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Volume 14
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Technologies, Volume 14, Issue 1 (January 2026) – 76 articles

Cover Story (view full-size image): The analyses and investigations conducted in this contribution have highlighted the potential for reliable, accurate, and dependable medical observation, assistance and intervention therapies through the integration of a smart digital environment. The medical implications explored include image-assisted interventions in robotic surgery and drug delivery, as well as monitoring and assistance approaches using wearable devices. The analysis corroborates that technological advances in smart material, wearable sensing, robotic actuation, and imaging, combined with smart digital monitoring, are shaping smart digital management. The latter is integrated into autonomous systems, reflecting valuable interaction between machines and personnel. This improves medical functionality, patient well-being, and staff convenience. View this paper
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62 pages, 4036 KB  
Systematic Review
Quantization of Deep Neural Networks for Medical Image Analysis: A Systematic Review and Meta-Analysis
by Edgar Fabián Rivera-Guzmán, Luis Fernando Guerrero-Vásquez and Vladimir Espartaco Robles-Bykbaev
Technologies 2026, 14(1), 76; https://doi.org/10.3390/technologies14010076 - 22 Jan 2026
Cited by 3 | Viewed by 1906
Abstract
Neural network quantization has become established as a key strategy for transitioning medical imaging models from research environments to clinical devices and resource-constrained edge platforms; however, the available evidence remains fragmented and focused on highly heterogeneous use cases. This study presents a systematic [...] Read more.
Neural network quantization has become established as a key strategy for transitioning medical imaging models from research environments to clinical devices and resource-constrained edge platforms; however, the available evidence remains fragmented and focused on highly heterogeneous use cases. This study presents a systematic review of 72 studies on quantization applied to medical images, following PRISMA guidelines, with the aim of characterizing the relationship among quantization technique, network architecture, imaging modality, and execution environment, as well as their impact on latency, memory footprint, and clinical deployment. Based on a structured variable matrix, we analyze—through tailored visualizations—usage patterns of Post-Training Quantization (PTQ), Quantization-Aware Training (QAT), mixed precision, and binary/low-bit schemes across frameworks such as PyTorch V 2.6.0, TensorFlow 2.19.0, and TensorFlow Lite, executed on server-class GPUs, edge/embedded devices, and specialized hardware. The results reveal a strong concentration of evidence in PyTorch/TensorFlow pipelines using INT8 or mixed precision on GPUs and edge platforms, contrasted with limited attention to PACS/RIS interoperability, model lifecycle management, energy consumption, cost, and regulatory traceability. We conclude that, although quantization can approximate real-time performance and reduce memory footprint, its clinical adoption remains constrained by integration challenges, model governance requirements, and the maturity of the hardware–software ecosystem. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Medical Image Analysis)
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19 pages, 8623 KB  
Communication
Influence of Performance Metrics Emphasis in Hyperparameter Tuning for Aircraft Skin Defect Detection: An Early Inspection of Weighted Average Objectives
by Christian Kurniawan, Nutchanon Suvittawat and De Wen Soh
Technologies 2026, 14(1), 75; https://doi.org/10.3390/technologies14010075 - 22 Jan 2026
Viewed by 554
Abstract
To address the limitations of traditional aircraft skin inspection, the aviation industry and academia have increasingly been exploring the integration of computer vision technologies into the defect detection process. These implementations of computer vision technologies rely on the performance of underlying neural network [...] Read more.
To address the limitations of traditional aircraft skin inspection, the aviation industry and academia have increasingly been exploring the integration of computer vision technologies into the defect detection process. These implementations of computer vision technologies rely on the performance of underlying neural network models, whose effectiveness is highly influenced by their hyperparameter configuration. To obtain optimum hyperparameters, an optimization procedure is often employed to optimize a certain combination of the model’s performance metrics. However, in the aircraft skin defect detection domain, studies to inspect the effect of different emphases in the performance metrics considered in this objective function are still not widely available. In this paper, we present our early observations regarding the influence of different performance metrics’ emphases during the hyperparameter tuning process on the overall performance of a computer vision model employed for aircraft skin defect detection. In this preliminary inspection, we consider the utilization of YOLOv12 and the Bayesian Optimization approach for the defect detection model and hyperparameter optimizer, respectively. We highlight the possible performance degradation of the model after a hyperparameter tuning procedure when the weight factor distribution of the performance metrics is not carefully considered. We note several weight factors of interest that could serve as initial possible “safe spots” for further exploration. Full article
(This article belongs to the Special Issue Aviation Science and Technology Applications)
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28 pages, 11222 KB  
Article
Robustness Enhancement of Self-Localization for Drone-View Mixed Reality via Adaptive RGB-Thermal Integration
by Ryuto Fukuda and Tomohiro Fukuda
Technologies 2026, 14(1), 74; https://doi.org/10.3390/technologies14010074 - 22 Jan 2026
Viewed by 789
Abstract
Drone-view mixed reality (MR) in the Architecture, Engineering, and Construction (AEC) sector faces significant self-localization challenges in low-texture environments, such as bare concrete sites. This study proposes an adaptive sensor fusion framework integrating thermal and visible light (RGB) imagery to enhance tracking robustness [...] Read more.
Drone-view mixed reality (MR) in the Architecture, Engineering, and Construction (AEC) sector faces significant self-localization challenges in low-texture environments, such as bare concrete sites. This study proposes an adaptive sensor fusion framework integrating thermal and visible light (RGB) imagery to enhance tracking robustness for diverse site applications. We introduce the Effective Inlier Count (Neff) as a lightweight gating mechanism to evaluate the spatial quality of feature points and dynamically weigh sensor modalities in real-time. By employing a 20×16 grid-based spatial filtering algorithm, the system effectively suppresses the influence of geometric burstiness without significant computational overhead on server-side processing. Validation experiments across various real-world scenarios demonstrate that the proposed method maintains high geometric registration accuracy where traditional RGB-only methods fail. In texture-less and specular conditions, the system consistently maintained an average Intersection over Union (IoU) above 0.72, while the baseline suffered from complete tracking loss or significant drift. These results confirm that thermal-RGB integration ensures operational availability and improves long-term stability by mitigating modality-specific noise. This approach offers a reliable solution for various drone-based AEC tasks, particularly in GPS-denied or adverse environments. Full article
(This article belongs to the Special Issue Image Analysis and Processing)
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22 pages, 2341 KB  
Article
Acquisition Performance Analysis of Communication and Ranging Signals in Space-Based Gravitational Wave Detection
by Hongling Ling, Zhaoxiang Yi, Haoran Wu and Kai Luo
Technologies 2026, 14(1), 73; https://doi.org/10.3390/technologies14010073 - 21 Jan 2026
Viewed by 520
Abstract
Space-based gravitational wave detection relies on laser interferometry to measure picometer-level displacements over 105–106 km baselines. To integrate ranging and communication within the same optical link without degrading the primary scientific measurement, a low modulation index of 0.1 rad [...] Read more.
