted: a coastal-port prefecture-level subset derived from the national regional carbon dataset (28 coastal port-related prefectures), split chronologically into 70% training and 30% test sets; the Yangtze River Delta ship dataset integrating AIS, ship properties and fuel data is partitioned via tonnage-based stratified sampling into 7:2:1 training–validation–test subsets, with an independent test subset for short-term ship-type forecasting. On the independent ship test set for maritime greenhouse gas prediction, the proposed model achieves an R2 of 0.96, 0.95 and 0.93 for container, bulk and oil ships respectively, which only applies to ship-scale forecasting rather than regional carbon prediction. Special ablation experiments show that single T-GNN converges within 72 epochs, single EEMD-XGBoost has a high-frequency fitting error of 6.82%, and the complete dual-branch model reaches a spatial feature capture rate of 94.65% with only a 3.47% fitting error, despite a 27.52 ms single-sample inference time, proving the synergy of the two branches. The model outperforms baselines under abnormal and sparse data conditions. This interpretable framework supports traceable refined carbon management and provides quantitative engineering implications for port zoning control, differentiated ship emission reduction and regional low-carbon policy implementation.

Technologies doi: 10.3390/technologies14100630

Authors: Ronnie Concepcion Jeanette Pao Rhen Anjerome Bedruz Ronnel Agulto Jared Jan Abayan Edza Aria Wikurendra

Natural and anthropogenic disasters are rising in frequency and severity, and reactive, single-technology response models are now the principal bottleneck in safeguarding planetary health. This article develops Hyperautomated Hazard Mitigation (HHM), in which robots, unmanned aerial vehicles (UAVs), and artificial intelligence (AI) services operate as one integrated, self-optimizing system rather than as isolated tools. Hazard mitigation is treated as a specific resilience behavior of a multi-agent engineering system, building on established proactive-resilience theory rather than presenting that framing as new. This framework-oriented review synthesizes evidence and proposes an architecture. It reports no new experimental validation. It maps hazard-mitigation functions onto the six layers of the Reference Architectural Model Industrie 4.0 (RAMI 4.0) with the standards and interfaces that realize each layer; classifies robotic and UAV functions across natural and anthropogenic hazards using one set of four capability classes; expresses ethical and regulatory requirements as quantified engineering constraints; operationalizes health- centered evaluation metrics; and appraises the maturity of the evidence in each hazard domain. It concludes that the architecture is tractable with existing standards, that the actuation stage is the least mature and least evaluated, and that the decisive gap is evaluative: the field has no shared benchmark and rarely reports health outcomes.

Technologies doi: 10.3390/technologies14100629

Authors: Šundov Grbić Vučetić Milin

Elevated string-rail systems, in which unmanned electric vehicles run on steel wheels along a prestressed rail carried on slender supports, have been proposed as a way of moving containers between a quay and an inland terminal without using the road network. Their developers claim gains in ship handling speed, terminal land use and emissions at the same time, but none of these claims has been tested against measured port performance. This study assesses such a link at eight European container ports handling between 0.4 and 14.2 million TEU. The assessment combines two distinct components: a discrete-event simulation of the quay interface, calibrated against the Container Port Performance Index to within 1.5% of reported ship time at berth, and separate analytical calculations of required yard area, emissions and cost. The simulation covers the crane and horizontal-transport interface only; stacking, retrieval, reshuffling and yard-crane operations are outside its boundary. Internal model behaviour is benchmarked against independently published terminal operating figures, and the logic, inputs and scenario behaviour of the model were assessed by four specialists in container terminal operations. Because no published source gives the energy use of the vehicle, it is derived from physics, specifically from rolling and aerodynamic resistance, as 0.482 kWh per TEU-km. On an analytical capacity calculation, removing half of the road-borne inland volume would reduce required stacking area by 7.7 to 31.6%, equivalent to between 4.2 and 108.6 hectares of terminal area under a stated proportionality assumption, and a conditional comparison of emission factors gives a reduction of 60 to 97% on the diverted traffic alone. Avoided road haulage gives a positive annual balance at seven of the eight ports, with simple payback of 3.5 to 5 years at the five largest diverted flows. The benefit depends on how much of a port’s traffic actually leaves by road: gateway ports with heavy road use gain most, while a transhipment hub such as Piraeus gains least despite its size. Ship handling improves by at most 3.8%, well below the 5 to 10% claimed, and the improvement comes from avoiding the drive across the yard rather than from a faster crane cycle. Measured against the price of carbon, the investment is poor value, but measured against the cost of road haulage, it is not, which suggests such links should be planned as land and traffic infrastructure rather than as an emissions measure.

Technologies doi: 10.3390/technologies14100628

Authors: Richard Amesimenu Johnson Nwogu Haijun Gong

3D-printing via masked stereolithography (mSLA) enables the fabrication of complex polymer components with high resolution and excellent surface quality. However, mSLA-printed parts often exhibit incomplete polymerization in their as-printed (green) state, resulting in reduced mechanical performance and structural stability. Ultraviolet (UV) post-curing is therefore essential to enhance the degree of conversion and improve material properties. In this study, the effects of UV post-curing time on the mechanical and thermal behavior of mSLA-printed elastic photopolymer resin were investigated. Specimens were fabricated using a mSLA 3D printer and subjected to varied post-curing time under controlled conditions. Characterization was carried out using differential scanning calorimetry (DSC), uniaxial tensile testing (ASTM D412 Type C), and hardness testing (Durometer Type-M). The results indicate that increasing post-curing time promotes further polymerization and improves polymer network. Consequently, improvements in tensile properties, such as elastic modulus, and surface hardness, were observed. However, extended curing may lead to reduced ductility due to increased network rigidity. Overall, the study establishes a post-curing and property relationship and demonstrates the effectiveness of post-curing in improving stiffness. These findings provide practical insights for optimizing post-processing conditions in mSLA-based 3D-printing of elastic photopolymer materials.

Technologies doi: 10.3390/technologies14100627

Authors: Mladen Bošnjaković Marko Katinić

The quality of welded joints is critical to the safety, reliability, and durability of engineering structures in manufacturing, energy, transportation, and shipbuilding. Advances in non-destructive testing (NDT) have enabled the collection of large digital datasets, creating new opportunities for artificial intelligence (AI) in automated weld quality assessment. This paper reviews recent research on AI-based quality control of welded joints, focusing on machine learning, deep learning, computer vision, and real-time monitoring. A systematic review and critical analysis of recent scientific literature examines weld imperfections, major NDT methods, digital datasets, and AI performance in defect detection, classification, segmentation, and quality prediction. The findings show that deep learning architectures, including convolutional neural networks, object detection and segmentation models, hybrid frameworks, and transfer learning, can significantly improve the speed, consistency, and objectivity of weld inspection while achieving high detection performance across different NDT modalities. However, several challenges limit industrial implementation, including insufficient data quality and availability, class imbalance, domain shift, limited model generalization, the lack of standardized benchmark datasets, and the need for explainable and trustworthy AI that complies with industrial standards and regulatory requirements. Unlike previous reviews focused primarily on individual inspection methods or specific AI techniques, this paper integrates advances in NDT, AI, explainable artificial intelligence, and intelligent manufacturing into a unified perspective, highlighting their interrelationships and practical implications for industrial deployment. The findings indicate that AI should support, rather than replace, welding and NDT experts. Future research should focus on multimodal sensing, explainable AI, digital twins, standardized datasets, and real-time intelligent quality control systems for Industry 4.0 and smart manufacturing.

Technologies doi: 10.3390/technologies14100626

Authors: Rustem Kaiyrov Zhumadil Baigunchekov Azamat Mustafa Med Amine Laribi Tanin Alibek Aida Toleuishova Algazy Zhauyt Yerik Nugman Mukhagali Sagyntay Nurtay Albanbay

Optical systems for unmanned aerial vehicle (UAV) detection and tracking are predominantly deployed on stationary platforms, which limits operational mobility and increases the vulnerability of fixed installations under adverse operating conditions. To address this limitation, this study proposes a novel 6+3-RRS+RR hybrid robotic system for UAV detection and tracking from a mobile ground vehicle. The system integrates a 3-RRS parallel manipulator that compensates for vertical displacement, roll, and pitch of the vehicle body with a two-axis pan–tilt mechanism that provides independent camera pointing. A unified kinematic model is developed using homogeneous transformation matrices. Analytical inverse-kinematic solutions are derived for the 3-RRS and pan–tilt mechanisms, whereas the forward kinematics of the parallel manipulator is solved numerically using the Newton–Raphson method. Jacobian matrices are formulated to establish velocity relationships and disturbance-compensation conditions. Serial and parallel singularities are analyzed using scaled Jacobian condition numbers. Numerical examples confirm the consistency of the forward and inverse kinematic solutions and determine generalized-coordinate ranges that avoid singular and near-singular configurations. The proposed architecture separates disturbance compensation from target pointing within a unified hybrid system and provides a kinematic foundation for future control-system development and dynamic disturbance compensation on mobile robotic platforms.

Technologies doi: 10.3390/technologies14100624

Authors: Bahar Memarpour Kimia Memarpour Kimia Shirini Sina Samadi Gharehveran Siamak Pedrammehr Hussain Mohammed Dipu Kabir

Advanced Persistent Threats (APTs) are particularly challenging for enterprise intrusion detection because they are time-evolving, distributed, and difficult to detect under changing network conditions. Conventional machine-learning-based intrusion detection systems (IDSs) often rely on node-level features and may be vulnerable to concept drift, topological changes, and severe class imbalance, potentially achieving high overall accuracy while overlooking rare but critical malicious activities. This study proposes a Graph-Aware Deep Reinforcement Learning (GADRL) framework that integrates Graph Neural Networks (GNNs), Double Deep Q-Networks (DDQNs), and Prioritized Experience Replay (PER) for adaptive APT detection. Network traffic is modeled as a temporal graph to learn spatial–temporal relational representations of communication patterns and neighborhood interactions. These representations are then provided to a DDQN-based threat-hunting agent, while PER prioritizes high-severity and rare attack experiences during training. The framework is evaluated using five temporally ordered snapshots of enterprise network traffic to examine its performance under evolving network conditions. On an unseen future snapshot, GADRL achieves 100.00% recall, outperforming both a Random Forest baseline and a graph-agnostic reinforcement-learning ablation. This improvement in recall is accompanied by increased false-positive alerts, indicating a deliberate trade-off that prioritizes minimizing missed intrusions over reducing false alarms. Overall, the results demonstrate that combining topology-aware graph representation learning with priority-aware reinforcement learning can improve adaptive detection of evolving APTs under temporally changing network conditions.

Technologies doi: 10.3390/technologies14100625

Authors: Calvin Ser Thong Seah Wai Leong Eugene Wong Harry Sze Li Lim Hiong Yap Gan

In-transit additive manufacturing (AM) offers the potential to continue point-of-need production while a host transportation platform remains in motion. Although fabrication has been demonstrated aboard moving maritime platforms, comparable experimental evidence during sustained transit aboard land-based platforms—specifically heavy wheeled vehicles—remains limited. This review examines whether differences in platform dynamic environments may contribute to this asymmetry through interactions between environmental excitation, installed-system dynamics, and fabrication response. A structured narrative synthesis integrates evidence from additive manufacturing, structural dynamics, transportation vibration, and environmental qualification, focusing on maritime vessels and heavy wheeled vehicles as representative platforms, with material extrusion as the principal analytical case. Maritime environments combine low-frequency global vessel motion with higher-frequency machinery, propulsion, hydrodynamic, and transient excitation, whereas heavy wheeled vehicles exhibit suspension- and body-related response alongside broadband road-induced vibration and transient events. Representative material-extrusion structural modes span approximately 10–80 Hz, creating opportunities for spectral coincidence with both environments through distinct excitation mechanisms. Such overlap indicates potential dynamic amplification rather than deterministic degradation, since response also depends on excitation magnitude, damping, transmissibility, boundary conditions, and process-specific disturbance coupling. An environment–structure–process framework is proposed to support vibration-aware characterization and qualification of in-transit AM systems.

Technologies doi: 10.3390/technologies14100623

Authors: Yiying Wei Minhan Xu Hui Wang Bingjun Liu Jun Xiao Zhengan Yang

Automatic recognition of driving behavior at the state and driver levels is important for personalized driver assistance, safety assessment, and actuarial modeling. However, TICC-style inverse covariance clustering relies on globally fixed regularization and switching penalties, which may be unreliable under cluster imbalance and insufficiently responsive to local changes in nonstationary driving behavior. This study proposes SATIC, a self-adaptive temporal inverse covariance clustering framework that integrates adaptive model estimation with adaptive temporal segmentation. In the M-step, a vectorized Newton solver for sparse inverse covariance estimation is combined with cluster-specific regularization. This design reduces solver runtime from 12.7 s to 7.2 s, and the iteration count from approximately 850 to 10–30. In the E-step, equal-weight fusion of continuity coefficients over 0.3–2.0 s produces time-varying switching penalties without trainable fusion parameters. Under an idealized squared-loss formulation, a preselected constant switching weight can incur minimax regret of Ω(T), whereas online gradient descent attains O(log T). The linear regret bound indicates that the cumulative excess loss of the fixed strategy may grow with sequence length and that its average regret need not vanish. In contrast, the logarithmic bound yields vanishing average regret for the online adaptive strategy. This result motivates an adaptive switching penalty that tracks local temporal variations. SATIC was evaluated against four baselines on OCSLab, UAH-DriveSet, NGSIM, and a real-vehicle dataset. On NGSIM, vehicle-level comparisons favored SATIC for the Calinski–Harabasz and Davies–Bouldin indices after false-discovery-rate correction. The raw p-values were 0.022 and 0.015, with rank-biserial correlations of 0.51 and 0.55, respectively. Additional experiments on the real-vehicle dataset evaluated its applicability under practical driving conditions, where its overall performance remained comparable to the baselines and varied across drivers. Exploratory analyses further related the inferred states to physics-defined driving categories and identified three interpretable driver groups. Overall, SATIC provides an interpretable adaptive framework for analyzing nonstationary driving sequences, although the magnitude of its empirical benefits varies across datasets, evaluation metrics, and drivers.

Technologies doi: 10.3390/technologies14100622

Authors: Mahdzir Jamiaan Chin Fhong Soon Kim Seng Chia Naznin Sultana

Photobioreactor (PBR) microalgae cultivation involves complex multivariate physicochemical interactions in which subtle disturbances may affect multiple parameters simultaneously before visible culture degradation occurs. Conventional monitoring approaches based on manual sampling and univariate threshold inspection are often inadequate for capturing these coupled temporal dynamics in cultivation environments. However, the relative effectiveness of different unsupervised anomaly detection (AD) methods for modeling complex multivariate environmental data remains insufficiently understood, highlighting the need for a systematic comparative evaluation. This study aims to evaluate three unsupervised AD models, namely Isolation Forest (IForest), One-Class Support Vector Machine (OC-SVM), and Local Outlier Factor (LOF), for monitoring C. vulgaris PBR cultivation using multivariate real-time IoT sensor data. The observations comprising oxidation-reduction potential (ORP), electrical conductivity (EC), potential of hydrogen (pH), and water temperature were collected and analyzed as multivariate time-series data. Percentile-based thresholds (P90, P95, and P99), derived from the normal training data, were evaluated to examine the trade-off between anomaly sensitivity and false alarm reduction. Within the threshold-sensitivity analysis, IForest achieved a recall of 0.9750 and an F1-score of 0.9656 at P95, together with an ROC-AUC of 0.9957 and a PR-AUC of 0.9959. Temporal anomaly profiling further revealed that anomaly clusters were concentrated during the adaptation and stationary growth phases. These findings demonstrate the potential of unsupervised AD models as practical tools for data-driven monitoring and anomaly profiling in microalgae PBR cultivation systems.

Technologies doi: 10.3390/technologies14100621

Authors: Dolores de Fez Ainhoa Molina-Martín Mª José Luque David P. Piñero

The aim of the present study was to develop a reference database in a healthy population for a test measuring color discrimination thresholds and contrast sensitivity, with the purpose of establishing reference values that contribute to optimizing their use in vision research and clinical assessment. All measurements were performed using four portable electronic display devices (iPads), each of which underwent a specific colorimetric characterization. Experimental measurements were carried out using the Optopad test battery, which includes a test for the assessment of color discrimination thresholds and a test for measuring chromatic and achromatic contrast sensitivity functions. Mean color discrimination and contrast sensitivity threshold values and the reference region defined by the 2.5th and 97.5th percentiles for age decades in the [10, 80] age range were obtained for healthy subjects. These results allow us to define normative intervals with potential diagnostic applications, and information about the mean values of the contrast sensitivity functions for the different age ranges can be used, together with a spatio-chromatic model, to predict age-related changes in visual perception.

Technologies doi: 10.3390/technologies14100620

Authors: Yang Shang Yucheng Huang Ziang Li

Reflective photoplethysmography (PPG) is sensitive to sensor–skin contact conditions. This engineering feasibility study examined synchronized relative-load voltage and Red/IR PPG in one participant using a MAX30101 sensor, nRF52833 microcontroller, and adjacent force-sensitive resistor (FSR). Five repeated trials followed a fixed low–medium–firm–medium-return sequence. Mean FSR voltages were 500.0 ± 22.1, 1179.8 ± 87.6, and 1996.6 ± 71.3 mV for the ascending stages. The 15 trial-stage summaries were repeated observations from the same participant. Higher FSR voltage was associated with lower IR AC/DC (within-trial r = −0.886; descriptive trial-fixed-effect slope, −0.0223 percentage points per 100 mV). Mean IR AC/DC was 0.560 ± 0.152% at low load, 0.218 ± 0.011% at firm load, and 0.381 ± 0.099% during medium return, compared with 0.330 ± 0.074% at the initial medium stage. Preliminary artifact controls documented setup-specific waveform changes. The adjacent FSR provides an uncalibrated spatial proxy rather than optical-site force or pressure. Fixed loading order prevents separation of load from time, adaptation, and loading history. These observations establish acquisition feasibility in the tested participant and arrangement, without establishing a causal pressure–response relationship, externally validated quality thresholds, or performance across users and devices.

