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.

Technologies doi: 10.3390/technologies14060317

Authors: Fernando Quiroz-Vazquez Victor Cardenas Mario Gonzalez-Garcia Gerardo Espinosa-Pérez Manuel A. Barrios

The increasing adoption of energy storage systems has driven the development of inductive power transfer (IPT) chargers operating under static and dynamic current references, while maintaining robust performance in the presence of disturbances such as misalignment. This article presents an experimental and analytical comparison of three classical current controllers—PI, PI with feed-forward loop (PI+FF), and integral (I)—applied to a low-power inductive power transfer charger (BC-IPT). In addition, a simple and practical criterion for controller gain selection is proposed and evaluated under identical operating conditions, using a 164 W experimental platform with unidirectional power transfer. The controllers (PI, PI+FF, and I) are compared in terms of settling time, overshoot, phase margin, gain margin, and disturbance rejection capability. The experimental results show that adjustable settling times between 1 and 12 ms can be achieved for static and dynamic current references. An overshoot below 8% was obtained, along with stable performance under the evaluated variations in input voltage and coupling factor. The settling time can be directly adjusted using the proposed gain-selection criterion. Overall, the results demonstrate that, under the studied operating conditions (including a 164 W platform, unidirectional power flow, and the selected topology), classical controllers provide an appropriate balance among dynamic performance, robustness, and tuning simplicity for current-regulated IPT battery charging applications.

Technologies doi: 10.3390/technologies14060316

Authors: Emad-ul-Haq Qazi Muhammad Hamza Faheem Abdulrazaq Almorjan

Phishing and malicious websites are a serious threat on the internet. Attackers use fake links to trick users and steal their private information. Detecting these links is difficult because attackers change their tricks often. Many old methods cannot detect new or hidden threats. Some recent models use deep learning (DL), but they are large, slow, and hard to use in real-time systems. In this paper, we present a lightweight and accurate model called MiniLM-CNNLSTM. It combines a small transformer model (MiniLM) with a hybrid DL network using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers. The transformer learns the meaning of URLs. The CNN finds important patterns. The LSTM captures the order of characters. We also add handcrafted features that help the model detect tricky URLs. We test our method on two public datasets: the Phishing Site URLs dataset and the Malicious URLs dataset from Kaggle. We use 3-fold cross-validation and early stopping to ensure fair and stable results. The MiniLM-CNN-LSTM model outperformed previous benchmarks by achieving an average three-fold cross-validation accuracy of 98.98%, a precision of 98.63%, a recall of 98.29%, an F1-score of 98.46%, and a false positive rate of 0.68%. The proposed model has a higher accuracy, precision, recall, F1-score and a lower false positive rate, which enhances the accuracy by 1.88, precision by 3.77, recall by 4.17 and decreases the false positive rate by 61.58% compared with the strongest baseline (Distil BERT + CNN-LSTM), showing significant practical improvements. The results show that our approach is fast, small, and highly effective. It can detect phishing and malicious links with high accuracy. This makes it a good choice for real-time security systems like browsers, email filters, or firewalls.

Technologies doi: 10.3390/technologies14060315

Authors: Razvan Udroiu Corina Birleanu Florin Popister Horea Goia Marius Pustan Mircea Cioaza

Ti-6Al-4V ELI (Grade 23) fabricated by Laser Powder Bed Fusion (LPBF) exhibits well-known susceptibility to adhesive wear and tribo-oxidation under dry sliding, yet the tribological consequences of in-process laser remelting remain poorly characterized. This study investigates the influence of an in-layer laser scan strategy (single-scan and double-scan), normal forces in the 5–15 N range, and a sliding speed of 0.10–0.20 m·s−1 on the dry sliding tribological response of additive manufactured Ti-6Al-4V ELI. A full factorial experimental design was carried out and the most significant factors and their contributions to the coefficient of friction, specific wear rate, and contact temperature were identified by a statistical method using a general linear model (GLM). The optimal parameters for both of the scan strategies were predicted using a response surface methodology (RSM). Furthermore, to assess the effect of the laser scan strategy and the in-layer remelting on the local mechanical properties, a microscale and nanoscale indentation was carried out. The results show that the normal load was the dominant factor with a contribution of 89.3% for the coefficient of friction, 54% for the specific wear rate, and 40.5% for the temperature. A significant load–scan strategy interaction that governed the wear behavior was detected. The double-scan strategy exhibited higher wear at 5 N but lower wear at 15 N than the single-scan, a counter-intuitive reversal attributed to the load-threshold tribolayer stabilization promoted by the remelting-induced near-surface microstructural modification. The novelty of this study was the setup of a robust GLM–RSM framework for predictive modeling and optimization of additively manufactured surfaces under tribological loading.

Technologies doi: 10.3390/technologies14060314

Authors: Christopher J. Rodriguez-Cortes Panfilo R. Martinez-Rodriguez Diego Langarica-Cordoba Gerardo Vazquez-Guzman Juan A. Villanueva-Loredo Jose M. Sosa

This paper presents a model-based control strategy for a power factor correction system that employs a high conversion-ratio DC–DC converter. The proposed system consists of two stages. In the first stage, a full-bridge diode rectifier is connected to the grid through a passive filter to improve the quality of the injected current. Two passive AC input filters, namely L and LCL configurations, are evaluated to analyze their impact on grid current quality and overall system performance. The second stage is a high-step-up DC–DC converter based on the switched-inductor technique, which provides a high voltage conversion ratio. A model-based approach is employed to derive the control design from the averaged system model. The resulting control structure consists of a current tracking loop and a voltage regulation loop. A proportional-resonant controller is used to ensure current tracking and achieve a near-unity power factor, while a proportional-integral controller regulates the output voltage. Experimental validation is carried out using a low-power laboratory-scale prototype to assess the effectiveness of the proposed approach. The results demonstrate adequate current tracking and satisfactory dynamic performance within the tested operating conditions.

