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Search Results (59,232)

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20 pages, 1126 KB  
Article
Semi-Supervised Vertebra Segmentation and Identification in CT Images
by You Fu, Jiasen Feng and Hanlin Cheng
Tomography 2026, 12(3), 33; https://doi.org/10.3390/tomography12030033 - 3 Mar 2026
Abstract
Background/Objectives: Automatic segmentation and identification of vertebrae in spinal CT are essential for assisting diagnosis of spinal disorders and for preoperative planning. The task is challenging due to the high structural similarity between adjacent vertebrae and the morphological variability of vertebrae. Most [...] Read more.
Background/Objectives: Automatic segmentation and identification of vertebrae in spinal CT are essential for assisting diagnosis of spinal disorders and for preoperative planning. The task is challenging due to the high structural similarity between adjacent vertebrae and the morphological variability of vertebrae. Most existing methods rely on fully supervised deep learning and, constrained by limited annotations, struggle to remain robust in complex scenarios. Methods: We propose a semi-supervised approach built on a dual-branch 3D U-Net. Mamba modules are inserted between the encoder and decoder to model long-range dependencies along the cranio–caudal axis. The identification branch employs a 3D convolutional block attention module (3D-CBAM) to enhance class discriminability. A unified semi-supervised objective is formulated via teacher–student consistency: for each unlabeled sample, weakly and strongly augmented views are generated, and cross-branch consistency is enforced, together with confidence-based filtering and class-frequency reweighting. In addition, a connected-component analysis is used to enforce anatomically plausible sequential continuity of vertebral indices in the outputs. Results: Experiments on VerSe 2019 and 2020 show that, on the public VerSe 2019 test set (with VerSe 2020 scans used as unlabeled training data), the supervised baseline achieved a Dice score of 89.8% and an identification accuracy of 92.3%. Incorporating unlabeled data improved performance to 91.6% Dice and 97.5% identification accuracy (relative gains of +1.8 and +5.2 percentage points). Compared with competing methods, the proposed semi-supervised model attains higher or comparable segmentation accuracy and the highest identification accuracy. Conclusions: Without additional annotation cost, the proposed method markedly improves the overall performance of vertebra segmentation and identification, offering more robust automated support for clinical workflows. Full article
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26 pages, 5114 KB  
Article
Experimental Accuracy Evaluation of UAV-Based Homography for Static and Dynamic Displacement Monitoring of Structures
by Ante Marendić, Dubravko Gajski, Ivan Duvnjak and Ana Kosor
Sensors 2026, 26(5), 1593; https://doi.org/10.3390/s26051593 - 3 Mar 2026
Abstract
Structural displacement monitoring is an essential component of structural health monitoring of bridges, providing valuable information for performance evaluation, numerical model validation, and damage detection. While conventional contact-based sensors provide high accuracy, their installation is often complex, costly, and disruptive to traffic. Recent [...] Read more.
Structural displacement monitoring is an essential component of structural health monitoring of bridges, providing valuable information for performance evaluation, numerical model validation, and damage detection. While conventional contact-based sensors provide high accuracy, their installation is often complex, costly, and disruptive to traffic. Recent developments in unmanned aerial vehicle (UAV) platforms and vision-based measurement techniques offer a flexible, non-contact alternative; however, platform motion remains a major source of uncertainty. This study evaluates the accuracy and operational feasibility of UAV-based homography for static and dynamic displacement monitoring. The proposed approach is validated through three complementary experimental campaigns: a controlled calibration field test, a beam static load test, and bridge monitoring under traffic loading, with direct comparison to LVDT and RTS measurements. Under controlled conditions, sub-millimetre vertical precision was achieved, with RMSE values below 0.3 mm. In full-scale bridge applications, the method captured traffic-induced displacement trends with errors generally within 1–2 mm compared to LVDT data and with RMSE values below 1.4 mm. The results demonstrate that, when appropriate reference point configuration and imaging geometry are ensured, UAV-based homography provides a practical and sufficiently accurate solution for bridge displacement monitoring which is especially important in applications where sensor installation is difficult or unsafe. Full article
(This article belongs to the Special Issue Novel Sensor Technologies for Civil Infrastructure Monitoring)
32 pages, 3437 KB  
Article
AIS-Based Recognition of Typhoon-Related Ship Responses: A Dual-Behavior Framework
by Xinyi Sun, Jingbo Yin, Yingchao Gou, Shaohan Wang, Ningfei Wang, Min Chen and Xinxin Liu
J. Mar. Sci. Eng. 2026, 14(5), 487; https://doi.org/10.3390/jmse14050487 - 3 Mar 2026
Abstract
Typhoon avoidance is critical for ship maneuvering safety under extreme meteo-ocean conditions. This study proposes a data-driven framework that converts AIS trajectories into interpretable course deviation and speed change responses for navigational decision support. After AIS cleaning, temporal resampling, and matching with gridded [...] Read more.