Technologies doi: 10.3390/technologies14100619

Authors: Huimin Shen Mingfeng Jiang Jiangling Chen Xiaoyu He Dingchang Zheng Ling Xia

Sudden cardiac death (SCD) remains difficult to anticipate from routine risk stratification, and the reliability of electrocardiographic (ECG) predictions remains underreported. In this study, SCD prediction is investigated retrospectively using ECG segments sampled at predefined lead times before ventricular fibrillation (VF) onset. We propose ST-GODE-UQ, which represents 5-s ECG segments as frequency-band spectral-similarity graphs, performs continuous-depth latent learning with a graph neural ordinary differential equation (ODE), and combines heteroscedastic logit variance with Monte Carlo (MC) dropout. Evaluation used subject-wise grouped five-fold outer cross-validation in two complementary settings: a cross-database comparison of ECG segments recorded 30–35 min before VF with healthy normal-sinus-rhythm recordings, and a within-cohort temporal-control comparison of 30–35 and 80–85 min pre-VF segments from the same SCD subjects. ST-GODE-UQ achieved mean accuracies of 94.14% and 93.00% and mean ROC-AUCs of 94.78% and 93.60% in the cross-database and temporal-control evaluations, respectively. Its pooled out-of-fold ROC-AUC was numerically higher than that of the graph neural network (GNN) + Transformer baseline, although the paired difference was not statistically significant. Misclassified predictions showed higher median uncertainty across all three reliability measures, and controlled Gaussian-noise and baseline-wander perturbations progressively increased the aleatoric estimate. These findings support the feasibility of uncertainty-aware SCD prediction from predefined pre-VF ECG windows under subject-independent evaluation and motivate further validation in prospective and clinically heterogeneous settings.

Technologies doi: 10.3390/technologies14100618

Authors: Jibin Li Jun Han Jun Wang Yudi Ai Qi Lei

Multiaxial fatigue damage remains a critical barrier to the reliable design of metallic engineering components, particularly under complex non-proportional loading paths. Conventional critical-plane and energy-based models rely heavily on empirical assumptions and struggle to capture intricate path-dependent damage evolution, leading to limited accuracy and generalizability. To overcome these challenges, we propose a novel data-driven framework that integrates heterogeneous information sources through a three-branch parallel neural architecture. Specifically, a deep neural network (DNN) extracts static material intrinsic properties; a bidirectional gated recurrent unit (BiGRU) enhanced with a multi-head self-attention mechanism captures long-term temporal dependencies and adaptively weights key stages in loading histories; and a one-dimensional convolutional neural network (1D-CNN) extracts local strain fluctuation patterns. The three streams are fused to produce a comprehensive damage representation for end-to-end life prediction. The output of the model is the base −10 logarithm of fatigue life, i.e., lg(Nf). Evaluated on a public multiaxial fatigue dataset comprising 40 metals and 1167 samples, the proposed model achieves a coefficient of determination (R2) of 0.9052, achieving promising performance on a fixed data split (R2 = 0.9052); ablation studies demonstrate the complementary nature of the three feature extraction branches, though 5-fold cross-validation suggests that the performance gain requires further validation with larger datasets. Systematic ablation studies confirm that the multi-source fusion strategy and the attention mechanism are pivotal to the performance gain. This work offers a robust and effective data-driven solution for high-accuracy fatigue life assessment, and points toward future integration of physical knowledge to further enhance extrapolation and transparency.

Technologies doi: 10.3390/technologies14100617

Authors: Puttapong Somjai Rachasak Phanumpha Kittisak Khuwaranyu Duangkamol Ruen-ngam Paisarn Muneesawang

Peer-to-peer (P2P) energy trading in industrial settings is rarely validated with metered data. This paper studies the E-Infinity Ecosystem, coupling the X-Changer trading platform with Energinx IoT smart meters that limit reverse power flow (RPF) at the meter, deployed at a ceramic factory in Saraburi, Thailand. Contributions include (i) coupling meter-level export limitation with interval-level bilateral matching under conventional billing; (ii) an analytical bound on export above the admitted level; (iii) a proof that two-building allocation loses no energy relative to pooled metering; and (iv) metered evidence with uncertainty, economic, and benchmark analysis. Two PV buildings were metered at 10 min intervals over 118 days (January–April 2025): Building 1 generated 573,082 kWh (14.47% self-sufficiency) and Building 2 309,484 kWh (21.10%), with roles following the PV-to-load ratio. Estimated trading potential was 44,275 kWh (96.3% from Building 2 to Building 1), avoiding 185,953 THB at 4.2 THB/kWh. A 12-month dataset gave a 90.00 ± 0.33% matching rate and a ±7.8% seasonal interval on annual volume. Volumes are meter-based estimates; RPF suppression and control dynamics were not measured directly.

Technologies doi: 10.3390/technologies14100616

Authors: Saleha Bano Amna Shabbir Safdar Rizvi Nurashikin Saaludin

This paper presents a fully differential fourth-order low-pass filter (LPF) designed for ultra-low-power ECG acquisition systems. The design utilizes a dynamic threshold MOSFET (DTMOS)-based flipped voltage follower (FVF) topology to meet the stringent requirements of wearable and implantable biomedical electronics. By leveraging DTMOS input transistors and operating the circuit in the subthreshold region, the filter achieves extreme power efficiency, consuming only 1.35 nW from a 0.5 V supply. The proposed architecture was simulated using 150 nm CMOS technology in Cadence Virtuoso. Simulation results demonstrate a cutoff frequency of 132.68 Hz with 0 dB passband gain, making it ideal for capturing the clinical frequency range of ECG signals. The filter exhibits a dynamic range (DR) of 70 dB, an input-referred noise of 34.77 μVrms and a third-order harmonic distortion (HD3) of 65.605 dB at 50 Hz and 50 mV pk-pk input. The proposed filter achieves a superior figure of merit (FOM) compared to existing state-of-the-art nano-power filters, providing an optimal balance between power consumption, linearity, and noise performance, making it a highly effective solution for wearable health care monitoring devices.

Technologies doi: 10.3390/technologies14100615

Authors: Svetlana Beglerova Ainur Shekerbek Amir Orazbay Saule Zhumagulova Gulden Murzabekova Akmaral Kassymova Akzhol Tussipkhanov Azamat Dnekeshev Akbota Yerzhanova

This study proposes a method for probabilistic time-series forecasting on graphs incorporating interpolation (IA-GTPF)—a decision-tree-based ensemble approach that combines observation quality metrics, historical load dynamics, information on neighboring nodes, residual correction, quantile regression, and conformal calibration. The method’s performance was evaluated using the Open Power System Data Household dataset. Fixed time boundaries defined distinct periods for training, validation, calibration, and testing. Only information available at the time of forecasting was used as input, while graph weights and preprocessing parameters were determined solely based on the training set. A separate calibration period was used to adjust confidence interval boundaries without altering point forecasts. On the main test set, the IA-GTPF method demonstrated the following metrics for a 15-min forecast horizon: a Mean Absolute Error (MAE) of 0.9824 kW, a Root Mean Square Error (RMSE) of 2.5408 kW, and a Coefficient of Determination (R2) of 0.98947. For the 60-min horizon, the corresponding values were 1.3940 kW, 4.2017 kW, and 0.97122. The QuantileForest model showed a lower MAE for the 15-min horizon (0.9695 kW); however, IA-GTPF achieved the lowest RMSE and highest R2 values. For the 60-min horizon, the IA-GTPF method demonstrated the lowest MAE and RMSE values among all evaluated models.

Technologies doi: 10.3390/technologies14100614

Authors: Amor Smida

This work presents a four-element multiple-input multiple-output (MIMO) implantable antenna for 915 MHz biotelemetry, built on a Rogers RO3010 substrate with overall dimensions of only 10×10×0.1 mm3. Getting the size this small required a semi-hexagon meandered slot geometry, which extends the electrical path considerably without touching the outer footprint. The four antenna elements are placed on the four sides of the substrate with 90° rotational symmetry over complete ground plane. Isolation was the main challenge (without any decoupling structure, port-to-port isolation was just 13.94 dB). Adding a row of through-substrate vias along the diagonal, together with the naturally low surface-wave activity of the 0.1 mm substrate, pushed this to 31.8 dB. The antenna covers 0.83–0.992 GHz (162 MHz bandwidth), which is enough to include the full 915 MHz ISM band. In simulation and in measurement, each element gives a realized gain of −26.3 dBi inside a head tissue model; the measured figure is −28.1 dBi, the small difference being consistent with fabrication and setup losses. Experimental work used minced meat as a tissue phantom. The ECC stays under 0.1 across the band, giving a diversity gain above 9.96 dB. A link budget analysis confirms the antenna can maintain a positive link margin at practical operating distances. Given its very small volume (10 mm3) size and very high isolation value (31.8 dB), this design is a practical option for compact implantable MIMO systems.

Technologies doi: 10.3390/technologies14100613

Authors: Jan Kowalski Marzena Lendo-Siwicka Grzegorz Wrzesinski Simon Rabarijoely Sylwia Szymanek Katarzyna Pawluk

The realisation of construction objects in the immediate vicinity of flood embankments is among the most demanding engineering processes. All earthworks, drainage, and structural works carried out near the embankments can affect the stability of slopes, filtration conditions, changes in groundwater levels, and local ground loosening. For this reason, it is essential to apply appropriate design, execution, and monitoring methods based on reliable geotechnical reconnaissance and proper implementation procedures. The article presents an analysis of the process of constructing a retention reservoir located near a flood embankment. Particular attention was paid to the impact of executing deep excavations secured with a system of retaining walls on the existing hydrotechnical structure. Based on the analyses conducted, the process of implementing the entire construction project was assessed, considering the potential for failures during and after the investment. A block diagram of the potential construction failure mechanism during the realisation of the retention reservoir near the flood embankment was also proposed. The retrospective EC7-II HYD screening gave ΛHYD = 2.84–3.47% for the Sk/W′k formulation, 58.44–71.42% for the u/σv formulation, and 2.89–3.53% for the Δu/σ′v formulation under VC2(a). Under VC2(b), the corresponding values were 2.69%, 55.31%, and 2.74%. The governing value was ΛHYD = 71.42%, below the 100% worksheet acceptance criterion.

Technologies doi: 10.3390/technologies14100612

Authors: Ting Zhou Hua-Chun Xiang Mao-Bin Lv Xin-Yu Yi

The development of complex equipment faces prominent challenges, including unequal status among participating agents, multi-objective full confrontation, strong super-conflict among multi-indicators, dynamic risk evolution, delayed on-site perception, and the absence of collaborative negotiation. Traditional risk management and control methods, based on the ideal assumptions of equal subjects and independent indicators, struggle to characterize and resolve super-conflict games dominated by super decision-makers. Furthermore, they lack dynamic early warning and closed-loop execution mechanisms linked to real-time perception, commonly suffering from drawbacks such as low early warning accuracy, high decision-making conflict, delayed response, and inefficient collaboration. To address these issues, this paper integrates multi-agent conflict negotiation with intelligent perception and learning technologies to propose an intelligent risk early warning and closed-loop control method for complex equipment development. The three-dimensional risk evolution dynamics model and Virtual Risk Center (VRC) are employed to decouple super-conflict indicators, while the industrial inspection unmanned aerial vehicle (UAV) perception relative motion model enables the unified mapping of physical risks and decision-making games. A super-conflict gray target negotiation (SCGTN) model is constructed to achieve stable consensus decisions among multiple parties under conflicting indicators. Based on Markov Decision Processes and the PPO algorithm, the optimal virtual risk trajectory is generated, which is then combined with the Archimedean spiral convergence trajectory to synthesize executable control trajectories. This forms an integrated system of UAV real-time perception → super-conflict resolution → intelligent decision-making → closed-loop regulation. Validated through a case study of large-scale complex aviation equipment development, the proposed method achieves field-validated risk early warning accuracy of 94.7% evaluated against real-world on-site ground truth labels. The numerical simulation results, whose parameters are fully calibrated against real-world engineering datasets, indicate that under simulated test conditions, our method yields simulation-predicted performance: it reduces decision-making conflict intensity by 49.3%, controls risk deviation error within 1.38%, and shortens closed-loop response time to 158 ms. Note that conflict reduction level, risk deviation error, and closed-loop response time are pure simulation outputs and have not been directly measured from physical on-site closed-loop experiments. Under the same simulation setup, the end-to-end response speed is 7.6 times faster than the peer dynamic closed-loop Digital Twin-Proximal Policy Optimization (DT-PPO) benchmark algorithm with identical online sensing and reinforcement learning architecture and roughly 4700 times faster than simulated counterparts of traditional static offline evaluation modes that rely on periodic manual statistics and offline meetings. It is adaptable to complex equipment development scenarios characterized by strong super-conflict, high dynamics, and unequal subjects, providing a theoretical framework and technical support for intelligent risk prevention and control throughout the full lifecycle of complex equipment.

Technologies doi: 10.3390/technologies14100611

Authors: Mustafa Abdul Salam

Vehicle-to-everything (V2X) communication has become an end-to-end cyber–physical evidence problem rather than a radio-interface problem alone: sensed events, transmitted messages, edge computation, trust decisions, and control or service outcomes must be evaluated as a connected chain. The nearest recent surveys clarify V2X architectures, datasets, protocols, cybersecurity, cooperative perception, and energy services, but they do not provide a study-level synthesis that systematically separates architectural claims from evaluated cross-layer effects while preserving validation context and metric compatibility. This paper addresses that gap through a systematic evidence-to-inference map, reported in accordance with PRISMA 2020, built on an analytical corpus of 235 full-text V2X/ICV research publications whose first public versions appeared from 1 January 2025 to 4 August 2026. The reference list contains 249 entries: 235 coded corpus publications and 14 supporting methodological, standards, or contextual sources outside the analytical denominator. Each corpus publication is assigned to one of ten mutually exclusive primary domains and coded across six analytical layers: sensing/perception, representation/fusion, communication, computation, trust/security, and decision/control/outcome. Claimed and evaluated layer integration are coded separately, validation maturity is assessed on an E0–E4 scale, and communication-to-outcome evidence is synthesized only when metric families, denominators, and experimental contexts are compatible. The synthesis identifies recurring fragmentation between broad cross-layer architectural claims and narrower evaluated evidence, limited high-maturity validation, incomplete communication-to-downstream-outcome linkage, and reporting heterogeneity that limits direct quantitative comparison across studies. The resulting framework produces evidence-gap maps, layer co-evaluation patterns, metric-bundle interpretations, domain-level engineering implications, and testable priorities for benchmark refinement. It provides a practical route for moving V2X evaluation from isolated layer performance toward operationally credible and auditable cross-layer evidence.

Technologies doi: 10.3390/technologies14100610

Authors: Hasnae El Khoukhi Assia Belatik Ali Belkhiri Meryem Cherrate My Abdelouahed Sabri Abdellah Aarab

Automatic sign language recognition has become an important research area for improving communication between deaf individuals and the hearing community. While vision-based approaches achieve high recognition performance, their dependence on cameras and environmental conditions limits their suitability for portable real-time applications. This paper presents a fully embedded Moroccan Sign Language (MSL) recognition system based on a dual smart glove equipped with ten MPU6050 inertial measurement units (IMUs). A dedicated dataset of approximately 8000 gesture sequences representing 20 common MSL gesture classes was collected from 80 participants. The acquired multivariate inertial signals were preprocessed and used to train a lightweight Long Short-Term Memory (LSTM) network, which was quantized and deployed on a Raspberry Pi Pico microcontroller using TensorFlow Lite Micro. Experimental results achieved an overall recognition accuracy of approximately 98%, with high precision, recall, and F1-score, with an average inference latency of 27.4 ± 1.0 ms on the embedded platform. The proposed platform demonstrates the feasibility of accurate and low-latency MSL recognition on resource-constrained embedded hardware under controlled acquisition conditions, representing an initial proof of concept toward future wearable assistive communication systems.

Technologies doi: 10.3390/technologies14100609

Authors: Lu Chen Siru Chen Meng Li Manfeng Gong Deyun Mo

Engineering mechanics laboratory teaching has long been constrained by high equipment costs, limited hands-on opportunities for students, and a lack of intuitive data visualization. To address these challenges, this paper presents a practical and low-cost experimental design method based on the Phyphox software and smartphone sensors. Two representative experimental modules were developed as examples: the measurement of the rigid body moment of inertia and the determination of Young’s modulus via the bending method. The experimental principles, apparatus construction, and data acquisition and processing methods were systematically analyzed. The results demonstrate that the proposed method achieves relative errors of 3.8% for the moment of inertia and 6.83% for Young’s modulus, confirming its feasibility and close agreement with theoretical values in engineering mechanics education. On this basis, the method was integrated into a structured 5E instructional framework, and a comprehensive instructional design was established, covering teaching objectives, key points and difficulties, implementation steps, and assessment methods. This study proposes a novel teaching method that transforms traditional engineering laboratory experiments into a low-threshold, data-and-analysis-integrated learning model. Experimental validation shows that the proposed method can reduce costs and enhance data visualization, thereby promising to improve students’ engagement in engineering mechanics education.

Technologies doi: 10.3390/technologies14100608

Authors: Vesela Karlova-Sergieva

Robust admissibility guarantees that the closed-loop poles remain within a prescribed region, but it does not reveal the locally sensitive portions of the root contours. In this study, a pole sensitivity mapping method is developed that combines modulus–phase decomposition, multiparametric assessment using singular values, and time-domain verification. The method is applied to an uncertain second-order plant with a PI controller and seven tunings numerically confirmed as robustly admissible. For the considered tunings, the maximum local pole sensitivity varies from 1.388 to 43.951, with the tuning having the lowest pole sensitivity not coinciding with having the lowest transient response sensitivity. The results show that the joint quantitative consideration of pole and time-domain sensitivities enables a more substantiated selection among robustly admissible tunings by localizing fragile regions and identifying the nature of pole motion.