Technologies doi: 10.3390/technologies14060313

Authors: Giulia Pierotti Manuel Chiachío Ruano Masoud Haghbin Noah Masegosa Cáceres Filippo Landi Pietro Croce

Addressing building vulnerability to climate hazards requires advanced tools to support adaptation decisions. To this end, the current study presents an Agentic Artificial Intelligence (Agentic AI) Optimization framework to enhance the climate resilience of existing buildings, bridging policy guidelines and a practical tool for optimized and context-aware retrofit strategies. Aligned with EU Guidance, the framework operationalizes a Climate Vulnerability Assessment (CVA) within a Multi-Objective Optimization (MOO) engine through a multi-agent architecture. Specialized subagents, including Requirements, Cost, Strategy, and XAI Agents, collaborate to understand user goals, manage budget constraints, optimize strategies, and produce explainable reports. Two metaheuristic optimizers, such as Multi-Objective Invasive Weed (MO-IWO) and Grey Wolf (MO-GWO), were coupled with Multi-Criteria Decision Making (MCDM) models to minimize building vulnerability and adaptation costs against multiple climate hazards (e.g., heat waves and heavy precipitation). Results show that, despite MO-GWO’s lower computational burden, MO-IWO performed more robustly and is selected as the superior optimizer for integration into the Agentic AI system. Ultimately, the framework provides a scalable approach to asset management, significantly improving decision-making for building retrofits.

Technologies doi: 10.3390/technologies14060312

Authors: Tony Punnoose Valayil Tarek H. Mokhtar

The Stewart–Gough Platform (SGP) is a spatial parallel manipulator offering high accuracy, rigidity, and adaptability, with applications spanning medical systems, marine engineering, agriculture, manufacturing, entertainment, aerospace, and architectural installations. This paper presents a comparative analytical and computational study of three SGP configurations: the regular SGP, with regular hexagonal base and top platforms; the Irregular-Parallel SGP, derived from the regular SGP by a novel graphical decomposition-and-modification procedure and characterized by similar symmetric hexagonal platforms with limbs preserved parallel; and the Irregular-Skewed SGP, in which the irregular hexagonal platforms of the Irregular-Parallel SGP are retained, but the limbs are connected in an inclined, alternating clockwise (or anticlockwise) topology. The Irregular–Skewed SGP is free from the constraint singularity that persists in the first two configurations and requires the shortest maximum actuator stroke. Static force analysis shows that the regular SGP and the Irregular–Parallel SGP both exhibit a rank-deficient rigidity matrix (rank = 3) across the geometric scaling range tested (radius ratios 1:2 to 1:10; inter-platform distances 100–1000 mm), whereas the Irregular-Skewed SGP achieves full rank (rank = 6) through inclined limb connectivity and is the only configuration capable of sustaining static equilibrium under the loading conditions examined. The forward kinematics of the Irregular-Parallel SGP is verified against a SolidWorks model: under a 9 mm uniform limb extension, the MATLAB and SolidWorks positions of node 7 agree to within 1.27 mm. The rotational workspace volume is equivalent across the three configurations, but the density of valid solution points within that workspace differs. The workspace within joint limits, alternating compression–tension force partition, and asymmetric stroke economy of the Irregular-Skewed SGP indicate applicability to kinetic facades and transformable interiors in architectural-robotics deployment.

Technologies doi: 10.3390/technologies14050311

Authors: Nikolaos Pellas

The integration of artificial intelligence (AI) in education has made significant advancements in personalized learning and adaptive instruction. However, current systems remain limited by three critical gaps: (a) fragmented architectures that decouple technical performance from ethical governance, (b) the treatment of fairness and accountability as external constraints rather than embedded design principles, and (c) reliance on single-modality data that inadequately represents complex learning environments. These restrictions hinder scalability and limit the capacity of AI systems to deliver equitable, transparent, and context-aware educational experiences. This study aims to address these challenges by designing and validating an ethics-aware, multi-agent conceptual framework for adaptive education in which personalization and responsible AI are co-developed as integrated system properties. The proposed architecture uses five coordinated agents: perception, pedagogy, assessment, feedback, and ethics monitoring. These five agents share one knowledge layer containing learner profiles, domain models, competency structures, interaction histories, and machine-readable policy rules. A four-stage feedback loop comprises: (a) outcome aggregation, (b) system evaluation and validation, (c) teacher review and intervention, and (d) agent update and policy refinement. It enables real-time adaptation, teacher oversight, and iterative system improvement. Adopting a design science research (DSR) methodology and mixed-methods evaluation across functional, pedagogical, ethical, and system-level dimensions, the proposed framework is expected to demonstrate improved learner modeling accuracy, enhanced knowledge tracing, and more robust multimodal engagement analysis compared to centralized and single-modality approaches. Based on design science evaluation against established benchmarks and component-level validation in a simulated learning management system (LMS), this theoretical framework is projected to improve learner modeling accuracy, enhance knowledge tracing, and enable more robust multimodal engagement analysis compared with centralized and single-modality approaches. These projections constitute theoretically derived hypothesis and remain subject to empirical validation in live deployment studies. This study’s theoretical contribution lies in demonstrating that ethics-by-design and adaptive personalization are architecturally compatible and mutually reinforcing design principles.

