Technologies doi: 10.3390/technologies14070417

Authors: Ubong Ukommi Mfonobong Uko Sunday Ekpo Ikpaya Ikpaya

The exponential growth of digital video traffic over satellite networks demands innovative approaches to optimize spectral efficiency while ensuring high quality of experience (QoE). Conventional Adaptive Coding and Modulation (ACM) schemes respond solely to Channel State Information (CSI), neglecting the perceptual importance of video content. This paper proposes a comprehensive Quality-Driven Adaptive Coding and Modulation (QACM) framework that dynamically allocates physical-layer resources based on joint channel conditions and content-aware quality metrics. The framework introduces a Quality Significance Factor (QSF) that quantifies video complexity and priority, enabling intelligent trade-offs between spectral efficiency and quality robustness. We implement a complete simulation testbed incorporating DVB-S2X-compliant ModCods with multiple code rates (1/2, 3/4, and 5/6) and higher-order constellations (up to 64QAM) over Additive White Gaussian Noise (AWGN) channels. Extensive experimental results using H.264/AVC sequences demonstrate that, while standard ACM achieves 30.14 dB PSNR for high-motion football sequences at 16 dB SNR with 64QAM-1/2, QACM improves this to 41.40 dB by switching to QPSK-1/2, representing an 11.26 dB gain. We provide comprehensive BER analyses across the 0–20 dB SNR range, statistical significance validation (p < 0.01 for quality improvements), computational complexity analysis showing 15.2% overhead, and detailed comparisons with prior arts. The framework demonstrates scalability to higher-order modulations while maintaining 23% weighted QoE improvement over conventional ACM. This work provides a validated, implementable cross-layer solution for next-generation satellite broadcasting systems.

Technologies doi: 10.3390/technologies14070416

Authors: Miklas Scholz

These are uncertain political and socio-economic times worldwide [...]

Technologies doi: 10.3390/technologies14070415

Authors: Subhra Mondal Subhankar Das Vasiliki G. Vrana

Agentic artificial intelligence (AAI) and retrieval-augmented generation (RAG) are increasingly embedded in organisational business intelligence (BI) and business process management (BPM). Unlike conventional AI, these systems set sub-goals, plan multi-step workflows, and retrieve and store information under limited human supervision. This article argues that such autonomy not only transmits human cognitive bias into organisational decisions but also amplifies that bias under identifiable conditions. We develop the Bias Amplification Model (BAM), a three-layer account of how bias enters and escalates. In the injection layer, human biases enter through goal framing, prompt design, and data scoping. In the propagation layer, the agent compounds these biases across autonomous execution steps. In the crystallisation layer, RAG memory encodes biassed outputs as retrievable organisational knowledge that later cycles treat as evidence. A feedback loop links the three layers, converting episodic human bias into structural organisational bias. We state seven propositions, specify amplifying and mitigating conditions, and ground the model in a PRISMA-guided synthesis of 47 studies. A controlled, replicated experiment with an autonomous agent in a supplier-selection pipeline provides initial empirical support for injection, propagation, the mitigating effect of a single governance checkpoint, and bias transmission across a task boundary via retrieved memory. The framework reframes AI-driven process optimisation as a possible source of silent decline in decision quality.

Technologies doi: 10.3390/technologies14070414

Authors: Xiaoyi Liu Yuhan Yin Yetong Zhang Kunxiao Wu Jianyong Zheng Fei Mei

To address unavailable global navigation satellite system (GNSS) signals, dense metallic equipment, valve-tower occlusion, and the insufficient robustness of single-sensor localization in unmanned aerial vehicle (UAV) inspection of converter valve halls, this paper proposes a non-line-of-sight (NLOS)-aware LiDAR-ultra-wideband (UWB) fusion localization method. The method uses LiDAR odometry to provide continuous local motion constraints and UWB ranging to provide global distance constraints. The geometric relationship among the UAV, UWB anchors, and valve-hall obstacles is used to evaluate the NLOS risk of each UWB link, and the equivalent ranging variance is adaptively adjusted before tight fusion optimization. To avoid overextending simulation conclusions, this study focuses on localization-layer modeling and simulation-based validation rather than full energized valve-hall flight deployment. In the grouped-bushing valve-hall scenario, the proposed method achieves an RMSE of 0.30 m, a mean error of 0.29 m, a P95 error of 0.43 m, and a maximum error of 0.48 m, reducing the RMSE by 50.0% compared with ordinary tight LiDAR-UWB fusion. Additional Monte Carlo tests under different trajectories, anchor layouts, anchor installation errors, and obstacle densities further verify the robustness of the proposed weighting mechanism. The results indicate that the method can suppress LiDAR accumulated drift and reduce the influence of UWB NLOS ranging in GNSS-denied metallic indoor environments, while real converter-valve-hall flight tests under energized electromagnetic conditions remain necessary before engineering deployment.

Technologies doi: 10.3390/technologies14070413

Authors: Andrzej Szymon Borkowski Paulina Jarema Magdalena Kładź Anatolii Smoliar

Building Information Modeling (BIM) usually represents a building as a static snapshot of the model’s state. Dynamic extensions, such as Internet of Things(IoT)-enabled sensing or immersive visualization, already exist, but the underlying data model remains state-based. The Industry Foundation Classes (IFC) standard does not define a formal mechanism that would link the same physical element across successive phases of a building’s life cycle. Design, construction, and operation are recorded in separate IFC files, and the same element is assigned different Globally Unique Identifiers (GUIDs) in each. The result is fragmentation of the element’s identity, loss of the history of property changes, and the inability to formulate cross-phase queries. This paper proposes the BIM-Phase ontology based on the fundamental Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) ontology, which solves this problem by introducing a distinction between a building element as an endurant and its life cycle phases as perdurants. The ontology comprises nine classes, six object relations, and six axioms expressed in Web Ontology Language 2 Description Logic (OWL 2 DL). Phase properties and relations are represented using a reification pattern, which maintains full compatibility with the expressiveness of OWL 2 DL. The ontology was validated using an example of a single-family residential building developed in Autodesk Revit. Three structural elements (external wall, floor slab, and column) were tracked across three phases of the life cycle. Eight competency questions covering scalar, constitutional, and mereological changes were defined and mapped to ontology constructs, confirming that the BIM-Phase enables the recording of changes and the formulation of cross-phase queries that are impossible in classic IFC. All eight questions were answered correctly on the published knowledge graph, and the HermiT reasoner confirmed the logical consistency of the model. The findings show that preserving element identity across phases requires only a minimal ontological layer on top of existing standards. We recommend introducing persistent, phase-independent identifiers of building elements alongside IFC GUIDs, as this single change enables full lifecycle change tracking.

Technologies doi: 10.3390/technologies14070412

Authors: Rakesh Nath Tiwari Uruvakili Jyothi Prabhakar Singh Pradeep Kumar Chinneeramma Gari Kalitha Jampala Harsha Vardhan Reddy

In this manuscript, a graphene-based four-port linear MIMO antenna is proposed for terahertz (THz) applications. The antenna is designed on a polyimide substrate with a compact footprint of 60 µm × 240 µm × 4 µm. A modified triangular patch with a tip-fed configuration is employed to enhance asymmetric current distribution, enabling dual-band operation at 2.88 THz and 4.36 THz. A parametric analysis is carried out by varying the chemical potential and relaxation time of graphene to optimize the antenna performance in terms of S-parameters. The proposed design achieves an inter-element isolation > 20 dB, with a realized gain exceeding 3.28 dBi and radiation efficiency above 65% across both operating bands. Furthermore, key MIMO diversity parameters are evaluated, demonstrating an envelope correlation coefficient (ECC) ≤ 0.008 and channel capacity loss (CCL) ≤ 0.27 bits/s/Hz over the entire operating range. The total active reflection coefficient (TARC) remains stable under varying excitation phase conditions, indicating minimal mutual coupling between adjacent ports. An equivalent circuit model is also developed to validate the simulated results. The antenna is analyzed using CST Microwave Studio, and the obtained results confirm its suitability for emerging THz wireless applications.

Technologies doi: 10.3390/technologies14070411

Authors: Khutso I. Mashigo Michael K. Ayomoh Louwrence Erasmus Tshifhiwa G. Nenzhelele

Wheel (mis)alignment is one of the factors influencing vehicle safety, tire wear, and energy efficiency. While alignment procedures are well established in automotive workshops, recent advances in sensing, connectivity, and data-driven methods have led to renewed academic interest. This goes against existing research, which remains fragmented around vehicle types and methodologies. This study conducts a scoping review of wheel alignment monitoring and detection methods, with a focus on passenger vehicles. Guided by PRISMA-ScR, 453 studies were identified, of which 386 were excluded and 13 were duplicates and thus removed, resulting in a small number remaining for thematic analysis. Three dominant methodological approaches emerged: (i) traditional measurement methods, (ii) sensor-based vehicle dynamics analysis, and (iii) data-driven methods employing machine learning and vehicle telemetry. The findings revealed limited research on vehicle applications, especially for intelligent, integrated, and scalable alignment technologies and real-time, in-service monitoring applications. Other challenges included data quality, calibration, and cost-effectiveness. Therefore, the development of an integrated real-time wheel misalignment detection and reporting framework grounded in systems engineering and enterprise architecture principles is proposed.

Technologies doi: 10.3390/technologies14070410

Authors: Alexandr Solochshenko Karina Turzhanova Alexander Pastukh Valery Tikhvinskiy Yelizaveta Vitulyova Olga Abramkina Viktors Gopejenko Farida Abdoldina

The 8.025–8.400 GHz band is one of the key X-band downlink ranges for modern Earth observation satellites, enabling high-rate transmission of imagery and sensor data for agriculture, environmental monitoring, greenhouse gas assessment, disaster response and security-related applications. The potential introduction of 5G networks into this band raises serious concerns about harmful interference to Earth observation ground stations cand, consequently, about the continuity and growth of the global Earth observation data chain. This paper investigates the feasibility of sharing this downlink band between Earth observation systems and 5G networks using a Monte Carlo simulation framework. The model includes a low-Earth-orbit Earth observation satellite with dynamically tracking ground stations and dense urban, suburban and rural deployments of 5G base stations and user devices, together with established radio-propagation and clutter models and representative protection objectives for satellite downlinks. The results suggest that, to keep interference at acceptable levels, ground stations would need to be located far from 5G deployments, which is difficult to achieve in practice and could seriously limit the future expansion of Earth observation infrastructure.

Technologies doi: 10.3390/technologies14070409

Authors: Evangelos Makris Panayiotis Tsanakas

Modern healthcare, biomedicine, and translational medicine are becoming increasinglydependent on innovations and achievements in bioinformatics and biomedical engineering [...]

Technologies doi: 10.3390/technologies14070408

Authors: Vladimir Yuhimenko Ron Harush Riccardo Mandrioli Mor M. Peretz Alon Kuperman Vitaly Gitis

Active ripple suppression branches (ARSBs) are widely employed in switching power converters interfacing ripple-sensitive devices such as batteries, supercapacitors, hydrogen electrolyzers, fuel cells, and photovoltaic panels. Conventional ARSBs share the main converter DC-link voltage and require inductance comparable to that of the primary power stage, resulting in high semiconductor voltage stress and bulky magnetic components. Recent studies have proposed supplying the ARSB from a lower auxiliary voltage source, significantly reducing both inductance value and semiconductor voltage ratings. This paper shows, however, that lowering the ARSB rating while keeping the series capacitance value unaltered inherently increases residual current ripple, degrading ripple-cancellation performance. It is then demonstrated that this limitation should be overcome by increasing the ARSB capacitance in inverse proportion to the rating reduction, thereby restoring ripple suppression performance. Furthermore, it is revealed that for converters operating at a fixed duty cycle, a unique operating point exists where the ARSB capacitor can be eliminated without sacrificing the ripple attenuation ability of the circuit. The resulting capacitorless implementation reduces component count, size, complexity, and cost while improving ripple suppression. Simulation and experimental results validate the theoretical analysis and confirm the feasibility and effectiveness of the proposed capacitorless open-loop operating ARSB.

Technologies doi: 10.3390/technologies14070407

Authors: Hongzhi He Zichen Kang Zixi Kang Shide Du Renjie Lin

Multi-view learning can effectively exploit the consistency and complementarity among multiple data sources and has become a major research direction in semi-supervised classification. However, the existing methods commonly suffer from several limitations, including the loss of view-specific information caused by premature feature fusion, interference from redundant inter-view noise, and the limited discriminative capability of consensus representation. These issues severely restrict classification performance under low-label settings. To address these limitations, this paper proposes Dual-branch Multi-view Learning with Dual-contrastive Information Bottleneck. The proposed framework constructs a decoupled dual-branch graph convolutional architecture to explicitly separate view-specific representations from cross-view consensus representation, thereby alleviating feature homogenization at the structural level. Furthermore, we design a dual-contrastive information bottleneck optimization mechanism, where the CLUB constraint minimizes redundant mutual information across views to suppress noise, while the InfoNCE constraint maximizes the mutual information between consensus representation and labels to enhance discriminative capability. Additionally, we employ an adaptive attention fusion module to dynamically integrate the dual-branch representations, further refining task-relevant features. The experiments conducted on nine public datasets demonstrate that the proposed method achieves favorable performance improvements over most of the selected comparison methods in semi-supervised classification tasks.

Technologies doi: 10.3390/technologies14070406

Authors: Titus Ifeanyi Chinebu Kennedy Chinedu Okafor Henrietta Onyinye Uzoeto Ogochukwu Militus Ifenze Juliet Onyinye Nwigwe Diovu Remigius Chidiebere Ijeoma Peace Okafor Ijeoma Madonna Onwusuru Wisdom Okafor Onukwube Victor Apeh

Dental caries remains one of the most prevalent yet preventable non-communicable diseases worldwide, disproportionately affecting populations with limited access to dental care and persistent socioeconomic inequalities. Early-stage lesions frequently remain undetected because of their asymptomatic nature, inadequate screening infrastructure, and the absence of continuous monitoring technologies, resulting in preventable complications and increased healthcare costs. To address these challenges, this study proposes an Internet of Things (IoT)-enabled intraoral wearable sensing device (I-OWSD) for continuous, quantitative, real-time monitoring of biomarkers associated with caries progression. The proposed framework integrates intraoral wearable sensing, cloud-based telemedicine services, and artificial intelligence (AI)-assisted analytics to support preventive oral healthcare and remote clinical decision-making. Two primary contributions are presented. First, a fractional-order delay-type model (FODM) based on the Caputo–Fabrizio derivative is proposed to capture the memory-dependent and nonlocal dynamics of caries progression. Mathematical analysis establishes the model’s non-negativity, boundedness, existence, uniqueness, and stability properties. Second, a biocompatible intraoral sensor interface is designed to enable continuous data acquisition and secure wireless communication with digital health platforms. Simulation results based on the proposed FODM suggest that, under an estimated adoption rate of 67.49%, the I-OWSD framework could reduce caries prevalence by approximately 15% while improving opportunities for early intervention and preventive care. The findings demonstrate the potential of combining fractional-order modelling, wearable sensing, and AI-driven teledentistry to advance continuous oral health monitoring and preventive dental care.

Technologies doi: 10.3390/technologies14070405

Authors: Margarita Guadalupe García-Barajas Claudia E. Pérez-García Abraham Ulises Chávez-Ramírez Ana A. Feregrino-Pérez Alejandra Álvarez-López Juvenal Rodríguez-Reséndiz Vanessa Vallejo-Becerra

Electrospun composite nanofibers represent an emerging strategy for the development of efficient fertilizer systems, as they enable modulation of the structural properties of the nanofibrous network and, consequently, the transport and release processes of nutrients. In this study, polyvinyl alcohol (PVA) nanofibers loaded with two different urea contents (0.09 g and 0.36 g) were fabricated and characterized to investigate how urea incorporation modifies the nanofiber morphology and influences urea release kinetics. SEM and EDS analyses confirmed that increasing urea content promotes surface roughnes and reduced nanofiber diameters, whereas XRD and FTIR demonstrated a decrease in crystallinity and the formation of hydrogen-bonded interactions between PVA chains and urea molecules, indicating that urea is incorporated within the PVA network rather than being superficially adsorbed on the nanofiber surface. These structural changes govern water retention and release kinetics: the 0.36 g formulation exhibited a 100-h induction period followed by multiphase diffusion, while the 0.09 g system displayed immediate release but lower final concentrations. Kinetic modeling revealed excellent fitting to the Higuchi and second-order models, confirming diffusion-controlled urea release modulated by internal interactions. The nanofiber network thus behaves as an active regulator of nitrogen mobility, overcoming the limitations of conventional coating-based fertilizers. These findings demonstrate the potential of PVA/urea nanofibers as scalable platforms for sustainable nitrogen delivery in agriculture, bridging morphology-driven polymer design with environmental performance.

Technologies doi: 10.3390/technologies14070404

Authors: Mashary N. Alrasheedy Sabrina Tiun Fariza Fauzi

Arabic multi-label emotion detection (MLED) in social media remains challenging because dialectal variation, implicit affective cues, and polarity-opposed emotions may occur within the same post. Existing Arabic MLED studies have mainly emphasized thresholded predictive performance, with limited attention to whether model confidence remains reliable under emotionally conflicting conditions. In this study, we propose CONCORD-Emo (CONflict-aware Compositional Representation for Emotion Detection), a reliability-aware framework for Arabic MLED. The framework adopts established label-wise attention, mixture-of-experts routing, Monte Carlo (MC) dropout, and post hoc temperature scaling as supporting mechanisms, while its architecture-level contribution is the conflict-conditioned integration of a residual global anchor with a conflict-aware fusion gate supervised by an automatically derived polarity-conflict target. We evaluated the framework on three Arabic benchmarks: SemEval-2018-Ar, ExaAEC, and SemEval-2025-Arq using predictive and reliability-oriented criteria. CONCORD-Emo remains competitive with strong MARBERT-based baselines. On SemEval-2025-Arq, it attains point estimates of 0.471 for Jaccard, 0.606 for micro-F1, and 0.582 for macro-F1. Paired bootstrap confidence intervals show that most predictive differences include zero, whereas the lower Expected Calibration Error and Brier scores on SemEval-2018-Ar and ExaAEC are consistently supported relative to the controlled baselines. Conflict-conditioned analysis shows that polarity-conflict instances yield lower predictive performance and higher Brier scores than blended-emotion instances. Taken together, these results support a reliability-aware evaluation of Arabic MLED in which polarity conflict, calibration, uncertainty estimation, and selective prediction are examined alongside predictive performance.