Space-based gravitational wave detection relies on laser interferometry to measure picometer-level displacements over 105–106 km baselines. To integrate ranging and communication within the same optical link without degrading the primary scientific measurement, a low modulation index of 0.1 rad is required, resulting in extremely weak signals and challenging acquisition conditions. This study developed mathematical models for signal acquisition, identifying and analyzing key performance-limiting factors for both Binary Phase Shift Keying (BPSK) and Binary Offset Carrier (BOC) schemes. These factors include spreading factor, acquisition step, modulation index, and carrier-to-noise ratio (CNR). Particularly, the acquisition threshold can be directly calculated from these parameters and applied to the acquisition process of communication and ranging signals. Numerical simulations and evaluations, conducted with TianQin mission parameters, demonstrate that, for a data rate of 62.5 kbps and modulation indices of 0.081 rad (BPSK) or 0.036 rad (BOC), respectively, acquisition (probability ≈ 1) is achieved when the CNR is ≥104 dB·Hz under a false alarm rate of 10−6. These results provide critical theoretical support and practical guidance for optimizing the inter-satellite communication and ranging system design for the space-based gravitational wave detection missions. Full article
(This article belongs to the Section Information and Communication Technologies)
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25 pages, 6614 KB  
Article
Timer-Based Digitization of Analog Sensors Using Ramp-Crossing Time Encoding
by Gabriel Bravo, Ernesto Sifuentes, Geu M. Puentes-Conde, Francisco Enríquez-Aguilera, Juan Cota-Ruiz, Jose Díaz-Roman and Arnulfo Castro
Technologies 2026, 14(1), 72; https://doi.org/10.3390/technologies14010072 - 18 Jan 2026
Viewed by 733
Abstract
This work presents a time-domain analog-to-digital conversion method in which the amplitude of a sensor signal is encoded through its crossing instants with a periodic ramp. The proposed architecture departs from conventional ADC and PWM demodulation approaches by shifting quantization entirely to the [...] Read more.
This work presents a time-domain analog-to-digital conversion method in which the amplitude of a sensor signal is encoded through its crossing instants with a periodic ramp. The proposed architecture departs from conventional ADC and PWM demodulation approaches by shifting quantization entirely to the time domain, enabling waveform reconstruction using only a ramp generator, an analog comparator, and a timer capture module. A theoretical framework is developed to formalize the voltage-to-time mapping, derive expressions for resolution and error, and identify the conditions ensuring monotonicity and single-crossing behavior. Simulation results demonstrate high-fidelity reconstruction for both periodic and non-periodic signals, including real photoplethysmographic (PPG) waveforms, with errors approaching the theoretical quantization limit. A hardware implementation on a PSoC 5LP microcontroller confirms the practicality of the method under realistic operating conditions. Despite ramp nonlinearity, comparator delay, and sensor noise, the system achieves effective resolutions above 12 bits using only native mixed-signal peripherals and no conventional ADC. These results show that accurate waveform reconstruction can be obtained from purely temporal information, positioning time-encoded sensing as a viable alternative to traditional amplitude-based conversion. The minimal analog front end, low power consumption, and scalability of timer-based processing highlight the potential of the proposed approach for embedded instrumentation, distributed sensor nodes, and biomedical monitoring applications. Full article
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21 pages, 3790 KB  
Article
HiLTS©: Human-in-the-Loop Therapeutic System: A Wireless-Enabled Digital Neuromodulation Testbed for Brainwave Entrainment
by Arfan Ghani
Technologies 2026, 14(1), 71; https://doi.org/10.3390/technologies14010071 - 18 Jan 2026
Cited by 2 | Viewed by 1192
Abstract
Epileptic seizures arise from abnormally synchronized neural activity and remain a major global health challenge, affecting more than 50 million people worldwide. Despite advances in pharmacological interventions, a significant proportion of patients continue to experience uncontrolled seizures, underscoring the need for alternative neuromodulation [...] Read more.
Epileptic seizures arise from abnormally synchronized neural activity and remain a major global health challenge, affecting more than 50 million people worldwide. Despite advances in pharmacological interventions, a significant proportion of patients continue to experience uncontrolled seizures, underscoring the need for alternative neuromodulation strategies. Rhythmic neural entrainment has recently emerged as a promising mechanism for disrupting pathological synchrony, but most existing systems rely on complex analog electronics or high-power stimulation hardware. This study investigates a proof-of-concept digital custom-designed chip that generates a stable 6 Hz oscillation capable of imposing a stable rhythmic pattern onto digitized seizure-like EEG dynamics. Using a publicly available EEG seizure dataset, we extracted and averaged analog seizure waveforms, digitized them to emulate neural front-ends, and directly interfaced the digitized signals with digital output recordings acquired from the chip using a Saleae Logic analyser. The chip’s pulse train was resampled and low-pass-reconstructed to produce an analog 6 Hz waveform, allowing direct comparison between seizure morphology, its digitized representation, and the entrained output. Frequency-domain and time-domain analyses demonstrate that the chip imposes a narrow-band 6 Hz rhythm that overrides the broadband spectral profile of seizure activity. These results provide a proof-of-concept for low-power digital custom-designed entrainment as a potential pathway toward simplified, wearable neuromodulation device for future healthcare diagnostics. Full article
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21 pages, 502 KB  
Article
Electrodermal Response Patterns and Emotional Engagement Under Continuous Algorithmic Video Stimulation: A Multimodal Biometric Analysis
by Carolina Del-Valle-Soto, Violeta Corona, Jesus GomezRomero-Borquez, David Contreras-Tiscareno, Diego Sebastian Montoya-Rodriguez, Jesus Abel Gutierrez-Calvillo, Bernardo Sandoval and José Varela-Aldás
Technologies 2026, 14(1), 70; https://doi.org/10.3390/technologies14010070 - 18 Jan 2026
Viewed by 951
Abstract
Excessive use of short-form video platforms such as TikTok has raised growing concerns about digital addiction and its impact on young users’ emotional well-being. This study examines the relationship between continuous TikTok exposure and emotional engagement in young adults aged 20–23 through a [...] Read more.
Excessive use of short-form video platforms such as TikTok has raised growing concerns about digital addiction and its impact on young users’ emotional well-being. This study examines the relationship between continuous TikTok exposure and emotional engagement in young adults aged 20–23 through a multimodal experimental design. The purpose of this research is to determine whether emotional engagement increases, remains stable, or declines during prolonged exposure and to assess the degree of correspondence between facially inferred engagement and physiological arousal. To achieve this, multimodal biometric data were collected using the iMotions platform, integrating galvanic skin response (GSR) sensors and facial expression analysis via Affectiva’s AFFDEX SDK 5.1. Engagement levels were binarized using a logistic transformation, and a binomial test was conducted. GSR analysis, merged with a 50 ms tolerance, revealed no significant differences in skin conductance between engaged and non-engaged states. Findings indicate that although TikTok elicits strong initial emotional engagement, engagement levels significantly decline over time, suggesting habituation and emotional fatigue. The results refine our understanding of how algorithm-driven, short-form content affects users’ affective responses and highlight the limitations of facial metrics as sole indicators of physiological arousal. Implications for theory include advancing multimodal models of emotional engagement that account for divergences between expressivity and autonomic activation. Implications for practice emphasize the need for ethical platform design and improved digital well-being interventions. The originality and value of this study lie in its controlled experimental approach that synchronizes facial and physiological signals, offering objective evidence of the temporal decay of emotional engagement during continuous TikTok use and underscoring the complexity of measuring affect in highly stimulating digital environments. Full article
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27 pages, 11232 KB  
Article
Aerokinesis: An IoT-Based Vision-Driven Gesture Control System for Quadcopter Navigation Using Deep Learning and ROS2
by Sergei Kondratev, Yulia Dyrchenkova, Georgiy Nikitin, Leonid Voskov, Vladimir Pikalov and Victor Meshcheryakov
Technologies 2026, 14(1), 69; https://doi.org/10.3390/technologies14010069 - 16 Jan 2026
Viewed by 1032
Abstract
This paper presents Aerokinesis, an IoT-based software–hardware system for intuitive gesture-driven control of quadcopter unmanned aerial vehicles (UAVs), developed within the Robot Operating System 2 (ROS2) framework. The proposed system addresses the challenge of providing an accessible human–drone interaction interface for operators in [...] Read more.