Technologies doi: 10.3390/technologies14050310

Authors: Yongsheng Ma Guobao Zhang Yongming Huang

Control-barrier-function-based safety filters are promising for autonomous driving, but most existing formulations treat obstacle perception as deterministic or account only for bounded ego state-estimation errors. This becomes limiting when obstacle existence, position, motion, and sensing quality vary online. We present a sensor-health- and belief-aware risk-adaptive high-order control barrier function (HOCBF) safety filter for dynamic obstacle avoidance. The method uses obstacle belief from a perception/tracking module, inflates residual obstacle uncertainty according to an object-wise sensor-health score, and converts upper-tail risk into adaptive HOCBF tightening through conditional value-at-risk (CVaR). Sensor health enters the controller through both covariance inflation and online CVaR confidence scheduling. The resulting quadratic program combines deterministic ego-error robustness with probabilistic perception uncertainty while minimally modifying the nominal control input. The zero-slack solution guarantees forward invariance of the risk-tightened safe set under the stated assumptions, whereas the slack-activated mode provides a quantified least-violation fallback rather than a strict safety guarantee. Simulations on a nonlinear 3-DOF bicycle model evaluate critical cut-in, sudden perception degradation, merge-bottleneck, fixed-CVaR, sensitivity, runtime-scaling, heterogeneous multi-obstacle, and heavy-tailed uncertainty cases.

Technologies doi: 10.3390/technologies14050309

Authors: Yongsheng Ma Hongwei Zhu Guobao Zhang Yongming Huang

Autonomous vehicles must enforce safety constraints even when their state estimates are corrupted by sensor faults and disturbances. This paper develops a separation-based robust safety-control framework that couples a fault-tolerant observer with a control barrier function (CBF) safety filter through an explicit estimation-error envelope. First, a uniformly ultimately bounded observer-error estimate is derived. This bound is then injected into an estimated-state robust CBF condition, yielding safety margins that account for both observation error and bounded disturbances. The construction is further extended to time-varying safe sets induced by moving obstacles. For implementation, the resulting condition is realized as a quadratic-program safety filter with high-order obstacle and lane constraints. Simulations on a nonlinear 3-DOF bicycle model evaluate bias faults, gust-like disturbances, dense traffic, and tightened stress tests. Compared with a standard CBF baseline and observer/safety-filter ablations, the proposed method preserves nonnegative safety margins while keeping slack activation negligible. Additional sensitivity experiments quantify the trade-off among safety margin, slack usage, observer accuracy, control conservatism, and QP computation time. The results support the proposed architecture as a practical bridge between bounded state estimation and fault-aware safety filtering.

Technologies doi: 10.3390/technologies14050308

Authors: Erick Alexander Noboa Lourdes Ruiz György Eigner Péter Galambos

The post-pandemic evolution of education involving mechatronics and machine learning has shifted the demand for robotic hardware from centralized laboratories to accessible laboratories in home environments. This paper presents a portable three-wheeled holonomic robotic platform designed for remote research and home office experimentation. The proposed system utilizes a modular design and low-cost philosophy comprising a custom embedded control system driven by an ESP32-WROOM microcontroller, which manages a closed-loop PID velocity controller using Hall effect feedback from three DC micromotors. In contrast, external nodes allow the reception, conditioning, and classification of 8-channel surface electromyography (sEMG) data sampled at 500 Hz. To address the non-stationarity and stochastic noise in raw sEMG signals, this study implements a hybrid Deep Learning (DL) architecture that complements 2D Convolutional Neural Networks (CNN) for spatial feature extraction with Long Short-Term Memory (LSTM) networks for temporal context awareness. This model decodes the neuromuscular intent of the user into real-time holonomic velocity vectors, achieving validation accuracies of 80.51% for horizontal movement, 84.86% for vertical translation, and 99.56% for the Fist/no-Fist state. By synthesizing advanced AI-based teleoperation with a portable design, this study establishes a scalable framework for the next generation of “laboratory-at-home” educational tools and research regardless of physical location.

Technologies doi: 10.3390/technologies14050307

Authors: Alexander Logunov Andrey Vorotyntsev Igor Prokhorov Alexey Maslov Artem Belousov Ivan Zanozin Evgeniya Logunova Artem Kulikov Sergei Zelentsov Alexander Ganov Ilia Senchenko Anton Petukhov Ilya Vorotyntsev

To accelerate the transition to sustainable energy, efficient methods for CO2-free hydrogen production and carbon utilization are needed. This study presents a new, sustainable approach for the simultaneous production of hydrogen, valuable hydrocarbons, and functional carbon materials by converting methane in low-pressure microwave plasma. Compared to traditional methane reforming methods (such as steam reforming), our plasma-based process operates at low temperatures, eliminates direct CO2 emissions, and enables the conversion of methane into three valuable products: (1) environmentally friendly hydrogen for fuel cells and energy storage systems, (2) a range of valuable organic products (C2H2, C2H4, C2H6), and (3) functional carbon films with self-improving catalytic properties. Optical emission spectroscopy (OES) and the Langmuir double probe method were used for plasma diagnostics, revealing an increase in the concentration of active species (CH, Hα, C2) and electron temperature upon argon addition. The structure, morphology, and impurity composition of the deposited films were investigated using X-ray diffraction (XRD), scanning electron microscopy (SEM), and inductively coupled plasma mass spectrometry (ICP-MS), respectively. Gas-phase byproducts were analyzed using gas chromatography–mass spectrometry (GC-MS). Argon addition at an Ar/CH4 ratio of 1 leads to the formation of carbon films with a more ordered structure, as confirmed by XRD data, and improved surface morphology. It was established that argon, by effectively participating in the excitation and dissociation processes of methane molecules through energy transfer from metastable states and increased electron temperature, optimizes plasma–chemical reactions, promoting the deposition of higher-quality carbon coatings.