Technologies doi: 10.3390/technologies14070403

Authors: Nikolay V. Gromov Albina V. Lebedeva Artem A. Sharkov Anna D. Grebenyukova Oksana D. Elshina Anastasiya M. Borisova Valentin Yu. Borisov Anton E. Malkov Lev A. Smirnov Tatiana A. Levanova Alexander N. Pisarchik

Automatic analysis of electroencephalography (EEG) recordings relies on large, high-quality labeled datasets. Manual segmentation by medical experts is resource-intensive and time-consuming. Moreover, to overcome potential subjectivity in labeling, independent annotation by at least two experts is required. Therefore, reliable automatic data labeling is essential for obtaining the large datasets needed to train robust AI models. In this paper, we show that a properly trained state-of-the-art deep neural network (DNN) achieves labeling performance comparable to inter-expert agreement in the task of segmenting epileptiform activity patterns. To this end, we first compiled a custom database of EEG recordings containing such patterns. Second, five experts based on part of these recordings independently assessed spike-wave index (SWI), which is a key diagnostic criterion that indicates the percentage of the EEG recording during which epileptic discharges are observed. Third, we compared the expert assessments with SWI calculated based on automatic segmentation by the trained DNN. Our results demonstrate that the 1D U-Net architecture achieves competitive overall performance and aligns well with both expert assessments and expert-derived SWI values. Thus, automated segmentation and analysis of EEG recordings holds great promise for accelerating diagnosis and developing targeted therapeutic strategies for epilepsy.

Technologies doi: 10.3390/technologies14070402

Authors: Jie Chen Zhi Wang Wenquan Huang

Human pose detection is fundamental to quantitative sports training analysis in college general education courses, enabling an objective assessment of college students’ movement quality and the early identification of sports injury risks among non-professional athletes. At present, those detectors based on YOLO have encountered difficulties in capturing the continuous movement patterns of college athletes in routine training, maintaining the regression accuracy of different size posture targets, and maintaining the real-time calculation speed in the campus sports environment. Furthermore, most existing pose-estimation frameworks are optimized for general scenes and fail to address the unique challenges of college physical education settings, including non-standard student movements, diverse skill levels, and strict cost constraints for large-scale deployment. In order to solve these problems, we put forward PPLCNet-YOLOv11, which is a simplified human posture-estimation framework designed for college physical education. This model is optimized by three key improvements: (1) replacing the original backbone network with PPLCNet to enhance feature extraction, while strictly observing the strict FLOPs and parameter restrictions; (2) an enhanced Multi-Scale Attention Mechanism (MSAM) that combines adaptive scale perception, hierarchical channel attention, and pose-sensitive spatial attention to better represent elongated anatomical structures and multi-scale pose cues; and (3) an improved enhanced IoU loss function that incorporates scale-aware and aspect-ratio-aware penalty terms to refine the bounding box adjustment for atypical and sports-specific gestures. Experiments on both a dedicated college student sports pose dataset and two public benchmark datasets (COCO Keypoints 2017 and MPII Human Pose) demonstrate that PPLCNet-YOLOv11 achieves 77.8% mAP@0.5 and 37.09% mAP@0.95 based on the campus dataset, with 82.34% precision and 75.00% recall, while requiring only 2.62 M parameters and 6.38 GFLOPs. Extensive inference speed tests show that the model achieves 127 FPS on an NVIDIA RTX 4090 GPU, 38 FPS on an Intel i7-12700 CPU, and 16 FPS on a Jetson Nano edge device, meeting the real-time requirements of campus sports monitoring. Compared with mainstream lightweight YOLO variants and state-of-the-art specialized pose-estimation models, our proposed method improves mAP@0.5 by 4.93–12.6 percentage points based on the campus dataset. All experiments were repeated five times with different random seeds, and we report mean values with standard deviations and statistical significance tests to ensure result reliability. These results indicate that PPLCNet-YOLOv11 provides an accurate and resource-efficient solution for real-time pose evaluation in college physical training.

Technologies doi: 10.3390/technologies14070401

Authors: Jaafar Omar Jeanette Pao Melody Mae Maluya Immanuel Paradela Earl Ryan Aleluya Francis Jann Alagon Ronnie Concepcion Carl John Salaan

Adequate monitoring of flour storage bins in the food manufacturing industry can prevent profit loss from underproduction and overstocking. Manual volume measurement is labor-intensive and error-prone. With the need for efficient monitoring in mind, this study presents the design and development of volumetric measurement of the flour inside a storage bin using 2D-based rotating LiDAR to capture 3D point cloud data. The proposed system eliminates manual probing by fully automating the scanning and volumetric computation workflow. Instead of relying on discrete physical measurements inside the bin, the 2D rotating LiDAR continuously captures the interior walls and flour surface to generate a dense 3D point cloud. This removes the need for operators to insert rods or probes and thereby avoids human-induced measurement variability. Furthermore, because the system computes flour volume directly from geometric reconstruction rather than converting probe depths using a uniform surface assumption, it does not rely on a constant material density and is therefore more robust to compaction differences within the bin. The high-resolution point cloud also generates accurate mapping of non-uniform and irregular surface geometries, which captures true depressions, peaks, and sloped regions that manual methods typically miss. A dedicated web application was developed to send commands to the system for automated scanning and real-time volume computation. Successful real-world testing showed the system’s reliability, with an accuracy level of 1.013 ± 0.70% MAPE across varied flour quantities and surface contours.

Technologies doi: 10.3390/technologies14070400

Authors: Ioana-Octavia Bucur Daniel-Eugeniu Crunțeanu Mădălin-Constantin Dombrovschi

Vertical-axis wind turbines are attractive for urban energy applications, but modest efficiency still constrains their wider use. This study evaluates a passive flow-control solution consisting of 45°-inclined cavities introduced on the suction side of a NACA0012 airfoil. Two configurations were investigated, a baseline rotor and with a modified rotor with cavities placed over the last two-thirds of the suction side. The CFD component used 2D transient ANSYS Fluent (Version 19.2) simulations with Dynamic Mesh and 6DOF to compare the aerodynamic rotor response. Numerically, the modified configuration reached higher angular velocity, tip speed ratio, power coefficient, and aerodynamic power than the baseline, with the advantage increasing at higher wind velocities. The experimental component used fabricated polycarbonate rotor models and directly measured rotational speed, voltage, and current in a generator–rectifier–load chain. Based on five repeated measurements, at 14 m/s the modified rotor delivered an average useful electrical power of 1.314 ± 0.016 W, compared with 0.940 ± 0.014 W for the baseline rotor, corresponding to an increase of 39.79% in useful power and 13.22% in tip speed ratio. The 2D CFD model reproduced the experimental performance ranking, despite overpredicting absolute power levels.

Technologies doi: 10.3390/technologies14070399

Authors: Roberto Carlos Diaz-Velazco Alberto Gudiño-Ochoa Julio Alberto García-Rodríguez Jorge Ivan Cuevas-Chávez Eduardo Ruiz-Velázquez

Foundation time-series models have recently shown potential for forecasting complex temporal signals, but their behavior in patient-specific continuous glucose monitoring (CGM) forecasting remains insufficiently understood, particularly when only glucose history is available. This study provides a patient-level benchmark of foundation models for 30 min ahead glucose prediction in adults with type 1 diabetes mellitus (T1DM) under a strictly univariate CGM-only setting. Using the HUPA–UCM dataset from 25 individuals, we evaluated TimeGPT, Chronos, and Sundial against representative statistical, machine learning, and deep learning forecasters, including ARIMA, ETS, gradient-boosting models, recurrent networks, and neural forecasting architectures. Models were assessed using a local walk-forward validation strategy over the final 24 h of CGM data for each patient. Foundation models achieved the strongest global performance, with Sundial obtaining the lowest overall MAE (6.06mg/dL), while TimeGPT and Chronos remained among the most competitive approaches. However, patient-level analyses showed that this advantage was not uniform: ARIMA remained highly competitive in selected individuals, and no single model consistently dominated across the cohort. These findings suggest that foundation time-series models are promising tools for short-horizon CGM forecasting, but their use should be framed within patient-specific model selection rather than as universal replacements for classical forecasting methods.

Technologies doi: 10.3390/technologies14070398

Authors: Filip Cvitić Josip Bota Vladimir Cviljušac Jesenka Pibernik

This study presents a novel technological framework that integrates web-based 3D modeling and digital prototyping into interdisciplinary design education. Addressing the gap between traditional theoretical assessment and modern industry demands, the research investigates the implementation of interactive micro-websites and high-fidelity 3D product models as standard deliverables. Using a quasi-experimental design, the proposed digital workflow was tested on 53 final-year graphic design students at the University of Zagreb, divided into three groups based on the end users of their digital prototypes: real industry clients, peers, or academic mentors. The systemic reliability of the technological framework was measured through the technical quality of the final output (grades) analyzed via ANOVA, while user engagement with the digital process was tracked longitudinally. Results indicate that the implemented technological pipeline produced consistently high-quality outputs across all cohorts, with the client-facing group achieving the highest technical scores (M = 4.37; SD = 0.57). The lack of statistically significant variance between groups highlights a “ceiling effect,” demonstrating that the structured digital workflow itself is operationally stable and ensuring top-tier technical performance and prepress accuracy regardless of the evaluator. The study concludes that integrating advanced 3D web technologies and interactive public deliverables into the curriculum provides a scalable, industry-aligned technological model that successfully prepares design engineers for complex professional environments.

Technologies doi: 10.3390/technologies14070397

Authors: Stoica Dorel Mohammed Gmal Osman Gheorghe Lazaroiu Ovanisof Alina

This study presents a technical assessment of solar energy systems for integrated agricultural use and rural electrification. A model village comprising 30 households was considered, and high-resolution hourly load profiles were developed to characterize consumption dynamics, including peak demand and sectoral distribution across residential, agricultural, public, healthcare, and commercial users. A 60 kW photovoltaic (PV) system was designed in conjunction with an independent solar thermal installation for hot water supply. The system configuration was established through component sizing and numerical modeling, incorporating heat transfer mechanisms and operational constraints. Time-dependent simulations performed in MATLAB (R2022b) evaluated PV power output, battery storage cycling, and thermal system performance over a 24-h horizon. A comparative analysis of standalone PV, hybrid PV/T, and decoupled PV–thermal configurations was conducted based on performance and operational criteria. The results indicate that separated electrical and thermal subsystems achieve improved cost-effectiveness, enhanced reliability, and reduced maintenance requirements. The proposed approach demonstrates the technical viability of solar-based energy systems for rural applications, supporting energy autonomy, reduced fossil fuel dependence, and sustainable agricultural development.

Technologies doi: 10.3390/technologies14070396

Authors: Margita Ščasná Michal Hebnár Alexandra Kucmanová Maroš Sirotiak Veronika Kvorková Maroš Soldán Jan Hajzler Barbora Ludrovcová Marián Palcut

This study investigated carbonaceous materials prepared from Paulownia tomentosa leaves by hydrothermal carbonization, slow pyrolysis, and HCl post-treatment for the adsorption of metribuzin and tebuconazole from water. Hydrochars were prepared at 180–220 °C, pyrochars at 400–600 °C, and the pyrochar produced at 600 °C was further treated with HCl. The materials were characterized by yield, ash content, active and exchangeable pH, oxidizable organic carbon content, FTIR, SEM, and CO2-derived surface and pore properties. Increasing processing temperature reduced the yield in both conversion routes. Hydrochars retained more oxygen-containing and oxidizable organic structures, whereas pyrochars showed stronger carbonization, higher ash content, and higher CO2-derived surface area. HCl treatment decreased the ash residue, thereby resulting in improved CO2-accessible surface and pore properties and more fragmented morphology. Metribuzin adsorption was better described by the pseudo-second-order kinetic model across all sorbents, with the highest fitted equilibrium adsorbed amount observed following HCl treatment. Tebuconazole showed higher initial uptake toward most untreated materials, but its kinetic profiles were non-monotonic, with a decrease in the adsorbed amount at longer contact times. Consequently, the conventional PFO and PSO models did not adequately describe its complete kinetic behavior. Nonlinear isotherm modeling showed predominantly Freundlich-type fitting for metribuzin, suggesting heterogeneous adsorption sites, whereas tebuconazole was formally better described by Langmuir-type fitting, although with poorer fit quality for several materials. The results show that Paulownia tomentosa leaves are a suitable precursor for carbonaceous sorbents and that HCl-treated pyrochar is the most promising material for metribuzin adsorption.

Technologies doi: 10.3390/technologies14070395

Authors: Hazim Albedran Edina Koch

This work presents a generalized framework for solving the inverse kinematics problem of robotic manipulators by introducing two approaches: optimization-based and learning-based approaches within a unified architecture. The optimization-based inverse kinematic solver is formulated as a minimization problem of the error in localization of the end-effector. On the other hand, the artificial neural network’s inverse kinematic solver is presented as a subsequent operation to a given straightforward forward kinematics analysis. The two proposed formulations are applicable to robot manipulators with any synthesis and degrees of freedom. To validate the proposed solvers, a low-cost 6-DOF robotic platform was developed, including a host-embedded system that enables real-time interaction between a virtual environment and a physical manipulator. Different optimization algorithms and different learning algorithms were used and their effect was compared to evaluate the efficiency of the proposed framework. The two approaches were analyzed and compared with respect to accuracy, computational cost, and real-time suitability. Results show that optimization methods provide higher precision and require longer computation time, whereas the artificial neural network-based method achieves significantly faster responses with acceptable approximation error. Experimental validation demonstrates the robustness and practical applicability of the framework, which is recommended for high-degree-of-freedom manipulators where analytical and closed-form solutions do not exist.

Technologies doi: 10.3390/technologies14070394

Authors: Reshman Jabeen Manoochehr Rasekh Wamadeva Balachandran

The growing demand for real-time data synchronisation has increased the importance of supervisory control systems in industrial automation, smart grids, healthcare monitoring, and environmental applications. Low-power wireless sensor networks (LPWSNs) have emerged as key enablers of scalable and energy-efficient monitoring. However, achieving reliable synchronisation remains challenging due to latency, energy constraints, scalability limitations, security vulnerabilities, and data integrity concerns. This review examines the role of time synchronisation in supervisory control systems and evaluates how LPWSNs support real-time monitoring and decision-making. Established synchronisation protocols, including Reference Broadcast Synchronisation (RBS), the Flooding Time Synchronisation Protocol (FTSP), and the Timing-Sync Protocol for Sensor Network (TPSN), are analysed in terms of accuracy, energy efficiency, and scalability. Key optimisation strategies, such as clock drift compensation, data aggregation and compression, and edge computing, are also discussed. Recent advances, including artificial intelligence and machine learning (AI/ML)-based predictive synchronisation, blockchain, software-defined networking (SDN), and 5G-enabled LPWSNs, are reviewed across industrial, energy, healthcare, and agricultural applications. The review critically evaluates their benefits and trade-offs and identifies remaining challenges related to cybersecurity, energy efficiency, and large-scale deployment. Finally, future research directions are outlined to support robust, scalable, and efficient real-time synchronisation in LPWSNs.

Technologies doi: 10.3390/technologies14070393

Authors: Andrés Navas Perrone Ana Belén Tulcanaza-Prieto

Artificial Intelligence (AI) adoption has emerged as a critical priority for governments globally, driven by its transformative potential in improving public service delivery, governance efficiency, and innovation ecosystems. Despite this, substantial disparities exist in AI readiness and adoption levels across countries, necessitating an in-depth exploration of the factors influencing AI adoption. This study leverages data from the Oxford Insights Government AI Readiness Index to model cross-country patterns of government AI readiness through clustering, regression, classification, and explainable machine learning. A Random Forest regression model was first used to estimate the 2024 AI Government Readiness score using lagged 2023 indicators. However, because the dependent variable is a composite index constructed from conceptually related dimensions, this model is interpreted as a lagged score-approximation and benchmarking exercise rather than as an independent forecasting model. The main analytical contribution lies in the clustering-classification framework, which identifies four country-level AI readiness profiles and evaluates the indicators that most strongly distinguish countries across low, moderate-low, intermediate, and high readiness groups. SHAP and permutation-based interpretation methods are used to examine feature contributions, while recognizing that these results indicate model contribution rather than causal effects. The findings underscore the multifaceted nature of AI readiness, emphasizing the interplay between governance, digital infrastructure, and technological investment.