Technologies doi: 10.3390/technologies14100607

Authors: Nikiforos Okkalidis Georgios Giakoumettis Hristomir Yordanov Filippos Okkalidis Anastasios Siountas Georgios Plataniotis Panagiotis Bamidis Athanasios Tiliakos Emmanouil Papanastasiou

Conventional thermoluminescent radiation detectors (TLDs) are typically manufactured with fixed geometries—in this work, we attempt for the first time to employ additive manufacturing for the fabrication of customized radiation detectors, with the prospect of evolving geometrically complex and application-specific TLDs for incorporation into phantoms for use in medical radiology. While additive manufacturing has found applications in dose verification, quality assurance, imaging modalities, settings optimization of medical imaging devices, and the replication of soft and bone tissues, with a wide range of thermoplastic polymers used in extrusion printing having been extensively investigated for their radiological properties, there are no reported TLD examples, thus limiting the prospects of this technology. In this work, we manufacture composite polypropylene-CaSO4-dysprosium filaments for 3D-printed TLDs, and we evaluate their response by irradiating 3D-printed prototypes in a medical linear accelerator. Our results validate their functionality within the limits imposed by the melting points of the polymeric binders, effectively qualifying them as TLDs for detection—but not dosimetry. Thus, 3D-printed TLDs incorporated in phantoms can enhance radiological investigations for detecting exposure to potentially harmful radiation.

Technologies doi: 10.3390/technologies14100605

Authors: Oussama Dribel Souleymane Balde Fouad Riane Abdessamad Ait El Cadi

Bike-Sharing Systems (BSS) are increasingly viewed as a sustainable and flexible component of urban mobility, particularly in rapidly growing cities such as Casablanca. The effectiveness of such systems depends, critically, on strategic planning decisions, most notably the number and location of stations. This study proposes a spatially explicit modeling framework to support the design of a BSS network based on urban demand characteristics. Ten categories of urban attractors are identified and grouped into four main criteria: socioeconomic activities; education, culture, and health; leisure spaces; and public mobility hubs. A modified Huff model is used to estimate the spatial distribution of potential demand and to identify candidate locations for bike-sharing stations. Walking accessibility is represented by shortest-path distances on the pedestrian network. Building on these demand estimates, the station deployment problem is formulated as a minimum set covering location problem whose objective is to determine the smallest number of stations required to cover a predefined proportion of total mobility demand within a 1000 m walking-network distance. Coverage is constrained separately for each arrondissement, while stations located near administrative boundaries may serve demand in neighboring arrondissements. At the baseline γ=0.5, the model selects 45, 70, and 154 stations for coverage targets of 60%, 80%, and 100%, respectively. The proposed approach provides a systematic and data-driven basis for BSS planning and helps integrate bike sharing into a more efficient, environmentally sustainable, and multimodal urban mobility system in Casablanca.

Technologies doi: 10.3390/technologies14100606

Authors: Jiri Hajnys Quoc-Phu Ma Akash Nag Matus Gelatko Adam Skoloudik

Support structures remain a major limitation of laser powder bed fusion (LPBF), particularly for downward-facing surfaces prone to powder adhesion, dross formation, melt-pool instability, and staircase effects. This study investigates the downskin optimization of Inconel 718 (IN718), fabricated at a 35° inclination using a Renishaw RenAM 500S Flex. A Taguchi L25 orthogonal array was employed to evaluate three process parameters: hatch power (225–425 W), remelting times (0–4), and printing velocity (1100–1900 mm/s). Surface topography was characterized using an Alicona InfiniteFocus G5, with Sa and Sz selected as surface-quality indicators. Remelting was identified as the dominant factor for both responses, followed by hatch power and printing velocity. The response deltas for Sa were 22.70, 7.86, and 5.22 μm, respectively, while those for Sz were 68.9, 46.8, and 37.2 μm. The optimum mean responses were obtained at 375 W, one remelting operation, and 1700 mm/s for Sa, and 275 W, one remelting operation, and 1100 mm/s for Sz. The lowest measured Sa and Sz were 50.75 and 500.7 μm, respectively. The results demonstrate the potential of controlled remelting for improving downskin surface quality and reducing support requirements in LPBF.

Technologies doi: 10.3390/technologies14100604

Authors: Anastasiya Doroshenko Serhii Hlod Yurii Kynash

Detecting small, distant ground vehicles in real time on edge hardware requires models that jointly balance accuracy, computational efficiency, and robustness to degraded imagery; therefore, deployment suitability cannot be inferred from accuracy on general-purpose benchmarks alone. This study compares five detection architectures spanning two-stage and one-stage families under a standardized experimental protocol, using a purpose-built dataset of 5501 annotated frames covering three classes of ground vehicles. Each configuration was trained with three random seeds using a leakage-controlled data split, evaluated under a unified detection protocol, benchmarked directly on an NVIDIA Jetson Orin Nano Super, and tested using a reproducible, probability-based corruption protocol. To the best of our knowledge, no previous study has evaluated both detector families on a common ground-vehicle dataset dominated by small and distant instances, within a single framework combining multi-seed variance analysis, on-device performance measurement, controlled corruption testing, and training-protocol sensitivity analysis. Under each architecture’s canonical training regime, YOLO11n (mAP@0.5 = 0.811 ± 0.004) and YOLOv8n (0.807 ± 0.010) achieved comparable detection accuracy, outperforming the strongest two-stage configuration (0.775 ± 0.003) with substantially fewer parameters; disabling YOLO’s canonical online augmentation reversed this ranking. On the target edge platform, the one-stage models reached up to 61 FPS under TensorRT deployment, whereas Faster R-CNN configurations achieved, at most, 2.6 FPS. Under image corruption, Faster R-CNN retained 82.6% of its clean accuracy without corruption-specific training, exceeding YOLO’s 74.6–74.8% retention even after corruption-aware retraining from a 58.7–60.0% baseline. These results show that newer detector generations do not necessarily yield measurable gains in accuracy and that rankings can depend on training protocol choices; architecture selection should instead jointly weigh accuracy, latency, computational cost, and robustness. Under the evaluated conditions, compact one-stage detectors remain the most favorable deployment trade-off, while the standardized framework offers a reproducible basis for evaluating future architectures and edge platforms.

Technologies doi: 10.3390/technologies14100603

Authors: Yurii Syromiatnykov Marcin Niemiec

Agro-industrial organic wastes differ markedly in moisture, structure, biochemical composition, nutrient value, and contaminant risk, so pretreatment should be selected from the limiting feedstock constraint rather than from a generic technology hierarchy. This critical narrative review examines feedstock-adaptive valorisation routes centred on anaerobic digestion and subsequent recovery of energy, nutrients, carbon-rich fractions, fertilisers, humic-like materials, and plant-biostimulant products. Unlike technology-centred syntheses, the review uses the dominant feedstock constraint as a common comparison axis across pretreatment, biological stability, and product qualification. Mechanical, mechanochemical, cavitation-based, thermal, chemical, and biological treatments are compared in terms of the barrier they remove, their energy and material burden, downstream biological response, and recovered-product quality. The evidence shows that particle-size reduction, solubilisation, or methane yield alone is insufficient for process selection. A minimum-sufficient-pretreatment heuristic is therefore proposed: treatment intensity is increased only until a predeclared conversion or product-quality objective is reached while stability, safety, and positive incremental net benefit are retained. Zero pretreatment remains a valid outcome when concentration, co-digestion, feeding control, biomass retention, or another intervention better addresses the dominant constraint. The framework also highlights the need for harmonised energy reporting, mass balance, continuous validation, and product-specific qualification before laboratory improvements are translated to industrial operation.

Technologies doi: 10.3390/technologies14100602

Authors: Mustafa Fakhir Saadi Ercan Aykut

Grid-connected PV-wind systems experience strongly varying operating conditions that challenge fixed-gain control. This study evaluates a Fuzzy-PID controller with offline Grey Wolf Optimizer (GWO) tuning using two explicitly separated simulation layers: thesis-level MATLAB/Simulink system indicators and a reproducible reduced-order DC-link benchmark. The reproducible layer uses common disturbance traces, five synthetic stochastic weather profiles, independent-seed validation, and an equal-optimization-budget comparison between GWO-PID and GWO-Fuzzy-PID. With 30 wolves and 15 iterations for both architectures, the optimized PID and optimized fuzzy controller achieved nearly identical training objectives (0.08349 and 0.08401). Across 10 unseen seeds, GWO-PID produced slightly lower mean RMSE than GWO-Fuzzy-PID in all five scenarios; under Stormy operation, the values were 7.746 and 7.873 V, respectively, compared with 9.517 V for Fixed PID. Extended 30 s and 60 s tests confirmed that GWO-based tuning retained its principal benefit under severe disturbances, while the fuzzy layer did not provide a systematic RMSE advantage over an equally optimized PID. Rainy/Low-Wind diagnostics showed a persistent source-power deficit and near-continuous actuator current-limit operation, explaining the approximately 22 V short-horizon RMSE. Controller-only MATLAB R2017a timing was below 8 microseconds/update for the fuzzy implementations. The results therefore support GWO-based severe-disturbance robustness, while separating the contribution of optimization from that of fuzzy adaptation and avoiding a claim of universal controller superiority.

Technologies doi: 10.3390/technologies14100601

Authors: Ziyang Zhang Lin Guan Yuanchen Li

Vehicular Ad Hoc Networks (VANETs) have become an important communication technology for intelligent transportation systems, where efficient caching mechanisms are required to reduce content delivery latency and communication load. However, caching strategies inherited from conventional Mobile Ad Hoc Networks (MANETs) are often insufficiently adaptive to rapid topology changes, heterogeneous vehicular content interests, and the different caching characteristics of vehicles and Roadside Units (RSUs). This paper proposes PHVC, a Prior-Guided Hybrid Machine Learning-based VANET Caching strategy that combines lightweight Q-learning with supervised learning and conventional caching knowledge. PHVC separates cache replica placement and cache replacement into coordinated learning processes. Support Vector Machine (SVM)-based historical pattern learning provides auxiliary guidance for cache replica placement, whereas Least Recently Used (LRU) information is introduced as temporary prior guidance for cache replacement during the initial reinforcement-learning stage. Each vehicle and RSU independently maintains a local Q-table whose state representation is based on cache positions rather than global content identities, thereby limiting the state-space and storage requirements. A request-triggered delayed reward mechanism is further employed to associate caching decisions with subsequent cache-use outcomes. The strategy is evaluated using SUMO, OMNeT++, Veins, and INET under representative urban and highway scenarios. At the final simulation points, compared with the best-performing conventional benchmark for each metric, PHVC improves the cache hit ratio by approximately 13.0–26.1%, reduces average content delivery latency by 23.9–48.7%, and reduces normalised link load by 7.6–23.4%. The results demonstrate that the proposed prior-guided hybrid learning framework provides an effective performance–complexity trade-off for adaptive cache management in dynamic vehicular networks.

Technologies doi: 10.3390/technologies14100600

Authors: Molin Su Peng Ren Zhijie Gao Mingchao Bai Huabing Li Hongqiao Yan Yingli Li Ruijing Jiang Yue Zhao Kaimeng Wang Yuqi Zhou

Strain conversion between inner and outer surfaces is critical for the integrity assessment of cracked thin-walled tubes, yet its dependence on crack geometry remains insufficiently clarified. In this study, X46 tubular specimens with surface and through-wall cracks were tested under uniaxial tension, with full-field deformation measured by digital image correlation (DIC) and a validated 3D finite element model (FEM) employed. The effects of crack type, length and orientation on strain concentration and inner–outer strain transfer were systematically investigated. Results show that increasing crack length enhances local strain localization; through-wall cracks induce significantly stronger localization than surface cracks. Small surface cracks cause limited deformation disturbance with failure governed by global necking, whereas longer surface and through-wall cracks lead to crack-dominated fracture. Eventually, a unified inner–outer strain conversion model was established, which showed good internal agreement for the investigated tube geometry, material and loading conditions.

Technologies doi: 10.3390/technologies14100599

Authors: Bogdan-Flavius Komlosi Dumitru-Daniel Bonciog Valentin-Ioan Maranescu Aurel Gontean Adriana Berdich

The present study introduces the development and implementation of a control and monitoring system that relies on software designed for a 2 × 50 W Class AB amplifier. The primary objective of this research is to integrate the hardware platform with the designed software, ensuring proper functionality and continuously monitoring the inputs, with further potential for integration into smart home applications. Digital control is preferred due to its flexibility, configurability, and ability to provide repeatable system-level behavior, rather than inherent improvements in analog audio performance. The developed architecture is designed to improve control flexibility and support calibration capability for a more precise control of the sound levels without the need for a hardware change. This research looks into challenges encountered in the design of a Class AB amplifier platform, in this case, the response of the amplifier over the defined audio bandwidth and a practical user interface. The proposed solution focuses primarily on firmware-based control, user interaction, system management, and supervisory monitoring of operational states.

Technologies doi: 10.3390/technologies14100598

Authors: Qian Zhang Ying Wang

Accurate short-term net load forecasting is challenging due to the superimposition of meteorological influences, daily and weekly seasonal patterns, and irregular disturbances in the observed series. On the one hand, redundant meteorological input data risks hindering the learning process. On the other hand, methods that directly model the raw time series may struggle to distinguish these heterogeneous patterns. To address the above issues, this paper proposes a component-wise hybrid framework combining multiple seasonal-trend decomposition using loess (MSTL) and phototropic growth algorithm (PGA)-optimized TimeXer with the selected features. First, both the Pearson correlation coefficient and Shapley additive explanation are employed for meteorological feature selection. Then, MSTL is utilized to decompose the net load into trend, daily seasonal, weekly seasonal, and residual components. Following this, independent PGA-TimeXer models are trained to forecast the trend and seasonal components, while a light gradient boosting machine (LightGBM) is designed to forecast the residual component from lagged residual features. By recombining the forecasting results from each component, the final net load prediction is achievable. Finally, field measurement data from an actual 220 kV substation verify the effectiveness of the method proposed in this paper.

Technologies doi: 10.3390/technologies14100597

Authors: Kaio Henrique Ferreira Nogueira de Nogueira Jonathan Araújo Queiroz Letícia Cabral Correia Allan Kardec Duailibe Barros

Several studies have proposed decision-support systems for atrial fibrillation (AF) diagnosis using binary classification, distinguishing only healthy from diseased patients. We propose a four-class method that separates AF phenotypes from signal acquisition modality: normal sinus rhythm (SR), paroxysmal AF, persistent AF (LTAF), and intracardiac-acquisition AF (IAF). SR is the non-AF reference class rather than an AF subtype; IAF is the same arrhythmia recorded via intracardiac electrograms instead of surface ECG, a difference in acquisition modality, not an additional clinical phenotype. Each cardiac cycle is standardized by z-score and amplified by its own Shannon entropy, and four statistical measures (mean, variance, skewness, kurtosis) feed a k-nearest neighbors classifier. On the independent four-class test set, this representation reached 98.0% accuracy, with balanced accuracy and macro-F1 also at 98.0%. An ablation study, cross-checked with an SVM classifier, confirmed this gain comes from the representation itself, not from a specific classifier. Because each class came from a single database, we further tested whether the separability reflects AF phenotype or the source database: restricting sinus rhythm and AF to one database (AFDB) gave 90.9% balanced accuracy, and training on one database while testing on another (LTAFDB to AFDB) gave 84.2%. Both results are well above chance but below the four-class figure, showing that the representation carries a genuine physiological signal while confirming that part of the original accuracy reflects database-specific characteristics rather than the AF phenotype alone.

Technologies doi: 10.3390/technologies14100596

Authors: Zuzana Soltysova

Artificial intelligence (AI) is transforming organizations across all sectors, yet many AI initiatives fail despite significant technological investments. Existing research has primarily explained AI adoption through technological, organizational, and strategic readiness, while the preparedness of individuals to work with AI remains insufficiently conceptualized. This limitation is particularly relevant for small- and medium-sized enterprises (SMEs), where successful AI adoption depends heavily on employees’ willingness and ability to collaborate with intelligent systems. This paper introduces the concept of Human Readiness for AI Adoption as a human-centered perspective on AI implementation in SMEs. Human readiness is conceptualized as the degree to which individuals are cognitively, psychologically, and ethically prepared to understand, evaluate, accept, use, and responsibly collaborate with AI systems. Building on the literature on organizational readiness, AI readiness, and human-centered AI, the paper proposes a conceptual framework that positions human readiness as a prerequisite for trust in AI, successful human–AI collaboration, and sustainable AI adoption. The proposed framework extends existing AI adoption research by shifting the focus from technological capability to human preparedness. It contributes to the emerging discourse on human-centered AI while providing managers with a conceptual foundation for designing AI implementation strategies that foster trust, transparency, employee participation, and continuous learning. Finally, directions for future empirical validation of the proposed framework are discussed.

Technologies doi: 10.3390/technologies14100595

Authors: Manuel Alejandro Cardona-López Santiago Segura-Romo Aura Luz Barcenas-Medina Jaime Robles-García Moisés Salinas-Rosales Rolando Flores-Carapia

Since intercepted sensor data can reveal user activities, lightweight cryptography is essential for protecting information in resource-constrained devices. Although ARX-based lightweight schemes offer high computational efficiency, their lack of inherent non-linear substitution and permutation layers motivates their integration into secure cryptographic designs. This paper proposes LSEPI (Lightweight Sensor Encryption Using PI), a lightweight stream cipher for protecting electrical sensor data. LSEPI employs a single-round substitution-permutation design that combines the AES substitution box with a key-dependent dynamic permutation and a dynamically selected XOR mask derived from the secret key and the digits of the number π. To mitigate the computational cost of pseudorandom sequence generation, the first 20 KB of the decimal expansion of π are stored on both the ESP32 and Android device. Encryption is performed on the ESP32 during the simultaneous acquisition and computation of voltage, current, power, and energy, while decryption is performed on an Android application for user-selected time intervals. Statistical evaluation shows that LSEPI achieves an entropy of 7.9971 with a single encryption round, comparable to Speck-CTR with 32 rounds (7.9970) and Ascon (7.9969), and higher than the approximately 7.18 reported for recent lightweight proposals. The encrypted data also satisfies the chi-square goodness-of-fit criterion, exhibits correlation coefficients close to zero, and achieves average avalanche effects of 50.005% for one-bit plaintext changes and 50.08% for one-bit key changes. GLCM-based texture metrics are comparable to those of Speck-CTR and Ascon. The known-plaintext analysis indicates that recovering the key-dependent masking sequence requires determining or inverting the key-dependent permutation; thus, despite the use of XOR operations, the attack does not directly succeed. From a computational perspective, LSEPI requires 135 μs per sensor record after initialization, adding only 0.04% overhead to the computation of the electrical variables on the ESP32.