Technologies doi: 10.3390/technologies14050306

Authors: Chen-Hao Zhao Yi-Feng Ren Long-Kun Cao Hong-Yu Wang

In the area of transmission line inspection, one of the prominent areas of research has been to unite Unmanned Aerial Vehicles (UAVs) with neural network object detection algorithms. This area of research is challenging because of high computational resource consumption and poor infrared detection capabilities. In this study we propose an infrared image detection algorithm, named IISD-YOLO, using a modified version of the YOLOv11 network, to detect infrared transmission line insulator strings. Firstly, the original object detection layer was removed and replaced with the ShuffleNetv2 network to achieve the goal of a lightweight model; subsequently, based on the original feature extraction module C3k2, the Manhattan Self-Attention (MaSA) mechanism was introduced to design a new feature extraction module, C3k2-MaSA, which enhances the feature extraction capability for infrared objects; finally, the bidirectional feature pyramid network (Bi-FPN) is used to replace the original feature fusion module, enhancing the network’s ability to process and fuse information at different scales. The comparative experiments show that compared with the mainstream YOLO models, IISD-YOLO has improved by 4.5, 6.1, and 4.8 percentage points respectively on mAP@50 over YOLOv5, YOLOv8, and YOLOv10; furthermore, this model outperforms advanced models including YOLO-CIR, FA-YOLO, YOFIR, and RT-DETR, with improvements of 2.9, 9.1, 5.0, and 1.1 percentage points respectively on mAP@50. The ablation study shows that each improvement effectively enhances the overall performance. Compared with the original YOLOv11, the IISD-YOLO has increased its mAP@50 by 3.5 percentage points, while reducing the number of Params by 1.1 million and the computational GFLOPs by 2 G. These results confirm the superior performance of IISD-YOLO in infrared insulator string detection.

Technologies doi: 10.3390/technologies14050305

Authors: Barbora Darmová Iveta Fajnerová Lora Appel

Generalized anxiety disorder (GAD) is characterized by persistent, excessive, and difficult-to-control worry. The Contrast Avoidance Model (CAM) proposes that individuals with GAD use worry to sustain negative emotional arousal, thereby avoiding sharp negative emotional contrasts that would otherwise follow unexpected adverse events. A virtual reality (VR) protocol was developed to simulate such contrasts by alternating guided relaxation with brief anxiety-inducing scenarios (skyline plank, crowded elevator, and loose dog encounter). This study evaluated the usability and feasibility of this protocol in 20 subclinical adults aged 18–45 who met a screening threshold of GAD-7 ≥ 5, using a Meta Quest 3 headset and Polar H10 heart rate sensor. Exposure segments produced a significant decrease in RMSSD (β = −0.185, p < 0.001), consistent with reduced parasympathetic activity during exposure, whereas heart rate did not differ significantly between conditions. Subjectively, exposure increased SUDS (β = 2.23, p < 0.001) and SAM arousal (β = 1.95, p < 0.001), and decreased SAM valence (β = −2.68, p < 0.001) and dominance (β = −1.70, p = 0.005). Presence scores, cybersickness ratings, and qualitative feedback supported the usability of the protocol and identified concrete design refinements. These results support the feasibility of the protocol and provide a foundation for future controlled clinical evaluation.

Technologies doi: 10.3390/technologies14050304

Authors: Ioana Cornelia Cristina Crihană Josef Rebenda

This paper discusses how digital transformation takes place in public knowledge institutions by examining public libraries as socio-technical service ecosystems, and conceptualizes digital maturity. Based on Service-Dominant Logic and the socio-technical systems theory, this study explores digital maturity as a natural product of convergence in technological infrastructures, professional expertise, governance mechanisms, and community involvement. The data analysis is conducted on a structured 48-item questionnaire which, at its turn, is based on a sample of 101 members of library staff in public libraries in Romania. The Romanian dataset is contextualized by using a national comparative dataset comprising 363 respondents from France. We employ a mixed method of descriptive and inferential statistical analyses and thematic coding in order to investigate institutional adaptability, AI readiness, and service development trends. The results reveal the continuing movement from collection-centered models toward hybrid physical–digital service platforms and differences in digital maturity and overall strategic planning among institutions. The results demonstrate that digital maturity is sensitive to the organized coordination and the planning capability in institutions rather than to isolated technological adoption. Drawing from this evidence, the study proposes an analytical framework and a tempered analytical lens for interpreting digital transformation processes in public knowledge ecosystems, forming a solid foundation for more general investigations of institutional adaptation to digitally mediated environments.

Technologies doi: 10.3390/technologies14050303

Authors: Nurzada Amangeldy Aigerim Yerimbetova Marek Milosz Akmaral Kassymova Elmira Daiyrbayeva Nazira Tursynova

This paper proposes a unified methodological framework for evaluating heterogeneous approaches to avatar-based sign language visualization. The study introduces a four-dimensional analytical framework based on four independent criteria: (A1) pipeline architecture and degree of automation, (A2) data and annotation requirements, (A3) portability across sign languages and domains, and (A4) integration and accessibility. The framework is applied to a comparative analysis of three dominant paradigms: (P1) notation → animation (e.g., HamNoSys), (P2) writing-based representation → animation (e.g., SignWriting), and (P3) keypoint-based animation and Artificial Intelligence (AI) methods. The comparative assessment shows that the differences between the paradigms are structural and reflect trade-offs among linguistic accuracy, automation level, scalability, and user accessibility, rather than the superiority of any one technology. Overall, the structured comparative framework (A1–A4) is applied for analyzing three paradigms of sign language avatar generation. It enables a systematic evaluation of architectural, data-related, and practical characteristics, highlighting key trade-offs between linguistic accuracy, scalability, and accessibility.