Technologies doi: 10.3390/technologies14070392

Authors: Julián David Pastrana-Cortés Alejandra Gomez-Rivera Andrés Marino Álvarez-Meza Julian Gil-Gonzalez David Cárdenas-Peña

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition commonly assessed through clinical interviews, behavioral observation, and rating scales. Although electroencephalography (EEG) has emerged as a promising complementary tool for ADHD assessment, robust, subject-independent classification remains challenging due to inter-subject variability, limited datasets, and the need for interpretable computational models. This work introduces EEG-TACT, a compact end-to-end deep learning architecture for identifying ADHD subjects from EEG epochs. The proposed model integrates an EEGNet-inspired convolutional embedding, a Transformer encoder operator, and an attention-based pooling mechanism. Together, these components capture local spatiotemporal EEG patterns, contextual temporal dependencies, and task-relevant latent representations. EEG-TACT was evaluated on a publicly available EEG dataset using strict, subject-independent stratified group partitions, ensuring no data leakage across subjects in the training, validation, and test subsets. Learned temporal filter responses, class-conditioned self-attention maps, and latent-space projections provide model interpretability. An ablation study quantifies the contribution of each architectural component. Performance analysis includes evaluation at the fold, subject, and epoch levels, together with statistical significance comparisons against representative state-of-the-art architectures. EEG-TACT achieved competitive performance among the contrasted models, reaching subject-level accuracy of 87.5%, recall of 96.0%, and precision of 82.8%, while requiring only a few thousand trainable parameters. By exhaustively repeating the initialization, the proposed model demonstrated improved labeling reliability and achieved the best average ranking among the evaluated architectures. The reported results therefore support evidence that EEG-TACT provides a compact, stable, and interpretable model for EEG-based ADHD identification under subject-independent evaluation settings. They also motivate further validation on larger, multi-site, and medication-controlled datasets.

Technologies doi: 10.3390/technologies14070391

Authors: Jennifer Whewell Rebecca Peters Janusz Kulon

The rapid advancement of machine learning and artificial intelligence (AI) has created new opportunities to enhance diagnostic accuracy in dermatology, particularly within primary care settings. Computer-aided diagnosis (CAD) systems have demonstrated potential to support General Practitioners (GPs) by enabling earlier and more consistent identification of skin diseases. This review critically examines the literature on explainable artificial intelligence (XAI) for skin disease classification, with a specific focus on the evolution of explainability frameworks and the methodological implications of dataset selection. A comprehensive review of studies published between 2020 and 2025 was conducted across multiple academic databases, encompassing research on skin lesion detection, classification, and monitoring. The analysis reveals that deep learning architectures, particularly those leveraging transfer learning with models such as EfficientNet, ResNet, and Xception, frequently report high classification accuracies—often exceeding 90% when evaluated on single benchmark datasets. However, studies employing multiple datasets consistently demonstrate more stable and generalisable performance, albeit with modest reductions in reported accuracy, highlighting a critical trade-off between performance optimisation and real-world robustness. The review further identifies a clear temporal progression in the adoption of XAI techniques. Early studies relied on a broader range of post hoc explainability while later work increasingly consolidated around Grad-CAM, SHAP, and related attribution techniques, followed by gradual diversification into more specialised frameworks such as TCAVs (Testing with Concept Activation Vectors) and Prototype-based Networks. Despite these advances, the lack of clinically grounded explanations, limited integration of ethical considerations, and reliance on non-clinical imagery continue to constrain clinical applicability which we have explored using a GRADE-style narrative. Notably, evidence suggests that CAD systems can improve GP diagnostic accuracy for conditions such as melanoma and seborrhoeic keratosis; however, sustained clinical adoption remains contingent on transparent, reliable, and context-aware explainability mechanisms.

Technologies doi: 10.3390/technologies14070390

Authors: Paško Ivančić Zaloa Sanchez Varela Vice Milin Ivan Peronja

Maritime communication systems are among the most important infrastructure of global maritime safety and security. They consist of very high frequency (VHF) radio, the Global Maritime Distress and Safety System (GMDSS), contemporary satellite nets, Automatic Identification System (AIS) networks, and the emerging VHF Data Exchange System (VDES). These systems are essential for distress signaling, navigational coordination, and vessel traffic management. As maritime operations are experiencing accelerated digitalisation, the safety and security dimensions of maritime communication systems have attracted substantial and growing scientific attention. This study presents a comprehensive literature review and bibliometric analysis of the safety and security of maritime communication systems. Guided by the PRISMA 2020 guidelines and Systematic Literature Review (SLR) methodology, a structured search was conducted across three major scientific databases: Scopus, Web of Science (WoS), and IEEE Xplore. Starting from a raw pool of 6648 records retrieved between 2000 and 2026, the dataset was reduced through successive filtering to a final body of 68 high-relevance publications. Bibliometric analysis reveals a significant upward publication trend from 2015 onwards, with a marked acceleration after 2019. Thematic analysis identifies seven principal research clusters: GMDSS modernisation, AIS safety and security, VDES and VHF next-generation systems, maritime cybersecurity, satellite communications, risk assessment frameworks, and emerging technologies, including artificial intelligence and autonomous vessel communications. The review identifies significant research gaps, including the absence of integrated cross-system risk frameworks, insufficient attention to human factors in cybersecurity, limited studies addressing emerging regulatory, legal governance components and a brief analysis of the maritime communications market. This study provides a structured foundation for future research and policy development in maritime communication security.

Technologies doi: 10.3390/technologies14070389

Authors: Shubrajit Bhaumik Byreddy Lakshmi Manohar Reddy Viorel Paleu William Woei Fong Chong

The rapid growth of electric vehicles and electrified systems has increased the risk of bearing failures due to combined mechanical and electrical stresses. This study investigated the performance of hexagonal boron nitride nanoparticle-enhanced lithium grease under electrified conditions. Experiments based on a Taguchi L9 orthogonal array were conducted on deep groove ball bearings using a full-scale test rig at 1200 rpm with varying loads (100–300 N), currents (6–10 A), and hBN concentrations (0.1–1 wt.%). The tribo-electrical performance of nano-enhanced grease was compared with the base grease and commercial grease. It was observed that the base grease exhibited superior performance with a lower current flow, reduced vibration, and minimal surface degradation. In contrast, the hBN-enhanced grease exhibited inferior tribo-performance, with high vibrations and surface damage in electrified conditions. The surface analysis revealed features morphologically similar to white etching areas and micro-pitting. The FTIR results indicated grease degradation, while ICP-OES confirmed higher wear debris generation in the commercial and hBN-added greases. The present work indicates that additives like hBN nanoparticles do not necessarily improve performance under electrified conditions, making it important to consider the type of additives to be added during lubricant formulation. Thus, the findings emphasize the importance of lubricant formulation for controlling electrically induced bearing failures and provide insights for developing advanced greases for electric machinery applications.

Technologies doi: 10.3390/technologies14070388

Authors: Sathit Pairoch Pattarapong Phasukkit Nongluck Houngkamhang

Sleep-related respiratory disturbances are difficult to monitor continuously outside specialized laboratories because conventional polysomnography is resource-intensive and intrusive. This study presents a contactless edge-AI engineering prototype for detecting controlled voluntary respiratory-motion suppression and motion artifacts using a 60 GHz frequency-modulated continuous-wave radar. The system integrates a 60 GHz radar front end, lightweight local preprocessing, an INT8 one-dimensional convolutional neural network deployed on the Analog Devices MAX78000 CNN accelerator (Analog Devices Thailand, Chon Buri, Thailand), and an event-driven Raspberry Pi Zero 2W gateway for alert transmission. Evaluation was performed using a controlled healthy-volunteer dataset consisting of normal breathing, voluntary breath-holding-induced respiratory suppression, and deliberate motion artifact. The final valid test set contained 270 technically valid 30 s windows balanced across the three classes. The INT8 model achieved an overall accuracy of 92.6% (95% confidence interval: 88.8–95.2%), with a macro-averaged precision, recall, and F1-score of 92.6%, 92.6%, and 92.5%, respectively. Active CNN inference on the MAX78000 consumed 0.152 ± 0.011 mJ and was completed in 5.20 ± 0.11 ms, corresponding to approximately 280-fold lower active inference energy than Python 3.14.6/TensorFlow Lite 2.21.0-based execution on the Raspberry Pi Zero 2W. These results demonstrate the feasibility of privacy-aware, low-power respiratory-pattern classification at the edge. However, the study should be interpreted strictly as an engineering proof-of-concept based on controlled voluntary breathing and movement tasks in healthy volunteers. It is not a clinically validated apnea or obstructive sleep apnea detection system and did not include polysomnography, oxygen saturation measurement, airflow sensing, sleep staging, or diagnosed patient cohorts.

Technologies doi: 10.3390/technologies14070387

Authors: Andrii Sulym Tetiana Popova Ján Dižo Miroslav Blatnický Aleš Slíva

The article deals with the further development of national innovative subway cars with asynchronous electric drives and energy recovery systems through the introduction of capacitive energy storage. It has been determined that the assessment of the effectiveness of existing concepts for energy processes control of subway cars with asynchronous electric drives and capacitive energy storage under identical specified conditions remains a relevant issue. Five of the most promising concepts for managing energy processes were selected and idealized. Oscillograms of energy flows for the selected concepts are presented. Parameters for evaluating the effectiveness of the selected control concepts are presented. The scientific novelty lies in the development of a procedure for selecting a rational concept for controlling energy processes in subway rolling stock with asynchronous electric drives and CES, based on the application of a unified comparative analysis system using a comprehensive evaluation criterion. A scheme for replacing subway cars with asynchronous electric drives and capacitive energy storage is presented, and a mathematical model of energy flow processes for traction and regenerative braking modes has been developed based on this scheme. Algorithms for controlling energy processes between asynchronous electric drives, capacitive energy storage devices, and contact networks have been developed for each of the selected concepts. The efficiency of each of the five selected concepts for the same specified operating conditions of the subway cars, parameters of the asynchronous traction electric drive and capacitive energy storage device has been investigated using the developed mathematical model and the formulated comprehensive evaluation criterion. It was established that it is possible to save up to 18% of the electricity consumed from the contact network per braking-acceleration cycle under the specified operating conditions, parameters of the subway cars, asynchronous traction electric drive, and capacitive energy storage device. An additional possibility exists to reduce the installed power of the power supply system equipment by up to 33.5% under the specified operating conditions of a subway train with the proposed technical characteristics. It has been determined that the most rational concept for controlling energy processes in subway cars with asynchronous electric drives and capacitive energy storage is the fifth concept, which allows the use of stored energy from regenerative braking in both normal and emergency operation of the subway power supply system.

Technologies doi: 10.3390/technologies14070385

Authors: Somprasonk Gabbualoy Pattarapong Phasukkit Nongluck Houngkamhang

Real-time roadway surveillance now leans hard on automated detection. How a model trained in one geographic context actually behaves on another, though, is still underexplored for Southeast Asian deployments. We answer that question for Thai roadway closed-circuit television with a cross-domain validation framework. A YOLOv11n (Ultralytics v8.2.0; Ultralytics, Los Angeles, CA, USA) detector trained with focal loss feeds a confidence-aggregation step that turns per-detection scores into a per-frame accident score, and we put four aggregation operators head-to-head. Reliability comes from DeLong variance estimation paired with non-parametric bootstrap on 1245 Thai frames that carry 23 positive accident events. Under maximum-class aggregation the proposed configuration reaches a frame-level AUROC of 0.959 ± 0.020 across three random seeds. Under top-K aggregation it reaches 0.965 ± 0.018. Per-seed DeLong 95 percent intervals exclude chance performance throughout. We also evaluate three baseline configurations: YOLOv5su comes in at 0.738, YOLOv8n at 0.868, and a Chiang Mai-tuned YOLOv11n variant at 0.918. The architectural progression seen on standard benchmarks therefore carries cleanly into the cross-domain setting. The same Chiang Mai-tuned variant reached an in-domain mAP50 of 0.952 yet only 0.918 cross-region AUROC on a separate Thai region, which is a quiet but clear signal that geographic proximity within a country does not on its own remove distributional shift. Bounding-box localisation appears as a secondary diagnostic because the operational target here is frame-level alerting rather than pixel-precise annotation. Edge deployment optimisation falls outside the present scope. What the work leaves behind is a reproducible baseline and a statistical protocol that follow-up Southeast Asian roadway-safety research can build on.

Technologies doi: 10.3390/technologies14070386

Authors: Roberta Presta Flavia De Simone Lorenzo Bacchiani Roberto Girau

Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty. This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery. Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery. The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty.

Technologies doi: 10.3390/technologies14070384

Authors: Amna Shabbir Uzma Afsheen Muhammad Faizan Shirazi Abdul Rauf Syed Muhammad Meesam Abbas Shahid Saeed Abdul Samad Khan Safdar Rizvi Nurashikin Saaludin

Visually impaired individuals face significant challenges in autonomous mobility and situational awareness. Most existing assistive technologies address isolated tasks, such as object recognition or text reading, while failing to capture broader environmental context. This work addresses this limitation by proposing a scene-sensitive, low-cost assistive system that delivers holistic situational information. We present Vocal-Eyes, an intelligent smart glasses platform that provides periodic audio descriptions of the surrounding environment. The system employs a cloud-based neural processing pipeline in which visual features are extracted using a Transformer-based architecture. Relational context is modeled through scene graph generation, and scene graphs are translated into natural language via a graph-to-text module. A lightweight hardware prototype captures visual data locally, while computationally intensive processing is offloaded to the cloud to reduce power consumption. The experimental results show that relational, scene-based narration produces more coherent and informative descriptions than object-centric approaches while maintaining acceptable periodic latency. Cost analysis further indicates that Vocal-Eyes is significantly more affordable than comparable commercial smart glasses solutions. These results demonstrate that Transformer-based scene understanding with cloud-assisted processing is an effective and practical approach for developing accessible, context-aware assistive technologies for visually impaired users.

Technologies doi: 10.3390/technologies14070383

Authors: David Calderón Isaías González Antonio José Calderón

Hydrogen production and storage constitute a promising technology in the path towards a global energy scenario featured by renewable energy penetration, decarbonization, sustainable development and resilience. In particular, so-called green hydrogen is generated from renewable energy sources, generally produced in an electrolyzer by means of Proton Exchange Membrane (PEM) water electrolysis. To make these expectations reality, experimental and real-world facilities are required, dealing with challenging aspects such as new technologies and integration of equipment. Thus, this paper presents the implementation and operation of a pilot plant for green hydrogen generation and storage based on a commercial 30 kW PEM electrolyzer. The renewable source is a photovoltaic generator of 60.6 kW which supplies the hydrogen generator through an inverter. Furthermore, the deployment of a supervisory system entirely based on open-source technologies is reported. The equipment employed and the supervisory system developed in this work exhibit a level of complexity and scale that is uncommon in the literature. Therefore, this article is a novelty in the literature and aims to contribute to the advancement of green hydrogen production and storage by providing experimental data and descriptions of a fully functional plant operating under real-world conditions. The achieved results under real operation conditions prove the successful implementation of the pilot plant as well as the suitability of the supervisory system to effectively track the most relevant variables.

Technologies doi: 10.3390/technologies14070382

Authors: Filip Cvitić Tajana Koren Ivančević Nikolina Stanić Loknar

This study investigates the potential, limitations, and pedagogical implications of applying a parallel multi-model AI evaluation workflow, using ChatGPT, DeepSeek, and Uizard, to assess student design projects in higher education. Because design assessment involves both formal criteria and subjective creative interpretation, the study first established a human expert baseline based on three independent university professors. The human inter-rater reliability was low to moderate, with a mean pairwise Spearman’s ρ of 0.36 and Cronbach’s α of 0.60 for packaging design, and ρ of 0.43 and α of 0.69 for web design. This finding is central to the study, as it shows that the human benchmark in creative design assessment is itself variable and interpretive. Against this baseline, AI–human alignment remained limited and task-dependent. For packaging design, the AI ensemble showed only a weak positive association with the human expert baseline (Spearman’s ρ = 0.30, p = 0.031), which should be interpreted cautiously given the Bonferroni-adjusted significance threshold used in the study. For web design, no significant AI–human association was observed. Qualitative analysis of AI-generated rationales identified recurring limitations, including hallucination, aesthetic shield effects, and missed context, where visually polished work was rewarded despite deeper conceptual or structural weaknesses. The findings suggest that current AI systems can provide useful formative feedback on visible formal features, but they are not reliable as autonomous grading tools for complex creative work. AI-assisted assessment is therefore best understood as a supervised formative support mechanism, while final evaluation should remain grounded in human pedagogical judgment.

Technologies doi: 10.3390/technologies14070381

Authors: Varin Cholahan Worapong Tangsrirat Tattaya Pukkalanun

This paper introduces an adaptive fixed-time position controller (AFxTPC) designed for the lifting axis servo mechanism of a computer numerical control (CNC) plasma machine. It integrates a permanent magnet synchronous motor, gearbox, and ball screw into a unified electromechanical model. The proposed AFxTPC combines a fixed-time terminal sliding surface function with adaptive fixed-time sliding mode control to achieve fixed-time convergence, precise tracking, and robustness in the presence of parameter uncertainties. A specially designed reaching law guarantees accurate trajectory tracking, while the fixed-time terminal sliding surface function effectively minimizes chattering near the sliding manifold. Importantly, a novel fixed-time nonlinear disturbance observer is developed to simultaneously estimate the unmeasured system states and lumped disturbances in real time within a guaranteed initial-state-independent settling time. These estimated values are explicitly fed back into controller for active disturbance compensation. The stability of the overall closed-loop system is rigorously established using Lyapunov stability theory. Simulation results demonstrate that the proposed observer-based controller achieves superior performance compared with conventional proportional–integral–derivative (PID) and standard sliding mode controllers. It exhibits zero steady-state error, reduced overshoot, minimal chattering, and strong robustness over a wide range of operating conditions.

Technologies doi: 10.3390/technologies14060380

Authors: Alexander Mitov Tsonyo Slavov Jordan Kralev

This article presents the synthesis, implementation, and experimental study of a PI-based adaptive actor–critic displacement volume controller of an axial-piston pump intended for open-loop circuit hydraulic drive systems. The proposed control structure combines a conventional PI actor with an adaptive critic that estimates the infinite-horizon cost through Bellman-error minimization. By using the tracking error and its integral as actor inputs, the controller avoids the need for an accurate plant model while retaining a compact and practically implementable structure. The adaptive laws are derived using gradient-based learning, and a Lyapunov-based analysis establishes closed-loop stability for sufficiently small adaptation gains. The controller is implemented in a fixed-step Simulink® environment and deployed on a rapid prototyping platform with real-time communication to an industrial microcontroller and proportional valve amplifier. The experimental results obtained under four fixed loading conditions and dynamic load variations demonstrate a stable operation, bounded critic behavior, and a near-zero Bellman error during learning. Comparative tests against a classical PI controller, a Lyapunov-based model reference adaptive controller, and a generic actor–critic scheme show that the proposed PI-based actor–critic achieves the lowest performance index and the shortest settling times in most cases.