This paper presents Aerokinesis, an IoT-based software–hardware system for intuitive gesture-driven control of quadcopter unmanned aerial vehicles (UAVs), developed within the Robot Operating System 2 (ROS2) framework. The proposed system addresses the challenge of providing an accessible human–drone interaction interface for operators in scenarios where traditional remote controllers are impractical or unavailable. The architecture comprises two hierarchical control levels: (1) high-level discrete command control utilizing a fully connected neural network classifier for static gesture recognition, and (2) low-level continuous flight control based on three-dimensional hand keypoint analysis from a depth camera. The gesture classification module achieves an accuracy exceeding 99% using a multi-layer perceptron trained on MediaPipe-extracted hand landmarks. For continuous control, we propose a novel approach that computes Euler angles (roll, pitch, yaw) and throttle from 3D hand pose estimation, enabling intuitive four-degree-of-freedom quadcopter manipulation. A hybrid signal filtering pipeline ensures robust control signal generation while maintaining real-time responsiveness. Comparative user studies demonstrate that gesture-based control reduces task completion time by 52.6% for beginners compared to conventional remote controllers. The results confirm the viability of vision-based gesture interfaces for IoT-enabled UAV applications. Full article
(This article belongs to the Section Information and Communication Technologies)
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12 pages, 3085 KB  
Article
Data-Driven Interactive Lens Control System Based on Dielectric Elastomer
by Hui Zhang, Zhijie Xia, Zhisheng Zhang and Jianxiong Zhu
Technologies 2026, 14(1), 68; https://doi.org/10.3390/technologies14010068 - 16 Jan 2026
Viewed by 495
Abstract
In order to solve the dynamic analysis and interactive imaging control problems in the deformation process of bionic soft lenses, dielectric elastomer (DE) actuators are separated from a convex lens, and data-driven eye-controlled motion technology is investigated. According to the DE properties, which [...] Read more.
In order to solve the dynamic analysis and interactive imaging control problems in the deformation process of bionic soft lenses, dielectric elastomer (DE) actuators are separated from a convex lens, and data-driven eye-controlled motion technology is investigated. According to the DE properties, which are consistent with the deformation characteristics of hydrogel electrodes, the motion and deformation effect of eye-controlled lenses under film prestretching, lens size, and driving voltage, is studied. The results show that when the driving voltage increases to 7.8 kV, the focal length of the lens, whose prestretching λ is 4, and the diameter d is 1 cm, varies in the range of 49.7 mm and 112.5 mm. And the maximum focal-length change could reach 58.9%. In the process of eye controlling design and experimental verification, a high DC voltage supply was programmed, and eye movement signals for controlling the lens were analyzed by MATLAB software (R2023b). Eye-controlled interactive real-time motion and tunable imaging of the lens were realized. The response efficiency of soft lenses could reach over 93%. The adaptive lens system developed in this research has the potential to be applied to medical rehabilitation, exploration, augmented reality (AR), and virtual reality (VR) in the future. Full article
(This article belongs to the Special Issue AI Driven Sensors and Their Applications)
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39 pages, 9074 KB  
Article
Electromagnetic–Thermal Coupling and Optimization Compensation for Missile-Borne Active Phased Array Antenna
by Yan Wang, Pengcheng Xian, Qucheng Guo, Yafan Qin, Song Xue, Peiyuan Lian, Lianjie Zhang, Zhihai Wang, Wenzhi Wu and Congsi Wang
Technologies 2026, 14(1), 67; https://doi.org/10.3390/technologies14010067 - 16 Jan 2026
Viewed by 1323
Abstract
Missile-borne active phased array antennas have been widely used in missile guidance for their beam agility, multifunctionality, and strong anti-interference capabilities. However, due to space constraints on the platform and difficulty in heat dissipation, the thermal power consumption of the antenna array can [...] Read more.
Missile-borne active phased array antennas have been widely used in missile guidance for their beam agility, multifunctionality, and strong anti-interference capabilities. However, due to space constraints on the platform and difficulty in heat dissipation, the thermal power consumption of the antenna array can easily lead to excessive temperature, causing two primary issues: first, temperature-induced drift in T/R components, resulting in amplitude and phase errors in the feed current; second, temperature-dependent ripple voltage in the array’s secondary power supply, which exacerbates feed errors. Both issues degrade the electromagnetic performance of the array antenna. To mitigate these effects, this paper investigates feed errors and compensation methods in high-temperature environments. First, a synchronous Buck circuit ripple coefficient model is developed, and an electromagnetic–temperature coupling model is established, incorporating temperature-dependent feed current characteristics, and the law of electromagnetic performance changes is analyzed. On this basis, an electromagnetic performance compensation method based on a genetic algorithm is proposed to optimize the quantization compensation amount of the amplitude and phase of each element under the effect of high temperature. Full article
(This article belongs to the Special Issue Microelectronics and Electronic Packaging for Advanced Sensor System)
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21 pages, 830 KB  
Article
Predicting Breast Cancer Mortality Using SEER Data: A Comparative Analysis of L1-Logistic Regression and Neural Networks
by Mayra Cruz-Fernandez, Francisco Antonio Castillo-Velásquez, Carlos Fuentes-Silva, Omar Rodríguez-Abreo, Rafael Rojas-Galván, Marcos Avilés and Juvenal Rodríguez-Reséndiz
Technologies 2026, 14(1), 66; https://doi.org/10.3390/technologies14010066 - 15 Jan 2026
Viewed by 1245
Abstract
Breast cancer remains a leading cause of mortality among women worldwide, motivating the development of transparent and reproducible risk models for clinical decision making. Using the open-access SEER Breast Cancer dataset (November 2017 release), we analyzed 4005 women diagnosed between 2006 and 2010 [...] Read more.
Breast cancer remains a leading cause of mortality among women worldwide, motivating the development of transparent and reproducible risk models for clinical decision making. Using the open-access SEER Breast Cancer dataset (November 2017 release), we analyzed 4005 women diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma (ICD-O-3 8522/3). Thirty-one clinical and demographic variables were preprocessed with one-hot encoding and z-score standardization, and the lymph node ratio was derived to characterize metastatic burden. Two supervised models, L1-regularized logistic regression and a feedforward artificial neural network, were compared under identical preprocessing, fixed 60/20/20 data splits, and stratified five-fold cross-validation. To define clinically meaningful endpoints and handle censoring, we reformulated mortality prediction as fixed-horizon classification at 3 and 5 years, and evaluated discrimination, calibration, and operating thresholds. Logistic regression demonstrated consistently strong performance, achieving test ROC-AUC values of 0.78 at 3 years and 0.75 at 5 years, with substantially superior calibration (Brier score less than or equal to 0.12, ECE less than or equal to 0.03). A structured hyperparameter search with repeated-seed evaluation identified optimal neural network architectures for each horizon, yielding test ROC-AUC values of 0.74 at 3 years and 0.73 at 5 years, but with markedly poorer calibration (ECE 0.19 to 0.23). Bootstrap analysis showed no significant AUC difference between models at 3 years, but logistic regression exhibited greater stability across folds and lower sensitivity to feature pruning. Overall, L1-regularized logistic regression provides competitive discrimination (ROC-AUC 0.75 to 0.78), markedly superior probability calibration (ECE below 0.03 versus 0.19 to 0.23 for the neural network), and approximately 40% lower cross-validation variance, supporting its use for scalable screening, risk stratification, and triage workflows on structured registry data. Full article
(This article belongs to the Section Assistive Technologies)
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19 pages, 12656 KB  
Article
Automatic Detection of TiO2 Nanoparticles Using Dual-Coupled Microresonators and Deep Learning
by Andrés F. Calvo-Salcedo, Marin B. Marinov, Neil Guerrero González and Jose A. Jaramillo-Villegas
Technologies 2026, 14(1), 65; https://doi.org/10.3390/technologies14010065 - 15 Jan 2026
Viewed by 542
Abstract
The detection of titanium dioxide (TiO2) nanoparticles is a significant challenge due to their extensive industrial use and potential health and environmental impacts, which demand accurate, label-free approaches. This work presents an automatic detection system based on spectroscopy with optical [...] Read more.