Technologies doi: 10.3390/technologies14050302

Authors: Reemas Alsubaiei Farah AlHayek Mariam Alsahhaf Ghadah Alajmi Aliah Almutairi Karim Youssef Ghina El Mir Sherif Said Taha Beyrouthy Samer Al Kork

Millions of persons worldwide experience varying degrees of hearing loss, traditionally addressed through prosthetic solutions such as hearing aids and cochlear implants. However, a significant proportion of individuals cannot benefit from these technologies, cannot access them, or choose not to use them. In this context, non-prosthetic assistive technologies have emerged as a complementary paradigm, leveraging advances in sensing, artificial intelligence, and wearable computing to transform acoustic information into alternative perceptual representations rather than restoring auditory function. This survey provides a review of such systems, focusing on technologies that enhance environmental awareness, communication, and social interaction. Existing approaches are categorized along two main dimensions: the tasks they perform and the platforms on which they operate. Task-oriented analysis includes sound recognition (speech and non-speech), sound source localization, emotion recognition, sign language recognition, and related emerging functionalities. Platform-based analysis emphasizes wearable devices and mobile solutions enabling real-time and context-aware assistance. The survey further highlights key research trends, including real-time auditory scene analysis, portable processing, and artificial intelligence. It shows that recent studies increasingly demonstrate that combining auditory, visual, and haptic modalities improves robustness and usability in real-world conditions, particularly in noisy and dynamic environments. Finally, open challenges such as energy efficiency, latency, evaluation methodologies, and user acceptance are discussed. By synthesizing existing work and identifying open research directions, this survey aims to provide a structured foundation for future developments in intelligent, non-prosthetic assistive systems that redefine how auditory information is accessed and interpreted.

Technologies doi: 10.3390/technologies14050301

Authors: Ruimin Wang Takenao Sugi Takao Yamasaki

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early detection and reliable monitoring remain major clinical challenges. Electroencephalography (EEG) combined with machine learning has attracted growing interest as a scalable and non-invasive approach to AD detection, yet reported classification accuracies vary widely across studies and are rarely comparable or clinically translatable. One important reason is that the analytical pipeline—from data acquisition to model validation—involves numerous methodological choices whose inter-stage dependencies and reproducibility implications are rarely made explicit. In this narrative review, we adopt a methodological chain framework to make these dependencies explicit, organizing EEG-based AD research into five sequential stages: data acquisition, preprocessing, feature representation, modeling, and validation. Choices at each stage can shape downstream analyses, inflate reported performance, and reduce cross-study comparability in ways that are difficult to detect when stages are assessed independently. These effects are particularly consequential in EEG-based AD research, where cohorts are typically small and biomarkers are subtle. We make three primary contributions: (1) we describe inter-stage methodological dependencies that may contribute to reproducibility problems and performance inflation; (2) we synthesize major sources of methodological variability across representative EEG–AD studies and evaluate their differential impact on spectral, connectivity, and complexity features; and (3) we provide practical, stage-aligned recommendations culminating in a minimum reporting checklist.

Technologies doi: 10.3390/technologies14050300

Authors: Chaymae Yahyati Ismail Lamaakal Yassine Maleh Khalid El Makkaoui Ibrahim Ouahbi

Real-time psychological stress detection on wearable and edge devices requires models that are accurate, computationally efficient, and small enough for on-device deployment. This paper proposes a Micro-Attention CNN Hybrid Architecture for stress recognition using wearable bio-signals. The model uses six sensor channels, namely tri-axial acceleration, electrodermal activity, heart rate, and skin temperature, and classifies three stress levels: no stress, low stress, and high stress. This study is conducted on a public wearable sensor dataset collected from 15 nurses during hospital work, providing a realistic benchmark for continuous stress monitoring under practical conditions. The proposed architecture combines one-dimensional and depthwise separable convolutions with a lightweight attention module to emphasize the most informative temporal patterns in short multivariate signal segments. To support deployment on resource-constrained devices, we further apply structured pruning, selective quantization-aware training, and post-training quantization. The full-precision model achieves a Macro-F1 score of 99.63%, while the final compressed model retains 98.03% Macro-F1 with a model size of 1.76 kilobytes and a CPU inference latency of 0.40 ms. Additional analyses show that most residual errors occur near the boundary between low stress and neighboring classes, while simple post-compression calibration improves reliability. These results demonstrate that accurate and low-latency stress detection using wearable bio-signals is feasible on compact edge hardware without transmitting raw sensor streams off-device.

Technologies doi: 10.3390/technologies14050299

Authors: Gian Carlo Montanari Sukesh Babu Myneni

The Paschen law, especially in its linear approximation, is said to be useful for predicting the partial discharge inception voltage (PDIV) in insulation systems when considering different defect sizes and pressure values. Hence, it is often used for designing electrical insulation systems in aerospace applications. This paper presents a comparison between PDIV estimates provided by the Paschen law and a new model applicable to internal and surface discharges in electrical insulation systems under varying pressure and defect size or creepage distance. It is shown that the Paschen law estimates can often be very far from the measured PDIV values for both surface and internal defects and at pressures above and below standard atmospheric pressure (SAP), which can negatively affect the design and reliability of insulation systems. On the contrary, the proposed model provides accurate and consistent PDIV estimates, which are very close to those measured, for both internal and surface discharges. The lower limit of the model application/validation is 50 mbar from SAP.

Technologies doi: 10.3390/technologies14050298

Authors: Amin Amin Feri Yusivar Faiz Husnayain Aam Muharam

Cell imbalance in battery packs can cause premature termination during battery discharge and recharge processes. This condition can decrease the usable energy of the battery. The cost of batteries can reach 30–40% of the price of an electric vehicle, so battery cell balancing in a battery management system (BMS) and a battery thermal management system (BTMS) is very important to maximize battery capacity, safety, and life. In conventional active balancing studies, the cell-balancing process draws energy from the cells or battery pack, resulting in a reduction in battery pack energy due to power losses during the balancing process. This condition can reduce the range of electric vehicles. In this paper, a battery balancing system with a reduced number of switches and low cost, as well as the use of a cell charger, is proposed. The cell charger will draw energy from the electrical grid so that it can maximize the energy in the battery pack. A balancing current of 3 A from the cell charger is used in the balancing process. A 23S1P 100 Ah LiFePO4 battery pack, consisting of 23 cells, is used for validation. Test results show that the proposed battery balancing system can balance the voltage of 23 battery cells for 40 minutes from the highest and lowest voltage difference of 116.7 mV to 11.8 mV.