Technologies doi: 10.3390/technologies14060379

Authors: Essam Ali Mohamed Abdelrahem José Rodríguez Abdelfatah M. Mohamed Alaaeldin M. Abdelshafy

Continouous unmanned aerial vehicle (UAV) missions are fundamentally limited by Lithium-Polymer (LiPo) battery endurance under intermittent and power-constrained renewable energy conditions. This paper proposes an integrated energy management and charging framework for a photovoltaic (PV)-powered mobile station equipped with a hybrid energy storage system (HESS) and an automated battery replacement (ABR) mechanism. A lexicographic priority-based allocator sequentially serves ABR actuation, multi-slot LiPo charging, and Brushless DC (BLDC) propulsion, while the HESS compensates for PV intermittency. At the charging level, a constraint-aware constant current–constant voltage (CC–CV) strategy is enhanced by an adaptive neuro-fuzzy inference system (ANFIS) trained on optimization-derived labels using battery temperature and its rate of change, thus enabling anticipatory thermal current derating with smooth, discontinuity-free control action. Anti-windup proportional–integral (PI) regulation and bumpless mode transfer ensure stable CC-to-CV transitions. An event-triggered emergency mode accelerates battery readiness via a max-first selection policy. Comparative simulations against a PSO/DE-optimized PID benchmark over a full diurnal PV cycle demonstrate that the ANFIS controller reduces the CC-mode current tracking root-mean-square error (RMSE) by up to 96.9%, delivers higher charge throughput, and lowers battery degradation proxies, including SOC-weighted thermal dose and equivalent full cycles (EFC). The proposed framework reliably sustains continuous charge–swap–recharge logistics under fluctuating renewable generation.

Technologies doi: 10.3390/technologies14060378

Authors: Xiaoming Wang Wenguang Zhao Meichen Dong Hao Zheng Zidong Meng Yingyu Liang

The large-scale integration of distributed photovoltaics (DPVs) into the distribution network exacerbates voltage fluctuations and substantially increases network losses. To improve the voltage quality and economic efficiency of distribution networks, a Volt/Var optimization (VVO) model is established. Coordinating multiple heterogeneous devices, the model aims to minimize the total voltage deviation, the total network losses, and the regulation cost of discrete equipment simultaneously. Considering multi-constraint coupling characteristics, a quantitative method is proposed to evaluate the reactive power regulation potential of DPVs under intricate operating conditions. Then, the multi-strategy integrated rime optimization algorithm (MSIRIME) is utilized for the model solution. Fuch chaotic mapping generates uniformly distributed and ergodic initial populations. A dual-branch search mechanism combining the snow ablation optimizer with the rime optimization significantly enhances global exploration capabilities. The guided learning strategy balances exploration and exploitation for high-dimensional VVO, preventing local optima. Case tests on a modified IEEE 33-bus system demonstrate that the proposed model exhibits excellent effectiveness and robustness. Moreover, MSIRIME exhibits better optimization performance than some classic and recently proposed strategies, reducing the average network losses and voltage deviation over 30 independent runs by at least 5.87% and 52.22%, respectively, relative to those of the compared methods.

Technologies doi: 10.3390/technologies14060377

Authors: Branislav Šoškić Dejan Viduka Vladimir Kraguljac Dragan Rastovac Petra Balaban

The digital transformation of tourist facilities requires careful selection of technologies that can provide secure, stable and scalable network infrastructure. Due to the possibility of application in different sectors with different specificities, the focus of the research was placed on the implementation of smart tourist services. A hybrid multi-criteria decision-making model based on PIPRECIA and MVA models was applied for the research. Based on the literature and the opinions of experts in the field, evaluation criteria such as bandwidth, latency, energy efficiency, security and privacy, scalability, costs and interoperability were defined, and internet technologies such as Li-Fi, Wi-Fi 7, Wi-Fi 6, private 5G networks, Ethernet-over-Power (EoP), NB-IoT and LoRaWAN were defined. The results obtained put the security and privacy criterion at the top (0.2253), followed by scalability (0.1952) and bandwidth (0.1624). The obtained results indicate that Wi-Fi 7 achieved the highest weighted score (4.2247), followed closely by Li-Fi (4.2177) and Wi-Fi 6 (4.0771). Wi-Fi 7 demonstrated particularly strong performance in scalability, interoperability and bandwidth, making it highly suitable for environments with high user density. Li-Fi achieved very high scores in security and latency, which makes it particularly appropriate for security-sensitive smart tourism environments. Lower-ranked technologies such as NB-IoT and LoRaWAN proved valuable for supporting IoT and monitoring functions, rather than as primary communication infrastructure. The proposed model has proven to be a flexible, transparent and practical tool for strategic decision-making in the field of smart tourism. In addition to the basic application presented in the paper, the model has the potential to be adapted to different contexts and expanded with additional criteria or new technologies. The proposed hybrid approach can serve as a useful decision-making tool for tourism managers, system engineers and urban planners who are looking for optimal solutions for the development of digital infrastructure.

Technologies doi: 10.3390/technologies14060376

Authors: Zhuoshi Li Guili Gao Jialin Guo Dequan Shi

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems.

Technologies doi: 10.3390/technologies14060374

Authors: Ezequiel Rincon-Canalizo David Gutiérrez-Rosales Daniel Aguilar-Torres Omar Jiménez-Ramírez Rubén Vázquez-Medina

This study presents a hybrid controller that integrates fuzzy logic control and the Incremental Conductance method. This controller optimizes maximum power point tracking in a 330 W photovoltaic system by designing a DC-DC converter. The study evaluates how the number and distribution of membership functions, specifically three-, five-, and seven-function configurations, affect system performance using the Integral Square Error (ISE) and Integral Absolute Error (IAE) indices. The empirical results demonstrate that the seven-function architecture yields optimal performance, minimizing ISE and IAE to 0.1155 and 7.365×10−4, respectively. Furthermore, this optimal configuration attains an energy efficiency of 99.7%, notably outperforming the baseline three-function configuration, which exhibited a worst-case efficiency of 98.9 %. To assess robustness against dynamic environmental variations, this study subjects the optimal configuration to fluctuating irradiance and temperature profiles. Additionally, an analysis of computational resource consumption reveals that the proposed hybrid controller incurs a lower computational load for rule evaluation than three controllers reported in the recent literature. These findings demonstrate the system’s structural efficiency and superior optimization capability, achieving maximized photovoltaic energy harvesting at a low computational cost.

Technologies doi: 10.3390/technologies14060375

Authors: Soufiane Oualil Issam Ait Yahia Mohamed El Kamili Khalid Fardousse Ismail Berrada

The security of Controller Area Network (CAN) systems is critical for modern automotive safety, as their lack of built-in security mechanisms makes them vulnerable to cyberattacks. In this work, we propose CAN-TEMPO, an unsupervised anomaly detection framework that explicitly models the multi-periodic structure of CAN traffic. The proposed approach leverages a Temporal Multi-Periodic Oscillation (TEMPO) block, which uses frequency-domain analysis to transform one-dimensional CAN sequences into multi-scale two-dimensional representations. This design enables the model to capture both intra-period correlations and inter-period temporal variations. We evaluate CAN-TEMPO on multiple public CAN intrusion detection benchmarks under diverse attack scenarios and generalization settings. Experimental results show that CAN-TEMPO consistently outperforms state-of-the-art methods in terms of AUC-ROC and F1-score, while maintaining lower false positive rates and improved robustness across different vehicles and attack types. These findings demonstrate that explicitly modeling periodic structures enables more reliable and generalizable anomaly detection in automotive networks.

Technologies doi: 10.3390/technologies14060373

Authors: Cozmin Adrian Cristoiu Marius-Valentin Drăgoi Alexandra Cojocaru Paulina Spânu

This article presents the development and proof-of-concept testing of a vision-based safety monitoring tool for operators in simulated robotic cells in RoboDK. The proposed method uses a virtual camera placed above the cell and image processing techniques to analyze the relationship between the operator and the workspace swept by the robot. In an initial stage, the robot movement is recorded as a mask of the swept space, and areas irrelevant to the process can be excluded by user-defined polygons. In the monitoring stage, the operator is identified in the video stream by HSV segmentation, after which an adjustable clearance zone is generated around the detected contour. Based on the intersections between the operator, clearance, swept space mask and the mask of the current robot movement, the application provides four discrete states: SAFE, WARNING, DANGER and COLLISION. For the experimental validation in the virtual environment, the virtual contact moment is estimated separately, while the COLLISION state is defined as the intersection between the inflated operator contour and the current robot motion mask. Therefore, in this study, COLLISION does not indicate measured physical contact, but an image-based imminent-collision condition used for early warning. The test scenario was carried out in a virtual palletizing cell, which includes an articulated arm robot, conveyors, manipulated objects and a mobile dummy acting as an operator. The obtained results support the use of the method as an applicative simulation solution for the evaluation of the early detection of risk situations. The study is limited to the virtual environment and represents a basis for future research on the development of visual monitoring systems to increase safety in collaborative and industrial robotic cells.

Technologies doi: 10.3390/technologies14060372

Authors: Zhuo Chen Zhiwei Shen Lixiong Lin Erkang Chen Jiechao Wang Haowei Zhang Jiaxun Chen Qianjie Cheng Peng Chen

To address the limitations of traditional controllers for the considered six-degree-of-freedom multi-thruster underwater robot under strong nonlinearities and environmental disturbances, this paper proposes a particle swarm optimization–radial basis function–proportional–integral–derivative (PSO-RBF-PID) control algorithm. The proposed method combines the nonlinear identification capability of the RBF neural network, the global optimization capability of PSO, and the stable closed-loop structure of PID control, thereby enabling adaptive parameter tuning and disturbance compensation. Unlike existing PSO-PID- and RBF-based controllers, the proposed method combines offline global optimization and online adaptive gain tuning within a unified control framework. Although the framework is modular and can be extended to underwater robotic systems with different degrees of freedom by redefining the state vector, controller channels, and thrust allocation matrix, the present study validates the method through a six-degree-of-freedom multi-thruster underwater robot case study. Comparative simulations were conducted under the same model, disturbance conditions, sampling settings, and evaluation indices for six controllers: PID, cascade PID, fuzzy PID, FOPID, PSO-PID, and PSO-RBF-PID. For the considered 6-DOF multi-thruster underwater robot, PSO-RBF-PID achieved the best overall performance in steady-state error, settling time, overshoot, and IAE. This improvement is mainly attributed to the combination of PSO-based offline optimization and RBF-based online adaptive compensation.

Technologies doi: 10.3390/technologies14060371

Authors: Leandro Pazmiño-Ortiz Alan Cuenca-Sánchez Byron Loarte-Cajamarca María Pérez

Wearable technologies offer practical opportunities for assistive communication and educational support in introductory sign language learning. This paper presents an IoT-enabled smart glove for deterministic static sign language recognition over a bounded vocabulary of 15 isolated static gestures, comprising digits (0–9) and five vowel handshapes (A, E, I, O, U). The system is intended for foundational static gesture and posture practice and is not designed or validated for dynamic gestures, coarticulated signing, continuous sign language recognition, or sentence-level translation. The prototype integrates five 2.2-inch (55.9 mm) resistive flex sensors and an MPU6050 3-axis accelerometer, performs acquisition, exponential moving average filtering, user-specific calibration, normalization, and deterministic classification on a NodeMCU ESP32 board, and transmits selected processed variables to Arduino Cloud through MQTT for remote monitoring. A 10 s calibration routine maps user-specific open-hand and closed-fist responses into normalized flex-sensor ranges, allowing the same deterministic rule structure to operate across participants without model retraining. Experimental evaluation with 10 healthy adult participants aged 20–41 years (mean age: 27 years), all familiar with sign language and all providing written informed consent, produced a balanced dataset of 1500 labeled steady-state sensor vectors. The class-averaged recognition rate was 92.8%, and leave-one-subject-out validation produced a subject-wise accuracy of 92.80±2.03%, with individual participant accuracies ranging from 90.00% to 96.00%. The local embedded processing pipeline required less than 2 ms per cycle, the complete path including MQTT visualization produced approximately 150 ms end-to-end latency, and the device operated for up to 14 h using a 3.7 V, 1000 mAh Li-Po battery. The results indicate that calibrated deterministic sensor fusion can provide a low-cost, low-latency, edge-executed solution for bounded static sign-language gesture learning tasks while maintaining stable short-term subject-wise performance under controlled experimental conditions.

Technologies doi: 10.3390/technologies14060370

Authors: Julian Gil-Gonzalez David Augusto Cárdenas-Peña Alvaro Orozco-Gutiérrez Enrique D. Guijarro-Estelles Andres M. Álvarez-Meza

Obtaining high-quality labeled data for supervised learning is costly, motivating the use of crowdsourcing, which distributes the annotation process across multiple workers with varying levels of expertise. A key challenge in crowdsourced data is annotation sparsity, as each worker labels only a limited subset of instances. This sparsity can amplify class imbalance, reduce supervision for minority classes, and bias standard cross-entropy-based models toward the majority classes. To address this problem, we propose a correlated chained Gaussian process framework trained on a focal-loss-based variational objective (CCGPFL). This probabilistic framework jointly models latent ground-truth and instance-dependent annotator reliability while accounting for correlations among annotators. In addition, the focal-weighted objective mitigates the imbalance induced by sparse annotations by assigning greater importance to harder examples during training. Experiments on synthetic, semi-synthetic, and fully real multi-annotator datasets show that CCGPFL achieves competitive and often superior performance relative to state-of-the-art learning-from-crowds baselines in terms of Overall Accuracy (OA) and Area Under the ROC Curve (AUC).

Technologies doi: 10.3390/technologies14060369

Authors: Christian Gómez Benoit Garbinato

This research examines stakeholders’ perspectives in Colombia and Switzerland on blockchain traceability systems in the coffee industry. Adopting the Unified Theory of Acceptance and Use of Technology (UTAUT) as an interpretive framework, the study analyzes these perceptions through the constructs of performance expectancy, effort expectancy, social influence, and facilitating conditions. Using a quantitative cross-sectional design with an exploratory scope, we survey 360 participants, comprising 60 coffee supply chain companies and 300 consumers. Results reveal that 78.3% of stakeholders consider traceability essential, yet only 46.7% are familiar with blockchain. Stakeholders identify three primary benefits: improved transparency (91.7%), fraud prevention (88.3%), and enhanced security (86.7%). However, significant barriers persist: high implementation costs (95%), limited expertise (91.7%), and lack of awareness (93.3%). Geographic differences emerge: Colombian stakeholders prioritize cost reduction and fraud prevention, while Swiss participants focus on data management and privacy protection. Among consumers, 62.7% express interest in provenance information, 56.7% are willing to pay for blockchain systems, and 59% are interested in tipping farmers. The study classifies benefits and drawbacks across nine dimensions, providing a comprehensive framework for understanding the multidimensional impacts of blockchain on the coffee supply chain.

Technologies doi: 10.3390/technologies14060368

Authors: Andrés F. Calvo-Salcedo Marin B. Marinov Neil Guerrero González Jose A. Jaramillo-Villegas

Deterministic generation of dissipative Kerr solitons (DKSs) is a key requirement for practical microresonator-based frequency comb sources. Here, we present a design methodology for Si3N4 dual-coupled microring resonators (DCMs) that relates device geometry to the intrinsic and interaction parameters governing soliton formation. In particular, the auxiliary-ring geometry controls the avoided mode crossing, enabling targeted control of the interaction strength a and its modal position b through geometric design and refractive-index tuning. The resulting DCM configurations exhibit accessible DKS regions in the (Δ,|S|2) parameter space under constant pump power and linear detuning sweeps. These results provide a practical framework for the implementation of robust microresonator frequency comb sources with simplified control.

Technologies doi: 10.3390/technologies14060367

Authors: Evgeniya Ishchukova Faezeh Sadat Sajadi Sergei Petrenko Alexey Petrenko Alexey Nekrasov

We performed a cross-layer, system-level analysis of the post-quantum transition of blockchain architectures through a systematic review. The analysis, based on 108 peer-reviewed studies, moves beyond post-quantum cryptography (PQC) as merely a primitive substitution and examines how quantum pressures cascade through validation, propagation, interoperability, governance, and regulatory layers. Empirical results show that the authenticated payloads for lattice signatures grow from ~65–73 bytes (ECDSA) up to kilobyte-scale sizes, and verification overhead is increased by a factor of 2× to 5× depending on the deployment scenario. Such inflation can narrow block-capacity margins, increase propagation delay under fixed-interval regimes, and shift validator resource thresholds in heterogeneous networks. Moreover, the harvest-now–decrypt-later model creates a temporal asymmetry between the design options and the exposure window. These findings indicate that post-quantum resilience depends more on maintaining a structural balance among the tightly coupled technical and institutional stress channels than on the strength of the algorithm itself, and migration success ultimately depends on the ability to coordinate the management of these constraints, rather than on managing them separately.