Typhoon avoidance is critical for ship maneuvering safety under extreme meteo-ocean conditions. This study proposes a data-driven framework that converts AIS trajectories into interpretable course deviation and speed change responses for navigational decision support. After AIS cleaning, temporal resampling, and matching with gridded wind, wave, and current fields, rule-based sliding-window and regression procedures, informed by experienced captains and company staff, automatically generate proxy labels for deviation and speed reduction. Samples are stratified by vessel size to reflect differences in inertia and maneuverability, and XGBoost classifiers are trained with simple resampling to mitigate class imbalance. The framework is demonstrated on a single-event case study of Typhoon Yagi in the South China Sea, covering 8609 vessels and reconstructed sailing fragments. On the test set, the deviation model achieves 89.8% accuracy and high recall for deviation cases, while the speed change model reaches 82% balanced accuracy under the proxy-label setting. Results suggest a scale-dependent response: smaller vessels exhibit more frequent course deviation, whereas larger vessels more often reduce speed under severe wind-wave loading. The framework offers a proof-of-concept approach to derive behavior-based indicators from AIS and environmental data and may support situational assessment under adverse weather. Full article
39 pages, 1309 KB  
Review
Understanding and Mitigating Contaminant Exposure in Firefighting: Comprehensive Review of Firefighter PPE on Contamination, Health Risks, and Decontamination Methods
by Yulin Wu, Mengying Zhang, Rui Li and Guowen Song
Occup. Health 2026, 1(1), 12; https://doi.org/10.3390/occuphealth1010012 - 3 Mar 2026
Abstract
Firefighters are exposed to complex combustion products and to contaminants carried on personal protective equipment (PPE). Occupational exposure as a firefighter is classified as carcinogenic. This review summarizes the current evidence on exposure environments, routes of uptake, contamination and secondary exposure from PPE, [...] Read more.
Firefighters are exposed to complex combustion products and to contaminants carried on personal protective equipment (PPE). Occupational exposure as a firefighter is classified as carcinogenic. This review summarizes the current evidence on exposure environments, routes of uptake, contamination and secondary exposure from PPE, and the effectiveness and limits of decontamination approaches. Across incident types, smoke composition varies with the fuels and combustion conditions, but fine and ultrafine particles and semi-volatile organic chemicals are common. Biomonitoring confirms uptake after incidents. Self-contained breathing apparatus reduces inhalation exposure during active suppression, yet exposures persist through dermal absorption at ensemble interfaces and post-incident tasks. Protective ensembles can retain soot-bound polycyclic aromatic hydrocarbons, additive chemicals, and metals; volatiles and particles resuspension in vehicles and stations can extend exposure. Studies show that on-scene preliminary exposure reduction and laundering can lower contaminant burdens on PPE; however, removal remains incomplete and decreases when cleaning is delayed or when gear is aged. Emerging evidence raises additional concern for per- and polyfluoroalkyl substances from foams and coating materials, with limited data on exposure metrics and removability. The field lacks standardized, realistic contamination platforms and a dose-based definition of clean PPE. Integrated intervention studies linking exposure, secondary exposure pathways, biomarkers, and decontamination methods are needed to set performance-based targets and evaluate emerging hazards. Full article
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22 pages, 3320 KB  
Article
On the Effects of Motion Coupling on Linear and Quadratic Damping in Multi-DoF Modelling of Floating Offshore Wind Turbines
by Antonella Castellano, Guglielmo Balistreri, Oronzo Dell’Edera, Francesco Niosi and Marco Cammalleri
Appl. Sci. 2026, 16(5), 2448; https://doi.org/10.3390/app16052448 - 3 Mar 2026
Abstract
Accurate modelling of hydrodynamic damping remains a critical challenge in the dynamic analysis of floating offshore wind turbines (FOWTs), particularly when motion coupling between degrees of freedom is significant. This study addresses the limitations of conventional single-degree-of-freedom damping identification techniques by proposing a [...] Read more.