Technologies doi: 10.3390/technologies14090594

Authors: Sergey Grigoriev Pavel Potapov Valery Zhylinski Uladzimir Matys Catherine Sotova Filipp Milovich Elena Eganova Tatyana Borovik Alexey Vereschaka

This article presents the potential for enhancing the corrosion resistance of stainless steel components by depositing modifying coatings containing copper, chromium, and zirconium layers on their surfaces while optimising the deposition process parameters. Coatings with an outer zirconium nitride (ZrN) or chromium nitride (CrN) layer and adhesive-corrosion-resistant Zr–Cu–Zr, Cr–Cu–Cr, and Cu–Zr sublayers were studied. The highest deposition rate of the copper layer was achieved with a copper cathode arc current of Icat = 85 A and a substrate bias voltage of Usub = 100 V. The proposed coatings reduced corrosion intensity by up to 30 times compared to an uncoated sample and up to three times compared to coatings with an adhesive sublayer without copper (zirconium or chromium only). The use of the proposed coatings reduced corrosion intensity by up to 15 times compared to an uncoated sample and up to 1.6 times compared to coatings with an adhesive layer without copper (chromium only). The Cr–Cu–Cr–CrN coating provided the best corrosion resistance in a 3.0% NaCl solution with a calculated corrosion current icor of 0.36 μA/cm2. These results indicate that the inclusion of a copper layer in the adhesive sublayer provides additional corrosion protection. The sample with a Cr–Cu–Cr–CrN coating exhibited better corrosion resistance, but its adhesive bond to the substrate was not very strong. The sample with a Zr–Cu–Zr–ZrN coating demonstrated high adhesive strength with relatively high corrosion resistance.

Technologies doi: 10.3390/technologies14090593

Authors: Laura Velásquez Amal Poulose Tanja Behrendt Andreas Günther Herena Torio Edwin Chica Ainhoa Rubio-Clemente

Life cycle assessment (LCA) is a crucial tool for monitoring the environmental impact of renewable energy systems. However, current approaches may not account for local socio-economic conditions and structural readiness without a large number of local data sets. The up-to-date carbon footprint assessment does not account for the huge gap between the maturity of infrastructure and the social context in different countries. To help bridge this gap, this work presents a new context-adjusted carbon-footprint assessment framework that embeds national preparedness and technological maturity into the LCA of solar photovoltaic (PV) and wind energy systems. The proposed model moves beyond traditional emission tracking by coupling site-specific resource availability, measured through the capacity factor (CF), with a technology development index (TDI). This index is an important indicator of socio-economic status and connects local economic conditions and energy infrastructures’ readiness. By employing different development scenarios in context, this study demonstrates the direct influence of structural differences on sustainability. The new socio-technical LCA approach that takes socio-economic and resource-specific factors into account in the context of development can be regarded as a robust and scalable tool to assess energy transitions across global contexts to ensure that equitable and rigorously informed decisions are made.

Technologies doi: 10.3390/technologies14090592

Authors: Maged Abdullah Esmail

This paper investigates the performance of a cascaded dual-hop relay system comprising a radio-frequency (RF) hop followed by a free-space optical (FSO) hop. The RF channel was subject to Rayleigh fading, whereas the FSO channel experienced beta-distributed dust-induced irradiance fluctuations based on experimentally obtained channel parameters. A fixed-gain amplify-and-forward (AF) relay was employed, and the FSO link operated using intensity modulation/direct detection (IM/DD) with on–off keying (OOK). Unlike conventional mixed RF/FSO studies that primarily model the optical hop through atmospheric turbulence and pointing errors, this work examined the end-to-end effect of dust-induced fading in a cascaded RF/FSO architecture. An exact integral representation of the end-to-end signal-to-noise ratio (SNR) cumulative distribution function was formulated. A finite-series closed-form approximation of the end-to-end CDF was subsequently obtained, from which corresponding closed-form approximations for the outage probability, the average bit error rate (BER), and the ergodic capacity metric were derived. The accuracy of the proposed approximations was validated through numerical integration and Monte Carlo simulations. The results show that the finite-series expressions provided highly accurate and computationally efficient performance estimates in the moderate- and high-SNR regions, while a measurable deviation may occur under very low-SNR conditions. The developed framework provides useful analytical tools for evaluating cascaded RF/FSO systems operating over beta-distributed dust-fading channels.

Technologies doi: 10.3390/technologies14090591

Authors: Khadija Meghraoui Abdellatif Moussaid

Agriculture is under pressure from climate variability, labour shortages and the need to use water, fertiliser and pesticide more carefully. Artificial intelligence, and agents and multi-agent systems in particular, have been applied to these problems since the 1990s, but a new wave of systems built on large language models has appeared in the last two years. We use the term agentic artificial intelligence, defined operationally in this paper, for software that perceives part of its environment, decides what to do based on that perception, and then acts or advises, where the sequence of steps is determined by the system itself rather than fixed in advance; this covers both the classical agent tradition and the recent language-model wave, and we distinguish the two throughout. This review asks how far agentic approaches, in both traditions, have travelled into agriculture. We searched Scopus for work published between 2020 and 2026 and screened 4111 records against a protocol based on PRISMA 2020. After removing duplicates, off-topic domains and studies that did not report enough method or result detail, 181 studies remained. We coded each one along six dimensions, agricultural domain, agent organisational pattern, artificial intelligence backbone, data source, deployment setting and level of autonomy, and assessed the consistency of the coding through a blinded manual re-coding of 30 randomly selected records. This quality-control exercise assessed the reproducibility of the record-level classification; it was not intended as a full-text validation of every implemented mechanism. Three findings stand out. First, the field as a whole spans more than three decades, but the large-language-model subset is very young: such studies appear only from 2024, and 46 of the 47 of them were published in 2025 or 2026. Second, the capabilities that define agentic behaviour are unevenly reported. Collaboration is reported by 82 percent of studies, while planning and reasoning each appear in 31 percent, and memory and reflection in 6 and 4 percent; these figures describe what abstracts report rather than confirmed implementations, a distinction we treat carefully throughout. Third, tested evidence is thin. Only 33 studies report a field or real deployment, 68 remain conceptual, and just 11 report evaluation across more than one season or period. We contribute a taxonomy that separates classical and contemporary agentic approaches, a direct comparison between the two, a reported-capability matrix reported with raw counts as well as percentages, and a roadmap for future work that we label clearly as our own synthesis rather than a direct empirical finding. The picture that emerges is of a field with real momentum in its newest part and thin evidence overall, where the main task ahead is to move from architecture proposals to systems that are tested on real farms over more than one season.

Technologies doi: 10.3390/technologies14090590

Authors: Wenkai Yang Jie Ouyang Zhu Wang Ye Xie Jiale Meng Cong Zhang

Deep lacustrine–fluvial soft ground at the Songyanghu Terminal (Phase III), Yueyang Chenglingji Port, was treated using vacuum wellpoint dewatering combined with dynamic compaction. A two-dimensional coupled solid-mechanics-Darcy model in coupled COMSOL Multiphysics 6.3 model was used to examine deformation and excess pore-water-pressure response, while an archived 360-day settlement series was used to evaluate three empirical prediction methods. The field dataset contains 12 settlement observations at a constant 30-day interval from Day 30 to Day 360. The numerical outputs indicate surface settlement approaching approximately 100 mm by Day 100; at Day 10, localised excess pore-water pressure remained at roughly 100 kPa near the mid-depth drainage zone, while most of the model domain was substantially lower. To avoid treating the same observations as both fitting and validation data, the Asaoka, hyperbolic and exponential methods were recalibrated using data ending at Days 180, 240 and 300 and then evaluated against the subsequent withheld observations. Across these three windows, the mean RMSE values were 0.02, 0.59 and 0.19 mm for the Asaoka, hyperbolic and exponential methods, respectively. The Day-360 measurement of 108.84 mm is therefore used as a reference observation rather than as a proven final settlement. For this single site and dataset, the Asaoka method produced the lowest numerical withheld-data errors; however, the differences between the Asaoka and exponential predictions are smaller than the stated ±1 mm levelling accuracy and therefore do not establish statistically or physically significant superiority. Broader applicability requires validation at additional sites and with independent monitoring datasets. These results provide a case-specific basis for post-construction settlement assessment of similarly treated soft ground.

Technologies doi: 10.3390/technologies14090589

Authors: Naai-Jung Shih Chuan-Ci Huang Tzu-Ya Wang

How does an AI-generated three-dimensional (3D) model fit into BIM as a diagram to support fieldwork? This research addresses this question by decomposing a device or construction component into parts based on images, assembly diagrams and 3D models. A number of AI application programs and platforms were applied for 2D assembly catalogs and 3D reconstruction of 3DGS models. The AI-assisted diagram interpretation is generated mainly based on field imagery of assembled or disassembled devices or fixtures. The illustrated structure, which is subject to future updates, contributes to the composition usually required for field reference. Field imagery is also AI-reconstructed in 3D to validate the diagram. The result combines the advantages of image-based 3D part decomposition and the regeneration of 2D assembly diagrams. In total, about 29 architectural fixtures and MEP devices were regenerated, creating 190 models of supporting formats and 87 Gemini® or ChatGPT® diagrams. Two of the seven sets of prompt types were revised to emphasize orientation, leading to about 44.4% of all errors in the diagrams. The comparative evaluation of 3DGS models demonstrated standard deviations ranging from 0.165503 mm at an 80% sensitivity level to 3.786150 mm across various 3D applications. The novelty of AI-assisted image-to-BIM lies in reinterpreting a subject from its as-built form in a number of AI-assisted, open, and accessible approaches. The 3DGS model offers important advantages in terms of verification by combining the visual and structural details of an object. Combining diagrams and 3D modeling enables an evolving conversion to a BIM IFC structure with a part-mapping table. Supported by cloud access and smartphone interaction, 3D catalogs and AR present an imagery-to-AR approach for potential field assembly assistance.

Technologies doi: 10.3390/technologies14090588

Authors: Johannes Stübinger Fabio Metz

This paper presents a systematic, data-driven literature review of research on Physical Artificial Intelligence (AI) based on the top 100 Google Scholar publications related to the search terms “Physical Artificial Intelligence” and “Physical AI”. The rapid advancement of Physical AI, driven by the convergence of advanced sensor technologies and foundation world models, has resulted in a diverse and fragmented research landscape that lacks comprehensive quantitative overviews. To address this gap, we implement and apply an AI-assisted computational analysis pipeline to this domain. The collected publications are processed using a Large Language Model accessed via a Python-based Application Programming Interface (API), enabling a structured computational analysis of the literature to assist thematic categorization. Based on this approach, the publications are grouped into five data-driven thematic clusters reflecting primary research perspectives within the analyzed sample. Specifically, the identified clusters comprise “Sensor Infrastructure and Architectures”, “Core Learning and Modeling Methodologies”, “Sim-to-Real and Digital Twins”, “Applications”, and “Safety, Governance, and Ethics”. By synthesizing the literature in a structured manner, this work provides a consolidated overview of central research patterns, identifies key operational challenges, and highlights fragmentation across Physical AI research, establishing a solid foundation for future trustworthy autonomous systems.

Technologies doi: 10.3390/technologies14090587

Authors: Polina A. Fomina Grigorii A. Oloviannikov Dmitriy A. Serov Lev R. Sizov Ilya V. Baimler Pavel P. Chapala Sergey V. Gudkov Valeriy A. Kozlov

Polycaprolactone (PCL) is a promising polymer in the medical field and is already being used in the creation of suture materials and tissue engineering. Due to the limited properties of PCL, in many medical tasks it is necessary to modify it, for example, the addition of antimicrobial properties to reduce the risk of infection in tissue repair processes. In this work, the effect of the introduction of nanoparticles of titanium oxide (NPs-TiO2) and zinc (NPs-ZnO) into the polycaprolactone matrix on its antibacterial activity and biocompatibility was studied. The studied concentrations of NPs ranged from 0.001 to 0.5 wt. %, and the final composites were obtained by casting from a solution. An investigation into the antibacterial activity against the Gram-negative bacterium Escherichia coli revealed high efficacy of the PCL/NPs-TiO2 composites, which increased with rising nanoparticle concentration to 27.6% at a nanoparticle content of 0.5 wt. %. PCL/NPs-ZnO composites did not show such activity. The results obtained demonstrate that the key factor determining the biological activity of a composite is not only the nature of the nanoparticles but also their ability to maintain a nanoscale state during the manufacture of the final composite. PCL/NPs-TiO2 composites may be a promising candidate for the creation of antibacterial materials for medical purposes, for absorbable scaffolds or sutures.

Technologies doi: 10.3390/technologies14090586

Authors: Maxence Bigerelle Julie Lemesle Eddy Chevallier Yasser Diab Thomas Touret Christophe Changenet Fabrice Ville

Accurate modeling of the tooth friction coefficient is central to analyses of efficiency, vibration, and durability in enclosed gear drives. Physics-based models describe these contacts using a large set of coupled thermal, contact, and lubrication laws whose individual parameters have uncertainties that are difficult to propagate. This study proposes a compact alternative: the HAF model, a three-parameter phenomenological description of the friction coefficient as a function of the slide-to-roll ratio (SRR). The three parameters have distinct tribological interpretations: h (hysteresis) governs the steepness of the sigmoidal transition, a (attrition) governs the slope of the plateau, and ν (friction level) governs the overall magnitude. The model is calibrated using nonlinear regression with fourteen two-disc traction curves acquired with a one-factor-at-a-time (star) design around a reference operating point (1.6 GPa, 80 °C, and 20 m/s): the contact pressure (1.2, 1.6, and 1.9 GPa), the injection temperature (40, 80, and 100 °C), and the mean speed (10, 20, and 30 m/s) are each varied in turn, for both smooth and rough discs. Parameter uncertainty is quantified using the residual-resampling bootstrap (BIG) established in a companion paper and applied over 105 iterations; it yields near-Gaussian, weakly correlated parameter distributions. The three HAF parameters are then expressed as linear functions of load, temperature, speed, and roughness; least-squares inference across the fourteen conditions shows that eleven of the fifteen regression coefficients differ significantly from zero at the 5% level, with roughness having the strongest effect on the friction level (t = 7.98). Substituting these laws into the HAF equation and reoptimizing the resulting expression globally yields a single closed-form model that reproduces the measured friction coefficient with a residual spread of σ ≈ 0.001 in friction-coefficient units (R2 ≈ 0.996) and approximately Gaussian, zero-mean, and homoscedastic residuals with no evident systematic structure. Being differentiable and equipped with bootstrap confidence intervals, the model predicts friction throughout the tested operating envelope—across which the maximum friction coefficient varies by a factor of eight, from 0.0049 to 0.0388—and supports gradient-based optimization of low-friction operating conditions. The contribution of this study is a compact, interpretable, and statistically characterized predictive law for tooth friction, expressed in closed form as a function of the operating conditions and of the surface state.

Technologies doi: 10.3390/technologies14090585

Authors: Xianlong Shen Shu Zhang Ziyi Bao

Self-reported perceived-risk responses form an ordered behavioral outcome in which classification errors differ in both direction and magnitude. Existing EEG-based safety studies have mainly considered hazard categories or binary states, with limited attention to participant-independent modeling of graded perceived-risk responses. A leakage-controlled comparative modeling framework was evaluated for four self-reported response levels—high risk, medium risk, low risk, and safe—using 2634 non-overlapping 3 s stimulus-locked EEG epochs from 31 workers with complete usable recordings. The signals contained 16 channels sampled at 250 Hz. Because participants could respond during the 3 s analysis interval, the epochs typically contained the behavioral response and some post-response activity; the task should therefore be interpreted as decoding response-associated perceived-risk states rather than prospective pre-response prediction of objective workplace risk. Four spatiotemporal neural architectures, EEGNet, DeepConvNet, ShallowConvNet+SE, and EEG-TCN, were evaluated against SVM-RBF, gradient-boosted decision trees, and random forest models trained on 80 full-window power spectral density features. Participant identities were separated in an outer three-fold cross-validation scheme, with inner-participant validation for early stopping, and the procedure was repeated across three participant permutations. One out-of-fold probability vector was obtained for each trial in each permutation, and the three vectors were averaged to generate a consensus prediction. EEGNet achieved the highest trial-pooled balanced accuracy of 0.7447 (95% CI, 0.6796–0.7769), together with a Macro-F1 of 0.7494, Macro-AUROC of 0.9322, and Macro-AUPRC of 0.8295. EEG-TCN reached a balanced accuracy of 0.7333 and the highest participant-equal mean balanced accuracy (0.6644 ± 0.1090); its participant-level difference from EEGNet was not significant after Holm correction. Medium-risk responses remained the least separable category. Under the shared-scene, response-inclusive setting examined here, compact spatiotemporal neural models provided the strongest participant-independent decoding performance, while the change in model ranking between trial-pooled and participant-equal evaluation highlighted the influence of the selected estimand on model interpretation.

Technologies doi: 10.3390/technologies14090584

Authors: Bacem Mbarek Aref Meddeb Mohammad Al-Azawi

This article reviews the evolution and current status of using blockchain technology to improve Smart Grid security and trust. It also summarizes the main challenges faced by blockchain-based Smart Grid systems. The survey provides a clear explanation of the various risks that threaten the blockchain-based Smart Grid and explores how AI technologies can support intrusion detection systems in identifying anomalies. It further highlights the importance of emerging quantum-computing risks and the challenges of adopting post-quantum cryptography in resource-constrained smart meters. The current AI evidence is concentrated on offline detection of false data and traffic anomalies, while operational validation, uncertainty calibration, adversarial robustness, and deployment-cost reporting remain limited, partly due to the lack of publicly available real-time BSG datasets and the complexity of realistic Smart Grid simulations. Further, post-quantum migration is constrained by the memory, bandwidth, latency, and energy budgets of long-lived smart-meter hardware. The abstract also identifies end-to-end evaluation of hybrid classical/post-quantum signatures as a priority. These insights support the development of more secure and resilient Smart Grid systems. By addressing these challenges and implementing robust countermeasures, this article aims to enhance the overall security of Smart Grids and promote their long-term resilience.