Technologies doi: 10.3390/technologies14060366

Authors: Alessia Papale Gloria Lopiano Andrea Campagner Federico Cabitza

Artificial intelligence (AI) is increasingly being integrated into Clinical Decision-Support Systems (CDSSs), shifting attention from algorithmic performance alone to the broader sociotechnical conditions that shape effective human–AI collaboration. In this study, we investigated whether nine displacement-based structured coordination protocols can improve the collective diagnostic decision-making of hybrid human–AI teams (16 board-certified radiologists and a simulated AI model) in a radiological double-reading task for vertebral fracture detection from X-ray images. Among the protocols tested, the Accuracy-Oriented, Confidence-Oriented, and Presumptuous strategies achieved the highest (balanced) accuracy overall, with up to 97% among strong clinicians and 92% among weak ones, significantly outperforming simpler methods like majority voting. Conversely, approaches optimized for a single metric (e.g., sensitivity or specificity) introduced performance trade-offs. Benefits were strongest among less proficient clinicians, which exhibited substantial and consistent improvements, while proficient clinicians showed limited gains and occasional declines. Critically, Kasparov’s Law emerged as a comparative framework for empirically evaluating coordination quality relative to the diagnostic task, clinical objective, and clinician proficiency by identifying situations in which less proficient clinicians supported by superior coordination protocols outperformed more proficient clinicians operating under inferior ones. These findings demonstrate that coordination design is a critical determinant of hybrid human–AI decision-making, highlighting that a well-structured process can be more relevant than individual components’ performance and support process-centered approaches to the development and evaluation of CDSSs.

Technologies doi: 10.3390/technologies14060365

Authors: Javier Melero-Deza Pedro Arias-Perez Guillermo García Patiño Lenza Martin Molina Pascual Campoy

In recent years, reinforcement learning (RL) has been applied to frontier-based exploration to enhance a robot’s decision-making policy and improve exploration performance. In this work, we address this scenario with the aim of pushing forward the finding of the optimal frontier selection policy in unknown, unstructured environments, with RL deployed for a minimal sensing drone setup. We propose a novel policy architecture, featuring an attention module that uses the global map features captured by a convolutional neural network together with local frontier features in the form of scalar values, trained end-to-end with a scoring network using the Proximal Policy Optimization algorithm over a 2D randomized unstructured environment. Our approach demonstrates improved exploration efficiency in the evaluated scenarios, as it surpasses purely heuristic-based frontier selection strategies used as baselines for other RL methods, achieving shorter paths than the Nearest Frontier, the Hybrid Approach, and the TARE local horizon, as well as one-shot sim-to-real policy deployment.

Technologies doi: 10.3390/technologies14060364

Authors: Basem Almadani Md Moazzem Hossain Nafisa Tabassum Farouq Aliyu

Smartwatches are increasingly used in safety-critical scenarios, yet their optical heart-rate (HR) measurements often contain noise, artifacts, and missing data, undermining clinical trust. This paper presents Edge Beats, a data-curation layer and end-to-end architecture that enables the low-cost, open source PineTime smartwatch to function as a practical HR sensing node for distributed wearable systems. Heart-rate packets are streamed from PineTime to an ESP32 at the edge layer over Bluetooth Low Energy (BLE), then forwarded via an embedded Message Queuing Telemetry Transport (MQTT) broker to an edge server laptop for processing and visualization. A lightweight multi-stage algorithm cleans and smooths the HR stream using physiological boundary checks, a configurable data imputation technique, and exponential moving average (EMA) smoothing, all designed for real-time operation on resource-constrained hardware. We have evaluated the system over long monitoring sessions and compared the processed PineTime output against a commercial Huawei GT Pro 2 smartwatch. The system suppresses extreme spikes and short-term oscillations, yielding a more stable HR trace with qualitative agreement to the reference trends while keeping values in a physiologically plausible range. Network measurements show low latency (almost 3 ms one-way, 15 ms RTT) and stable throughput, and power measurements (100–450 mW for ESP32 and 3–70 mW for PineTime watch) confirm that continuous HR streaming over BLE and MQTT is feasible within the PineTime’s energy budget. These results imply that data stream processing combined with a modest publish–subscribe architecture improves the stability and usability of HR streams obtained from commodity wearable sensors, making PineTime a candidate as a complementary component for mission-critical health and safety systems.

Technologies doi: 10.3390/technologies14060363

Authors: Tamás István Unger Miklós Kuczmann

Deterministic radio wave propagation models provide high accuracy in complex outdoor environments but remain computationally impractical for large-scale network planning and spectrum management. In contrast, empirical and hybrid models offer low complexity at the expense of reduced accuracy, systematic bias, and limited terrain sensitivity. This paper proposes a unified delta learning framework that enhances fast baseline propagation models by learning a data-driven correction toward a deterministic Parabolic Equation Modeling (PEM) reference. A key novelty lies in a compact, physics-informed feature representation that replaces the full terrain profile with an 18-dimensional vector combining local geometric descriptors, global terrain characteristics, and baseline responses, enabling accurate correction with low-dimensional input. The study also provides the first systematic investigation of delta-based correction across multiple widely used propagation models. The framework is evaluated for free-space propagation, ITU-R P.1546, ITU-R P.1812, and ITU-R P.452 using ridge regression, kernel ridge regression, gradient boosting regression trees, and a neural network model. Model performance is assessed in terms of error reduction, bias mitigation, robustness across learning algorithms, and profile-level generalization to previously unseen propagation paths within the considered terrain categories. Results show substantial error reduction, with up to twofold improvement for simpler baseline models and consistent gains for hybrid models, while preserving computational efficiency.

Technologies doi: 10.3390/technologies14060362

Authors: Cornelio Morales-Morales Joseph Aaron Rodríguez-Cabello Mirna Castro-Bello Josefa Morales-Morales Vitervo López-Caballero Victor Alberto Gómez-Pérez

Diabetic foot ulcers represent a severe complication of diabetes mellitus, affecting millions of adults worldwide and often leading to hospitalization and amputation. Diabetic neuropathy increases the risk of plantar injuries, while the lack of continuous monitoring and delayed detection contributes to the progression of these lesions. This study presents a low-cost smart insole system for continuous plantar pressure monitoring and screening of plantar pressure patterns associated with diabetic neuropathy. The system integrates piezoresistive sensors distributed across key regions of the foot, connected to a low-power ESP32 microcontroller for data acquisition. Measurements are transmitted via Bluetooth Low Energy to a mobile application that enables real-time visualization, user management, and storage in a MySQL database for historical data consultation. Data processing employs a convolutional neural network configured to classify plantar pressure patterns between non-diabetic individuals and diabetic patients presenting neuropathic alterations. System validation demonstrated 88% accuracy, 88% recall, and 87% F1-score in classifying plantar pressure patterns. The results confirm that the combination of low-cost hardware and open-source software constitutes a viable and scalable solution for screening biomechanical alterations associated with diabetic foot complications.

Technologies doi: 10.3390/technologies14060361

Authors: Shiqi Jiang Hai Peng Dingqi Zhang Yupei Zhu

Aircraft skin defect recognition is a safety-critical visual inspection task in which lightweight models must maintain high diagnostic accuracy while suppressing false alarms caused by complex surface textures, illumination variations, and weak defect patterns. This study proposes HQCA-Net, a simulation-based hybrid quantum-classical channel attention network for reliable aircraft skin defect recognition. The core component, termed Residual Quantum Channel Attention (RQCA), embeds a 10-qubit variational quantum circuit into a classical ResNet-18 backbone to perform compact and structured nonlinear feature recalibration, introducing only 30 trainable quantum-gate parameters. The quantum circuit is evaluated using state-vector simulation, and this study focuses on model-level feature recalibration, reliability, and robustness within the evaluated dataset rather than implementation on physical quantum hardware. Experiments on a six-class aircraft skin defect dataset show that HQCA-Net achieves 97.93% classification accuracy and a global false positive rate of 0.49%, outperforming ResNet-18 and classical lightweight attention mechanisms including SE, ECA, and SimAM. Additional analyses using confidence calibration, Grad-CAM visualization, Gaussian noise perturbation, few-shot training, and circuit-depth ablation further indicate that the proposed RQCA module improves feature discrimination and false-alarm suppression under compact parameter constraints. These results suggest that the hybrid quantum-classical attention module can serve as a parameter-efficient nonlinear feature recalibration strategy for reliable visual defect inspection under the tested experimental conditions.

Technologies doi: 10.3390/technologies14060360

Authors: Dmytro Borovyk Oleksander Barmak Pavlo Radiuk Iurii Krak

Accurate identification of visually similar targets in Unmanned Aerial Vehicle (UAV) imagery is hindered by significant inter-class ambiguity and viewpoint variability. While hierarchical deep learning mitigates these challenges, existing architectures relieve manual design, introducing subjectivity and limiting cross-domain scalability. In this work, we propose an objective, data-driven method for the automated synthesis of hierarchical classification structures. Our approach uses a hybrid inter-class proximity metric that integrates geometric distances between latent-feature-space centroids with empirical misclassification probabilities. Using a hierarchical agglomerative clustering algorithm optimized via an inconsistency coefficient, we synthesize a coarse-to-fine cascade that deploys YOLOv11 for feature extraction and FT-Transformers for specialized identification. Experimental validation on the VisDrone2019 and UAV123 datasets demonstrates that the automatically generated hierarchy achieves a peak F1-score of 94.9%, outperforming the monolithic YOLOv11 model by 0.8% and matching human-designed cascades. Sensitivity analysis indicates an optimal hybrid weight range of 0.4–0.6. The findings confirm that our automated synthesis provides high adaptability and reliability for real-time edge AI deployments, ensuring robust performance in dynamic monitoring environments without requiring manual redesign.

Technologies doi: 10.3390/technologies14060359

Authors: Tahani Albalawi Amani Alzahrani

Medical text classification is an important task in biomedical natural language processing, but prediction errors remain problematic in high-stakes settings where reliability matters in addition to accuracy. To address this challenge, this paper proposes a learned selective deferral framework for biomedical sentence classification that allows uncertain predictions to be deferred under constrained review budgets. The framework combines a transformer-based classifier with uncertainty estimation, temperature scaling, and a learned deferral policy that predicts the likelihood of model error from multiple signals, including confidence, entropy, calibration-aware features, and Monte Carlo Dropout descriptors. Deferral decisions are applied under fixed budgets to improve the use of limited review capacity. Experiments on the PubMed 200k RCT dataset show that budget-constrained deferral reduces system-level risk. Using PubMedBERT as the primary backbone, deferring 20% of the highest-risk cases reduces system risk from 0.1108 to 0.0360. Compared with a calibrated confidence-threshold baseline, the learned policy provides modest but generally favorable improvements, with statistical significance observed at the 20% budget. Additional experiments across PubMedBERT, BioBERT, and SciBERT suggest that the framework transfers across biomedical transformer backbones, while calibration improves the reliability of confidence estimates and learned policies outperform random deferral.

Technologies doi: 10.3390/technologies14060358

Authors: Kiyan Afsari Christian Ritz May El Barachi

Epileptic seizure prediction using an Electroencephalogram (EEG) can improve patient safety by enabling early intervention, yet most existing approaches focus on short prediction horizons with limited personalization or computational efficiency. This study presents a unified deep learning framework evaluated across ten pre-ictal prediction windows up to 300 min before seizure onset, using recordings from 161 patients and 1023 seizure events. At the 5 min horizon, the generalized model achieved 96.30% accuracy and 91.62% sensitivity. Two complementary personalization strategies are introduced: incremental transfer learning, which progressively fine-tunes the generalized model using patient-specific data, and Dynamic Time Warping (DTW)-based similarity personalization, which constructs a morphology-aware training cohort from a single reference seizure. Personalized models consistently outperform generalized baselines, particularly at longer horizons, with the DTW-based approach achieving 89.68% accuracy using only 70 similar patients. Reliable prediction is demonstrated up to 60 min prior to onset, while model optimization reduces computational complexity with minimal performance loss, supporting deployment in resource-constrained clinical environments.

Technologies doi: 10.3390/technologies14060357

Authors: Vasyl Lozynskyi Yakiv Nemyrovskyi Valentyn Otamanskyi Ihor Shepelenko Oleksandr Melnyk Vasyl Levchenko Liubomyr Ropyak

Plastic forming of the workpiece is a key quality indicator during deforming broaching. This study aims at technological control over workpiece forming by establishing a relationship with technological factors, including broaching modes: interference, tool geometry, and workpiece wall thickness. The research methods used included numerical simulation of the deformation process and the stress–strain state of a plastic steel workpiece. The constructed simulation models allowed tracking stress and strain evolution on the inner and outer surfaces, revealing their differences. The approach’s originality lies in establishing the key influence of critical contact pressure in the deformation zone on strain state changes. Its appearance is influenced by interference, tool geometry, and workpiece wall thickness. Circumferential strain depends solely on interference and workpiece wall thickness, remaining independent of the angle, α. A relationship is provided to determine the interference ensuring the outer dimension. The calculation method for determining the processed hole diameter was improved, considering the real deformation zone scheme, simulation results, and elastic recovery. The relationship between the processed hole diameter, broaching modes, and workpiece wall thickness has been established. It is necessary to set the angle that ensures the absence of axial strain. A technological control scheme for forming is developed, and an application example is provided.

Technologies doi: 10.3390/technologies14060356

Authors: Carlos Polvorinos-Fernández María Centeno-Cerrato Luis Sigcha César Asensio Guillermo de Arcas Ignacio Pavón

Accelerometers embedded in consumer-grade smartwatches hold significant potential for health-related research applications, but their measurement reliability is often compromised. This limitation necessitates proper metrological characterization to ensure precision and consistency, particularly in health-related research contexts where reliable movement data are required. This study proposes a methodology for the simultaneous metrological characterization of multiple smartwatch accelerometers, enabling efficient and consistent bench-based measurement evaluation. The proposed methodology employs a seismic table to generate controlled vibrations within a frequency range of 1–8 Hz and acceleration amplitudes between 1 and 4 m/s2. Five commercial smartwatch units were tested, collecting acceleration data at sampling rate of 50 Hz. A reference accelerometer was used to assess the accuracy of smartwatch measurements, with errors and uncertainties quantified following ISO standards. Results demonstrate that simultaneous bench-based evaluation allows consistent comparison of measurement performance across devices while reducing the time required for the process. The analysis highlights variations in frequency response and amplitude accuracy across different smartwatch units, emphasizing the need for systematic metrological characterization when considering the future use of smartwatches in health-related research studies involving wearable movement monitoring.

Technologies doi: 10.3390/technologies14060355

Authors: Xiaoyi Liu Yuhan Yin Yetong Zhang Kunxiao Wu Jianyong Zheng Fei Mei

Extreme weather conditions impose significant challenges on distribution network inspection because UAV flight safety, energy consumption, vehicle mobility, and task coverage are strongly coupled under wind disturbances. To improve inspection efficiency and operational robustness, this paper proposes a weather-aware asynchronous vehicle–UAV cooperative scheduling method based on bi-level MODDPG–NSGA-II optimization. First, a dynamic wind field model and a wind-sensitive UAV energy model are established to describe the effects of background wind, vertical wind shear, and local gust disturbances on UAV motion and state-of-charge evolution. Then, an asynchronous vehicle–UAV collaboration mechanism is developed, allowing the vehicle to move toward downstream parking sites after UAV deployment while UAVs perform inspection and cross-site recovery under rendezvous and energy safety constraints. On this basis, a bi-level optimization framework is constructed, in which NSGA-II searches global coordination parameters and MODDPG learns adaptive multi-UAV scheduling policies in continuous decision spaces. Controlled wind-factor experiments show that, with the task scale fixed at 52 inspection tasks, the proposed method maintains 100% task coverage under 0–10 m/s wind conditions. As the reference wind speed increases from 0 m/s to 10 m/s, the mission completion time increases from 40.97 min to 70.24 min, while the minimum residual SOC decreases from 50.32% to 13.82%, which remains above the predefined safety threshold. Repeated stochastic trials and statistical significance analysis further indicate that the proposed method achieves shorter mission time and more stable task coverage than representative baselines under the same experimental conditions. The scope of this study is simulation-level validation; real-world flight tests and hardware-in-the-loop verification will be further investigated in future work.

Technologies doi: 10.3390/technologies14060354

Authors: Evren Atış Tamara Gajić Dragan Vukolić Marko D. Petrović Lyailya M. Mutalieva Sofija Radulović Dariga M. Khamitova Aigerim Kassymova Nina Đurica

The study applies a multiphase, multimethod research approach based on participatory methodology. It integrates the perspectives of professionals from the tourism and hospitality industry and academic experts with the aim of developing an integrated conceptual model of the influence of AI and IoT technologies on work processes, skill development, and job attractiveness in the industry. The research provides a comprehensive understanding of how digital technologies indirectly shape employment through changes in work organization and the development of transferable digital and socio-emotional skills. The paper aims to contribute to redefining the perception of work in tourism and hospitality by emphasizing the sector not only as a career choice, but also as a platform for acquiring skills transferable to other industries. The findings revealed that employees’ intentions to enter or remain in the industry are not directly influenced by AI and IoT technologies; rather, these effects are mediated through changes in work processes and, more importantly, through skill development. The study contributes theoretically by developing and empirically validating an integrated conceptual model that connects technology implementation, work transformation, skill development, and employment outcomes. From a practical perspective, the results highlight the importance of human-centered implementation strategies based on training, communication, and employee inclusion in order to maximize the benefits of digital technologies.

Technologies doi: 10.3390/technologies14060353

Authors: Flávio Barros Letícia Correia Caio Magno Christian Diniz Gean Sousa Allan Kardec Barros Luis Claudio Silva

Lossless compression of telemetry data in satellites is essential due to the stringent limitations of bandwidth and onboard storage. Traditional methods based on information theory and entropy coding, such as Huffman and Arithmetic coding, exploit statistical redundancy but still present opportunities for improvement when applied to data with low redundancy, large alphabets, and near-uniform symbol distributions. This study proposes On+, a novel lossless compression algorithm for satellite telemetry data. Using real telemetry data captured by the Aldebaran-1 CubeSat satellite, a dataset consisting of 600 binary files was created. The performance of the proposed algorithm was evaluated in comparison with classical methods (Huffman and Arithmetic coding) and several commercial compressors (.rar, .zip, .7z, .xz, and .gz). The On+ algorithm achieved an average compression rate of 29.19%, with a standard deviation of 1.26 and a median of 29.09%, outperforming the traditional Huffman coding and Arithmetic coding methods in terms of compression efficiency. Furthermore, it exhibited superior performance compared with all commercial solutions evaluated, many of which resulted in file expansion (negative compression rates). These results demonstrate the effectiveness and viability of the On+ algorithm for optimizing telemetry data compression in satellites.