The detection of titanium dioxide (TiO2) nanoparticles is a significant challenge due to their extensive industrial use and potential health and environmental impacts, which demand accurate, label-free approaches. This work presents an automatic detection system based on spectroscopy with optical frequency combs (OFC) in dual-coupled microresonators. The OFC generation was modeled through the Lugiato-Lefever equation, while propagation in distilled water containing TiO2 was simulated using the finite element method (FEM). The water–TiO2 mixture was described with the Yamaguchi model in a 5 × 5 mesh to represent non-uniform concentrations. From the norm of the electric field at a probe, a database of 11 classes (0–100%) with controlled Gaussian noise was constructed. A Transformer-based classifier was trained and compared with 1D-CNN and SVM under Monte Carlo validation (100 random 70/30 splits). The Transformer achieved 99.84 ± 0.01% accuracy with an inference time of 0.793 ± 0.05 s, while the 1D-CNN reached 99.64 ± 0.09% and the SVM 84.73 ± 1.48%. A repeatability test with 200 iterations confirmed deterministic DKS trajectories. The results demonstrate that combining dual-coupled microresonators, FEM, and Transformer architectures enables precise and efficient detection of TiO2 nanoparticles in aqueous solutions. Full article
(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)
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32 pages, 5143 KB  
Review
A Review of Research on Multi-Objective Process Parameter Optimization Technology for Grinding Machining
by Xiao Yang, Zhaohui Deng, Decai Zhu, Rongjin Zhuo, Xipeng Xu and Wei Liu
Technologies 2026, 14(1), 64; https://doi.org/10.3390/technologies14010064 - 15 Jan 2026
Cited by 2 | Viewed by 880
Abstract
The optimization of grinding is a multi-objective problem characterized by high dimensionality, non-linearity, and complexity. Solving this multi-objective optimization (MOO) problem is one of the most challenging tasks in the field of mechanical engineering. In-depth research on multi-objective parameter optimization technology for grinding [...] Read more.
The optimization of grinding is a multi-objective problem characterized by high dimensionality, non-linearity, and complexity. Solving this multi-objective optimization (MOO) problem is one of the most challenging tasks in the field of mechanical engineering. In-depth research on multi-objective parameter optimization technology for grinding is of great significance for improving processing efficiency, optimizing product quality, and reducing energy consumption. This paper takes the multi-objective optimization problem of grinding as its starting point. First, it introduces the basic theory of multi-objective optimization and two primary methods for solving such problems: optimization target dimension reduction and multi-objective optimization. Second, the key technologies of the two methods are reviewed, including the modeling method of the optimization problem, the multi-objective optimization algorithm for solving the optimization model, and the prior and posterior trade-off methods used to obtain the compromised optimal solutions. Finally, the existing problems of the multi-objective optimization methods in grinding processing are summarized and the future development trends are predicted. This paper aims to provide researchers with a comprehensive understanding of the multi-objective optimization technology in grinding processing, enabling them to make more reasonable decisions when dealing with actual multi-objective optimization problems. Full article
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21 pages, 2324 KB  
Article
A Seamless Mode Switching Control Method for Independent Metering Controlled Hydraulic Actuator
by Yixin Liu, Jiaqi Li and Dacheng Cong
Technologies 2026, 14(1), 63; https://doi.org/10.3390/technologies14010063 - 14 Jan 2026
Viewed by 604
Abstract
Hydraulic manipulators are vital for heavy-duty applications such as rescue robotics due to their high power density, yet these scenarios increasingly demand safe and compliant physical interaction. Impedance control is a key enabling technology for such capabilities. However, a significant challenge arises when [...] Read more.
Hydraulic manipulators are vital for heavy-duty applications such as rescue robotics due to their high power density, yet these scenarios increasingly demand safe and compliant physical interaction. Impedance control is a key enabling technology for such capabilities. However, a significant challenge arises when implementing impedance control on Independent Metering Systems (IMS), which are widely adopted for their energy efficiency. The inherent multi-mode operation of IMS relies on discrete switching logic. Crucially, when mode switching occurs during physical interaction with the environment, the unpredictable external forces can trigger frequent and abrupt switching between operating modes (e.g., resistive and overrunning), leading to severe chattering. This phenomenon not only undermines the smooth interaction that impedance control aims to achieve but also jeopardizes overall system stability. To address this critical issue, this paper proposes a seamless control framework based on a Takagi–Sugeno (T-S) fuzzy model. Two premise variables based on the physical characteristics of the system are innovatively designed to make the rule division highly consistent with the dynamic nature of the system. Asymmetric membership functions are introduced to handle direction-dependent switching, with orthogonal functions ensuring logical exclusivity between extension and retraction, and smooth complementary functions enabling seamless transitions between resistance and overrunning modes. Experimental validation on a small hydraulic manipulator validates the effectiveness of the proposed method. The controller eliminates switching-induced instability and smooths velocity transitions, even under dynamic external force disturbances. This work provides a crucial solution for high-performance, stable hydraulic interaction control, paving the way for the application of hydraulic robots in complex and dynamic environments. Full article
(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)
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22 pages, 2981 KB  
Review
Integration of Electric Vehicles into the Grid in the Americas: Technical Implications, Regional Challenges, and Perspectives
by Daniel Icaza-Alvarez, Giovanny Mosquera and Juan Moscoso
Technologies 2026, 14(1), 62; https://doi.org/10.3390/technologies14010062 - 14 Jan 2026
Cited by 1 | Viewed by 1155
Abstract
The transition to renewable energy is generating numerous changes across different continents, some with greater impact than others, but the progress achieved is recognized and widely accepted. In particular, there are various solutions that include electric vehicles as elements that influence grid behavior [...] Read more.
The transition to renewable energy is generating numerous changes across different continents, some with greater impact than others, but the progress achieved is recognized and widely accepted. In particular, there are various solutions that include electric vehicles as elements that influence grid behavior when connected. Higher levels of electric vehicle penetration can present opportunities and solutions related to energy storage, V2G connections encompassing the distribution system, and long-term evaluation. High participation in V2G connections maintains the availability of the electrical system, while the high proportion of variable renewable energy sources forms the backbone of the overall electrical system. This study presents a systematic review of V2G systems in the Americas. The design of the Sustainable Mobility scenario and the high participation of V2G maintain the balance of the electrical system for most of the day, simplifying storage equipment requirements. Consequently, the influence of V2G systems on energy storage is an important outcome that must be considered in the energy transition and presents development opportunities for the various countries that make up the Americas. The stored electricity will not only serve as storage for future grid use, but V2G batteries will also act as a buffer between generation from diversified renewable sources and the end-use stage. This article shows that research on the design of V2G energy systems in scientific publications is relatively recent, but it has gained increasing attention in recent years. In total, 151 articles published since 1995 have been identified and analyzed. The overall result indicates that North American countries have developed the most V2G applications, and their deployment in the coming years will be significant. Meanwhile, in South and Central America, these systems are not yet being fully utilized due to the lack of growth in the electric vehicle market. Full article
(This article belongs to the Special Issue Emerging Renewable Energy Technologies and Smart Long-Term Planning)
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24 pages, 3277 KB  
Article
FT-iTransformer: A Stock Price Prediction Model Based on Time–Frequency Domain Collaborative Analysis
by Zheng Zou, Xi-Xi Zhou, Shi-Jian Liu and Chih-Yu Hsu
Technologies 2026, 14(1), 61; https://doi.org/10.3390/technologies14010061 - 14 Jan 2026
Viewed by 2520
Abstract
The stock market serves as an important channel for investors to preserve and increase their assets and has attracted significant attention. However, stock price is affected by multiple factors and represents complex characteristics such as high volatility, nonlinearity, and non-stationarity, making accurate prediction [...] Read more.