Technologies doi: 10.3390/technologies14090583

Authors: Lei Wang Yuan Si Jun Wang Liqing Liao Wensheng Xie

Rail-surface defects are often small, low-contrast, and confused with steel texture or reflections, making it difficult to improve accuracy without increasing model cost. This study proposes ESiV-YOLOv8, a compact detector that assigns complementary modifications to feature selection, box regression, and multiscale fusion in YOLOv8n. EffectiveSE recalibrates deep backbone features, SIoU provides direction-aware regression, and a VoV-GSCSP/GSConv neck reduces redundant computation. Evaluation used a self-built four-class dataset of 4020 images, a near-duplicate-aware train-validation-test split, and matched seven-seed experiments. ESiV-YOLOv8 achieved 97.7% precision, 94.6% recall, 97.2% mAP@0.5, and 68.6% mAP@0.5:0.95. Relative to YOLOv8n, mAP@0.5:0.95 increased by 5.0 percentage points, while parameters and GFLOPs decreased by 17.1% and 12.2%, respectively. On the combined natural-condition subset, ESiV-YOLOv8 achieved 59.6% mAP@0.5:0.95, 5.6 percentage points above the baseline. The annotation audit yielded 98.0% class agreement and a mean box IoU of 0.89. Model-only and end-to-end latency increased by 1.4% and 2.2%, respectively. Overall, ESiV-YOLOv8 improves detection accuracy and reduces model scale with limited latency overhead on the tested backend.

Technologies doi: 10.3390/technologies14090582

Authors: Vladimir Hristov Ivan Shopov Aykut Ismailov Daniel Stoyanov

Motor-Driven Roller (MDR) conveyor systems are increasingly being used in modern warehouse and intralogistics systems due to their modularity, energy efficiency and the ability to implement the Zero-Pressure Accumulation (ZPA) principle. Despite their wide industrial application, most existing ZPA solutions rely mainly on reactive control based on the current occupancy status of the conveyor zones, without using prior information about the incoming loads. This limitation is especially significant when transporting cylindrical loads with different sizes and dynamic characteristics. In this work, a framework for adaptive predictive coordination of MDR conveyor networks (Adaptive Predictive Coordination for MDR Conveyors—APC-MDR) is proposed, designed for sorting and coordinating the transportation of cylindrical loads in real time. The proposed approach uses a photoelectric sensor and light beam interruption time measurement to estimate the diameter of each incoming load. The obtained information is used for both load classification and sorting-branch assignment, as well as for adaptive speed control based on the product size and the occupancy of the selected downstream branch. A mathematical model of the coordination mechanism has been developed, integrating load estimation, predictive sorting, adaptive speed coordination and preparation of downstream conveyor sections in a single PLC-based architecture. The system is implemented on an experimental MDR conveyor platform built with a Siemens SIMATIC S7-1212C PLC, ConveyLinx Ai2 modules and PROFINET communication. The conducted experimental studies show reliable classification and sorting of cylindrical loads, with a sorting accuracy of approximately 98.33%. The obtained results also show a reduction in the average transportation time and a limitation of the number of stop–start events compared to conventional reactive control strategies. The study confirms that using information about the geometric characteristics of the load as a predictive control variable can improve the coordination and efficiency of MDR conveyor systems while maintaining contactless ZPA operation.

Technologies doi: 10.3390/technologies14090579

Authors: Belayneh Endalamaw Dejene Yaregal Assabie Mulugeta Tadele Bethlehem Adnew Rosa Tsegaye Yordanos Sintayehu Akililu Alemu Rawleigh Howe Agnes Kiragga Tsinuel Girma Tesfa Tegegne Alemseged Abdissa

Background: Human immunodeficiency virus (HIV) remains a major public health challenge in sub-Saharan Africa, where linguistic diversity and limited digital health resources constrain patient education and antiretroviral therapy (ART) adherence. Amharic, one of Africa’s most widely spoken languages, remains underrepresented in clinically oriented conversational AI systems. Methods: We developed SmartHIVCare, a bilingual (Amharic–English) multimodal conversational system integrating retrieval-augmented generation, multilingual semantic retrieval, Amharic-specific text normalization, and speech-based interaction for ART education and adherence support. Performance was evaluated using automated response quality metrics, clinician-based human evaluation, latency analysis, error analysis, and an ablation analysis on 50 bilingual queries (25 English, 25 Amharic). Results: SmartHIVCare achieved BLEU scores of 0.448 ± 0.386 (95% CI: 0.341–0.555) and a BERTScore of 0.820 ± 0.114 (95% CI: 0.789–0.852). Human evaluation yielded high ratings for accuracy (4.8/5, 4.6/5), clarity (4.6/5, 4.6/5), safety (4.6/5, 4.6/5), and clinical usefulness (4.8/5, 4.4/5) for English and Amharic, respectively. Response latency scaled with modality: 3.2 s (text-to-text), 4.1 s (text-to-speech), 5.8 s (speech-to-text), and 6.4 s (speech-to-speech). Conclusions: SmartHIVCare demonstrates the feasibility of retrieval-grounded bilingual conversational AI for HIV education in underrepresented languages. This proof-of-concept evaluation focuses on technical feasibility and response quality and requires validation through larger real-world clinical studies.

Technologies doi: 10.3390/technologies14090581

Authors: Muhammad Zohaib

Sixth-generation (6G) communication technologies are projected to enable exceptionally low-latency services, massive device connectivity, edge intelligence, and real-time processing of more complicated data. However, meeting these requirements using conventional technology is challenging because continued transistor scaling is associated with increased leakage current, power density, heat generation, and fabrication complexity. Emerging nanoscale computing technologies, particularly quantum-dot cellular automata (QCA), provide a promising alternative by enabling binary information processing through electronic interactions between neighboring cells rather than conventional current-driven switching. In this study, several QCA-based arithmetic and logic circuits are designed, including a fault-tolerant full adder (FA), 4-bit and 8-bit ripple-carry adders (RCA), and a binary-coded decimal (BCD) adder architecture. The proposed BCD adder performs binary addition and activates a decimal correction stage whenever the intermediate result exceeds nine or produces a carry-out. The circuit structures are enhanced to reduce cell count, occupied area, propagation delay, and energy dissipation while maintaining reliable signal transmission. The results demonstrate the potential of the proposed QCA arithmetic circuits as compact and energy-efficient computational components for future 6G edge devices, nanoscale processors, and communication systems.

Technologies doi: 10.3390/technologies14090580

Authors: Veronica Hernandez-Ramirez Dora-Luz Almanza-Ojeda Oscar Almanza-Conejo Alan Ortega-Gonzalez Igor Guryev Mario-Alberto Ibarra-Manzano

According to the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder among older adults, surpassed only by Alzheimer’s disease. PD is characterized by a heterogeneous combination of motor and non-motor symptoms, with involuntary movements constituting a central clinical defining feature. The prodromal phase presents substantial diagnostic challenges, as early symptoms are often mild, nonspecific, and commonly misinterpreted because overt motor signs are absent. Significantly, these early alterations can precede formal diagnosis by up to two decades, highlighting the urgent need for effective early detection strategies. In this work, we propose a functional Magnetic Resonance Imaging (fMRI)-based image-processing and machine learning framework for early detection of PD. Statistical features are extracted to train a set of 25 machine learning kernels, from which the most accurate model is selected. We then apply the Minimum Redundancy Maximum Relevance (mRMR) algorithm to identify the most descriptive fMRI frames for each subject. We introduce a hierarchical classification strategy: an initial binary classification to distinguish control subjects from prodromal + PD cases, followed by a second binary classification to discriminate between Prodromal and PD. The experimental results demonstrate a maximum precision of 96.3% with 16 axial slices and 86.4% with a single slice, indicating the effectiveness of the preprocessing strategy and its potential as a non-invasive biomarker for early PD detection, even with reduced input data. These findings suggest that high classification performance can be achieved with a limited number of fMRI slices, facilitating data acquisition and reducing subject burden in clinical studies.

Technologies doi: 10.3390/technologies14090578

Authors: Nándor Bakai Olivér Rák Patrik Márk Máder Dóra Erika Simon Bálint Bachmann Tünde Jászberényi Gergő Szeledi Miklós Halada József Etlinger Márk Balázs Zagorácz

Emergency Medical Services (EMS) personnel require exceptional spatial awareness and rapid decision-making within the confined environment of an ambulance. While Virtual Reality (VR) offers a safe alternative to traditional training, the lack of high-fidelity, regionally accurate, and VR-optimized 3D ambulance models limits its application. This study presents a scenario-driven methodology for developing a VR-ready 3D ambulance environment prototype tailored for Emergency Medical Technician (EMT) training. Utilizing reality-capture techniques, terrestrial laser scanning was performed to accurately document the interior of a standard Hungarian ambulance simulator. The resulting point cloud underwent systematic processing, manual retopology, PBR shading, and the implementation of a custom dual-rigging animation system to optimize complex mechanical movements—such as stretcher operations—for standalone VR platforms. The workflow successfully reduced the vertex count to 25,373 while maintaining millimeter-level spatial fidelity. Technical evaluation confirmed that geometrical, functional, and material objectives were fulfilled, whereas pedagogical implementation remains incomplete. Structural accuracy and animation readiness were verified through preliminary inspection within Blender’s VR viewport inspector. However, interactive game-engine integration remains future work, and educational effectiveness has not yet been tested with EMT learners. Overall, this workflow delivers a 3D asset foundation that establishes the necessary technical basis for subsequent software implementation and clinical evaluation.

Technologies doi: 10.3390/technologies14090577

Authors: Atef Ibrahim Fayez Gebali

The rapid expansion of the Internet of Medical Things (IoMT) and intelligent assistive technologies has intensified the need for resource-optimized cryptographic hardware to protect sensitive biometric data. Cryptographic hardware performance relies primarily on modular arithmetic operations, especially field multiplication. Although the binary extension field provides carry-less arithmetic ideal for battery-powered devices, standard general-purpose processors lack the dedicated hardware required to execute these operations efficiently. This paper proposes a novel multicore-based modular multiplication algorithm that bridges this gap by exploiting the parallel coordination fabric and high-bandwidth interconnects of modern embedded multicore systems. Central to this work is the Progressive Product Reduction (PPR) paradigm, which optimizes hardware efficiency by integrating the multiplication and field reduction phases into a unified process, thereby minimizing intermediate data storage and computational depth. We introduce and analyze two distinct architectural strategies—Progressive Product Reduction with Column Division (PPR-CD) and Progressive Product Reduction with Row Division (PPR-RD)—and establish rigorous mathematical models to estimate hardware area, critical path delay, and exact operational latency across various core configurations. Our performance evaluation demonstrates that the PPR-RD architecture achieves superior Area-Delay Product (ADP) and energy efficiency, providing a scalable framework for securing sensitive biometric data in next-generation assistive devices. This implementation ensures robust cryptographic protection for assistive devices while maintaining energy autonomy and computational resilience essential for sustaining consumer trust, advancing global health equity, and driving financial stability in future digital economies.

Technologies doi: 10.3390/technologies14090575

Authors: Olja Šovljanski Lato Pezo Tiana Milović Luka Mejić Dragoljub Cvetković Aleksandra Kardoš Stojanović Ana Tomić

Bacterial self-healing cement-based materials (BSHCMs) couple microbial mineralization with cement-based material design to autonomously seal cracks and potentially restore durability. However, their performance depends on a complex interaction among bacterial viability and physiological state, mineralization pathway, carrier and nutrient systems, calcium availability, matrix chemistry, crack characteristics, moisture, and exposure history. This review, supported by bibliometric mapping of 805 Scopus-indexed records, examines these interdependencies through the specific lens of artificial intelligence (AI) readiness. The mapping revealed six interconnected research themes spanning bacterial mineralization, sustainable cement-based systems, matrix chemistry and transport, encapsulation, durability, and machine learning (ML). AI evidence was classified as directly demonstrated in bacterial self-healing systems, transferable from adjacent concrete and structural health monitoring applications, or prospective. Although existing ML studies report high internal predictive performance, their engineering generalizability remains limited by heterogeneous and frequently literature-derived datasets, random train–test partitioning, potential feature leakage, synthetic data dependence, insufficient uncertainty reporting, and scarce independent laboratory or field validation. The analysis further shows that sustainability and economic benefits cannot be assumed from the biological nature of the technology but must be demonstrated through service life extension relative to the additional burdens of cultivation, nutrients, carriers, and processing. Advancing BSHCMs toward trustworthy AI-supported engineering, therefore, requires harmonized and machine-readable datasets, matched attribution controls, delayed cracking and realistic exposure experiments, explicit uncertainty and negative result reporting, study-grouped and external validation, and pilot- to field-scale testing. AI should consequently be regarded not as a substitute for biological healing, but as a decision support layer whose value depends fundamentally on the quality, traceability, and transferability of the underlying experimental evidence.

Technologies doi: 10.3390/technologies14090576

Authors: Zheng Chen Dapeng Jiang Jinhao Chen Yizhuo Zhang

Tea bud detection is a key technology for enabling automated and intelligent tea harvesting. However, accurate detection remains challenging because tea buds are generally small, densely distributed, partially occluded, and visually similar to the surrounding background. In addition, resource-constrained devices impose strict requirements on model size and computational complexity. To address these challenges, a lightweight tea bud detection model, termed CLBD-YOLO, is proposed for resource-constrained tea bud detection applications. Based on YOLOv11n, a C3K2_GD module is designed for the backbone and neck to combine ghost-style lightweight feature generation with input-conditioned dynamic feature modeling. A three-level weighted BiFPN is employed for bidirectional multi-scale feature fusion, while LAE, DAT, and MPDIoU are incorporated for downsampling feature extraction, adaptive spatial representation, and bounding box regression, respectively. Experiments on the predefined TeaBud test subset containing 625 images show that CLBD-YOLO achieves 74.31% precision, 77.07% recall, 85.67% mAP50, and 62.69% mAP50–95. Compared with YOLOv11n, the parameter count is reduced from 2.582 M to 1.426 M and the computational cost from 6.3 G to 5.6 G FLOPs, corresponding to reductions of approximately 44.8% and 11.1%, respectively. For the pre-declared seed-0 run, mAP50 and mAP50–95 decrease by 0.22 and 1.40 percentage points, respectively; when averaged over the three matched random seeds, the corresponding decreases are 0.77 and 1.89 percentage points. Because near-duplicate screening revealed cross-subset augmented siblings in the predefined TeaBud split, these results are interpreted as within-distribution reference values rather than as strictly leakage-free generalization estimates. Direct evaluation on the 591-image external Xinyang Maojian tea bud validation subset using the fixed TeaBud-trained weights without additional fine-tuning or threshold tuning yields 81.2% mAP50 and 62.5% mAP50–95, respectively, for CLBD-YOLO, compared with 80.4% and 61.2% for YOLOv11n. These results show that the compact model remains competitive under this external data distribution. Overall, CLBD-YOLO substantially reduces model complexity while maintaining competitive tea bud detection performance, demonstrating a favorable accuracy–complexity trade-off for resource-constrained applications.

Technologies doi: 10.3390/technologies14090574

Authors: Oana Stefania Damian Radu Bosoanca Costel Ungureanu

Hydraulic transients induced by rapid changes in operating conditions represent a major challenge in the design and safe operation of offshore Firewater systems. During emergency events, such as fire pump start-up or rapid valve operations, pressure waves may propagate throughout the distribution network, generating water hammer effects capable of compromising the integrity and reliability of critical safety equipment. This study investigates the transient hydraulic response of an offshore Firewater ring-main system installed on a Floating Production Storage and Offloading (FPSO) unit using a detailed numerical model developed in PIPENET Transient. Three representative emergency operating scenarios were analysed, including fire pump start-up, deluge valve closure, and monitor valve closure. For each scenario, the hydraulic response of the original system configuration was compared with a modified pressure-protection arrangement involving relocation of the check valve immediately downstream of the fire-pump discharge flange and reduction of the pressure safety valve (PSV) set pressure from 17.5 barg to 16.5 barg. The simulations enabled the identification of critical pressure locations, evaluation of transient pressure propagation, and assessment of the effectiveness of the proposed mitigation strategy. The results demonstrate that the modified pressure-protection arrangement reduces the governing system-level pressure peaks and attenuates transient pressure oscillations under the investigated operating conditions. The proposed engineering methodology provides practical support for the design verification and optimization of offshore Firewater systems and contributes to improving the operational safety and reliability of safety-critical piping networks.

Technologies doi: 10.3390/technologies14090573

Authors: Eduardo Vázquez Aldo Méndez Leopoldo A. Garza Gerardo Romero Marco A Panduro Omar Elizarraras

Underwater Wireless Sensor Networks (UWSNs) are constrained by limited energy resources, high acoustic propagation delay, and topology variations caused by underwater mobility. This paper proposes a hybrid clustering framework that integrates the Binary Dragonfly Algorithm with Q-learning (BDA-QL) for adaptive cluster-head selection in UWSNs. The proposed method formulates clustering as a binary multi-objective optimization problem considering acoustic-aware energy consumption, end-to-end latency, and cluster load balance. Q-learning is incorporated to dynamically adjust the Dragonfly algorithm coefficients during the optimization stage, while the selected clustering configuration is evaluated under a controlled semicircular mobility model. Simulations were conducted with 100 nodes deployed in a 500 m × 500 m area over 500 simulation rounds and compared against GA, LEACH, C-LEACH, SS-GSO, CDFO-UWSN, and BDA. The results show that BDA-QL preserved the highest number of alive nodes, retaining 59 nodes at the final round, achieved the lowest final latency with 25.43 s, and delivered the highest number of packets, reaching 45,339 packets. BDA-QL provided the strongest overall trade-off across network lifetime, latency, packet delivery, and energy preservation. These findings suggest that reinforcement-learning-based coefficient adaptation can improve the robustness of Dragonfly-based clustering under underwater mobility conditions.