Technologies doi: 10.3390/technologies14060352

Authors: Fabian Khateb Pichai Suksaibul Tomasz Kulej Montree Kumngern

This study introduces an innovative mixed-mode universal biquad filter implemented using multiple-input operational transconductance amplifiers (MI-OTAs). Based on the advantage of OTAs, which possess multiple inputs, the proposed mixed-mode universal filter using MI-OTAs can implement both non-inverting and inverting standard filtering functions such as low-pass, high-pass, band-pass, band-stop, and all-pass filters in voltage-mode, transadmittance-mode, current-mode, and transimpedance-mode, which is the maximum capability of mixed-mode universal filters. The natural frequency of all filtering functions can be electronically controlled. Based on the multiple-input bulk-driven MOS transistor (MOST) technique, the OTA can also operate at very low supply voltage and provide wide-input voltage swing. The technique of MOST, operating in the weak inversion region, is used to achieve the low-power consumption of OTA. The MI-OTA circuit and mixed-mode universal filter were designed and simulated using Cadence Virtuoso, utilizing TSMC’s 65-nm CMOS technology. At a 0.5 V supply voltage, the filter demonstrated a simulated power consumption of 450 nW at a natural frequency of 156 Hz. In these ranges of power consumption and natural frequency, it can be expected that the proposed filter can be built as an versatile integrated circuit for low-frequency applications such as bio-signal processing. The design parameters were successfully validated through both post-layout extractions and discrete hardware prototyping utilizing commercially available LM13700N ICs.

Technologies doi: 10.3390/technologies14060351

Authors: Bartosz Korba Katarzyna Pawluk

The chronic digitalisation deficit within the construction sector induces design anomalies and human errors, leading to a severe erosion of investment profitability. This study aims to implement the automation of resource generation and validation processes, acting as a systemic safety barrier to stabilise analytical workflows. The proposed methodology relies on a Multi-Agent System (MAS) architecture embedded within the n8n environment and powered by Gemini-class language models. The framework integrates a deterministic PostgreSQL database within a Retrieval-Augmented Generation (RAG) architecture, enabling the precise, real-time processing of Construction Law regulations. Applying Chain-of-Thought reasoning alongside structured prompt templates helped eliminate model logic drift, ensuring comprehensive result reproducibility. The deployment of this platform induced a 96% acceleration in the pre-construction phase, reducing the formulation time of Work Breakdown Structure (WBS)/Critical Path Method (CPM) structures from a baseline of 480 min to an average of 20 min. The empirical data demonstrates a radical compression of operational costs (OPEX) concurrent with the marginalisation of the Human Error Probability (HEP) index to a residual level of < 1%. Ultimately, the solution drastically minimised the iterative overhead, confining the design cycle to a single execution while maintaining high level of compliance with the 7R (7 Rights) Logistics Directive.

Technologies doi: 10.3390/technologies14060350

Authors: Josephine Eskaline Joyce Shoney Sebastian

Kubernetes Horizontal Pod Autoscaler (HPA) primarily relies on resource-based metrics, such as CPU utilization, which are poorly suited to capturing the latency variability of AI inference workloads. In this paper, we propose a custom-metric-driven autoscaling approach that leverages inference latency histograms as first-class scaling signals for Kubernetes HPA. The proposed framework integrates a Prometheus Operator (PO)-based observability stack with the Prometheus Adapter to expose and aggregate per-pod inference latency metrics, enabling workload-aware scaling decisions. We evaluate the approach using four mid-scale transformer-based inference services, comprising two reasoning-like and two latency-stable workloads, under high-concurrency conditions. The experiments analyze latency variation, tail behavior, and replica dynamics across multiple autoscaling policies, including variations in scale-up aggressiveness (3 pods/30 s, 3 pods/60 s, 6 pods/60 s), inference-time thresholds, and stabilization windows. Compared to CPU-based autoscaling, inference-driven policies reduce mean response time by 18–27% for reasoning-like workloads and 12–20% for stable workloads. The results show that latency-variable workloads exhibit wider tails and higher variance, indicating the need for moderately aggressive scale-up strategies to avoid long-lasting latency spikes. Overall, the findings show that inference-latency-driven custom metrics significantly improve autoscaling efficiency and stability for transformer-based inference workloads in cloud-native environments.

Technologies doi: 10.3390/technologies14060349

Authors: Jie Ren Peng Wang Ao-Tian Gu Chunhui Gong Yi Yang

Alpha/beta (α/β) pulse shape discrimination (PSD) in liquid scintillation counting (LSC) is fundamentally limited by the charge comparison method (CCM) at low energies, where the entire tritium (3H) beta spectrum resides (0–18.6 keVee). The CCM figure-of-merit drops below 0.6 in this region, rendering it inadequate for simultaneous tritium and natural uranium alpha monitoring in nuclear power plant (NPP) liquid effluents. We present a one-dimensional convolutional neural network (1D-CNN) trained on an 80,000-waveform physics-based simulation dataset using established scintillation parameters for Ultima Gold AB. The proposed network achieves 97.4% overall classification accuracy and an area under the receiver operating characteristic curve (AUC) of 0.9981 on the held-out test set, representing improvements of 13.8 percentage points and 0.046 AUC over CCM. In the critical 0–18.6 keVee region, CNN accuracy exceeds 95% compared to below 60% for CCM—a greater than 35 percentage point improvement. Pulse amplitude discrimination (PAD), evaluated as a preliminary screening method, exhibits a 6.3% alpha spillover rate into the beta window, exceeding the regulatory limit of 3%. Gradient-weighted class activation maps (Grad-CAM) confirm that the network exploits physically meaningful pulse features rather than simulation artefacts. A comprehensive background suppression strategy combining dual-SiPM coincidence (24× reduction), anti-coincidence guard detector (5.8× reduction), composite passive shielding (10× reduction), and CNN-assisted discrimination reduces the system equivalent background to 1.83 ± 0.12 cpm, yielding a tritium minimum detectable activity (MDA) of 0.21 Bq/mL (10 mL sample, 30 min count), which satisfies the GB 14587 reference limit of 0.5 Bq/mL. After 8-bit post-training quantisation, the model achieves sub-microsecond inference latency on an embedded Xilinx Artix-7 Field-programmable gate array(FPGA), enabling real-time deployment in portable online monitoring systems.

Technologies doi: 10.3390/technologies14060348

Authors: Irina Biryukova Mikhail Palatnikov Diana Manukovskaya Sofja Masloboeva Roman Titov Olga Palatnikova Alexandra Kadetova Olga Tokko Natalya Teplyakova Il’ya Efremov Nikolay Sidorov

A series of co-doped LiNbO3:Mg:Er crystals were grown in a single technological cycle and under the same technological conditions by Czochralski. In each subsequent step of the growth cycle, the content of Mg and Er dopants decreased. The initial concentration of dopants in the melt was [Mg] = 4.0 mol% and [Er] = 0.78 mol%. The melt was obtained from a homogeneously doped batch. The batch included the Nb2O5:Mg:Er precursor synthesized by the liquid-phase method. The physicochemical features of crystallization were studied. The optical properties of the crystals were investigated using laser conoscopy and photoinduced light scattering. Macro- and microdefect structures were studied by optical microscopy. Quantitative phase analysis was performed for single-crystal samples. The defect structures of powdered LiNbO3:Mg:Er samples were determined by refining XRD patterns by Rietveld. The optical quality of doubly doped crystals corresponds to that of singly doped LiNbO3:Er crystals. Mg significantly reduces the transparency of LiNbO3:Mg:Er crystals in the ultraviolet and violet spectral ranges. The optimal dopant concentration in the melt was [Er] = 0.63 mol% and [Mg] = 3.0 mol%, and [Er] = 0.47 mol% and [Mg] = 3.07 mol% in crystal. The optical properties of LiNbO3:Mg:Er crystals make them promising active nonlinear optical materials for generating and converting laser radiation.

Technologies doi: 10.3390/technologies14060347

Authors: Ziyang Chen Jianyu Wei Hang Su Qiyong Liang Wei Yang Chaohua Mo Lingling Chen Feng Liu Jian Wang Xinghan Chen Xinqing Xiao

Facing global problems such as the energy crisis and climate change, in recent years, the bioelectrochemical system represented by plant microbial fuel cell (PMFC) has been widely studied. It is a frontier bioelectrochemical technology that combines plant photosynthesis, rhizosphere microbial metabolism, and electrochemical energy conversion. This paper focuses on the linkage application of PMFC and intelligent sensing technology in the paddy-field environment, systematically expounds the basic composition, working principle, and integration mode of this technology with paddy field ecology, and emphatically analyzes its realization path and application potential in self-powered external sensor deployment, rhizosphere biosensor, and multi-node sensor network integration. The analysis shows that PMFC has the unique advantage of in situ and continuous micro-power generation in flooded rice fields. Its output not only supports the intermittent operation of low-power sensors, but the output electrical signals can also reflect plant stress and environmental conditions, thereby possessing biosensing potential. However, the current system still faces key bottlenecks, such as low power density, easily disturbed electrical signals, and high cost of high-performance electrode materials, which restrict the actual deployment of rice fields. Through the collaborative optimization of electrode interface engineering, microbial community directional control, and low-power sensing fusion strategy, it is expected to promote the transformation of PMFC from principle verification to field intelligent monitoring application.

Technologies doi: 10.3390/technologies14060346

Authors: Gulnur Yerkebulan Akerke Akanova Zhantore Galymzhan Nazira Ospanova

Graded lexical resources aligned with the Common European Framework of Reference for Languages (CEFR) and lexical complexity prediction remain limited for low-resource Turkic languages, and the extent to which existing predictive models generalize to agglutinative morphology is unresolved. We introduce the first CEFR-graded lexicon for Kazakh, containing 4561 lemma–part-of-speech (POS) entries across A1–C1, and use it to test whether explicit morphology improves lexical complexity prediction. We compare handcrafted morphological features, XLM-RoBERTa contextual embeddings, and fusion models that combine both signal types on held-out CEFR classification. Our best model, a gated fusion of contextual embeddings with morphological features, achieves a macro-averaged F1 score of 0.360 and a mean absolute error of 1.125 on the held-out test set. Morphology provides useful information beyond character-level cues, contextual representations are strong on their own, and combining them yields the best supervised performance for this task. The paper therefore contributes a new CEFR resource for Turkic languages and evidence that morphology-aware modeling is useful for Kazakh lexical difficulty prediction. The results support Sustainable Development Goal 4 (Quality Education) by enabling objective assessment of learning-material complexity and adaptive Kazakh language learning. The derived lexicon and code are publicly available.

Technologies doi: 10.3390/technologies14060345

Authors: Carlos Polvorinos-Fernández Luis Sigcha Mayca Marín Valero Miriam Grande Guillermo de Arcas Ignacio Pavón

Assessing motor symptoms in Parkinson’s disease (PD) is challenging due to the progressive evolution of the condition and the variability of symptoms, which are not fully captured by periodic clinical visits. In this context, wearable sensors and machine learning (ML) have emerged as a viable path toward objective and continuous monitoring, although achieving robust generalization to free-living conditions remains a challenge. This work explores the egg-beating task, a simple everyday activity, as a digital approach for PD motor assessment using smartwatch-based inertial measurements and ML techniques. Twenty-two individuals with PD and sixteen healthy controls (HC) completed a one-minute egg-beating task while wearing a smartwatch equipped with tri-axial accelerometer and gyroscope sensors. Data were recorded both under supervised clinical conditions and during unsupervised home sessions. Time- and frequency-domain features were extracted from the inertial signals, and models trained exclusively on supervised recordings were then tested on supervised, unsupervised, and combined data. PD participants showed systematically lower movement amplitude, slower oscillation frequency, and a progressive drop in signal energy over the course of the task, all of which align with the characteristic features of bradykinesia. The support vector machine achieved the best overall performance, reaching 90% accuracy in distinguishing PD from healthy controls under supervised conditions, with a reduction of less than 4% when applied to unsupervised data. These results support the egg-beating task as a practical and ecologically valid method for real-world motor assessment, with potential for future use in remote monitoring and longitudinal assessment.

Technologies doi: 10.3390/technologies14060344

Authors: Flavio Morales Francis Rodríguez Luque-Nieto Miguel Angel Alfonso Ariza Quintana

Vehicle ad hoc networks (VANETs) can help prevent traffic accidents through wireless communication; however, most studies are based on simulations or static evaluations. This research paper presents the design, implementation, and experimental evaluation of a prototype early-warning system for vehicle proximity based on VANETs using ESP-NOW. The prototype utilizes five ESP32-CAM nodes equipped with MaxSonar sensors installed in vehicles and an RSU unit with a Raspberry Pi for vehicle-to-infrastructure (V2I) communication. Field tests were conducted in Quito, Ecuador, at speeds ranging from 10 to 70 km/h, measuring latency, packet loss, and received signal strength (RSSI). The results show average latencies of 9.9 ms at 10 km/h and 114.5 ms at 70 km/h, with packet loss rates of 2% and 60%, respectively. Statistical analysis reveals 95% confidence intervals for latency ranging from ±0.98 ms to ±6.90 ms, while obstacles introduce marginal attenuation (p = 0.051) with significant dispersion (σ = 5.85 dB). The Doppler shift is negligible (155.6 Hz), but the channel coherence time (2.7 ms) explains the observed degradation. Models were obtained that relate speed to latency (R2 = 0.994) and packet loss (R2 = 0.991). The prototype is viable for early collision warning at urban speeds (up to 60 km/h), outperforming human reaction time (1.5 s).

Technologies doi: 10.3390/technologies14060343

Authors: Haojinyun Le Yueying Ye Jiyao Li Bicheng Guo Shizhang Liu Xinjie Yuan Kaile Guo Zhenrong Xie Zhilong Xu Xianling Li

In steam turbines, blades operate in a high-speed wet steam environment and are often damaged by combined erosion from liquid droplets and solid particles. To reveal the mechanism of composite modification via high velocity oxy-fuel spraying (HVOF) and laser shock peening (LSP) on improving blade erosion resistance, an accelerated erosion experimental method was designed in this work. Five different processes were proposed, including UT, LSP, UT-HVOF, LSP-HVOF, and HVOF-LSP. The results indicate that compared with UT specimens, LSP treatment induces high compressive residual stress in the surface layer of 1Cr12Ni3Mo2VN stainless steel, which leads to shallower compound erosion pits. Compared with UT-HVOF and LSP-HVOF specimens, the HVOF-LSP specimen has the lowest coating porosity and the highest surface microhardness of 1500 HV0.5, representing an increase of 14.5% and 8.7% respectively. This demonstrates that LSP post-treatment can enhance the load-bearing capacity of HVOF coatings effectively. Microstructural analysis further reveals that the HVOF-LSP specimen presents the shallowest erosion pits and the longest penetration lifetime of the WC coating. Accordingly, the HVOF-LSP treatment can effectively improve the service life and protection performance of materials under accelerated erosion conditions, providing a technical reference for the long-term service of turbine blades.

Technologies doi: 10.3390/technologies14060341

Authors: Barbara Kopácsi Nándor Prontvai Blanka Törő Petra Kós Dóra Kozma Tamás Haidegger Viktória Alföldi Katalin Török Péter Prukner István Drotár Szilvia Kóra József Tollár

People living with neurological disorders frequently experience gait impairments that substantially reduce mobility, independence, and quality of life. This pilot study aimed to evaluate the feasibility, safety, and preliminary functional outcomes of integrating the EksoNR robotic exoskeleton (Ekso Bionics, San Rafael, CA, USA) into outpatient neurorehabilitation practice in individuals with chronic neurological impairments. Over an eight-month period, five participants with heterogeneous neurological conditions (two spinal cord injuries, one cerebellar ataxia, one ischemic stroke, and one spastic paraparesis) completed a four-week robotic gait training program consisting of 15 intervention sessions. Functional outcomes were assessed before and after the intervention using standardized clinical tests. Cardiovascular endurance was evaluated using the 6-Minute Walk Test (6MWT), while physical and psychological well-being were assessed with the Functional Independence Measure (FIM) and the Barthel Index, in addition to the WHO Quality of Life (WHOQOL) and EQ-5D-5L questionnaires. Mobility and balance were evaluated using the Timed Up and Go (TUG), Berg Balance Scale (BBS), Tinetti Performance-Oriented Mobility Assessment (POMA), and Walking Index for Spinal Cord Injury II (WISCI II), where applicable. In addition, device-recorded gait parameters, including step count, step length, walking distance, and walking duration, were analyzed. Significant improvements were observed in several device-derived gait parameters, including the number of steps performed with the exoskeleton (p < 0.001), step length (p = 0.003), walking distance (p = 0.002), and walking duration (p < 0.05). Significant improvements were also identified in balance performance (BBS: p = 0.006; Tinetti POMA: p = 0.001), cardiovascular endurance (6MWT: p = 0.017), and EQ-5D-5L scores (p = 0.038). Functional independence measures (FIM and BI), TUG performance, and WHOQOL domains did not demonstrate statistically significant changes. No serious adverse events or device-related injuries occurred during the intervention period. Due to the small and clinically heterogeneous sample, these findings should be interpreted as preliminary exploratory results. Nevertheless, the study supports the feasibility and potential clinical utility of EksoNR-assisted gait rehabilitation and provides a basis for larger controlled investigations.