The stock market serves as an important channel for investors to preserve and increase their assets and has attracted significant attention. However, stock price is affected by multiple factors and represents complex characteristics such as high volatility, nonlinearity, and non-stationarity, making accurate prediction highly challenging. To improve forecasting accuracy, this study proposes FT-iTransformer, a stock price prediction model based on time–frequency domain collaborative analysis. The model integrates a frequency domain feature extraction module and a multi-scale temporal convolution network module to comprehensively capture both time and frequency domain features, and then the extracted features are fused and input into iTransformer. It models the complex relationships among multiple variables through the self-attention mechanism, utilizes the feedforward network to capture temporal dependencies, and finally the prediction results are output through the projection layer. This study conducts both comparative and ablation experiments on six stock datasets to evaluate the proposed FT-iTransformer model. The results of comparative experiments show that, compared with seven mainstream baseline models, such as LSTM, Informer, and FEDformer, FT-iTransformer achieves superior performance on all evaluation metrics. Furthermore, the results of ablation experiments exhibit the contributions of each core module to the overall predictive performance, and confirming the validity of the model’s design. In summary, FT-iTransformer provides an effective framework for predicting stock price accurately. Full article
(This article belongs to the Topic Emerging AI+X Technologies and Applications)
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24 pages, 7600 KB  
Article
Integrated Study of Morphology and Viscoelastic Properties in the MG-63 Cancer Cell Line
by Guadalupe Vázquez-Cisneros, Daniel F. Zambrano-Gutierrez, Grecia C. Duque-Gimenez, Alejandro Flores-Mayorga, Diana G. Zárate-Triviño, Cristina Rodríguez-Padilla, Marco A. Bedolla, Jorge Luis Menchaca, Juan Gabriel Avina-Cervantes and Maricela Rodríguez-Nieto
Technologies 2026, 14(1), 60; https://doi.org/10.3390/technologies14010060 - 14 Jan 2026
Viewed by 900
Abstract
Cell morphology and its mechanical properties are crucial factors in cancer development, affecting migration, invasiveness, and the potential risk of metastasis. However, most studies address these aspects separately, limiting the understanding of how morphological complexity relates to cellular mechanics. This work presents an [...] Read more.
Cell morphology and its mechanical properties are crucial factors in cancer development, affecting migration, invasiveness, and the potential risk of metastasis. However, most studies address these aspects separately, limiting the understanding of how morphological complexity relates to cellular mechanics. This work presents an integrated approach that simultaneously quantifies morphology and viscoelasticity in the human osteosarcoma cell line MG-63. Stress–relaxation experiments and optical imaging of the same cells were performed using a custom-built system that couples Atomic Force Microscopy (AFM) with an inverted optical microscope. Morphometric parameters were extracted from cell contours, while viscoelastic properties were obtained by fitting AFM data to the Fractional Kelvin (FK) and Fractional Zener (FZ) models. Among the morphological descriptors, the Shape Complexity (SC) was proposed. It is derived from the Lobe Contribution Elliptical Fourier Analysis (LOCO-EFA), which captures fine-scale contour features overlooked by conventional metrics. Experimental results show that, in MG-63 cells, higher SC values are associated with greater stiffness, indicating a correlation between cell shape complexity and cell stiffness. Furthermore, loading-rate analysis shows that the FZ model captures strain-rate-dependent stiffening more effectively than the FK model. This methodology provides a first approach to jointly analyzing quantitative morphological parameters and mechanical properties, underlining the importance of combined studies to achieve a comprehensive understanding of cell behavior. Full article
(This article belongs to the Topic Bio-Inspired, Biomedical, Surgical, Social and AI-Integrated Bio-Mechanical Robotics)
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20 pages, 3939 KB  
Article
Quad-Band Truncated Square-Shaped MIMO Terahertz Antenna for Beyond 5G and 6G Communications
by Jeremiah O. Abolade, Pradeep Kumar and Dominic B. O. Konditi
Technologies 2026, 14(1), 59; https://doi.org/10.3390/technologies14010059 - 13 Jan 2026
Viewed by 685
Abstract
A compact quad-band multiple-input multiple-output (MIMO) antenna for terahertz communications is presented in this work. The proposed antenna consists of a truncated square patch with inverted-U-shaped and C-shaped slots. The operating frequencies of the proposed antenna are 0.38 THz, 0.43 THz, 0.61 THz, [...] Read more.
A compact quad-band multiple-input multiple-output (MIMO) antenna for terahertz communications is presented in this work. The proposed antenna consists of a truncated square patch with inverted-U-shaped and C-shaped slots. The operating frequencies of the proposed antenna are 0.38 THz, 0.43 THz, 0.61 THz, and 0.7 THz, with reflection coefficients of −13.8 dB, −22.1 dB, −27.3 dB, and −14.8 dB, respectively, and a −10 dB impedance bandwidth of 9 GHz, 18 GHz, 18 GHz, and 21 GHz, respectively. The peak gain values of a single element antenna at 0.38 THz, 0.43 THz, 0.61 THz, and 0.7 THz are 3.3 dB, 4.8 dB, 4.7 dB, and 5.5 dB, respectively. The dual-triangular MIMO configuration was investigated. The peak gains of the MIMO configurations at 0.38 THz, 0.43 THz, 0.61 THz, and 0.7 THz are 10.6 dB, 12.2 dB, 15.6 dB, and 15.2 dB, respectively. The envelope correlation coefficient (ECC) and the diversity gain (DG) of the proposed antenna were investigated and are presented herein. The proposed MIMO antenna demonstrates lower coupling and higher isolation at the operating frequency bands. Therefore, it is a suitable candidate for beyond 5G and 6G wireless communications applications, such as for nanodevices used in the internet of things and in wearables. Full article
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23 pages, 5201 KB  
Article
HiFiRadio: High-Fidelity Radio Map Reconstruction for 3D Real-World Scenes
by Ke Liao, Mengyu Ma, Luo Chen, Yifan Zhang and Ning Jing
Technologies 2026, 14(1), 58; https://doi.org/10.3390/technologies14010058 - 12 Jan 2026
Viewed by 797
Abstract
The reconstruction of high-fidelity radio maps is pivotal for wireless network planning but remains challenging due to the tension between physical accuracy and computational efficiency. We propose HiFiRadio, a novel framework that achieves a breakthrough in this balance by integrating centimeter-resolution 3D environmental [...] Read more.