Technologies doi: 10.3390/technologies14090571

Authors: Molin Su Gang Yu Zhijie Gao Huajun Tao Huitao Li Zihui Gao Yingli Li Yue Zhao Mingchao Bai Hongqiao Yan Kai Song

Centrifugally cast 35Cr45NiNb alloy has been widely employed in ethylene-cracking furnace tubes owing to its excellent carburization and creep resistance. However, the influence of microstructural degradation and temperature on its high-temperature tensile behavior remains poorly investigated. In this study, an accelerated aging method at 1200 °C for 230 h (A1) and 430 h (A2) was employed to simulate approximately 4 and 8 years of service at 1050 °C, based on the Larson-Miller parameter. The equivalence was validated by the nearly identical precipitate area fractions of the A1 specimen (16.6%) and an ex-service specimen (14.8%). Combined with SEM and EBSD characterization, tensile tests at 950, 1000, and 1050 °C were conducted to elucidate the relationship between microstructure and high-temperature tensile properties. During aging, the skeletal interdendritic M7C3 carbides transformed into blocky M23C6, NbC evolved into the brittle G-phase (Ni16Nb6Si7), fine secondary M23C6 precipitates formed, and the initially continuous primary-carbide network progressively coarsened. Yield and ultimate tensile strengths decreased monotonically with increasing temperature, whereas aging produced pronounced hardening at the expense of ductility, as secondary-carbide precipitation strengthening outweighed the weakening of the primary carbide network. The fracture mode transitioned from mixed quasi-cleavage fracture at 950 °C, initiated by stress concentration at coarse phase interfaces, to ductile rupture at 1000 and 1050 °C. GND analysis further revealed an aging-dependent transition in the dominant deformation mechanism, from dislocation pile-up at the carbide network, to recrystallization after prolonged aging.

Technologies doi: 10.3390/technologies14090572

Authors: Abdulrahman M. Alanazi

Ultrasonic pulse-echo imaging is one of the most widely used non-destructive evaluation (NDE) modalities for monitoring the structural integrity of reinforced-concrete civil infrastructure such as bridge decks, tunnel linings, and dam walls. In this acquisition geometry, a single low-frequency transducer is mechanically raster-scanned over the accessible top surface of the specimen and records one A-scan per scan position, simultaneously serving as transmitter and receiver. However, commonly used reconstruction algorithms such as the Synthetic Aperture Focusing Technique (SAFT) and Reverse Time Migration (RTM) tend to produce reconstructions of limited quality on this class of data because they do not adequately model the round-trip propagation kernel that is specific to the monostatic geometry, they do not separate the strong near-surface direct-arrival reflection from the bulk image, and they do not account for the persistent aggregate-induced clutter that contaminates every A-scan in concrete media. In this paper, we propose a clutter-aware reconstruction method for monostatic ultrasound acquisition (CARMA), whose main innovation is the joint integration of a monostatic-specific round-trip propagation model, a dedicated near-surface direct-arrival subspace, and a data-adaptive low-rank clutter subspace within a unified model-based reconstruction framework. Unlike existing reconstruction approaches, CARMA explicitly accounts for the co-located transmit–receive geometry through a squared-cosine round-trip directivity model while simultaneously separating scan-dependent direct-arrival contributions and aggregate-induced clutter from the desired reflectivity image. To verify the method under fully controlled and repeatable conditions, we generate intensive, physically realistic full-wave simulations with the k-Wave pseudo-spectral acoustic solver that reproduce a representative civil-infrastructure inspection scenario: three reinforced-concrete specimens with a stepped back wall of varying thickness, ten embedded ground-truth defects spanning steel tendon ducts and low-impedance polystyrene inclusions, a monostatic raster-scanned pulse-echo acquisition, and randomly distributed aggregate scatterers that reproduce the clutter of real concrete. Results on these intensive k-Wave simulations indicate that CARMA reconstruction yields approximately 2× lower localization error than RTM and approximately 4× lower localization error than SAFT, while recovering the deepest embedded defect with substantially better localization and contrast than the comparison methods.

Technologies doi: 10.3390/technologies14090570

Authors: Guiheng Zou Kok Beng Gan

Balancing production resilience with environmental, social and governance (ESG) performance is difficult when disruptions, policy constraints and stakeholder expectations interact over time. This paper proposes a six-node dynamic network data envelopment analysis architecture whose admissible weight intervals are adjusted by an LSTM learner using IoT-derived shock states. The application covers 96 aggregate monthly periods from 2018 to 2025 across three countries and four technology-based manufacturing groups. Figure-grounded diagnostics indicate mean resilience scores of 0.7016 for the basic IoT-DEA benchmark and 0.7134 for the complete model (paired difference = 0.0118; 12-month moving-block bootstrap 95% CI = 0.0015–0.0245; p = 0.001), while mean uncertainty falls from 0.0536 to 0.0205, a 61.8% reduction. Threshold sensitivity shows that governance-delay and skill boundaries vary by industry and carbon-constraint severity rather than constituting universal standards. In the calibrated policy simulation, the combined carbon-tax and green-subsidy path reaches approximately 0.90 by month 96, compared with 0.79 under no policy; this contrast is interpreted as a scenario result, not a firm-level causal treatment effect. NSGA-III and SHAP then connect the measured constraints to Pareto-efficient portfolios and sector-specific managerial priorities. The framework’s main supported contribution is more stable, temporally explicit ESG-resilience diagnosis with transparent limits on causal and cross-sectional inference.

Technologies doi: 10.3390/technologies14090569

Authors: Vladimir Malikov Sergey Voinash Farmon Mamatov Aliya Moldakhmetova Amangeldi Kanaev Evgeniy Y. Remshev Alexander Katasonov

Metal-cutting operations can generate resource losses not only through the kerf itself but also through subsequent reworking, removal of altered edge material, and processing of workpieces that later prove unsuitable. This study develops and experimentally evaluates an automated eddy-current inspection system intended to characterize metal edges immediately after cutting. The system combines a miniature high-frequency eddy-current transducer, three-axis positioning, digital signal acquisition, and software-based processing. A clad D16AT aluminum alloy specimen with edges produced by laser cutting, cold sawing, and hot shearing was scanned. The air-to-metal transition profiles were described by a logistic function, yielding an electromagnetic transition coordinate xc, a transition parameter s, and the coefficient of determination R2. The fitted xc values were 7.30, 8.76, and 8.93 mm for cold-sawn, laser-cut, and hot-sheared edges, respectively; s was 0.64, 0.59, and 0.67 mm, while R2 was 0.961, 0.959, and 0.941. These quantities are interpreted as comparative electromagnetic descriptors and not as direct measurements of heat-affected-zone depth or defect probability. A scenario calculation based on the displacement of the electromagnetic transition relative to the geometric edge gave apparent material-removal indices of 0.192, 1.127, and 1.236 g per 40-mm edge. Under this explicitly model-based scenario, the laser-cut edge was 8.8% lower than the hot-sheared edge. Complementary measurements showed concordant ordering of the electromagnetic descriptors with roughness, burr height, HV0.1, altered-zone depth, conductivity, and removed-layer mass; the apparent and measured masses differed by 0.3–1.1% for this specimen. The results demonstrate that automated eddy-current mapping can differentiate edge states and provide structured data for routing decisions in resource-efficient and zero-defect manufacturing. Independent-specimen replication, fully traceable physical characterization, and production-scale validation are required before the descriptors can be used as acceptance thresholds or as direct estimates of actual waste.

Technologies doi: 10.3390/technologies14090568

Authors: Oscar Walduin Orozco-Cerón Orlando Joaqui-Barandica Diego F. Manotas-Duque

This study addresses a regime-conditioned question in electricity theft detection: when only monthly billing series and inspection-confirmed labels are available, which supervised model families recover irregular consumption without relying on the temporal resolution of advanced metering infrastructure (AMI)? The working sample comprises 4000 utility customers and 864 confirmed theft cases, each represented by 53 monthly kWh values from January 2021 to May 2025. After majority-class undersampling that retains all theft observations and construction of a balanced 1:1 learning set, eight classifiers are compared under an 80/20 stratified split: K-Nearest Neighbors, Decision Tree, Support Vector Machine, Random Forest, two dense multilayer perceptrons, Long Short-Term Memory, and a one-dimensional Convolutional Neural Network. Performance is assessed through threshold-optimized accuracy together with precision, recall, F1-score, the area under the receiver operating characteristic curve (AUC), and confusion matrices. On the hold-out test set, Random Forest and the compact dense network both reach an accuracy of 0.685; Random Forest attains the highest AUC (0.748) and F1-score (0.677). Even so, these models miss about one-third of the hold-out theft accounts (59 and 65 false negatives out of 173). Sequential deep models underperform on this short monthly regime. The results support ensembles and compact dense networks for monthly theft screening and indicate that AMI-oriented sequential gains do not transfer automatically to 53-point billing vectors under the present protocol.

Technologies doi: 10.3390/technologies14090567

Authors: Yuqi Wen Yingtao Niu Yusi Zhang

Addressing the dual threats of malicious jamming and time-varying fading faced by wireless communication links in complex dynamic electromagnetic adversarial environments, existing intelligent anti-jamming methods predominantly focus on single-dimensional resource optimization under quasi-static channels. This focus neglects the nonlinear superposition effects of multi-path deep fading and dynamic strong jamming in the time-frequency domain, making it challenging for systems to balance transmission reliability and system energy efficiency in physical environments where fading and suppression coexist. To address this issue, this study proposes a joint intelligent anti-jamming method for channel switching and transmit power control based on a Deep Q-Network (DQN). Initially, a composite communication environment model incorporating Markov time-varying fading and jamming is constructed. Subsequently, the joint resource scheduling problem is formulated as a Markov Decision Process. The environment state space is reconstructed by integrating continuous channel state estimation and jamming observation features, accompanied by the design of a highly aggregated two-dimensional discrete action space for both channel and power. Finally, a composite reward function evaluating both communication success rates and power consumption costs is proposed to guide the agent in multi-dimensional resource joint optimization. Simulation results demonstrate that the proposed algorithm effectively extracts implicit features under the composite state of fading and jamming. When encountering extreme deep fading or full-band blocking, the agent strategically triggers a silent mechanism to avoid exorbitant invalid energy consumption penalties, while precisely matching interference-free channels with the minimum effective transmit power during favorable communication windows. Simulation results show that compared with traditional xx algorithms, the proposed method significantly improves the dynamic successful transmission rate and system energy efficiency in complex, highly dynamic scenarios, achieving an effective optimization of anti-jamming reliability and low power overhead.

Technologies doi: 10.3390/technologies14090566

Authors: Jamil Ahmad Habiba Almetnawy Ahed Orabi Mustaqeem Khan Haleem Farman Farman Ullah

Medical-image retrieval systems must balance semantic relevance with storage and search cost while remaining robust to errors introduced by hierarchical routing. This work presents an anatomy-aware hierarchical contrastive hashing framework for radiograph classification and retrieval. A calibrated ConvNeXt-Tiny classifier first estimates anatomical-region probabilities, after which a shared Swin-Tiny encoder and lightweight anatomy-specific heads produce fine-grained predictions and compact binary codes. The hashing objective combines embedding-level supervised contrastive learning with hash-space semantic supervision, route-specific binary prototypes, a sign-margin constraint, quantization, and route-wise bit balance. Confidence-adaptive multi-route database indexing and top-r query routing are used to reduce irrecoverable failures caused by hard Stage-1 assignment. Experimental results on IRMA and MURA datasets reveal that the proposed framework improves retrieval performance and efficiency over competing deep-feature and hashing-based approaches. The Stage-1 classifier on IRMA achieved 97.05% sample-level accuracy, 93.15% macro recall, and 95.33% macro-F1, whereas Stage 2 reported 96% accuracy and 93.2% macro-F1. 128-bit hash codes achieved Precision@20 of 0.96, and mAP of 0.871. On MURA, Stage 1 achieved 96.93% anatomy-classification accuracy, 96.44% macro-F1, and a calibration error of 0.0126. The 14-class Stage 2 model achieved 75.13% accuracy and 74.84% macro-F1. Joint anatomy–abnormality retrieval showed a code-length-dependent trade-off: 32-bit codes obtained the highest mAP of 0.646, while 256-bit codes achieved the best early-rank performance with Precision@10 of 0.631 and nDCG@10 of 0.626. Study-level normal/abnormal prediction achieved an AUROC of 0.860, AUPRC of 0.857, and accuracy of 80.46%. These results support the use of anatomy-aware routing and compact semantic hashing for efficient radiograph retrieval, while also showing that abnormality-level discrimination remains substantially more challenging than anatomical categorization.

Technologies doi: 10.3390/technologies14090565

Authors: Marina I. Aleutdinova Viktor V. Fadin

The development of new principles for controlling the technical systems requires the use of reliable actuators. A sliding steel/steel contact under a high-density electric current could serve as one of their elements. The search for the factors reducing the wear of the contacts is a subject of scientific and commercial interest. One such factor is the melting that occurs within a sliding steel/steel electrical contact space. The overall goal of this study is to describe the characteristics of an M2 steel/steel sliding electrical contact during the formation of a melt-like state in the contact zone. Dry sliding of M2 steel (sample) against C45 steel (counterbody) under an alternating electric current at a density higher than 100 A/cm2 is performed as a model experiment using the well-known “pin-on-ring” tribo-loading configuration. The formation of tribolayers on the sample and the counterbody is demonstrated using a scanning electron microscope and a MICRO MEASURE 3D-station non-contact device. According to the EDX analysis, the contact layers predominantly contain oxygen and iron. Two sectors with different morphological features are observed on the M2 steel sliding surface. A melt-like state is visible in one of the sectors. This state is assumed to form within a certain sublayer. A method for calculating this sublayer thickness is proposed. This thickness does not exceed 10 μm. The calculation model has revealed that the thickness of this sublayer depends mainly on the external impact power rather than on the atomic and phase composition of the tribolayer. An increase in the contact current density is consistent with an increase in the calculated thickness of this sublayer, an increase in the electrical conductivity of the contact, and a decrease in the coefficient of friction (COF). The proposed analysis of the obtained results could serve as a basis for broader generalizations.

Technologies doi: 10.3390/technologies14090564

Authors: Vasileios P. Rekkas Sotirios Sotiroudis George V. Tsoulos Stavros Koulouridis Zaharias D. Zaharis Mohammad A. Matin Panagiotis Sarigiannidis George Karagiannidis Christos. G. Christodoulou Sotirios K. Goudos

As bandwidth-intensive applications proliferate and the usage of wireless devices surges, fifth-generation (5G) and beyond (B5G) networks are challenged to enhance coverage, reduce latency, and improve efficiency. The application of machine learning (ML) models for received signal strength (RSS) estimation is a powerful tool. This study evaluates various ML models—categorical boosting (CatBoost), extreme randomized trees (ETs), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost)—for effective estimation of RSS. Additionally, we apply explainable artificial intelligence (XAI) methodologies, especially the Shapley additive explanation (SHAP) framework. Our investigation reveals the sophisticated mechanisms within these models, notably highlighting the exceptional accuracy of the ET model. We further introduce SHAPRP, in which SHAP attributions reduce the input space and sparse regression selects a compact subset of the ET ensemble. The results are obtained from a single-operator rural/semi-rural campaign and constitute a case study, where the trained estimator is deployment-specific and is not a pre-trained model applicable to arbitrary 5G/B5G scenarios, so what transfers is SHAPRP itself.

Technologies doi: 10.3390/technologies14090563

Authors: Min Hui Tan Qin Xiang Ng Sharna Si Ying Seah Favian Fang Yu Lim Patrick Jia Jing Lee Brenda Geok Ting Ong Elaine Kee Chen Siow Wai Pong Wong Jade Gek Sang Soh Hozaidah Binte Hosain

Background: Immersive virtual reality (VR) may support rehabilitation and patient engagement; however, little is known about how frontline healthcare professionals perceive its clinical relevance and feasibility in post-acute community care settings. This study explored healthcare professionals’ perceptions of the potential clinical uses, usability, and implementation requirements of patient-facing immersive VR in a community hospital providing post-acute care in Singapore. Methods: Using purposive sampling, nineteen healthcare professionals from a subacute hospital in Singapore participated in individual semi-structured interviews. Participants discussed their expectations of VR, experienced approximately 10–15 min of a reminiscence-oriented virtual tour of a local (now defunct) village, using an Oculus Quest head-mounted display, and subsequently reflected on its usability and potential applications. The Unified Theory of Acceptance and Use of Technology 2 informed the interview guide and was used as a sensitising framework. Interviews were transcribed verbatim and analysed using hybrid deductive–inductive framework analysis. Results: Four themes were identified: (1) clinical value was conditional on therapeutic relevance; (2) anticipated patient acceptability depended on person–content fit and social context; (3) simple navigation did not remove physical and sensory barriers; and (4) clinical integration required a supporting service model. Conclusions: Healthcare professionals viewed immersive VR as potentially useful but not yet ready for routine clinical implementation. Adoption in post-acute care should be guided by therapeutic purpose, accessibility, and integration into existing workflows rather than technological novelty alone. Future research and development should prioritise co-designed therapeutic content, patient selection, accessibility, workflow integration, and implementation support.

Technologies doi: 10.3390/technologies14090562

Authors: Yinyin Li Lei Liu Yeguo Sun Qingyu Liu

Detecting traffic signs in real-world roadway scenes remains a demanding task due to extensive category diversity, the prevalence of diminutive targets, and interference from cluttered surroundings. To overcome these obstacles, we present YOLO-PPA, a YOLOv11n-based detector strengthened through multi-path feature aggregation and attention-enhanced representation learning. First, a Parallelized Patch-Aware Attention (PPA) mechanism is embedded in place of the standard C3K2 block, simultaneously capturing fine-grained local textures and broad contextual semantics while adaptively amplifying informative spatial regions critical for small objects. Second, a high-resolution P2 detection head is appended to the feature pyramid, recovering fine spatial cues that would otherwise be attenuated across successive downsampling stages, and this design is particularly beneficial for recognizing signage occupying only a handful of pixels. Third, the Normalized Gaussian Wasserstein Distance (NWD) replaces the conventional CIoU metric as the regression loss, offering a smoother optimization landscape for tiny instances where even single-pixel displacements can destabilize standard IoU-based objectives. Evaluated on the TT100K benchmark, YOLO-PPA surpasses the YOLOv11n baseline by 2.1% in precision, 3.7% in recall, 4.3% in mAP@50, and 3.0% in mAP@50:95, confirming its effectiveness for small-scale traffic sign recognition in complex driving environments.