Accurate modelling of hydrodynamic damping remains a critical challenge in the dynamic analysis of floating offshore wind turbines (FOWTs), particularly when motion coupling between degrees of freedom is significant. This study addresses the limitations of conventional single-degree-of-freedom damping identification techniques by proposing a novel multi-degree-of-freedom identification procedure capable of including off-diagonal coupling terms in the estimation of both linear and quadratic damping matrices. The aim is to assess whether viscous cross-coupling effects can be explicitly identified within a multi-degree-of-freedom lumped-parameter framework and to evaluate their impact on motion prediction. The methodology employs a hybrid optimisation approach, combining a genetic algorithm with a gradient-based solver. The procedure is applied to a taut-leg moored semi-submersible floating platform, focusing on surge–pitch coupling and using both experimental wave-basin data and high-fidelity CFD free-decay simulations. The results show that diagonal damping coefficients can be robustly identified even under coupled free-decay conditions, whereas the inclusion of off-diagonal viscous terms does not significantly improve the reconstruction of free-decay responses. Moreover, the simultaneous calibration of the added mass matrix enabled by the proposed procedure further improves agreement with the reference data. Although the findings highlight limited identifiability of viscous cross-coupling effects from free-decay tests, this paper provides a flexible tool for more advanced damping identification in operational and extreme conditions. Full article
(This article belongs to the Section Energy Science and Technology)
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27 pages, 1917 KB  
Article
Machine Learning and Approximated Estimation Approaches for Process Design in Drug Synthesis
by Andrea Repetto, Gianguido Ramis and Ilenia Rossetti
Chemistry 2026, 8(3), 32; https://doi.org/10.3390/chemistry8030032 - 3 Mar 2026
Abstract
The continuous-flow technologies in organic synthesis for the production of active pharmaceutical ingredients (APIs) are nowadays more and more applied. In-silico process design is a powerful tool able to support organic synthesis in the field of scale-up and process development. Process design feasibility [...] Read more.
The continuous-flow technologies in organic synthesis for the production of active pharmaceutical ingredients (APIs) are nowadays more and more applied. In-silico process design is a powerful tool able to support organic synthesis in the field of scale-up and process development. Process design feasibility and reliability depend on the availability of a well-defined chemical reaction kinetic scheme, information which is usually derived from experimental datasets collected on purpose. The latter approach is time-consuming and demanding in terms of resources. Different possibilities are here proposed to valorize widely available experimental data from explorative works with different approaches, depending on the nature, richness, and structure of the datasets. The kinetic parameters (i.e., reaction order, kinetic constant, and activation energy) of some interesting organic reactions have been approximately estimated by applying different computational methodologies, thanks to built-in experimental databases. The numerical algebra approach dealing with linear and non-linear regression analysis for the kinetic parameters has been initially considered and related to the database information for oseltamivir synthesis. The Bayesian statistic was applied to the ibuprofen case through the application of the Markov Chain Monte Carlo (MCMC) method for reaction order estimation. At last, a Machine Learning (ML) approach has been applied to the Rolipram and Pregabalin case study. The in-house developed T-ReX experimental kinetic constant database was exploited, with application of the k-Nearest neighbor algorithm for classification and regular expression pattern recognition. Advantages and limitations of the three approaches are discussed. Full article
(This article belongs to the Special Issue AI and Big Data in Chemistry)
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28 pages, 6904 KB  
Article
The Priming Effect of Auxiliary Line Construction on Mathematical Creative Thinking: An fNIRS Study
by Chunli Zhang, Kai An, Jiacheng Li, Qinchen Yang, Meihui Song and Li Wang
J. Intell. 2026, 14(3), 40; https://doi.org/10.3390/jintelligence14030040 - 3 Mar 2026
Abstract
Auxiliary line construction has been identified as a crucial approach to fostering mathematical creative thinking. However, existing studies have only focused on the correlations between auxiliary line construction tasks and mathematical creative thinking, without investigating whether engaging in auxiliary line construction can improve [...] Read more.