The reconstruction of high-fidelity radio maps is pivotal for wireless network planning but remains challenging due to the tension between physical accuracy and computational efficiency. We propose HiFiRadio, a novel framework that achieves a breakthrough in this balance by integrating centimeter-resolution 3D environmental meshes with semantic-aware propagation modeling. At its core, HiFiRadio introduces a semantic-enhanced 3D indexing structure that efficiently manages complex terrain data, enabling real-time classification of signal paths into line-of-sight, non-line-of-sight, and vegetation-obstructed categories. This classification directly guides a hybrid propagation model, which dynamically applies dedicated loss calculations for buildings and foliage, grounded in physical principles. Extensive experiments demonstrate that HiFiRadio attains an accuracy comparable to commercial ray-tracing tools while being orders of magnitude faster. It also significantly outperforms existing learning-based baselines in both accuracy and scalability, a claim further validated by field measurements. By making high-fidelity, real-time radio map reconstruction practical for large-scale scenes, HiFiRadio establishes a new state of the art with immediate applications in network planning, UAV pathing, and dynamic spectrum access. Full article
(This article belongs to the Topic Challenges and Future Trends of Wireless Networks)
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23 pages, 6250 KB  
Article
Refining Open-Source Asset Management Tools: AI-Driven Innovations for Enhanced Reliability and Resilience of Power Systems
by Gopal Lal Rajora, Miguel A. Sanz-Bobi, Lina Bertling Tjernberg and Pablo Calvo-Bascones
Technologies 2026, 14(1), 57; https://doi.org/10.3390/technologies14010057 - 11 Jan 2026
Viewed by 676
Abstract
Traditional methods of asset management in electric power systems rely upon fixed schedules and reactive measurements, leading to challenges in the transparent prioritization of maintenance under evolving operating conditions and incomplete data. In this paper, we introduce a new, fully integrated artificial intelligence [...] Read more.
Traditional methods of asset management in electric power systems rely upon fixed schedules and reactive measurements, leading to challenges in the transparent prioritization of maintenance under evolving operating conditions and incomplete data. In this paper, we introduce a new, fully integrated artificial intelligence (AI)-driven approach for enhancing the resilience and reliability of open-source asset management tools to support improved performance and decisions in electric power system operations. This methodology addresses and overcomes several significant challenges, including data heterogeneity, algorithmic limitations, and inflexible decision-making, through a three-module workflow. The data fidelity module provides a domain-aware pipeline for identifying structural (missing) values from explicit missingness using sophisticated imputation methods, including Multiple Imputation Chain Equations (MICE) and Generative Adversarial Network (GAN)-based hybrids. The characterization module employs seven complementary weighting strategies, including PCA, Autoencoder, GA-based optimization, SHAP, Decision-Tree Importance, and Entropy Weighting, to achieve objective feature weight assignment, thereby eliminating the need for subjective manual rules. The optimization module enhanced the action space through multi-objective optimization, balancing reliability maximization and cost minimization. A synthetic dataset of 100 power transformers was used to validate that the MICE achieved better imputation than other methods. The optimized weighting framework successfully categorizes Health Index values into five condition levels, while the multi-objective maintenance policy optimization generates decisions that align with real-world asset management practices. The proposed framework provides the Transmission and Distribution System Operators (TSOs/DSOs) with an adaptable, industry-oriented decision-support workflow system for enhancing reliability, optimizing maintenance expenses, and improving asset management policies for critical power infrastructure. Full article
(This article belongs to the Special Issue AI for Smart Engineering Systems)
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16 pages, 1360 KB  
Article
Enhancement of Building Heating Systems Connected to Third-Generation Centralized Heating Systems
by Ekaterina Boyko, Felix Byk, Lyudmila Myshkina, Elizaveta Nasibova and Pavel Ilyushin
Technologies 2026, 14(1), 56; https://doi.org/10.3390/technologies14010056 - 11 Jan 2026
Cited by 1 | Viewed by 453
Abstract
In third-generation centralized heating systems, qualitative regulation of the heat transfer medium parameters is mainly performed at heat sources, while quantitative regulation is implemented at central and individual heating points, with buildings remaining passive heat consumers. Unlike fourth-generation systems, such systems generally do [...] Read more.
In third-generation centralized heating systems, qualitative regulation of the heat transfer medium parameters is mainly performed at heat sources, while quantitative regulation is implemented at central and individual heating points, with buildings remaining passive heat consumers. Unlike fourth-generation systems, such systems generally do not employ renewable energy sources, thermal energy storage, or low-temperature operating regimes. Third-generation centralized heating systems operate based on design high-temperature schedules and centralized control, without considering the actual thermal loads of consumers. Under conditions of physical deterioration of heating networks, hydraulic imbalance, and operational constraints, the actual parameters of the heat transfer medium supplied to buildings often deviate from design values, resulting in deviations of thermal conditions at the level of end consumers and disruptions of thermal comfort. This study proposes the concept of an intelligent active individual heating point (IAIHP), designed to provide adaptive qualitative–quantitative regulation of heat transfer medium parameters at the level of individual buildings. Unlike approaches focused on demand-side management, the use of thermal energy storage, or the integration of renewable energy sources, the proposed solution is based on the application of a local thermal energy source. The IAIHP compensates for deviations in heat transfer medium parameters and acts as a local thermal energy source within the building heat supply system (BHSS). Control of the IAIHP operation is performed by a developed automation system that provides combined qualitative and quantitative regulation of the heat transfer medium supplied to the BHSS. The study assesses the potential scale of IAIHP implementation in third-generation centralized heating systems, develops a methodology for selecting the capacity of a local heat source, and presents the operating algorithm of the automatic control system of the IAIHP. At present, the reconstruction of an individual heating point of a kindergarten connected via a dependent scheme is being carried out based on the developed project documentation. Modeling and calculations show that the application of the IAIHP makes it possible to ensure indoor thermal comfort by reducing the risk of temperature deviations, which are otherwise typically compensated for by electric heaters. The proposed concept provides a methodological basis for a gradual transition from third-generation to fourth-generation centralized heating systems, while equipping the IAIHP with an intelligent control system opens opportunities for improving the energy efficiency of urban heating networks. The proposed integrated solution and the developed automatic control algorithms exhibit scientific novelty and practical relevance for Russia and other countries operating third-generation centralized heating systems, including Northern and Eastern European states, where large-scale infrastructure modernization and the implementation of fourth-generation technologies are technically or economically constrained. Full article
(This article belongs to the Section Construction Technologies)
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26 pages, 1505 KB  
Systematic Review
Comparative Experimental Studies on Superior Cognitive Domains: AI Versus Humans
by Raquel Ayala-Carabajo and Joe Llerena-Izquierdo
Technologies 2026, 14(1), 55; https://doi.org/10.3390/technologies14010055 - 10 Jan 2026
Viewed by 1143
Abstract
This study analyzes the performance of artificial intelligence in processes known as “cognitive” (according to scientific literature) in comparison with the performance of human cognitive processes, analyzing experimental and/or empirical studies. The PRISMA process and bibliometric analysis were used to identify and analyze [...] Read more.
This study analyzes the performance of artificial intelligence in processes known as “cognitive” (according to scientific literature) in comparison with the performance of human cognitive processes, analyzing experimental and/or empirical studies. The PRISMA process and bibliometric analysis were used to identify and analyze relevant research. A total of 291 studies were analyzed, which were grouped into five categories corresponding to the identified cognitive processes. The results show that only 10.3% of the studies report accuracy rates between 90% and 100% in their performance. The evidence suggests that AI can perform comparably to humans, but not with absolute efficiency. The experimental studies focus mainly on the “decision-making” process (56%), followed, in order of importance, by the processes of “analysis and evaluation” (25%), “judgment and reasoning” (8.6%), “comprehension and learning” (5.5%), and other “specific processes” (4.8%). The most significant contribution of this study is the comparative relational structure between human cognitive processes versus AI processes. Full article
(This article belongs to the Section Information and Communication Technologies)
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41 pages, 80556 KB  
Article
Why ROC-AUC Is Misleading for Highly Imbalanced Data: In-Depth Evaluation of MCC, F2-Score, H-Measure, and AUC-Based Metrics Across Diverse Classifiers
by Mehdi Imani, Majid Joudaki, Ayoub Bagheri and Hamid R. Arabnia
Technologies 2026, 14(1), 54; https://doi.org/10.3390/technologies14010054 - 10 Jan 2026
Cited by 3 | Viewed by 5114
Abstract
This study re-evaluates ROC-AUC for binary classification under severe class imbalance (<3% positives). Despite its widespread use, ROC-AUC can mask operationally salient differences among classifiers when the costs of false positives and false negatives are asymmetric. Using three benchmarks, credit-card fraud detection (0.17%), [...] Read more.