Technologies doi: 10.3390/technologies14090561

Authors: Bruno Kostura Radim Škuta Vladislav Kurka Vlastimil Matějka Marek Velička Facundo Barraque Michal Ritz Jana Seidlerová Silvie Vallová Jozef Vlček

The recovery of zinc from mixed steelmaking and blast furnace sludge was investigated through a direct comparison of the hydrometallurgical acetic acid leaching process and pyrometallurgical processing using coke dust as a reductive agent, with particular emphasis on the structure and functional properties of the resulting ZnO products. The mixed sludge contained 9.71 wt.% ZnO, with zinc occurring predominantly in the form of stable ferritic phases, mainly franklinite ((ZnFeII)Fe2O4). Hydrometallurgical treatment using 1 mol·dm−3 acetic acid resulted in only a limited relative decrease in Zn content (16.8%) based on solid-phase concentrations, as mainly readily soluble zinc phases were dissolved, while ferrite-bound zinc remained largely unaffected. In contrast, pyrometallurgical treatment of the sludge–coke dust mixture at 1200 °C enabled highly effective zinc volatilization, reducing the residual Zn content below 0.05 wt.% and resulting in a relative decrease in Zn content of 99.5% based on solid-phase concentrations. Characterization by XRD, FTIR, SEM, and EDS revealed substantial differences between the products obtained by the two processing routes. The pyrometallurgically prepared ZnO-rich product exhibited higher crystallinity, well-developed rod-like morphology, and substantially lower Fe and Pb contamination than the hydrometallurgically prepared material. In contrast, the hydrometallurgical product showed lower crystallinity and elevated Fe and Pb contents and therefore represented a highly contaminated ZnO-rich oxide mixture. Photocatalytic tests using Rhodamine B showed measurable but low activity of the pyrometallurgical product, with approximately 18% degradation after 120 min of UV irradiation, whereas the hydrometallurgical product was practically inactive. Overall, carbothermic treatment provided substantially more effective Zn removal than acetic acid leaching, although the recovered ZnO-rich product exhibited only limited photocatalytic activity.

Technologies doi: 10.3390/technologies14090560

Authors: Mohamed A. Sobhy Ahmed H. EL-Ebiary Mahmoud A. Attia Ahmed O. Badr

In order to improve maximum power point tracking (MPPT) and energy extraction in grid-connected photovoltaic (PV) systems under various climatic conditions, this research proposes a Secretary Bird Optimization-tuned Super-Twisting Sliding Mode Controller (SBOA–STSMC). The efficacy of traditional MPPT and adaptive control techniques is frequently compromised by nonlinear dynamics, abrupt changes in irradiance, and partial shading. In order to overcome these restrictions, the suggested method uses the Secretary Bird Optimization Algorithm (SBOA) to optimize STSMC parameters, resulting in improved disturbance rejection and transient response. Five increasingly difficult simulation scenarios are used to evaluate the controller. A single-array grid-connected system under stepwise irradiance fluctuations, combined irradiance–temperature disturbances, severe atmospheric dynamics, and low-irradiance operation are examined in Scenarios 1–4. The performance of adaptive controllers tuned using the Harmony Search Algorithm (HSA) and Invasive Weed Optimization (IWO) is compared with the traditional Incremental Conductance approach. In the fifth scenario, which deals with dynamic partial shading in a dual-array arrangement, the suggested approach is contrasted with HSA-based and IWO-based adaptive controllers and the traditional Perturb and Observe (P&O) technique. Furthermore, a sensitivity analysis is carried out for ±50% parameter modifications, demonstrating a small change in the total injected energy. An additional severe-disturbance test under rapid irradiance variations is performed, together with a ±50% sensitivity analysis of the grid-side choke inductance under the same severe profile. Statistical repeatability is further evaluated through 30 independent runs of the SBOA. A separate statistical comparison based on 30 independent runs of SBOA, TLBO, and PSO is also performed using the Wilcoxon rank-sum test, supporting the superior and consistent performance of SBOA. In addition, real-time validation is conducted using a Speedgoat real-time platform to demonstrate the practical implementation capability of the proposed controller. The results confirm improved dynamic response, reduced oscillations, enhanced robustness, and increased energy injected into the grid across all considered operating conditions.

Technologies doi: 10.3390/technologies14090559

Authors: Raimundo Castillo Marc Schmidt Arisbel Cerpa-Naranjo José O. Martínez

This study investigates the optimization of airflow distribution and pressure drops in H14 HEPA minipleat filters through the introduction of the hot-melt cell as the fundamental hydraulic unit governing local flow behavior. A coupled analytical framework integrating Falkner–Skan boundary-layer theory, Darcy–Weisbach channel friction, and Darcy porous-medium flow was developed and experimentally tested using velocity measurements obtained in a 600 m3/h test bench operating at a frontal velocity of 0.45 m/s under laminar-flow conditions. Ten primary geometric configurations and 21 hot-melt distribution scenarios (totaling 31 cases plus an optimized design case) were systematically evaluated by varying cell width (W), inlet height (Hi), and pleat length (L). Experimental and analytical results reveal significant velocity heterogeneity in the vicinity of the filter surface, which progressively decreases with distance from the filter, while localized velocity amplification is observed near the hot-melt separators. The analysis demonstrates that hydraulic diameter, pleat angle, and hot-melt spacing are the dominant parameters governing pressure drop generation and flow redistribution. Among the configurations investigated, a model-predicted optimized design (W = 46.25 mm, L = 55.00 mm, 230 pleats) yields a theoretical pressure drop reduction of up to 29.23% without significantly compromising the effective filtration area. These results provide an analytical framework for pre-prototyping optimization, although experimental verification of physical prototype validation for mechanical integrity and the preservation of initial efficiency, among other governing physical quantities, remains essential. The results demonstrate that the proposed hot-melt cell concept provides a practical engineering framework for the aerodynamic optimization of minipleat HEPA filters, enabling improved flow uniformity and reduced energy consumption in cleanroom applications.

Technologies doi: 10.3390/technologies14090558

Authors: Eswar Karri Archana Pallakonda Rayappa David Amar Raj Janusz Starczewski Cristian Randieri

Fingerprint presentation attack detection (PAD) remains an open problem owing to continually developing spoofing schemes and large variations in sensors and acquisition conditions. In this paper, we propose SFAD-Net, a lightweight dual-stream spatial–frequency attention network for fingerprint presentation attack detection. The framework integrates complementary spatial and wavelet-based frequency representations with a lightweight attention-guided fusion mechanism to improve discriminative feature learning while maintaining computational efficiency. We evaluate SFAD-Net on the LivDet 2011, 2013, and 2015 datasets using the standard intra-sensor evaluation protocol, together with an additional cross-sensor evaluation on the LivDet 2015 dataset to investigate the impact of sensor-induced domain shifts. The results demonstrate that the proposed method achieves an accuracy of 99.51% on the LivDet 2011 Sagem sensor while maintaining strong intra-sensor presentation attack detection performance across the independently evaluated LivDet benchmark datasets. At the dataset level, SFAD-Net achieves mean ACER values of 1.56%, 1.17%, and 2.60% for LivDet 2011, 2013, and 2015, respectively, together with low APCER and BPCER across the evaluated sensors. The cross-sensor evaluation further highlights the challenges posed by sensor-induced distribution shifts and provides additional insight into the behavior of the proposed framework under unseen sensor conditions. Extensive ablation studies systematically evaluate the contribution of the dual-stream design, wavelet-based frequency representation, and attention-guided feature fusion. In addition to its detection performance, SFAD-Net contains 0.41 M parameters, requires 2.56 GFLOPs, and has a model size of 4.86 MB, achieving 12.31 FPS in the local computational benchmarking setup. Direct deployment on a 4 GB Raspberry Pi 5 further provides evidence of its practical feasibility. Among the evaluated reduced-precision representations, FP16 substantially reduces model size while preserving PAD performance close to FP32, whereas INT8 achieves 35.99 ms latency and 27.79 FPS but introduces a noticeable degradation in PAD performance under the evaluated LivDet 2015 Crossmatch condition.

Technologies doi: 10.3390/technologies14090557

Authors: Qian Zhang Lu Liu Huiping Zheng Xueting Cheng Juan Wei Ji Zhang Yuxiang Li

To address the challenges of rapid voltage fluctuations and operational economy in new power systems with high wind power integration under sandstorm weather, this paper proposes a multi-timescale cooperative voltage control strategy for new power systems. On the second-level timescale, a discrete state-space model of wind turbines and reactive power devices is established considering sudden wind speed changes. Model predictive control (MPC) is then used to rapidly calculate the optimal reactive power references for wind turbines, static var generator (SVG), and on-load tap changer (OLTC), thereby effectively ensuring rapid stabilization of the grid-connection point voltage. On the minute-level timescale, a two-stage topology reconfiguration method is adopted. A feasible radial network is first generated through a sequential switch opening strategy, followed by iterative optimization via a switch exchange strategy. This approach rapidly identifies the optimal switch configuration to minimize network losses and improve operational economy. Simulation results demonstrate the effectiveness of the proposed strategy in voltage regulation and loss reduction, highlighting its capability to enhance the robustness of new power systems under sandstorm weather.

Technologies doi: 10.3390/technologies14090556

Authors: Rômulo Barreto Mincache Leonardo Gabiato Catharin Lucas de Oliveira Teixeira Thelma Elita Colanzi Yandre Maldonado e Gomes da Costa Valéria Delisandra Feltrim

Academic dropout is costly for students and institutions, and support actions are most effective early, when little information beyond enrollment records is available. However, the most informative predictors are derived from academic trajectory data, such as grades and course progression, which only become available after students have completed one or more terms. This study investigates dropout prediction using supervised machine learning (ML) applied to records of 66,820 students across 45 undergraduate programs of the State University of Maringá, Brazil (2002–2022), trained exclusively on static enrollment-time variables, since these were the only institutional data available. Although this restricts the information the models can use, it also allows students who may be at risk to be flagged very early. Decision Tree, Random Forest, and eXtreme Gradient Boosting (XGBoost) models were evaluated in ten training configurations, covering the complete dataset, the complete-generations subset, a temporal cohort split, academic centers, individual programs, and resampled variants. Evaluation was based on per-class precision, recall, and F1-score, together with threshold-free and calibration measures (PR-AUC, ROC-AUC, and Brier score). XGBoost performed best in the institution-wide configurations, reaching a dropout recall of 0.64 at a precision of 0.51 with undersampling and, without resampling, a PR-AUC of 0.586 against a dropout prevalence of 0.364, which corresponds to 1.61 times the performance of random ranking. Under the temporal split, recall at the default threshold fell from 0.37 to 0.18, while ROC-AUC changed little (0.708 to 0.679); this loss reflects miscalibration under changing dropout prevalence and can be corrected without retraining the model. The main contributions are an evaluation methodology for enrollment-time dropout prediction that controls information leakage and includes temporal validation, and a deployment protocol that uses the model outputs to prioritize student support when follow-up capacity is limited.

Technologies doi: 10.3390/technologies14090555

Authors: Adrián Belarra Irene Hernández-Girón Peter Homolka Diego García-Pinto Margarita Chevalier

An anthropomorphic patient-derived 3D-printed breast phantom (4.8 cm compressed thickness, 12.2% volumetric breast density) was evaluated for image-quality assessment in contrast-enhanced mammography (CEM). It comprises four 1.2 cm plates printed via fused filament fabrication (PLA/ABS). Five replicates of one of the plates (target) containing a glandular-tissue surrogate and four cavities for iodinated disks with same iodine area density per plate (0.2–0.5–0.75–1.0–1.5 mg/cm2) were fabricated. As a reference, a homogeneous PET-G phantom was printed. CEM images (low-energy (LE), high-energy (HE), and DES) were acquired in three different systems. Iodine signal linearity (mean pixel value, signal difference, signal-difference-to-noise ratio (SdNR)) and background cancellation (residual signals, texture cancellation coefficient (TCC), parameter β from noise power spectrum (1D-NPS)) were estimated. Evaluation across the three systems demonstrated the phantom provides appropriate measurements of iodine signal linearity (R2 > 0.986, based on mean values across the four inserts) and background cancellation (TCC:0.04–0.11). Fitting 1D-NPS = αf−β within 0.25–1.0 mm−1 yielded β values ≈ 3.1–4.0, compatible with the literature, for patient LE and HE images, whereas DES reduced β to ≈1.1. In the homogeneous phantom, β remained invariant between LE and DES images (β ≈ 0.6). The local glandular ratio at each position of the iodinated disks enabled evaluating the impact of background on iodine signal.

Technologies doi: 10.3390/technologies14090554

Authors: Prisca Viviani Cecilia Testa Federico Lissandrello Luca Magagnin

The increasing demand for miniaturized, flexible and wearable electronics has accelerated the development of printed electrochemical energy-storage devices capable of combining high performance with scalable and cost-effective manufacturing. Among emerging electrode materials, MXenes have attracted significant attention owing to their exceptional electrical conductivity, hydrophilic surface chemistry, tunable interlayer structure and excellent solution processability, making them particularly suitable for jet printing technologies. In particular, inkjet printing (IJP) and aerosol jet printing (AJP) enable maskless, high-resolution and material-efficient fabrication of micro-scale energy-storage devices while offering excellent compatibility with flexible substrates. This review provides a comprehensive overview of the use of MXene-based inks in jet-printed energy-storage devices, emphasizing the relationships between MXene physicochemical properties, ink formulation, printing processes and electrochemical performance. First, the structural characteristics, synthesis strategies and electrochemical charge-storage mechanisms of MXenes are discussed, together with the rheological, colloidal and stability requirements governing ink printability. The fundamental principles of IJP and AJP are then critically analyzed, highlighting the influence of solvent systems, printability criteria, processing parameters and deposition conditions. Recent advances in jet-printed MXene-based supercapacitors and micro-supercapacitors are comprehensively and critically reviewed, with particular attention to how material design, ink formulation, and printing strategies affect device performance. Battery-related studies, which remain comparatively limited, are discussed as an emerging application area highlighting the broader potential of jet-printed MXenes for electrochemical energy storage. Finally, the major challenges hindering the large-scale implementation of MXene-based printed energy-storage systems, including oxidation stability, restacking, ink shelf life, and manufacturing scalability, are critically discussed.

Technologies doi: 10.3390/technologies14090553

Authors: Max Gonzalo Chiluisa Saragosin Alexander Aguila Téllez

This study evaluates a hierarchical workflow for fault diagnosis in transmission systems by integrating established machine-learning techniques for fault-type classification and zonal fault location with a complementary double-ended traveling-wave procedure for point-location estimation. The contribution lies at the level of process integration and comparative evaluation rather than in the proposal of a new machine-learning or traveling-wave algorithm. The methodology was evaluated using the IEEE 9-bus test system. Symmetrical and asymmetrical short-circuit scenarios were automatically simulated at multiple positions along six transmission lines using DIgSILENT PowerFactory, and the resulting oscillographic records were exported in COMTRADE format, producing a database of 2952 fault events. Phase voltages and currents, together with positive-, negative-, and zero-sequence components, were used to evaluate Decision Trees, Self-Organizing Maps (SOM), Artificial Neural Networks (ANN), and k-Nearest Neighbors (KNN) for fault-type classification and zonal fault location. Under the simulated noise-free conditions and the adopted fixed hold-out partition, all four algorithms correctly classified the 591 fault-type testing observations, yielding 100% test-set accuracy. This result characterizes the specific evaluation subset considered in the study; repeated, cross-validated, or grouped partitions were not performed, and neighboring simulated fault positions may therefore be represented across the training and testing subsets. For zonal fault location, the ANN exhibited the strongest and most consistent observed performance in the retained 590-event evaluation set, with class-specific recall values between approximately 0.96 and 0.99 across the six fault zones, whereas the Decision Tree provided a favorable compromise between zonal discrimination and computational efficiency. Some model-specific hyperparameter values from the original executions are unavailable in the retained experimental record, which limits exact replication of those original configurations; the reported results correspond to the evaluated executions documented in this study. As a complementary third component of the workflow, the double-ended traveling-wave procedure based on discrete wavelet analysis was illustrated for one AG event simulated at 25% of the transmission-line length, producing a normalized point-location estimate of approximately 25.07% from the local terminal. This single-event analysis demonstrates the operation of the traveling-wave processing sequence, while broader multi-event validation is outside the present experimental scope. Overall, the results demonstrate the coordinated application of fault-type classification, zonal fault location, and traveling-wave-based point-location refinement within a common diagnostic workflow. The findings should be interpreted within the deterministic simulation conditions, fixed evaluation subsets, and experimental records considered in this study.