Technologies doi: 10.3390/technologies14050297

Authors: Xiang Wei Songjie Peng Baosheng Zhao

Robotics integration across manufacturing, healthcare, and hazardous environments demands robust performance evaluation. This study proposes a comprehensive Task–Environment–System–Metric (TESM) framework to link operational tasks and environmental constraints with quantifiable metrics. Based on TESM, a multi-level evaluation system is established, covering kinematic/dynamic performance, perception, human–robot interaction (HRI), reliability, and lifecycle economics. We systematically review key evaluation methodologies, including mechanistic modeling, digital twin simulation, physical testing, and multi-criteria decision-making (MCDM). Furthermore, typical engineering applications—ranging from industrial manipulators and mobile robots to collaborative and field systems are analyzed to demonstrate practical implementation. Despite significant progress, challenges persist regarding unified standards, testing fidelity, and the “black box” nature of data-driven assessments in safety-critical scenarios. This review concludes by identifying future research directions, such as establishing benchmark testing platforms, improving lifecycle assessment schemes, and developing modular evaluation tools. These advancements aim to ensure the scalable and reliable deployment of robotic systems in complex engineering environments.

Technologies doi: 10.3390/technologies14050296

Authors: Giovanni Rafael Caicedo Escorcia Liliana Vera-Londoño Jaime Andres Perez-Taborda

Water quality monitoring remains a critical challenge for achieving Sustainable Development Goal 6, particularly in rural and resource-constrained environments where conventional laboratory-based methods are costly and slow. This study presents the development and field validation of an Artificial Intelligence of Things (AIoT) ecosystem for intelligent, low-cost, and real-time water quality assessment using edge computing and generative artificial intelligence. The system integrates a laboratory-developed multiparameter probe measuring temperature, pH, dissolved oxygen, and electrical conductivity with a mobile application and a cloud-based backend. Field validation was conducted in riverine environments in the municipality of Pueblo Bello (Cesar, Colombia), where the system was deployed for in situ data acquisition and real-time inference. A supervised Artificial Neural Network (ANN) was trained to classify water quality based on a Water Quality Index (WQI) ground truth derived from a public dataset, employing KNN-based missing data imputation, interquartile range outlier filtering, stratified balancing, and grid search hyperparameter optimization. The best-performing model achieved 85.1% accuracy and an AUC of 0.87 using only four physical parameters and was successfully deployed in TensorFlow Lite format on both the embedded probe and the mobile application with sub-millisecond inference time. Integration with a generative AI backend provides contextual natural-language interpretations of measurements. These results demonstrate that reduced-parameter edge AI systems can provide reliable environmental diagnostics while enhancing accessibility and citizen engagement for participatory water monitoring.

Technologies doi: 10.3390/technologies14050295

Authors: Shuoyu Wang Taiki Miyaji

The number of patients with lower-limb dysfunction is increasing each year due to aging, illness, accidents, and other factors. Without timely rehabilitation and rapid recovery of walking function, further physical and mental deterioration may be accelerated, potentially leading to long-term bedriddenness. This study discusses gait training in rehabilitation therapy from the perspectives of kinesiology, cognitive science, walking function, and safety, and an omnidirectional gait training robot was developed. This study proposed a control system construction method for an omnidirectional gait training robot based on both prescription-based training and autonomous training. In the prescription-based training system, the target values are derived from the training prescription, and the control objective is to guide the patient to walk along the robot’s prescribed path and speed. In the autonomous training system, the target values are automatically generated based on the patient’s walking intentions, and the control objective is for the robot to safely follow the patient’s movement. A necessary condition for robot-assisted autonomous gait training is effective fall prevention. A fall prediction strategy based on foot position information and handrail pressure data was developed. Using this strategy, the robot can predict falls immediately before they occur, similar to a physical therapist, thereby reducing the risk of falls during gait training. Experimental results demonstrate the feasibility of implementing this strategy.

Technologies doi: 10.3390/technologies14050294

Authors: Judith Gálvez-García Vicente Torres-García Juan Ramón Rodríguez José Ángel Barrios Alberto Cavazos

This paper presents a sizing and optimization methodology for LCL filters tailored to high-capacity modular power systems. The approach prioritizes the strategic selection of the resonance frequency, an asymmetric inductance design, and strict harmonic current limits. The methodology is validated through a case study simulation of a 126 MW photovoltaic plant in a region of Mexico, analyzing its 2.34 MW inverter architecture. The simulations show that precise capacitor sizing for reactive power management, combined with a passive resistive damping strategy, ensures compliance with grid interconnection standards (IEEE 1547) and power quality standards (IEC 61000). This approach simplifies practical implementation by eliminating the need for complex active damping control algorithms. Additionally, dynamic decoupling is validated through time-domain step responses, and frequency-domain sensitivity analysis confirms robust stability margins even under ±20% variations in passive parameters. Ultimately, the system achieves voltage total harmonic distortion (THD) levels below 0.18%, demonstrating a scalable solution for maintaining grid stability.