Auxiliary line construction has been identified as a crucial approach to fostering mathematical creative thinking. However, existing studies have only focused on the correlations between auxiliary line construction tasks and mathematical creative thinking, without investigating whether engaging in auxiliary line construction can improve mathematical creativity. As a well-established research paradigm, cognitive priming can elicit changes in thinking within a short period. Based on this idea, the present study adopted the cognitive priming paradigm combined with functional near-infrared spectroscopy (fNIRS) technology, and randomly assigned 42 Chinese college students to an auxiliary line group or a control group. The students’ brain activity was monitored in real time during the priming phase (the auxiliary line group completed geometric problems requiring auxiliary line construction, while the control group finished proof problems with pre-set auxiliary lines) and the post-test phase (both groups completed a mathematical creative thinking test). The behavioral results showed that the auxiliary line group achieved significantly higher scores in fluency and originality of mathematical creative thinking than the control group in the post-test phase. The fNIRS data revealed that during the priming phase, the auxiliary line group exhibited stronger activation of the right superior frontal gyrus and higher variability in dynamic functional connectivity; meanwhile, in the post-test phase, the right superior frontal gyrus and right middle frontal gyrus maintained robust neural activation, and brain functional connectivity exhibited a lower clustering coefficient and attenuated small-world network properties. This study confirms that short-term engagement in auxiliary line construction exerts a priming effect on the fluency and originality of mathematical creative thinking, which may be associated with the enhanced activation of specific brain regions and the dynamic adjustment of brain functional connectivity. These findings provide theoretical and empirical evidence for the cultivation of mathematical creative thinking. Full article
(This article belongs to the Section Studies on Cognitive Processes)
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41 pages, 4807 KB  
Review
From Microscopy to Nanoscopy: Contemporary Physical Methods in Mitochondrial Structural Biology
by Semen V. Nesterov, Anton G. Rogov and Raif G. Vasilov
Int. J. Mol. Sci. 2026, 27(5), 2361; https://doi.org/10.3390/ijms27052361 (registering DOI) - 3 Mar 2026
Abstract
Mitochondria play a crucial role in cellular bioenergetics, signaling, and metabolism; yet, many fundamental mechanisms such as the proton transfer along the membranes, the link between membrane curvature and oxidative phosphorylation, and the nanoscale organization of enzyme supercomplexes remain poorly understood due to [...] Read more.
Mitochondria play a crucial role in cellular bioenergetics, signaling, and metabolism; yet, many fundamental mechanisms such as the proton transfer along the membranes, the link between membrane curvature and oxidative phosphorylation, and the nanoscale organization of enzyme supercomplexes remain poorly understood due to the limitations of classical biochemical approaches. This review addresses this gap by systematically analyzing the contemporary physical methods used to investigate the mitochondrial structure and function from the micro to nano scale. It covers advanced fluorescence and super-resolution microscopy, electron and volume electron microscopy, and scanning probe techniques, as well as cryo-electron tomography for resolving supramolecular assemblies in near-native conditions. The review highlights the applications of the modern fluorescent probes, expansion and phase microscopy, and machine-learning-based image analysis for a quantitative assessment of the mitochondrial morphology, membrane potential, and dynamics in living cells and tissues. Complementary spectroscopic and scattering methods, including Raman spectroscopy, NMR, and X-ray and neutron scattering, are discussed as tools for probing the redox state, metabolite composition, and membrane organization. Emphasis is placed on integrating high-resolution experimental data with advanced computational frameworks to test competing models of mitochondrial function and pathology, and to guide the development of biomimetic and biomedical technologies. Full article
11 pages, 3612 KB  
Communication
Planar Microwave Sensor for the Characterization of Milk
by Foo Wei Lee, Kim Ho Yeap, Yong Jun Tan, Han Kee Lee, Kok Weng Tan, Kim Hoe Tshai, Nor Faiza Abd Rahman, Pek Lan Toh, Ming Hui Tan, Nuraidayani Effendy and Siu Hong Loh
Electronics 2026, 15(5), 1059; https://doi.org/10.3390/electronics15051059 - 3 Mar 2026
Abstract
This paper presents the development and analysis of a planar microwave sensor designed for detecting adulteration in milk by evaluating the purity of milk in a water-based solution. The sensor comprises a pair of complementary split-ring resonators (CSRRs) fabricated on an FR4 substrate, [...] Read more.