Technologies doi: 10.3390/technologies14060342

Authors: Hajar Majjate Youssra Bellarhmouch Adil Jeghal Ali Yahyaouy Loubna Laaouina Hamid Tairi Khalid Alaoui Zidani

The rapid evolution of online learning environments has generated diverse and complex data ecosystems. Recommender systems play a central role in leveraging such heterogeneous data to support personalised learning experiences. However, many deep learning-based recommender systems still rely on identifier-based representations that capture co-occurrence and collaborative patterns while overlooking the semantic information embedded in educational activities and the temporal dynamics of learner behaviour. To address these limitations, this study proposes a collaborative-enhanced semantic–sequential recommendation framework for educational platforms that combines structured semantic representation learning, sequential behavioural modelling, and collaborative preference modelling. The proposed architecture integrates a parameter-efficient MiniLM adaptation strategy to extract semantic representations from structured item-related educational metadata and a bidirectional recurrent encoder to model temporal learning patterns from behavioural logs. A gated fusion mechanism is then used to combine semantic and contextual information into learner representations, which are further integrated with collaborative user–item embeddings for top-K recommendation using a Bayesian personalised ranking objective. Experiments conducted on the EdNet-KT1 dataset under chronological splitting, full-corpus ranking, and fixed candidate-sampling protocols show that the collaborative-enhanced model achieves the highest-ranking performance among popularity-based, matrix factorisation, neural collaborative filtering, recurrent sequential, self-attention sequential, and ablation baselines. The model obtains an NDCG@10 of 0.1344 under full-corpus ranking and 0.5383 under candidate sampling, with statistically significant but practically modest improvements over the strongest baselines. Additional ablation, efficiency, and gate analyses indicate that semantic–contextual modelling is most effective when used as a residual enhancement to collaborative recommendation rather than as a standalone replacement. These results suggest that parameter-efficient semantic–sequential modelling, when combined with collaborative preference signals, offers a promising direction for scalable and evidence-based educational recommender systems.

Technologies doi: 10.3390/technologies14060340

Authors: Bahiru Bewket Mitikie Walied A. Elsaigh

This investigation examines the novel application of bacterial concrete as a sustainable substitute for traditional concrete within the construction sector, utilizing bibliometric analysis in conjunction with machine learning. The main aim of the study is to gain insights into the application and potential benefits of using bio-based concrete in the construction industry. A comprehensive search of all publications indexed in Scopus was carried out for the period spanning from 2020 to 14 March 2025, followed by meticulous screening and extraction of relevant documents. The dataset obtained from Scopus was processed in strict accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to uphold transparency and replicability throughout the systematic review process. A descriptive analysis was undertaken to evaluate publication trends over time. The research on bio-concrete combined with machine learning is highly concentrated in Asia, Europe, and the USA; in contrast, vast areas of Africa show no research output regarding self-healing concrete based on this data extraction. Various types of bacteria, including Bacillus species, are explored for their calcium carbonate precipitation capabilities in this review. Microbial-induced calcite precipitation process reduces carbon emissions associated with cement production and extends concrete lifespan by sealing cracks.

Technologies doi: 10.3390/technologies14060339

Authors: Ivan Tomilov Rodion Zamotaev Natalia Gusarova Aleksandra Vatian

Early stopping is a standard form of implicit regularization in neural sequence models, but criteria based solely on validation loss can become unstable or weakly informative in noisy, non-stationary, or weakly separated regimes. We propose Symbolic Early Stopping (SES), a representation-aware hybrid stopping criterion that monitors the evolution of validation hidden-state organization during training. At each epoch, SES constructs a Mapper-based symbolic abstraction of hidden representations extracted from a fixed monitored layer, transforms latent trajectories into symbol sequences, and summarizes them through a compact set of symbolic–dynamic descriptors capturing sequential complexity, transition uncertainty, and geometric dispersion. These descriptors are aggregated into a single symbolic stability score, which is combined with validation-loss monitoring to detect convergence of the learned representation. We evaluate SES on recurrent, bidirectional recurrent, and encoder-only Transformer architectures across multiple time-series regimes with different levels of structural regularity and noise. The results indicate that SES frequently terminates training substantially earlier than conservative loss-based baselines while preserving a competitive quality–efficiency trade-off relative to oracle validation-based stopping. Robustness experiments under additive input noise show that the symbolic monitoring signal remains informative under moderate perturbations, although its advantage is not uniform across all datasets and model classes. A layer-wise analysis further suggests that useful stopping signals may emerge before the final validation curve fully stabilizes, reflecting earlier organization of latent representations. Overall, SES provides an interpretable and computationally tractable framework for representation-level early stopping in neural sequence modeling.

Technologies doi: 10.3390/technologies14060338

Authors: Suradet Tantrairatn Poommin Phinphimai Nattapong Phuangmalee Pawarut Karaked Nutchanan Petcharat Auraluck Pichitkul Atthaphon Ariyarit

This paper presents a connected perception framework for blind-spot awareness by connecting an external camera system with a lightweight autonomous robot. The proposed system combines real-time object detection, localization, position prediction, and collision avoidance to enhance environmental perception beyond the limitations of onboard sensing. A YOLOv11-based detection model is employed for obstacle detection, achieving high accuracy with a mean average precision (mAP@0.5) of 0.991. For obstacle localization, the external camera system achieves centimeter-level accuracy, which is further improved using Multiple Linear Regression (MLR)-based correction, reducing the localization error by approximately 75.77%. In addition, position prediction models for both camera-based and autonomous vehicle systems demonstrate strong performance, with coefficients of determination (R2) exceeding 0.98. The system also achieves effective collision avoidance, successfully stopping in all tested scenarios with response times ranging from 0.2 to 0.45 s. The integration of external and onboard perception enables effective blind-spot mitigation and improves situational awareness within simulated blind-spot corner scenarios representing real-world occlusion challenges. The results validate the system-level integration of these modules as a practical framework for addressing sensing limitations in autonomous robotic applications.

Technologies doi: 10.3390/technologies14060337

Authors: Fatma A. S. Alwafi Reza Saatchi

Path planning is critical for multi-robot systems (MRS), directly affecting the operation efficiency, execution time, and operational cost. Despite extensive research and successful applications of multiple algorithms, achieving globally optimal solutions in cluttered or dynamic environments remains a significant challenge. Issues such as scalability with an increasing number of robots, computational efficiency, system robustness, and coordination complexity continue to drive the development of more reliable approaches. This study reviews modelling approaches, optimisation criteria, and solution algorithms based on the roadmap planning methods that are widely used for multi-robot path planning (MRPP). It focuses on three graph-based algorithms: MRPP algorithm, central algorithm (CA), and the optimisation central algorithm (OCA). These algorithms utilise visibility graphs (VG) for environment representation and Dijkstra’s algorithm for shortest path computation, while incorporating algebraic connectivity to improve coordination, safety, and scalability. In addition, the technological context and implementation platforms, including simulation environments, cloud robotics, and AI-based frameworks, are conceptually examined. The potential applications of these methods in assistive robotics are highlighted, particularly in supporting a safe and reliable navigation in healthcare and human-centred environments. The article synthesises theoretical and practical insights, identifies current limitations and challenges, and outlines future research directions for efficient, scalable, and robust MRPP.

Technologies doi: 10.3390/technologies14060336

Authors: Oscar Danilo Montoya Jesús C. Hernández Javier Rosero-García

The optimal reconfiguration of electrical distribution feeders is a fundamental strategy for reducing active power losses and improving voltage profiles, yet it remains a challenging mixed-integer nonlinear programming (MINLP) problem due to the combinatorial explosion of radial topologies and the nonlinearities introduced by power flow equations. This paper proposes a novel master–slave methodology that integrates a Chu and Beasley genetic algorithm (CBGA) with a minimum spanning tree (MST)-based repair mechanism to address these challenges. In the master stage, the CBGA explores the binary space of switching decisions via steady-state population management, duplicate elimination, and stagnation restart policies. A key contribution lies in the MST-based repair procedure, which ensures that every individual generated by crossover and mutation is projected onto a feasible radial and connected configuration, effectively confining the search to the constrained solution space without recourse to penalty functions. A systematic weight-design rule preserves the Hamming distance between infeasible offspring and repaired solutions, minimizing the distortion of genetic information. The slave stage evaluates each candidate topology using a successive approximations power flow solver, assessing electrical feasibility and computing active power losses. The proposed methodology is validated on multiple test feeders, ranging from small 9- and 24-bus networks to large-scale benchmarks including 33-, 69-, 84-, 136-, and 415-bus systems. A comparison against the deterministic sequential switch opening method (SSOM) and a specialized tabu search demonstrates that the CBGA-MST consistently matches the best-known optima in the literature, achieving loss reductions of up to 9.63% compared to SSOM on the 415-bus system. A statistical analysis over 100 independent runs confirms the algorithm’s robustness, with zero standard deviation for networks of up to 69 buses and a standard deviation of only 2.99 kW (0.51%) for the 415-bus system. The findings confirm that the proposed approach offers superior scalability, robustness, and solution quality, positioning it as a practical and effective tool for distribution system operators seeking to enhance network efficiency under peak load conditions.

Technologies doi: 10.3390/technologies14060335

Authors: Siyu Bao Mengge Sun Lulu Guo

Accurate regulation of air flow rate and cathode pressure is crucial for enhancing the efficiency and durability of proton exchange membrane fuel cell (PEMFC) systems. However, the PEMFC air supply system exhibits strong nonlinearity, multivariable coupling, and sensitivity to parameter variations and external disturbances, which make precise mathematical modeling extremely challenging and consequently limit the effectiveness of conventional model-based control approaches. To address these issues, a data-driven sliding mode control strategy that requires neither prior model information nor structural knowledge is developed. First, a dynamic linearized data model capable of describing both separable and inseparable disturbances is constructed, and its dynamic equivalence to the original nonlinear system is rigorously established. A wavelet neural network is then employed to estimate the unknown parameters online, thereby improving estimation accuracy. Based on these data models, a data-driven sliding mode controller is designed for the coordinated regulation of air flow rate and cathode pressure. Furthermore, a novel hyperbolic reaching law is introduced to adaptively adjust the convergence rate and effectively alleviate chattering. Theoretical analysis proves that the proposed control scheme guarantees convergence to a quasi-sliding mode. Finally, comparative simulations with benchmark controllers demonstrate the effectiveness and superiority of the proposed method.

Technologies doi: 10.3390/technologies14060334

Authors: Renzo Fernández Néstor Zamora Victor Coloma Nino Vega Tomás Gavilánez

The use of prosthetic devices can significantly improve the quality of life of individuals with limb amputations. However, existing prosthetic hands face multiple engineering and manufacturing challenges, making them economically inaccessible to a large portion of the population. This study focuses on the design and analysis of a cost-effective prosthetic hand capable of performing five fundamental grasp types: tripod, cylindrical, spherical, lateral, and pinch. The development process began with a biomechanical analysis of the human hand, followed by the derivation of a kinematic model. To ensure anthropomorphic fidelity, finger trajectories were synthesized using a computer vision-based algorithm that captured natural human motion. These trajectories were then mapped to the prosthetic control system. Experimental validation was conducted through rigorous goniometric analysis of the prototype’s execution. The results demonstrated the system’s effectiveness in replicating functional grasps, with a Root Mean Square Error (RMSE) within acceptable thresholds for assistive tasks. While the prototype achieved high motion correspondence, higher deviations were observed in distal joints due to mechanical transmission resistance and spring-return torque requirements. This work provides a scalable framework for tendon-driven prostheses, balancing advanced trajectory synthesis with a robust and accessible mechanical architecture.

Technologies doi: 10.3390/technologies14060333

Authors: Cesar Solis Jorge Morales Carlos Montelongo Sergio Palomino

This paper addresses the data-driven identification of open-chain robot morphology from finite windows of heterogeneous signals, including commanded joint references, measured joint states, and end-effector pose observations. Unlike conventional calibration procedures that assume a known kinematic topology, the proposed formulation estimates both discrete structural quantities and continuous kinematic coordinates: the number of active joints, the revolute/prismatic token sequence, Product-of-Exponentials (POE) screw axes, and the home pose of the end effector. A temporal transformer encoder is used as the main estimator and compared with a gated recurrent unit (GRU) baseline on the same dataset, with the same output heads and a multitask physics-aware objective. The continuous target is expressed in POE coordinates rather than as a Denavit–Hartenberg table because POE directly represents spatial joint axes and avoids several frame-assignment ambiguities. Simulated results on a noisy benchmark of 48 serial-robot families show that both sequence models recover the discrete structure on the tested in-library trajectories, while their continuous reconstruction errors reveal different trade-offs in screw-axis, home-pose, and trajectory reconstruction accuracy. The study also discusses inactive-slot masking, out-of-library behavior, synthetic-to-real limitations, persistent excitation, and the role of the learned model as an initialization for subsequent calibration refinement.

Technologies doi: 10.3390/technologies14060332

Authors: Zoltán Szilágyi Csaba Hajdu Bálint Farkas Péter Galambos Károly Széll

Industrial robotic systems increasingly operate as heterogeneous ecosystems in which production, maintenance, quality assurance, safety, and human–machine interaction are coupled through shared data and cross-layer constraints. Existing modeling approaches remain structurally fragmented: hierarchical taxonomies support decomposition, graph-based models primarily encode pairwise relations, and analytical layers are commonly attached as external pipelines. This paper proposes a hypergraph-based framework for the multi-contextual state representation of industrial robotic systems. The framework combines a multi-layer problem taxonomy, a formal definition of context as an active semantic processing unit, and a directed hypergraph model with signed incidence for representing dependency, interpretative, compositional, and cross-layer constraint relations without binary decomposition. The model is instantiated on grasping and maintenance examples and translated into a numerical interface for downstream analytical processing. Quantitative results are also reported. Benchmarking shows near-linear compile-time and star-expansion scaling, while comparison with pairwise encodings confirms lower representational overhead for higher-order relations. In a canonical grasping scenario, one-cycle hypergraph-grounded inference remains in the microsecond range on CPU, with a median latency of 2.264 µs. These results indicate that the proposed framework is computationally tractable as a representational substrate for context-aware analysis. The contribution of the paper is not a new control algorithm, but a formal representation and numerical translation layer for future learning-based and rule-based analytical methods.

Technologies doi: 10.3390/technologies14060331

Authors: Juan Minango Alberto Paradisi Silvia Marion Andreza Lona

Institutional adoption of blockchain technology in supply chains, healthcare, and public administration remains constrained. Organizations that manage digital assets on behalf of large numbers of non-technical users lack custody architectures suited to their scale. Existing approaches either require users to manage private keys directly; rely on centralized custodians that store encrypted keys; or depend on distributed protocols such as multi-party computation, which impose substantial infrastructure and coordination overhead. This paper presents CryptoVault, a stateless custody architecture for institutional blockchain deployments that derives private keys on demand from a single master seed using BIP-44 hierarchical deterministic (HD) wallets, eliminating persistent storage entirely. Only an AES-256-GCM-encrypted derivation index is persisted per wallet; the corresponding private key is re-derived at signing time and discarded immediately after use, ensuring no private key material ever rests on disk. The security model requires the simultaneous compromise of three independent components (the encrypted derivation index, the encryption key, and the master seed) for full key recovery, compared to two components in custody systems that persist encrypted private keys. An empirical evaluation under concurrent load demonstrates 13 to 22 ms steady-state signing latency on development hardware, with re-derivation accounting for approximately 4 to 7% of that total, confirming that on-demand derivation introduces negligible overhead. Thus, CryptoVault has been validated against an agricultural cooperative deployment as a representative institutional scenario, with an architecture that generalizes to any organization managing wallets on behalf of users who have no direct interaction with cryptographic material. A reference implementation is available as open-source software.

Technologies doi: 10.3390/technologies14060330

Authors: Shuquan Peng Mingxi Li Ling Fan Jiehui Lu

Quasi-zero stiffness (QZS) isolators provide excellent vibration isolation performance at low frequency. This paper presents an innovative flexural–torsional buckling QZS isolator, which depends on its linear negative stiffness to provide a more stable dynamic response than other QZS isolators. First, the force and stiffness characteristics of the flexural–torsional buckling toggle under vertical load are simulated, and it is proposed that they can be fitted with a piecewise function and its derivative. Next, the cross-sectional dimensions, and height-to-span ratios are discussed to determine their contributions to the static characteristics. Then the dynamic model of the QZS isolator is established and analyzed by a harmonic balanced method and the solutions are validated by numerical analysis. Finally, the comparison with an ordinary QZS isolator shows that the advantages of the proposed isolator are the linear negative stiffness and a certain load-bearing capacity at equilibrium position rather than the zero capacity of common isolators. The static characteristics of the proposed QZS isolator indicate that the negative stiffness is significantly influenced by the cross-sectional width, with the slope k increasing by 8.6 times as the width increases from 1 cm to 1.5 cm. The proposed mechanism exhibits an approximately linear negative stiffness with a maximum static bearing capacity of about 1000 N at the equilibrium position, contrasting with the nonlinear, non-capable negative stiffness of the ordinary Euler buckled beam model. The dynamic characteristics demonstrate excellent performance, operating effectively with ultra-low transmissibility. This study provides an innovative negative stiffness mechanism and a corresponding isolator based on flexural–torsional buckling, offering a potential solution for a wide range of large-scale engineering vibration problems.