Technologies doi: 10.3390/technologies14050293

Authors: Vedran Jurdana

Compressed sensing (CS) applied in the ambiguity domain offers an effective approach for recovering time–frequency distributions (TFDs) of non-stationary signals from sparse representations. Existing methods predominantly rely on the Wigner–Ville distribution (WVD) as the initial representation due to its simplicity and high auto-term concentration. However, the WVD performs poorly for signals with higher-order frequency-modulated (FM) components and is highly sensitive to interference and noise, which then propagate into the reconstruction. This paper introduces the systematic use of the L-Wigner distribution (LWD) as the initial representation for CS-based reconstruction, providing front-end adaptability to signal characteristics. By generating a controllable family of TFDs ranging from the spectrogram to cross-term-free polynomial WVDs, the LWD enables effective suppression of interference and noise while simultaneously enhancing auto-term localization for nonlinear FM components. The proposed LWD-based reconstruction framework is evaluated against the conventional WVD-based method using several state-of-the-art reconstruction algorithms, whose parameters are jointly optimized through a multi-objective meta-heuristic framework to ensure a fair comparison. Experiments on noisy synthetic signals and real-world gravitational-wave data demonstrate consistent performance gains. On synthetic signals, the proposed approach reduces the average reconstruction error index by up to 36.6%, improves the ℓ1-reconstruction error by up to 75.8%, and achieves complete elimination of cross-term energy. In addition, robustness analysis under additive white Gaussian noise shows up to a 75% improvement in ℓ1 performance. For real gravitational-wave data, the method reduces the mean reconstruction index by up to 5.8% while maintaining auto-term preservation and eliminating cross-term artifacts. These results establish the LWD-based initialization as an effective and general strategy for TFD reconstruction in complex signal environments.

Technologies doi: 10.3390/technologies14050292

Authors: Bin Li Wai Yie Leong

The evolution from Industry 4.0 to Industry 6.0 represents a paradigm shift—moving from automation toward an integrated model that incorporates intelligentization, sustainability, and human-centric resilience. While numerous conceptual frameworks have been put forward, empirical research remains scarce, primarily because of the absence of standardized indicators derived from verifiable corporate disclosures. To fill this research gap, the present study develops three quantifiable indices—Intelligence (INT), Sustainability (SUS), and Resilience & Human-centric (RES)—by extracting data from the annual reports and ESG disclosures of 100 Chinese A-share manufacturing enterprises (covering 2022–2024). Fixed-effects panel regression models are employed to assess the impact of these indices on financial performance (ROA, ROE, EPS), market valuation (Tobin’s Q), and sustainability outcomes (ESG ratings). Our findings reveal that INT is the most significant predictor of profitability, with statistically significant positive effects on ROA and ROE—effects that are particularly pronounced among high-tech enterprises. This supports the view that digital capabilities serve as strategic assets. SUS also demonstrates a positive influence on performance, especially in non-high-tech enterprises, where eco-efficiency, regulatory compliance, and ESG-linked financing help offset technological disadvantages. RES contributes to operational and financial stability by enhancing human capital, safety protocols, and organizational practices that reduce performance volatility. Collectively, these results indicate that different types of enterprises follow distinct yet converging pathways toward Industry 6.0: high-tech enterprises capitalize on intelligence to generate innovation rents, while non-high-tech enterprises increasingly rely on sustainability and resilience as strategies to build legitimacy. This study makes significant contributions in three aspects: Methodologically, it differs from previous research that relies on questionnaires and interviews. Instead, it quantifies Industry 6.0 through auditable large-sample key indicators, enhancing the objectivity and operability of the indicators. Empirically, it provides the first empirical evidence on the development path of Industry 6.0 based on data from Chinese manufacturing enterprises. In practical terms, it offers clear references for enterprises and policymakers on the core indicators and their construction framework that should be prioritized during the transformation to Industry 6.0. By linking the index derived from enterprise disclosures with quantifiable performance results, this study effectively bridges the gap between theoretical conceptions and practical applications. It further emphasizes that Industry 6.0 is not merely a technological upgrade but a systematic transformation driven by digitalization, sustainability, and resilience aimed at enhancing enterprise performance and achieving sustainable industrial development.

Technologies doi: 10.3390/technologies14050291

Authors: Shuquan Peng Yilin Qi Ling Fan Wanqi Huang Yan Zhou

Against the backdrop of growing demand for environmentally friendly reinforcement in geotechnical engineering, natural fiber reinforcement combined with microbial-induced calcium carbonate (MICP) and enzyme-induced calcium carbonate (EICP) technologies has garnered significant attention due to their eco-friendly and efficient advantages. However, few studies have reported the combined application of these three techniques for sand consolidation. This study employs a combined MICP-EICP approach with natural fiber reinforcement to enhance the overall strength of sandy soils and investigate related rock fracture permeability phenomena. Tests conducted include calcium carbonate content, unconfined compressive strength, permeability coefficient, and permeability flow rate. Results indicate that when brown fiber length is 6 mm and dosage is 0.8%, the unconfined compressive strength of MICP-EICP composite specimens reaches a maximum of 0.61 MPa, calcium carbonate content peaks at 7.07%, and permeability coefficient drops to a minimum of 0.0044 cm/s. This composite method offers a highly promising and sustainable improvement solution for geotechnical engineering applications such as sand consolidation, crack sealing, and cultural relic restoration. It not only optimizes mechanical properties but also enhances the utilization rate of waste materials.

Technologies doi: 10.3390/technologies14050290

Authors: Guilherme Almeida Mariana Seiça Licínio Roque

To activate conscious reflection regarding personal identity and identity-building processes in our daily lives is an increasing social concern. With this aim, we designed an Alternate Reality Game that invites participants to collectively explore these themes. Participants played with a prototype, evoking themes of identity through emergent dynamics from gameplay and interpersonal interactions. We analyzed participants’ appropriation of the prototype through logged activity, direct observation and interviews. The identified dynamics enabled iterative redesign and further exploration of the players’ interaction and behaviors. From this process, we synthesized four design insights as our main findings that may guide further research in the field: (1) how to explore design–play–reflect as a co-design process supported on individual appropriation, (2) how ARGs generate reflective social phenomena, such as varied social identity and power narratives, (3) how ARG design can open doors to balance power dynamics, and (4) how ARG designs can become generative social theories. Our main contributions, alongside the designed prototype, are these four insights, and their potential scalability to other ARG designs that seek to provoke social phenomena and collaborative interventions.