This study re-evaluates ROC-AUC for binary classification under severe class imbalance (<3% positives). Despite its widespread use, ROC-AUC can mask operationally salient differences among classifiers when the costs of false positives and false negatives are asymmetric. Using three benchmarks, credit-card fraud detection (0.17%), yeast protein localization (1.35%), and ozone level detection (2.9%), we compare ROC-AUC with Matthews Correlation Coefficient, F2-score, H-measure, and PR-AUC. Our empirical analyses span 20 classifier–sampler configurations per dataset, combined with four classifiers (Logistic Regression, Random Forest, XGBoost, and CatBoost) and four oversampling methods plus a no-resampling baseline (no resampling, SMOTE, Borderline-SMOTE, SVM-SMOTE, ADASYN). ROC-AUC exhibits pronounced ceiling effects, yielding high scores even for underperforming models. In contrast, MCC and F2 align more closely with deployment-relevant costs and achieve the highest Kendall’s τ rank concordance across datasets; PR-AUC provides threshold-independent ranking, and H-measure integrates cost sensitivity. We quantify uncertainty and differences using stratified bootstrap confidence intervals, DeLong’s test for ROC-AUC, and Friedman–Nemenyi critical-difference diagrams, which collectively underscore the limited discriminative value of ROC-AUC in rare-event settings. The findings recommend a shift to a multi-metric evaluation framework: ROC-AUC should not be used as the primary metric in ultra-imbalanced settings; instead, MCC and F2 are recommended as primary indicators, supplemented by PR-AUC and H-measure where ranking granularity and principled cost integration are required. This evidence encourages researchers and practitioners to move beyond sole reliance on ROC-AUC when evaluating classifiers in highly imbalanced data. Full article
(This article belongs to the Special Issue Data Science and Big Data in Biology, Physical Science and Engineering—3rd Edition)
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26 pages, 25891 KB  
Article
LiDAR-Based Skin Depth Analysis of Subterranean RF Propagation in Sandstone and Limestone Caves
by Atawit Jantaupalee, Sirigiet Phunklang, Peerasan Khamsalee, Piyaporn Krachodnok and Rangsan Wongsan
Technologies 2026, 14(1), 53; https://doi.org/10.3390/technologies14010053 - 10 Jan 2026
Viewed by 946
Abstract
This study investigates radio frequency (RF) wave propagation in sandstone and limestone cave environments, emphasizing the use of LiDAR-derived three-dimensional (3D) models to characterize cave geometry and support waveguide-based propagation analysis incorporating skin depth effects. RF transmission and reception measurements were conducted under [...] Read more.
This study investigates radio frequency (RF) wave propagation in sandstone and limestone cave environments, emphasizing the use of LiDAR-derived three-dimensional (3D) models to characterize cave geometry and support waveguide-based propagation analysis incorporating skin depth effects. RF transmission and reception measurements were conducted under line-of-sight (LOS) conditions across frequency bands from Low Frequency (LF) to Ultra-High Frequency (UHF). Comparative results reveal distinct attenuation behaviors governed by differences in cave geometry and electrical properties. The sandstone cave, with a more uniform geometry and relatively higher electrical conductivity, exhibits lower attenuation across most frequency bands, whereas the limestone cave shows higher attenuation due to its irregular structure. LiDAR-based 3D models are employed to extract key geometric parameters, including cavity dimensions, wall roughness, and wall inclination, which are incorporated into the proposed analytical framework. The model is further validated using experimental field measurements, demonstrating that the inclusion of LiDAR-derived geometry and skin depth effects enables a more realistic representation of underground RF propagation for communication system analysis. Full article
(This article belongs to the Section Information and Communication Technologies)
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24 pages, 3823 KB  
Article
Enhanced Fall-Risk Protection in Building Projects Using a BIM-Based Algorithmic Approach
by Márk Balázs Zagorácz, Olivér Rák, Patrik Márk Máder, Viktor Norbert Rácz, Nándor Bakai, József Etlinger and Tünde Jászberényi
Technologies 2026, 14(1), 52; https://doi.org/10.3390/technologies14010052 - 10 Jan 2026
Viewed by 848
Abstract
Health and safety concerns at construction sites have become increasingly significant, especially with the rapid technological development and the opportunities it brings. Since fall-from-height incidents are the most frequent construction accidents in the field, this paper focuses on a fall risk prevention method [...] Read more.
Health and safety concerns at construction sites have become increasingly significant, especially with the rapid technological development and the opportunities it brings. Since fall-from-height incidents are the most frequent construction accidents in the field, this paper focuses on a fall risk prevention method for building construction sites by integrating algorithm-based techniques with BIM models and introducing a smart adaptive system that automatically detects danger zones and places requiring safety equipment regardless of the layout complexity and design modifications. Moreover, the work reveals the optimal quantities and material takeoffs for the suggested safety plan over time, based on the construction sequence. It provides a 4D BIM simulation of building projects, in which the appropriate configurations, quantities, lengths, and costs of the required safety equipment can be derived at any chosen time interval within the construction stage. Full article
(This article belongs to the Section Construction Technologies)
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32 pages, 11443 KB  
Article
Development and Optimization of Antennas for 860–960 MHz RFID Applications and Their Impact on the Human Body
by Claudia Constantinescu, Claudia Pacurar, Sergiu Andreica, Marian Gliga, Laura Grindei, Laszlo Rapolti, Dana Terec and Adina Giurgiuman
Technologies 2026, 14(1), 51; https://doi.org/10.3390/technologies14010051 - 9 Jan 2026
Viewed by 1570
Abstract
Radio Frequency Identification (RFID) systems operating in the 860–960 MHz frequency range are widely used in applications such as supply chain management, retail, access control, healthcare, and transportation. This study presents the design, modeling, and fabrication of two antennas for this frequency range, [...] Read more.
Radio Frequency Identification (RFID) systems operating in the 860–960 MHz frequency range are widely used in applications such as supply chain management, retail, access control, healthcare, and transportation. This study presents the design, modeling, and fabrication of two antennas for this frequency range, followed by a comparative analysis to identify the antenna with superior gain. Key parameters, including corner fillets and chamfering, as well as antenna length, were varied to evaluate their impact on gain and S-parameters for the initial antenna considered the best from the two structures analyzed, aiming to optimize performance while minimizing size and keeping the frequency unchanged. Additionally, the antennas’ interaction with the human body was assessed through numerical modeling by evaluating the electric and magnetic fields and calculating the specific absorption rate for a human leg and hand in order to analyze the impact of these types of antennas on the human body. The dimensions of the initial structure were minimized while the antenna operated in the same frequency range, leading to a small decrease in the gain. It was discovered that when analyzing the values of the parameters of interest regarding the interaction with a human body, the RFID will not exceed them when considering the human hand, but it will harm a human foot when not placed at a specific distance from it. Full article
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13 pages, 961 KB  
Communication
Impact of Background Removal on Cow Identification with Convolutional Neural Networks
by Gergana Balieva, Alexander Marazov, Dimitar Tanchev, Ivanka Lazarova and Ralitsa Rankova
Technologies 2026, 14(1), 50; https://doi.org/10.3390/technologies14010050 - 9 Jan 2026
Cited by 1 | Viewed by 608
Abstract
Individual animal identification is a cornerstone of animal welfare practices and is of crucial importance for food safety and the protection of humans from zoonotic diseases. It is also a key prerequisite for enabling automated processes in modern dairy farming. With newly emerging [...] Read more.