Technologies doi: 10.3390/technologies14060329

Authors: Edimar José de Oliveira Lucas Santiago Nepomuceno Arthur Neves de Paula Raphael Paulo Braga Poubel Leonardo Willer de Oliveira

Multi-Stage Transmission Network Expansion Planning (MS-TNEP) is critical for adapting power grids to long-term renewable integration. However, the simultaneous incorporation of N-1 security, active power losses, and uncertainties regarding the spatial and temporal growth of power generation capacity imposes prohibitive computational complexity. This paper proposes a probabilistic MS-TNEP model evaluated over a 20-year horizon. To overcome this computational intractability, a hybrid decomposition framework is employed. The investment subproblem determines the discrete decisions for network investment via a metaheuristic, while the probabilistic operation subproblem utilizes linear programming to assess the operational feasibility of these decisions under multiple spatial and temporal growth of power generation capacity scenarios, active power losses, and N-1 contingencies. Furthermore, a novel Pack-Based Grey Wolf Optimizer (PBGWO) is introduced. The approach is validated on the Garver and the Southern Brazilian equivalent systems under multiple scenarios for the growth of both wind and conventional power generation capacity. Comparative analysis against the Genetic Algorithm, the standard Grey Wolf Optimizer, and the Whale Optimization Algorithm reveals that PBGWO is a highly competitive approach for MS-TNEP problems, consistently identifying the most cost-effective expansion plan.

Technologies doi: 10.3390/technologies14060328

Authors: Peng Jin Xiaozhou Hu

The research investigates lubrication characteristics of a three-stage planetary transmission system under first and second gear conditions. A whole-system CFD model and a planetary carrier bearing CFD model are established. Oil distribution is simulated using a UDF dynamic mesh technique. A dedicated test bench is designed and built for a multi-stage planetary transmission system to measure oil flow data at the outlets of each planetary stage. By comparing the simulation and experimental results, the CFD model is confirmed. The oil distribution in the planetary transmission system is followed. In the first gear condition, the oil distribution within the second stage is significantly lower than that in the other two stages, and mainly converges onto the meshing surfaces of gears. In the second gear condition, the planetary carrier remained stationary, resulting in limited oil distribution in the first stage. Meanwhile, the third-stage planetary carrier bearings exhibit insufficient oil distribution across different gear conditions. To address this issue, several structural optimization structures for the numerical model of the third-stage planetary carrier bearings are compared in terms of theoretical oil supply rates and oil volume fraction distribution characteristics. Among these, constrained by the fixed positions between the oil inlet and oil holes, the structures with different numbers of oil holes in the planetary carrier lead to an oil flow rate reduction due to flow division and pressure loss induced by turbulence at high rotational speed, failing to meet the oil demand. Optimization of oil-hole diameter enlargement, the oil flow rate increases proportionally with the hole diameter. A diameter of 5 mm satisfies the theoretical oil flow rate demand, yet an asymmetric oil distribution is observed between the two inner bearings. Building upon the initial design with two oil holes, a 5 mm diameter design, a 1 mm axial leftward offset of the oil hole position, and a 20° oil-guiding inclination on the outer hub reduce the oil distribution asymmetry between the two inner bearings from 64.5% to 13%. The oil volume fraction increases from 0.005 to 0.069 in the inner bearing and from 0.001 to 0.013 in the outer bearing, resulting in a substantial improvement in overall bearing lubrication performance.

Technologies doi: 10.3390/technologies14060326

Authors: Chotirose Prathom Ryoga Sato Shinya Watanabe Satoshi Kondo Kazuhiko Sato Yoshifumi Okada

Cardiovascular diseases (CVDs) are the leading cause of global mortality, highlighting the need for efficient and reliable automated electrocardiogram (ECG) analysis. While deep learning methods have achieved high classification accuracy, their large model sizes and computational demands limit clinical deployment. This study proposes a lightweight multiscale framework, the FPN–ECA–ELM, integrating a feature pyramid network (FPN), efficient channel attention (ECA), and an extreme learning machine (ELM) for automated CVD classification using standard 12-lead ECG images. The FPN enables efficient multiscale feature fusion by combining feature maps from different network depths to generate high-resolution semantically enriched representations. ECA performs channel-wise feature recalibration, and the ELM replaces conventional fully connected layers, further reducing computational cost. Under an inter-patient evaluation protocol, the model achieved 87.08% accuracy and 87.07% weighted F1-score for binary classification, and 78.06% accuracy and 78.34% weighted F1-score for five-class classification, demonstrating competitive classification performance. The model contains only 1.73 million parameters, with a size of 6.59 MB, requiring 0.21 GFLOPs, and achieves an inference time of 0.69 ms per sample. These results illustrate a favorable balance between accuracy and efficiency, supporting practical deployment in resource-constrained clinical and edge-computing environments.

Technologies doi: 10.3390/technologies14060327

Authors: Mauro Daniel Castillo Pérez Jesús Jaime Moreno Escobar Hugo Quintana Espinosa Erika Yolanda Aguilar del Villar

Since many children with Down syndrome have difficulties with emotion recognition, there is a significant application gap in assistive technologies and affective computing that could be addressed. Conventional deep learning methods, which depend on the standard raster-scan flattening operation, achieve limited accuracy in this population because they fail to preserve spatial locality. In this paper, we propose a novel Hilbert space-filling curve optimization for neural network flattening layers, specifically designed not only to address these gaps in assistive technologies for this vulnerable group who are currently underserved by affective computing, but also to provide a framework for researchers seeking to fine-tune the architecture of artificial neural networks. Our approach retains spatial coherence using Hilbert indexing, implemented as flexible Keraslayers that are compatible with standard architectures such as VGG16 and ResNet50. A comprehensive analysis across multiple datasets reveals a 4% improvement in emotion recognition accuracy compared to Hilbert. The Hilbert optimization achieves 71% precision in Down syndrome emotion classification while reducing processing overhead by approximately 5%. By closing the emotion recognition gap with spatial-aware deep learning, our work contributes to more equitable AI for healthcare and advances the development of assistive technologies for neurodiverse populations, with near-term clinical utility in pediatrics and broader applications in affective computing.

Technologies doi: 10.3390/technologies14060325

Authors: Dongfang Wang Yusheng Zhang Guobao Dong Yonghui Xu Yu Huang Baodi Xie Changan Wei

The construction of resource interface protocol templates is a key prerequisite for the unified integration of live, virtual, and constructive (LVC) resources in complex simulation and test environments. However, real-world protocol documents are usually heterogeneous in format, inconsistent in description, and rich in nested structures and implicit semantics, which makes manual analysis inefficient and error-prone. To address this issue, this paper proposes an intelligent construction method for LVC resource interface protocol templates based on large language models. First, raw protocol documents are converted into a unified Markdown representation, and a semantic understanding module is used for main-table identification, minimum-unit splitting, and auxiliary-table association. Then, a protocol item type identification expert module is designed to recognize complex structures such as frame headers, ordinary items, dynamic items, struct items, branch items, sub-protocol items, and checksum items. Finally, the extracted information is integrated into structured intermediate results for automatic XML template generation. Experiments on a representative test set composed of 20 protocol tables from real-world LVC resource interface documents show that the proposed method achieves a main-table extraction accuracy of 0.9761, a type recognition F1-score of 0.9769, an XML generation success rate of 1.0, and a node consistency of 0.9478. These results demonstrate that the proposed method can effectively improve the automation and engineering applicability of protocol template construction.

Technologies doi: 10.3390/technologies14060324

Authors: Gloribel Vázquez-Cornejo Sasirot Khamkure Prócoro Gamero-Melo Victoria Bustos-Terrones Ulises Carrasco-Dehesa Audberto Reyes-Rosas Arely M. López-Martínez Carlos D. Silva-Luna María L. Rivera-Huerta Edson B. Estrada-Arriaga Juan G. Garcia-Maldonado

This study investigates the structure–performance relationship of Fe3+- and Zr4+-modified layered double hydroxides (LDHs) for fluoride removal from water. Mg–Al LDHs with different metal loadings (Zr0.05, Zr0.1, Fe0.8, and Fe1) were synthesized via ultrasound-assisted coprecipitation and characterized using XRD, SEM–EDS, FTIR, XPS, and N2 physisorption. Among the synthesized materials, Zr0.05-LDH exhibited the highest adsorption performance. Response surface methodology identified adsorbent dosage as the most influential parameter, achieving a maximum fluoride removal efficiency of 98.17% under optimal conditions (pH ≈ 5, adsorbent dose of 0.88 g/L, and initial fluoride concentration of 12.6 mg/L). Zr0.05-LDH showed the largest specific surface area (261 m2/g) and a maximum adsorption capacity of 137 mg/g, as described by the Langmuir isotherm model. Kinetic studies indicated rapid adsorption, with equilibrium reached at approximately 180 min. Fluoride removal was governed primarily by inner-sphere complexation at Zr4+ and Fe3+ sites, accompanied by anion exchange and electrostatic interactions. The adsorbent retained 89% of its capacity after five regeneration cycles. Groundwater tests from Durango, Mexico, demonstrated effective fluoride reduction below Mexican and WHO guideline limits despite competing anions. These results demonstrate the potential of modified LDHs for fluoride-contaminated groundwater treatment.

Technologies doi: 10.3390/technologies14060323

Authors: Zicheng Wang Duange Guo Xingyu Shi Haoren Luo Yanjian Peng Shuaihu Li

Admittance-based analysis of wind farm-integrated power systems is inaccurate across varying operating points (OPs) resulting from wind speed fluctuations and shifting grid conditions. Existing methods can be classified as model-driven, which require detailed system modeling and struggle with parameter extraction, and as data-driven, which often lack physical interpretability, suffer from high dimensionality, and provide insufficient coverage of training frequency points. This study introduces an AM reconstruction framework that integrates equivalent circuits with a support vector machine (SVM). The approach first applies vector fitting and an equivalent-circuit transformation to decompose the admittance response into first- and second-order subcircuits, thereby representing the frequency-domain characteristics with low-dimensional, more physically interpretable parameters. Subsequently, an SVM establishes a nonlinear mapping between OPs and equivalent-circuit parameters, enabling the reconstruction of continuous admittance transfer functions for new OPs. This framework transforms the modeling of high-dimensional frequency-domain data into a low-dimensional physical parameter prediction problem, thereby avoiding error accumulation from interpolation over discrete frequency points. The proposed method is validated using a direct-drive permanent magnet synchronous generator (PMSG) wind turbine model connected to the IEEE 14-bus test system. Frequency-domain simulations and error analyses under previously unseen OPs confirm the method’s high prediction accuracy and strong generalization capability.

Technologies doi: 10.3390/technologies14060322

Authors: Zhao Sheng

With the rapid expansion of rail transit networks and increasing operational density, foreign object intrusion on tracks has emerged as a critical threat to train safety. Conventional manual inspection methods suffer from low efficiency, high miss rates, and inadequate real-time performance, failing to meet the stringent requirements of modern intelligent railway maintenance. While deep learning offers a promising paradigm shift, existing models often struggle with complex background interference and multi-scale target detection in railway scenarios. To address these challenges, this paper proposes CMF-Net, a unified detection framework for railway track foreign object detection. The CGG module serves as a lightweight feature extraction unit in the backbone, mitigating gradient vanishing and overfitting. The MSAF module enables adaptive multi-scale feature fusion via dual attention (CBAM), enhancing small-object detectability. The FGAF module captures fine-grained edges and textures through a four-branch decomposed convolution and fine-grained attention, suppressing complex background interference. The BiFPN module restructures the neck for efficient bidirectional cross-scale feature fusion. Furthermore, the TPSA module injects explicit railway-domain prior knowledge by fusing a learnable rail-centerline distance-decay field with the CBAM spatial attention map, guiding the detector to focus on operational danger zones and reducing false positives. Experiments on the OFBDs dataset demonstrate that CMF-Net achieves a mean Average Precision (mAP50) of 89.2% and an mAP50:95 of 64.5%, surpassing the baseline YOLOv5s by 4.8 pp and 5.3 pp, respectively. The model maintains a compact parameter size of 5.4 M, a computational cost of 15.2 GFLOPs, and real-time inference capability (56.2 FPS). Edge-deployment feasibility is validated via on-device benchmarking on three Jetson platforms (Nano, Xavier NX, and Orin Nano), where INT8 TensorRT inference achieves 16.2, 108.7, and 153.8 FPS, respectively, under one-hour continuous-inference soak tests with peak power below 16 W and steady-state junction temperatures within safe thermal margins. Statistical significance testing (p < 0.05) confirms the stability of these performance gains. These results indicate that CMF-Net provides rapid and accurate detection of various track intrusions, enabling robust real-time monitoring in dynamic railway environments and enhancing operational safety and intelligence.

Technologies doi: 10.3390/technologies14060321

Authors: Yujie You Yuzhu Ji Salavat Gumerovich Mudarisov Ilnur Rinatovich Miftakhov Feixiang Zhao Ming Xiao Le Zhang

Biologically related time-series data characterize the dynamic evolution of biological systems, including genetic inheritance, disease diagnosis, and the biological microenvironment. However, accurate prediction of these data remains challenging due to their pronounced time-varying, non-stationary, and noisy characteristics. Existing approaches often fail to capture latent shifts of biologically related time series, limiting both predictive performance and time-varying pattern recognition capability. Thus, in this study, we first propose a time-varying neural network (TVNN) model that combines frequency-domain information with Koopman theory. TVNN-model Koopman transition matrices are used to model global dynamics and local time-varying behaviors for pattern extraction. Secondly, a time-varying pattern recognition large language model (TVPRLLM) is introduced to recognize and classify the extracted time-varying patterns, enabling the identification of potential pattern categories. Thirdly, we have developed a biology-related time-series predictive platform that can offer visualization, data analysis, and predictive services. Experimental results demonstrate that the TVNN model outperforms existing mainstream methods in predicting biology-related time-varying time series, and that it achieves competitive forecasting performance, though its behavior depends strongly on the design of the frequency-domain decomposition. Additional robustness analyses reveal that the choice of Fourier masking strategy can materially affect both RMSE and long-horizon stability. We further show that Koopman-derived time-varying representations are highly discriminative for dynamic state recognition.

Technologies doi: 10.3390/technologies14060319

Authors: Shunzuo Qiu Zhaoliang Zhu Yan Yang Qin Liu Yan Jiang Caixia Xian

Aiming at the problems of insufficiently tight sealing of all-metal dissolvable frac plugs and the poor fracturing effect in the extraction of shale gas, the effects of structural parameters on the performance of metal dissolvable ball seat sealing rings was analyzed using numerical simulation and an experimental method. The key structural factors affecting performance were identified. The problem of stress concentration at the contact position between the sealing ring and the slip of the existing structure was discovered. To solve the above problems, a combination structure sealing ring was designed. Then the performance comparison analysis of the two structures and optimal structural parameters were carried out. Under the same sealing force, the combination structure sealing ring can be smoothly sealed, and the stress distribution of the upper sealing ring is uniform. This indicates that the performance of the combination structure sealing ring is superior, and the optimal cone angle and thickness obtained are 9° and 17 mm, respectively. Based on the optimized structural parameters, experiments were conducted. After being pressurized at room temperature to 51 MPa and stabilized for 15 min, the pressure gradually decreased to 47.4 MPa, indicating a secondary setting. After unloading, the lower end face of the dissolvable ball seat has no liquid leakage. Under high temperature, a pressure of 51 Mpa was applied; the pressure inside the wellbore remained basically unchanged. During the process of applying pressures of 60 MPa and 70 MPa, there was also a decrease in pressure, indicating the presence of secondary sealing. The above results indicate that the optimized combined metal sealing ring has strict sealing and good pressure-bearing performance. At the same time, the reliability of the simulation results was verified. The designed sealing ring was applied to the shale gas horizontal well deployed in Changning block, China. The application results show that when the displacement remains unchanged, the casing pressure increases from 51 MPa to 60 MPa, and continues to maintain the displacement. The pressure did not fall back to 51 MPa, proving that the formation pressure is released. The successful on-site application once again verifies the safe and reliable performance of the all-metal sealing ring.

Technologies doi: 10.3390/technologies14060320

Authors: Petar Nakić Marija Habijan Danijel Marinčić Marko Martinović

Accurate multi-class segmentation of the prostate in T2-weighted magnetic resonance imaging (MRI) into the peripheral zone (PZ), central gland (CG) and tumour is essential for targeted biopsy guidance and treatment planning. We present DSBANet, an encoder–decoder architecture that combines a pretrained ResNet-50 encoder, Atrous Spatial Pyramid Pooling, Multi-Scale Attention Fusion on skip connections, a Feature Fusion Module, deep supervision and boundary refinement. We evaluate eight architectures across three input dimensionalities (2D, 2.5D, 3D), yielding 24 models trained under identical conditions on the Prostate158 dataset. DSBANet achieves the best anatomy segmentation with PZ DSC of 0.8176 and CG DSC of 0.7888 among 2D models. To address the severe class imbalance of the tumour class, we further train DSBANet 2D with a class-weighted cross-entropy term and tumour-positive slice oversampling, raising per-case tumour DSC from 0.003 to 0.170 (a sixty-fold absolute improvement). A systematic eight-variant ablation study, evaluated under matched-pairs effect-size analysis, identifies the SE-Residual blocks and skip-connection attention as the largest contributors to tumour segmentation, while every architectural component contributes a directionally consistent gain.

Technologies doi: 10.3390/technologies14060318

Authors: Qi-Qin Xie Shi-Jian Liu Zheng Zou

Tooth segmentation from dental meshes is a fundamental step in clinical applications such as computer-aided orthodontics and dental implantation. Compared with mature image segmentation, deep learning-based mesh segmentation research is currently in a high-speed development stage. This study follows a dual-flow personalized feature learning scheme based on meshes and researches high-resolution mesh segmentation problems for clinical needs, proposing a dual-flow deep learning architecture called Position Shape Network (PSNet). Its basic idea includes continuously adjusting the feature map size in the network layer to enhance the model’s generalization ability and designing a reasonable branch structure to personalize the learning of position attributes represented by coordinates and shape attributes represented by surface perimeter area. In addition, it is proposed that the resolution of the validation set should be determined by comprehensively analyzing and simplifying errors to ensure the credibility of the model evaluation. Under this evaluation system, PSNet was compared with relevant authoritative methods in experiments, and the results verified the rationality and efficiency of the method and viewpoint proposed in this paper.