Technologies doi: 10.3390/technologies14050289

Authors: Simon Otte Felix Bayer Sebastian Schabel Jürgen Fleischer

The performance of lithium-ion batteries is strongly determined by material properties, which are provided in technical data sheets but often in inconsistent formats and terminology. Automated extraction of these parameters could enable downstream applications such as process optimization, traceability, and hazard assessment. However, current approaches are unsuitable for industrial use. This work presents a prototype NLP-based extraction pipeline for material and safety data sheets. Using fine-tuned SpaCy models, F1-scores above 0.7 are achieved for key parameters such as CAS number, molecular mass, and density. The resulting structured material database provides a foundation for data-driven applications in battery cell production. The feasibility of domain-specific NLP for automated material information extraction is demonstrated and potential pathways for integration with process control and optimization workflows are discussed.

Technologies doi: 10.3390/technologies14050288

Authors: Uhamir Patrick Stefanija Klaric Sara Havrlisan

Duplex stainless steel (DSS) blends impressive mechanical and chemical characteristics to withstand aggressive environments. Its fabrication by Gas Metal Arc Welding-Additive Manufacturing is an emerging research topic. However, its sensitive grain structure and alloy composition are prone to deterioration by repeated thermal shocks. Whether optimal weld parameters can resolve these challenges without additional costs from special fillers, gases, or mechanisms is a valid question. In this study, how different wire feed speeds, travel speeds, and weld voltages, chosen from a set of preliminary beads, translate into wall dimensions, phase formation and distribution, morphological transformation, and elemental segregation is investigated. The unique DSS microstructures were characterised using scanning electron microscopy and energy-dispersive spectroscopy to reveal differences in microstructural evolution and ferrite-austenite (α-γ) structure. The deposited walls exhibited satisfactory geometric quality with negligible distortions. However, the height suppression was noticeable at the deposition energy (DE) of 755 J/mm. Metallographic analysis revealed low γ phase formation (<30%) at low DE (230 J/mm) and excessive γ formation (>70%) in the high DE wall (755 J/mm). The parameters WFS:TS = 15, TS = 35 cm/min, WFS = 525 cm/min, and V = 20.804 volts suppressed the elemental segregation while maintaining a suitable phase balance without post-processing.

Technologies doi: 10.3390/technologies14050287

Authors: Guillermo Vera-Amaro Rodolfo Vera-Amaro Miguel Felix Mata-Rivera José Rafael Rojano-Cáceres

Recent research on web accessibility has explored the use of artificial intelligence (AI), particularly large language models (LLMs), to support accessibility remediation. However, the field lacks a theoretical perspective explaining how LLMs can be integrated to systematically support this process. This study proposes a theory of LLM-assisted web accessibility remediation. It is built through the integration of qualitative evidence, prior literature, accessibility standards, and empirical studies on LLM-based remediation. The resulting theory provides an explanatory framework describing how LLMs can assist web accessibility remediation through iterative cycles of analysis, transformation, and validation, and identifies key factors including prompting strategies, input representations, and validation mechanisms. This work provides a conceptual foundation for understanding and systematically studying LLM-assisted accessibility remediation, and supports both research and practice by guiding future studies and informing the design of models, methods, tools, and accessibility engineering practices.

Technologies doi: 10.3390/technologies14050286

Authors: Franziska Geiger Henning Bathel Sascha Spors Rainer Bader Daniel Kluess

The authors wish to make the following corrections to their paper [...]

Technologies doi: 10.3390/technologies14050285

Authors: Raluca Andreea Roșu Daniel-Eugeniu Crunțeanu Emilia Georgiana Prisăcariu Oana Dumitrescu

Liquid rocket engine injectors play a fundamental role in determining combustion efficiency, stability, and overall propulsion performance. This review paper provides a comprehensive analysis of liquid-injector technologies used in space propulsion systems, with emphasis on their historical evolution, atomization mechanisms, and cross-domain insights from aviation fuel injection systems. The study begins by examining the fundamental processes governing liquid jet breakup, including primary and secondary atomization, ligament formation, and droplet generation, together with the non-dimensional parameters that control these phenomena. The historical development of injector architectures -from early orifice-based and impinging designs to modern coaxial and pintle configurations—is then discussed in relation to increasing performance requirements and combustion stability challenges. A comparative perspective with aviation gas turbine injectors is introduced to highlight similarities in atomization physics and differences in operating conditions and design constraints. The paper also reviews experimental and numerical approaches used to characterize spray formation and injector performance. The results indicate that injector geometry and flow conditions strongly influence mixing efficiency, droplet size distribution, and combustion–acoustic coupling mechanisms. The study concludes that integrating cross-domain knowledge and modern design techniques is essential for advancing injector performance in next-generation propulsion systems.

Technologies doi: 10.3390/technologies14050284

Authors: Andres Martin-Megino Blanca Lopez Ivan Vidal Francisco Valera

Quantum Key Distribution (QKD) enables two parties to securely share encryption keys by leveraging the principles of quantum mechanics, offering protection against eavesdropping. In practical implementations, QKD systems often rely on a layered architecture where a key manager stores secret key material in a buffer and delivers it to higher communication layers as needed. However, this buffer can be depleted under high demand, requiring efficient replenishment strategies that minimize resource waste. Given the importance of optimizing time and resources in quantum cryptography protocols, we introduce a variable-length adaptation of the BB84 protocol designed to meet user-defined output key length constraints in non-ideal scenarios. We present a method for dynamically configuring the protocol’s initial parameters to generate secret keys of a desired length. To validate our approach, we developed simulation tools to model general QKD networks and discrete-variable protocols. These tools were used to implement and evaluate our strategies, which were developed within the BB84 framework but can be extended to other QKD protocols under reasonable assumptions. The results highlight their usefulness in optimizing quantum resource usage and supporting key management, contributing to the long-term goal of scaling and strengthening secure quantum networks.