Individual animal identification is a cornerstone of animal welfare practices and is of crucial importance for food safety and the protection of humans from zoonotic diseases. It is also a key prerequisite for enabling automated processes in modern dairy farming. With newly emerging technologies, visual animal identification based on machine learning offers a more efficient and non-invasive method with high automation potential, accuracy, and practical applicability. However, a common challenge is the limited variability of training datasets, as images are typically captured in controlled environments with uniform backgrounds and fixed poses. This study investigates the impact of foreground segmentation and background removal on the performance of convolutional neural networks (CNNs) for cow identification. A dataset was created in which training images of dairy cows exhibited low variability in pose and background for each individual, whereas the test dataset introduced significant variation in both pose and environment. Both a fine-tuned CNN backbone and a model trained from scratch were evaluated using images with and without background information. The results demonstrate that although training on segmented foregrounds extracts intrinsic biometric features, background cues carry more information for individual recognition. Full article
(This article belongs to the Special Issue Image Analysis and Processing)
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39 pages, 1059 KB  
Systematic Review
Ground Enhancement Materials for Grounding Systems: A Systematic Review of Factors, Technologies and Advances
by Hugo Martínez Ángeles, Cesar Augusto Navarro Rubio, Luis Angel Iturralde Carrera, Leonel Díaz-Tato, José Gabriel Ríos Moreno, Mario Trejo Perea, Roberto Valentín Carrillo-Serrano and Juvenal Rodríguez-Reséndiz
Technologies 2026, 14(1), 49; https://doi.org/10.3390/technologies14010049 - 8 Jan 2026
Cited by 1 | Viewed by 1811
Abstract
Grounding Systems (GS) play a critical role in electrical safety, lightning protection, and the reliable operation of power and renewable energy infrastructures, particularly in high-resistivity soils. In this context, Ground Enhancement Materials (GEM) are widely used to reduce soil resistivity and improve grounding [...] Read more.
Grounding Systems (GS) play a critical role in electrical safety, lightning protection, and the reliable operation of power and renewable energy infrastructures, particularly in high-resistivity soils. In this context, Ground Enhancement Materials (GEM) are widely used to reduce soil resistivity and improve grounding performance. This systematic review analyzes and synthesizes recent advances (2018–2025) in GEM applied to GS, with emphasis on their electrical performance, durability, and environmental sustainability. The review covers conventional GEM, industrial waste-derived materials, and hybrid formulations, evaluating their effectiveness under different soil types and moisture conditions. Comparative analysis of the literature indicates that GEM derived from industrial byproducts and hybrid composites often exhibit superior long-term resistivity reduction due to enhanced moisture retention and material-soil interactions, especially in clay-rich and heterogeneous soils. Sustainability considerations such as environmental impact, material availability, and long-term stability are increasingly influencing GEM selection and design. Overall, this review provides a structured framework for understanding the factors governing GEM performance while highlighting current trends, challenges, and future research directions in the development of sustainable grounding solutions. Full article
(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)
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27 pages, 4631 KB  
Article
Multimodal Minimal-Angular-Geometry Representation for Real-Time Dynamic Mexican Sign Language Recognition
by Gerardo Garcia-Gil, Gabriela del Carmen López-Armas and Yahir Emmanuel Ramirez-Pulido
Technologies 2026, 14(1), 48; https://doi.org/10.3390/technologies14010048 - 8 Jan 2026
Viewed by 875
Abstract
Current approaches to dynamic sign language recognition commonly rely on dense landmark representations, which impose high computational cost and hinder real-time deployment on resource-constrained devices. To address this limitation, this work proposes a computationally efficient framework for real-time dynamic Mexican Sign Language (MSL) [...] Read more.
Current approaches to dynamic sign language recognition commonly rely on dense landmark representations, which impose high computational cost and hinder real-time deployment on resource-constrained devices. To address this limitation, this work proposes a computationally efficient framework for real-time dynamic Mexican Sign Language (MSL) recognition based on a multimodal minimal angular-geometry representation. Instead of processing complete landmark sets (e.g., MediaPipe Holistic with up to 468 keypoints), the proposed method encodes the relational geometry of the hands, face, and upper body into a compact set of 28 invariant internal angular descriptors. This representation substantially reduces feature dimensionality and computational complexity while preserving linguistically relevant manual and non-manual information required for grammatical and semantic discrimination in MSL. A real-time end-to-end pipeline is developed, comprising multimodal landmark extraction, angular feature computation, and temporal modeling using a Bidirectional Long Short-Term Memory (BiLSTM) network. The system is evaluated on a custom dataset of dynamic MSL gestures acquired under controlled real-time conditions. Experimental results demonstrate that the proposed approach achieves 99% accuracy and 99% macro F1-score, matching state-of-the-art performance while using fewer features dramatically. The compactness, interpretability, and efficiency of the minimal angular descriptor make the proposed system suitable for real-time deployment on low-cost devices, contributing toward more accessible and inclusive sign language recognition technologies. Full article
(This article belongs to the Special Issue Image Analysis and Processing)
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15 pages, 257 KB  
Article
Exploring Users’ and Clinicians’ Perceptions of an Intelligent Dynamic System for Multi-Component Motorized Wheelchairs
by Claudine Auger, Annabelle de Serres-Lafontaine, Charlie Bouchard, Audrey Labelle, François Routhier and Krista L. Best
Technologies 2026, 14(1), 47; https://doi.org/10.3390/technologies14010047 - 8 Jan 2026
Viewed by 1381
Abstract
Introduction: Motorized components on power wheelchairs (PWC) enable repositioning to pre-programmed positions (e.g., tilt, leg support, verticalization) to prevent prolonged static positions. Smart technologies can track positioning information and give feedback according to clinical recommendations and personal goals. This study aimed to explore [...] Read more.
Introduction: Motorized components on power wheelchairs (PWC) enable repositioning to pre-programmed positions (e.g., tilt, leg support, verticalization) to prevent prolonged static positions. Smart technologies can track positioning information and give feedback according to clinical recommendations and personal goals. This study aimed to explore users’ and clinicians’ perceptions of an intelligent dynamic seating (IDS) system prototype comprising a PWC with motorized multi-components connected to a web interface. Methods: A purposive sample of six PWC users and eight clinicians were recruited in this exploratory descriptive qualitative study. Semi-structured interviews included viewing a video of the IDS and images of the web interface. Interviews were transcribed, deductively coded, and thematically analyzed using a conceptual model for evaluating eHealth interventions. Results: Clinicians found the IDS system intuitive to use, customizable, relevant in terms of positioning and clinical recommendations, and timesaving. Powered wheelchair users perceived benefits that could motivate behavioural change, autonomy, health, and inclusion. Concerns related to familiarity with complex technology, funding, cognitive requirements, and technical and health risks were raised. Conclusion: The results inform improvements for the integration of the IDS in clinical practice to respond to the positioning needs of PWC users. Full article
(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives—Second Edition)
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