This paper presents the development and analysis of a planar microwave sensor designed for detecting adulteration in milk by evaluating the purity of milk in a water-based solution. The sensor comprises a pair of complementary split-ring resonators (CSRRs) fabricated on an FR4 substrate, measuring 30 mm × 50 mm × 1.6 mm, with a dielectric constant of 4.4 and a loss tangent of 0.022. The device’s performance was assessed using a vector network analyzer (VNA) by varying the ratio of full-cream milk to water in a 50 mL solution, starting from 60% and increasing in 10% increments up to 100%. Measurements focused on return loss (RL) at resonant frequency 1.5425 GHz, which exhibited minimal frequency shifts but significant variations in RL magnitude with changing milk concentration (M). To establish a mathematical relationship between RL and M, we segmented the data into two ranges—60% to 80% and 80% to 100% milk concentrations—and applied second-order polynomial regression for each segment. The quadratic equations derived from this regression allowed us to express M in terms of RL. Verification of this method was conducted using arbitrary samples of milk concentrations: 61%, 62%, 72%, 88%, 93%, and 95%. Discrepancies between different quadratic solutions for the same RL values were resolved by normalizing the return losses against pure water and comparing the resulting normalized values with those from known concentrations. This comparison allowed for the accurate selection of the appropriate quadratic equation based on the closest match. Our normalization approach revealed distinct patterns correlating RL magnitudes, enabling us to select the appropriate quadratic equation segment based on minimal discrepancies. The analysis confirmed that by excluding negative and complex solutions and solutions which are beyond the stipulated range of the curve segment, the accuracy of the sensor in determining milk concentration exceeded 83.5%. This study demonstrates the potential of the proposed microwave sensor in ensuring milk quality by effectively quantifying milk purity. Full article
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20 pages, 77395 KB  
Article
Underwater Moving Target Localization Based on High-Density Pressure Array Sensing
by Jiamin Chen, Yilin Li, Ruixin Chen, Wenjun Li, Keqiang Yue and Ruixue Li
J. Mar. Sci. Eng. 2026, 14(5), 484; https://doi.org/10.3390/jmse14050484 - 3 Mar 2026
Abstract
The artificial lateral line sensing principle provides a promising approach for underwater target perception and the navigation of underwater vehicles in complex flow environments. However, the highly nonlinear hydrodynamic mechanisms in complex flow fields make it difficult to establish accurate analytical models, which [...] Read more.
The artificial lateral line sensing principle provides a promising approach for underwater target perception and the navigation of underwater vehicles in complex flow environments. However, the highly nonlinear hydrodynamic mechanisms in complex flow fields make it difficult to establish accurate analytical models, which limits the development of high-precision perception and localization methods for underwater moving targets. In this study, a high-fidelity simulation model is established to characterize the pressure field variations induced by a moving source on an artificial lateral line pressure array. The influences of source velocity and sensing distance on the sensitivity and discretization characteristics of the pressure array are systematically investigated. Simulation results indicate that the sensor density of the pressure array is strongly correlated with the spatial resolution of the acquired pressure data, and a resolution of 50 sensors per meter is selected as the best-performing configuration by balancing sensing accuracy and sensor quantity. Under this configuration, the pressure distribution induced by the moving source exhibits clear and distinguishable spatiotemporal features, making it suitable for deep learning-based modeling. Furthermore, a large-scale temporal pressure dataset is constructed based on high-fidelity simulations under multiple motion directions and velocity conditions, and a spatiotemporal neural network is employed to predict the position of the underwater moving source. Experimental results demonstrate that, for straight-line underwater motion scenarios, the average localization error is within 7 cm, and a classification accuracy of 71% is achieved in practical engineering experiments. These results indicate that the proposed artificial lateral line pressure array design and deep learning-based prediction framework provide a feasible and effective solution for underwater target perception and localization in complex flow environments. Full article
(This article belongs to the Section Ocean Engineering)
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22 pages, 4704 KB  
Article
A Few-Shot Fish Detection Method with Limited Samples Using Visual Feature Augmentation
by Daode Zhang, Shihao Zhang, Wupeng Deng, Enshun Lu and Zhiwei Xie
Appl. Sci. 2026, 16(5), 2441; https://doi.org/10.3390/app16052441 - 3 Mar 2026
Abstract
In recirculating aquaculture systems, fish detection is an essential component for maintaining effective farming operations. The availability of high-quality fish datasets is limited because of the richness of fish species, and the annotation of large-scale data, which is used to train models, is [...] Read more.