Technologies doi: 10.3390/technologies14090552

Authors: Anuchai Bunsan Sunisa Kunarak

The integration of unmanned aerial vehicles (UAVs) as aerial base stations has emerged as a key enabler for next-generation wireless networks, particularly in disaster recovery, temporary events, and infrastructure-deficient regions. However, multi-UAV deployments introduce severe co-channel interference due to spectrum reuse and overlapping coverage areas, while existing spectrum allocation methods either rely on centralized optimization with limited scalability or on reinforcement learning frameworks that lack spatial awareness of interference sources. To address these challenges, this paper proposes a Hybrid DeepMUSIC-assisted Cooperative Multi-Agent Deep Reinforcement Learning (MADRL) framework for intelligent spectrum allocation and interference management in multi-UAV 6G networks. The proposed framework integrates a hybrid interference localization module, which fuses the classical MUltiple SIgnal Classification (MUSIC) algorithm with a deep neural network to accurately estimate the direction of arrival (DoA) of interference sources, into a DeepMUSIC-enhanced state representation used by cooperative Deep Q-Network (DQN) agents trained under a Centralized Training and Decentralized Execution (CTDE) paradigm, enabling coordinated yet fully distributed spectrum allocation decisions. Extensive simulations demonstrate that the proposed Hybrid DeepMUSIC module reduces the mean DoA estimation error to approximately 0.105°, more than an order of magnitude better than classical MUSIC and standalone DeepMUSIC estimators. Compared with seven baseline algorithms spanning heuristic, optimization-based, single-agent, and cooperative multi-agent reinforcement learning approaches, the proposed framework achieves the highest network throughput, SINR, spectrum efficiency, and energy efficiency, together with the fastest and most stable training convergence, reaching a stable cooperative reward of 76.246 within approximately 371 training epochs. The framework further maintains near-linear computational scaling with the number of UAV agents, confirming its suitability for real-time deployment in dense, AI-native multi-UAV 6G wireless communication systems.

Technologies doi: 10.3390/technologies14090551

Authors: Touseef Ur Rehman Saba Latif Muhammad Talha Shabbir Meijin Guo Muhammad Rameez Ur Rahman

Overlapping cells in plant suspension-culture microscopy pose a particular challenge, for instance, segmentation because a single pixel may belong to more than one cell. Most standard instance-segmentation methods are not designed for this setting and tend to treat overlapping objects as mutually exclusive regions. We instead represent each cell as an independent full-cell instance and introduce MC-SlotNet, an architecture that separates competitive object-slot feature assignment from mask decoding. This allows multiple predicted masks to occupy the same image region. We further introduce a mask-level multiplicity-consistency loss that encourages the predicted number of masks covering a pixel to agree with the underlying cell occupancy. We evaluate MC-SlotNet on a newly annotated dataset of 53 Siraitia grosvenorii suspension-culture micrographs containing 4131 full-cell instances acquired at 4×–40× magnification. Using grouped five-fold cross-validation and an overlap-preserving evaluation protocol, we compare the method with Mask R-CNN, SOLOv2, and Mask2Former. MC-SlotNet achieves the best performance on AP50 (0.800), mAP50:95 (0.565), F150 (0.842), all-ground-truth Dice (0.761), AJI+ (0.754), overlap-region Dice (0.705), and overlap-instance recall (0.828). Its AP75 (0.649) is comparable to Mask2Former’s (0.651). MC-SlotNet also has the lowest inference time among the evaluated methods, at 1.113 s/image. These results indicate that decoding full-cell masks independently, rather than enforcing an exclusive partition of image pixels, is well-suited to instance segmentation in plant suspension-culture microscopy images with substantial cell overlap.

Technologies doi: 10.3390/technologies14090550

Authors: Yuxuan Li Jie Hua Weidong Huang Ali Anaissi

Energy systems are crucial to residential life and industrial production. During practical operation, these systems may experience various anomalies that disrupt the stability of system operation. Recent years have witnessed remarkable progress in power system anomaly detection. However, existing methods still suffer from two limitations. First, detection algorithms neglect privacy protection, although privacy security is also a critical issue in energy systems. Second, existing studies have difficulty characterizing latent dependencies and topology changes, which limits detection performance. To bridge these gaps, we present a power system anomaly detection method that integrates physics-informed sparse graph temporal modeling with homomorphic encryption, enabling anomalous-event identification and anomalous-bus localization under privacy-preserving conditions. Specifically, we construct a sparse graph using the power-grid topology and normal measurement residuals. We then obtain system-state predictions through polynomial graph temporal prediction and physics-guided affine correction and use anomaly scores to diagnose anomalous conditions. Furthermore, we employ homomorphic encryption to perform ciphertext computation for the affine prediction model without exposing historical measurement data, thereby enabling privacy-preserving remote anomaly detection. We conduct experiments on IEEE bus benchmarks to verify the effectiveness of the proposed method under multiple anomaly scenarios.

Technologies doi: 10.3390/technologies14090549

Authors: Abdelkrim Adila Khedidja Kendouci Nadir Bouchetata Habib Benbouhenni Houssam Eddine Ghadbane Nicu Bizon

Efficient maximum power extraction in variable-speed wind energy conversion systems (WECSs) remains challenging because of nonlinear turbine dynamics, continuously varying wind conditions, measurement disturbances, and mechanical-parameter uncertainties. This study presents a robust nonlinear integral backstepping control (BC) strategy for generator-speed regulation within a Tip-Speed Ratio (TSR)-based Maximum Power Point Tracking (MPPT) framework. The proposed controller combines nonlinear backstepping stabilization with integral compensation to improve reference tracking and reduce persistent tracking errors. The turbine-generator mechanical inertia is explicitly incorporated into the control formulation, providing a physically consistent representation of the mechanical dynamics and enabling systematic evaluation of parameter uncertainty. A comprehensive comparative assessment is conducted in MATLAB/Simulink using four control strategies: proportional-integral (PI), integral-proportional (IP), sliding-mode control (SMC), and the proposed integral BC. The controllers are evaluated under five complementary scenarios: variable wind speed, measurement noise, abrupt stepwise wind-speed variations, ±20% mechanical-inertia uncertainty, and a 10-ms rotor-speed measurement delay. Performance is assessed using the Integral of Squared Error (ISE), Integral of Absolute Error (IAE), and Integral of Time-weighted Absolute Error (ITAE), together with statistical measures across the five scenarios. Under the baseline variable-wind condition, BC achieves ISE = 24.4164, IAE = 1.539, and ITAE = 0.716, outperforming PI, IP, and SMC in all three indices. Under abrupt stepwise wind-speed variations, BC further achieves ISE = 0.00110, IAE = 0.0056, and ITAE = 0.0529, demonstrating rapid transient error suppression. The proposed controller remains stable under ±20% mechanical-inertia variations and a 10-ms measurement delay. Across the five scenarios, BC achieves the lowest mean ISE, IAE, and ITAE values of 19.353, 1.231, and 2.642, respectively, as well as the lowest standard deviations for ISE and IAE. SMC exhibits particularly consistent performance under measurement noise and the lowest standard deviation for ITAE. Overall, the results demonstrate that the proposed integral BC provides the most favorable balance of tracking accuracy, transient performance, and robustness among the investigated strategies. The improved rotor-speed regulation supports operation near the optimal TSR and effective aerodynamic power extraction. The findings highlight the potential of the proposed approach for robust MPPT control of variable-speed WECSs.

Technologies doi: 10.3390/technologies14090548

Authors: Tamara Gajić Dragan Vukolić Nina Đurica Marija Krstić Lazar Krstić Dejan Sekulić Andrea Ivanišević Marijana Dukić Mijatović Ivan Kosogor

The application of artificial intelligence (AI) and the Internet of Things (IoT) is transforming smart hotel services by simultaneously creating technological benefits and privacy-related concerns. Although previous studies have extensively examined the effects of AI and IoT on tourist experiences, they have predominantly adopted an individual perspective, with limited attention to potential interdependence among co-present guests. This study examines how technological benefits (AI usefulness, IoT convenience, and personalization) and technological risk (perceived privacy risk) are associated with tourists’ satisfaction and electronic word-of-mouth (eWOM) intention, while also investigating whether these outcomes exhibit dependence across social, spatial, and temporal structures of guest co-presence. The study draws on survey data collected from tourists staying in AI-and IoT-enabled hotels in Hungary, Croatia, and Serbia. The findings show that AI usefulness, IoT convenience, and personalization are positively associated with satisfaction and eWOM intention, whereas perceived privacy risk is negatively associated with both outcomes. Furthermore, technological benefit constructs exhibit significant positive indirect associations across guest co-presence structures, whereas privacy risk exhibits less consistently statistically significant indirect associations, particularly for eWOM intention. These findings indicate the coexistence of positive technology evaluations and privacy concerns and reveal different patterns of co-presence-associated interdependence rather than demonstrating that technological benefits statistically dominate privacy risks. These findings contribute to smart hospitality research by extending the benefit–risk perspective beyond exclusively individual-level evaluations and highlighting the importance of considering conditional interdependence among co-present guests when evaluating AI- and IoT-enabled hotel services.

Technologies doi: 10.3390/technologies14090547

Authors: Manuela Gómez-Suta Julian D. Echeverry-Correa Paula M. Herrera-Gómez

Impulsivity is a common trait and is understood as a symptom of various disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD). We previously proposed ImpulsivityBank protocol, which is a standardized discourse protocol for retrieving speech samples that enables the identification of the speech features related to the impulsivity trait in children and adolescents. ImpulsivityBank protocol presents three elicitation methods (recall task, storyboard, picture description) and quantifies general language abilities using a battery of linguistic assessments. In this paper, we analyze the current data from the Impulsivity corpus, which is a corpus that consists of speech samples collected using our protocol. We performed an automated linguistic-feature analysis of the Impulsivity corpus to examine speech features related to the impulsivity trait in children and adolescents. We present the results of both the classification and prediction systems considering diverse experimental scenarios. Our results indicate that the speech features from the storyboard consistently yielded the best-performing classification and prediction systems among the evaluated elicitation methods. We conclude that ImpulsivityBank protocol facilitates the collection of speech samples from which a range of linguistic features can be extracted and explored in relation to the impulsivity trait in children and adolescents. The consistency observed across the classification and prediction models suggests that multiple speech features jointly contributed to the fitted models’ outputs. Our main contribution lies in speech analysis, particularly in the extraction and study of speech features that may be associated with impulsivity.

Technologies doi: 10.3390/technologies14090546

Authors: Yongxiang Zhang Deliang Chen Quanrui Hao Zihan Hong Hengyun Wei

Existing wind power optimization control strategies often lack a qualitative analysis of the relationship between wind power energy and system frequency, which may lead to insufficient or excessive frequency support, thereby reducing the effectiveness of wind power frequency support or triggering a secondary frequency drop. To address this issue, a receiving-end system frequency optimization control strategy based on the available rotor kinetic energy of sending-end wind farms is proposed. First, the mathematical expression of the approximately first-order response in the initial stage of optimized frequency dynamics is clarified. The quantitative relationship between the available rotor kinetic energy of wind farms and the frequency support level is derived, and a target frequency design method is developed by combining a conservative evaluation of effective frequency regulation energy. Second, according to the deviation between the target frequency and the measured frequency, the total frequency regulation demand calculation, the approximate calculation of synchronous generator mechanical power variation, and the wind farms’ frequency regulation command calculation are dynamically executed within each control step. In this way, the outputs of wind farms and synchronous generators are coordinated to regulate the frequency close to the target value. Finally, a simulation model is built in MATLAB/Simulink to verify the effectiveness of the proposed frequency control target design method and control strategy under different operating conditions, as well as their robustness against parameter acquisition errors, communication delays, and power disturbance estimation errors.

Technologies doi: 10.3390/technologies14090545

Authors: Areej Matook Alqurashi Tariq M. Khan Qazi Emad Ul Haq

AI-generated and locally manipulated images can support impersonation, forged evidence, identity-document abuse, and other forms of digital fraud, making reliable content-authenticity analysis increasingly important. This paper presents STeREx-Net, a three-class forensic framework for distinguishing real, fully synthetic, and locally tampered images using frozen diffusion-derived residual evidence, spatially aligned RGB features, multi-task prediction heads, and reviewable visual evidence. On the official balanced 60,000-image SID-Set test, the original model achieved 96.24% accuracy, 96.26% macro F1, and a multiclass Matthews correlation coefficient of 0.944. Separate matched three-seed ablations showed that diffusion-residual evidence is materially useful relative to RGB-only input; however, residual-only and simple-fusion controls outperformed the proposed fusion on the clean SID-Set, so fusion superiority is not claimed. Frozen robustness testing further revealed strong condition dependence: SID-Set macro F1 decreased from 0.9655 on clean images to 0.7987 under JPEG Q75 and 0.6330 under JPEG Q50, while generator-stratified AIS-4SD results also varied substantially. Zero-shot transfer to FantasyID failed to detect tampered samples, whereas leakage-safe restricted adaptation partially recovered tampered recall to 0.3447 and reduced the expected calibration error from 0.7815 to 0.2077, at the cost of lower real-image recall. A validation-selected localization intervention increased Dice from 0.2817 to 0.3413 but remained precision-biased and non-uniform across manipulation sizes. Quantitative explanation analysis supported in-domain decision faithfulness and benign-transformation stability, but these properties did not transfer consistently to external data. STeREx-Net should therefore be viewed as a human-supervised forensic research framework with explicitly characterized robustness, localization, and generalization boundaries rather than as a universally robust detector.

Technologies doi: 10.3390/technologies14090544

Authors: Yasmeen M. Hussein Raaed F. Hassan Raad Farhood Chisab

We present a comprehensive empirical study of attention mechanisms for eye-based driver drowsiness detection, evaluating 13 model variants across accuracy, calibration, cross-dataset generalization, and FPGA edge deployment. We introduce the Guided Dual-Attention Unit (GDAU), which combines position-aware spatial attention with SE channel attention. The data pipeline first splits the full imbalanced dataset (103,803 images, 5.9:1 ratio) into stratified train/validation/test subsets; then, it applies undersampling only to the training set. On the naturally imbalanced test set, no attention mechanism significantly outperforms the others on an identical backbone: Channel attention achieves the highest raw accuracy (83.86%), while CBAM-L achieves the highest balanced accuracy (86.77%) and ROC AUC (0.927). GDAN achieves 82.83% accuracy (85.99% balanced) with only 2.19 M parameters—half of the baseline’s 4.29 M. Component ablation confirms spatial–channel complementarity (+2.37% balanced accuracy over baseline). No single model dominates all calibration metrics. Cross-dataset transfer fails for all architectures (48–57%, near random chance). On the physical Xilinx Kria KV260, DPU-accelerated inference achieves 0.481–1.116 ms (896–2077 FPS), confirming real-time edge deployment feasibility.

Technologies doi: 10.3390/technologies14090543

Authors: David José Pereira Ferreira Jaime Fernandes Teixeira Ronaldo Moreira Salles

The digital transformation of construction has accelerated the adoption of intelligent digital technologies (IDTs) for project monitoring and control. However, existing evidence remains fragmented across technology-focused studies, with limited attention to how these technologies affect project management control processes. This study consolidates the state of the art through an SLR and a complementary bibliometric analysis. The SLR synthesizes recent evidence on IDTs used in construction monitoring and control by identifying core technology groups, principal applications, benefits, technical limitations, and organizational barriers. The bibliometric analysis of Web of Science publications maps the evolution and intellectual structure of the field, including publication growth, leading journals, thematic clusters, and emerging trends. The findings show that IDTs can substantially improve monitoring accuracy, timeliness, transparency, and decision support, particularly when implemented as interoperable systems rather than isolated tools. The evidence also indicates persistent barriers related to interoperability, data quality, implementation costs, skills shortages, organizational readiness, and resistance to change. Bibliometric and lexical results further suggest a field-wide transition from isolated data acquisition and basic automation toward integrated, intelligence-driven monitoring architectures combining BIM, computer vision, machine learning, and digital twins. This study contributes by proposing an Integrated IDT-Based Monitoring and Control Framework and by outlining future research directions on socio-technical adoption, integration capability, and decision-oriented project control systems in construction.

Technologies doi: 10.3390/technologies14090542

Authors: Petru Todos Ghenadie Tertea Ilie Nucă Vadim Cazac Costică Nițucă Alin Dragomir

The main scope of this research was to complete and validate the analysis of stator winding topologies of six-phase AC machines using the winding quality factor by determining fault tolerance and validating it through experimental tests. This paper proposes a unified and practical methodology for evaluating the performance of stator windings in six-phase induction machines, with emphasis on magnetic field quality and fault-tolerant operation. The approach combines analytical modeling of the magnetomotive force (MMF) with its graphical representation using the MMF polygon, enabling an efficient assessment of harmonic content through a global indicator, referred to as the winding quality factor. Several representative winding topologies are analyzed within a common framework, including single-layer and double-layer configurations with full-pitch and short-pitch coils, suitable for generating homologous series of six-phase machines. The study considers both normal operating conditions and post-fault regimes, particularly operation with a single three-phase set. The results reveal the strong influence of winding topology on harmonic distortion and overall machine performance, highlighting the trade-offs between magnetic field quality and fault tolerance. It is shown that appropriate winding design can reduce spatial harmonics and improve robustness under degraded operating conditions. The theoretical findings are validated through experimental investigations, demonstrating good agreement between analytical predictions and measured data. A parallel analysis was performed between the theoretical findings and experimental data, and the results conform to the authors’ expectations.

Technologies doi: 10.3390/technologies14090541

Authors: Cesar H. Valencia-Niño Zuly Alexandra Mora-Pérez Sebastian Muñoz-Vásquez Paolo A. Ospina-Henao Jorge G. Díaz-Rodríguez

Passive and fixed-damping transfemoral prostheses cannot adapt their resistance to the phase-dependent demands of human gait, and microprocessor-controlled commercial knees remain out of reach for most amputees. We present a fuzzy logic controller that modulates a magnetorheological (MR) damper directly from gait phase and knee joint angle, since linear state-feedback and discrete PI designs are valid only at a single linearization point and require retuning across the gait cycle. The controller is formalized as a fuzzy-basis-function expansion with established coverage and Lipschitz continuity; the universal-approximation property of Mamdani systems grounds fuzzy logic theoretically but does not certify this 8-rule controller’s performance, established empirically instead. A dissipativity-based Lyapunov argument and a numerical sweep establish local closed-loop stability and bounded, rate-limited actuation. The damper couples to the knee through a shaft–bearing–housing assembly sized by free-body and Goodman fatigue analysis and verified by finite-element analysis, with the control pipeline embedded on an ESP32 microcontroller in a 2 kg prototype, corresponding to Technology Readiness Level (TRL) 5–6. In a single-subject case study with one transfemoral amputee, the controller achieved the lowest mean RMSE (0.0557 over three trials) against a non-disabled gait reference among five compared conditions, improving on the best fixed voltage by 20.2% and a passive prosthesis by 6.8×; these are single-subject feasibility results, not a claim of generalizable performance.