In recirculating aquaculture systems, fish detection is an essential component for maintaining effective farming operations. The availability of high-quality fish datasets is limited because of the richness of fish species, and the annotation of large-scale data, which is used to train models, is often labor-intensive and time-consuming. The presence of different fish species across batches introduces further challenges for consistent detection performance. This work introduces a few-shot learning approach for fish detection, utilizing a customized dataset as novel classes and the Fish4Knowledge dataset for base classes, thereby establishing a framework that enhances adaptability in data-scarce scenarios. Within the model architecture, multi-scale feature extraction is enhanced through an attention mechanism, which is integrated as a dedicated module to strengthen representation learning, thus enhancing the model’s capability to differentiate visually similar fish species. Two distinct customized fish datasets are employed to evaluate the robustness of the proposed method. Experimental results show that the proposed model performs competitively against TFA, Meta-RCNN, and VFA. In the base-training phase, it achieves a mAP of 0.775, slightly surpassing VFA, while in the 1-shot, 5-shot, and 10-shot fine-tuning settings, it obtains mAP values of 0.152, 0.247, and 0.265, respectively. A similar trend is observed on a subset of black fish, with mAP scores of 0.169, 0.253, and 0.286 in the corresponding few-shot settings. These results indicate that the proposed approach can maintain relatively stable detection accuracy and adaptability across different fish batches, offering a practical solution for fish detection tasks in aquaculture when annotated data is scarce. To further demonstrate the efficacy and practical utility of the proposed methodology, a case study in fish farming confirms that the enhanced model achieves consistent and precise detection across diverse fish species, even when trained with limited annotated data. Full article
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30 pages, 8087 KB  
Article
A Novel SLAM Approach for Trajectory Generation of a Dual-Arm Mobile Robot (DAMR) Using Sensor Fusion
by Narendra Kumar Kolla and Pandu Ranga Vundavilli
Automation 2026, 7(2), 42; https://doi.org/10.3390/automation7020042 - 3 Mar 2026
Abstract
Simultaneous Localization and Mapping (SLAM) is essential for autonomous movement in intelligent robotic systems. Traditional SLAM using a single sensor, such as an Inertial Measurement Unit (IMU), faces challenges including noise and drift. This paper introduces a novel Cartographer-based SLAM approach for DAMR [...] Read more.
Simultaneous Localization and Mapping (SLAM) is essential for autonomous movement in intelligent robotic systems. Traditional SLAM using a single sensor, such as an Inertial Measurement Unit (IMU), faces challenges including noise and drift. This paper introduces a novel Cartographer-based SLAM approach for DAMR trajectory generation in indoor environments to reduce drift errors and improve localization accuracy. This SLAM approach integrates multi-sensor data with extended Kalman filter (EKF) fusion from wheel odometry, an RGB-D camera (RTAB-Map), and an IMU for precise mapping with DAMR trajectory generation and is compared with the heading reference trajectory generated by robot pose estimation and frame transformation. This system is implemented in the Robot Operating System (ROS 2) for coordinated data acquisition, processing, and visualization. After experimental verification, the DAMR trajectories generated are closer to the reference trajectory and drift errors are tuned. The experimental results revealed that the DAMR trajectory with multi-sensor data integration using the EKF effectively improved the positioning accuracy and robustness of the system. The proposed approach shows improved alignment with the reference trajectory, yielding a mean displacement error of 0.352% and an absolute trajectory error of 0.007 m, highlighting the effectiveness of the fusion approach for accurate indoor robot navigation. Full article
(This article belongs to the Section Robotics and Autonomous Systems)
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23 pages, 760 KB  
Article
Trajectory Data Publishing Scheme Based on Transformer Decoder and Differential Privacy
by Haiyong Wang and Wei Huang
ISPRS Int. J. Geo-Inf. 2026, 15(3), 106; https://doi.org/10.3390/ijgi15030106 - 3 Mar 2026
Abstract
The proliferation of Location-Based Services (LBSs) has generated vast trajectory datasets that offer immense analytical value but pose critical privacy risks. Achieving an optimal balance between data utility and privacy preservation remains a challenge, a difficulty compounded by the limitations of existing methods [...] Read more.
The proliferation of Location-Based Services (LBSs) has generated vast trajectory datasets that offer immense analytical value but pose critical privacy risks. Achieving an optimal balance between data utility and privacy preservation remains a challenge, a difficulty compounded by the limitations of existing methods in modeling complex, long-term spatiotemporal dependencies. To address this, this paper proposes a trajectory data publishing scheme combining a Transformer decoder with differential privacy. Unlike traditional single-layer approaches, the proposed method establishes a systematic generation–generalization framework. First, a Transformer decoder is integrated into a Generative Adversarial Network (GAN). This architecture mitigates the gradient vanishing issues common in RNN-based models, generating high-fidelity synthetic trajectories that capture long-range correlations while decoupling them from sensitive source data. Second, to provide rigorous privacy guarantees, a clustering-based generalization strategy is implemented, utilizing Exponential and Laplace mechanisms to ensure ϵ-differential privacy. Experiments on the Geolife and Foursquare NYC datasets demonstrate that the scheme significantly outperforms leading baselines, achieving a superior trade-off between privacy protection and data utility. Full article
(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)
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35 pages, 10613 KB  
Systematic Review
Current Trends in Artificial Intelligence for Recognizing Work Postures to Prevent Work-Related Musculoskeletal Disorders: Systematic Review and Meta-Analysis by Occupational Activity
by Philippe Gorce and Julien Jacquier-Bret
Bioengineering 2026, 13(3), 298; https://doi.org/10.3390/bioengineering13030298 - 3 Mar 2026
Abstract
The use of artificial intelligence (AI) to recognize postures is a promising approach for the prevention of work-related musculoskeletal disorders (WMSDs). The aim was to conduct a systematic review with meta-analysis to assess the performance of work posture recognition systems during occupational activity. [...] Read more.
The use of artificial intelligence (AI) to recognize postures is a promising approach for the prevention of work-related musculoskeletal disorders (WMSDs). The aim was to conduct a systematic review with meta-analysis to assess the performance of work posture recognition systems during occupational activity. The results were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Google Scholar, IEEE Xplore, PubMed/MedLine, and ScienceDirect databases were screened without date restrictions. Two authors independently selected articles and extracted data. Studies were included if they presented a performance analysis of an AI deep learning (DL) or machine learning (ML) method that assessed the WMSD risk associated with working postures. Only peer-reviewed studies written in English including accuracy, precision, specificity, sensitivity, or F1-score values were included. The risk of bias was assessed using the Prediction Model Study Risk of Bias Assessment Tool. Of the 157 unique records, 58 studies were selected. The five performance parameters were investigated and averaged for seven occupational activities, eight posture categories, and the AI methods (ML vs. DL). Statistical analyses showed that DL methods produced better results. The reported systems detected sitting and standing postures with high accuracy. The solutions proposed in Manufacturing and Construction were the most numerous and the most effective on average. The major limitation lies in the wide variety of methods used. This analysis is a valuable source of information for designing new detection systems that are effective, ergonomic, easy to use, and acceptable so that humans remain at the center of the production process as defined by Industry 5.0. Full article
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28 pages, 8142 KB  
Article
Enabling Circular Reuse of Sandwich Panels Through UAV Inspection, Deep Learning, and BIM-Based Material Passports
by Rui Barros Garcia, Ruben Pereira Silva, Tomás Simões Jorge, José Santos, Luiza Assunção, Pedro Oliveira, Ricardo Santos, Micael S. Couceiro and Diogo Ribeiro
Sustainability 2026, 18(5), 2454; https://doi.org/10.3390/su18052454 - 3 Mar 2026
Abstract
Transitioning toward a circular economy requires not only solutions involving technical component reuse but also mechanisms that reduce risk and increase confidence among market stakeholders. Steel-faced sandwich panels, widely used in façades and roofs, constitute a significant urban material stock, yet their reuse [...] Read more.
Transitioning toward a circular economy requires not only solutions involving technical component reuse but also mechanisms that reduce risk and increase confidence among market stakeholders. Steel-faced sandwich panels, widely used in façades and roofs, constitute a significant urban material stock, yet their reuse is constrained by information asymmetry, liability concerns, and the absence of verifiable condition data. In this study, we develop an integrated end-to-end workflow—combining controlled panel recovery, Unmanned Aerial Vehicle (UAV) inspection, deep learning-driven damage detection, and Building Information Modeling (BIM)-linked material passports—to enable traceable, evidence-based reuse decisions. Validated through a pilot façade assembly and disassembly process, the methodology successfully quantified 4845.90 cm2 of mechanical damage across 10 panels, with all orthomosaic and detection outputs fully integrated into the digital passport environment. By standardizing component-level condition records, this approach reduces perceived risk and provides the technical assurance necessary to unlock a trusted second-hand marketplace for sandwich panels. Framed within an urban metabolism perspective, the findings demonstrate how digital transparency can bridge the gap between material recovery and market valuation. Full article
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