Sensors最新文献

{"title":"Use of CR-39 Dosimeters for the Imaging of Neutron Beam Profiles in the 100 keV-10 MeV Energy Range.","authors":"Margherita Simoni, Leonardo Baldassarre, Carlo Cazzaniga, Laura Fazi, Mattia Gaboardi, Leandro Gemmiti, Maria Kastriotou, Matthew Krzystyniak, Anna Marsicano, Marco Martellucci, Triestino Minniti, Anna Prioriello, Roberto Senesi, Valentin Suteica, Giovanni Romanelli","doi":"10.3390/s25185865","DOIUrl":"10.3390/s25185865","url":null,"abstract":"<p><p>We provide a beam shape characterization of the VESUVIO spectrometer, at the ISIS Neutron and Muon Source, employing CR-39 solid-state nuclear track detectors and combining techniques including optical and electron microscopy, as well as Monte Carlo transport simulations. In particular, we show, through comparison with irradiation with 14 MeV neutrons at the NILE Facility at ISIS, that the majority of defects on the etched surface of the dosimeters irradiated on VESUVIO were induced by neutrons with energies between 100 keV and 10 MeV. Our results were compared to previous characterizations of the VESUVIO beam shape performed in either the thermal or fast energy ranges, and we conclude that the VESUVIO beam has a constant shape from thermal-neutron energies up to 10 MeV, composed of an umbra (intensity above 90% of the maximum) with radius 1.1 cm, and surrounded by a penumbra (intensity above 1% of the maximum) that extends up to 2.5 cm.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473978/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pixel-Level Segmentation of Retinal Breaks in Ultra-Widefield Fundus Images with a PraNet-Based Machine Learning Model.","authors":"Takuya Takayama, Tsubasa Uto, Taiki Tsuge, Yusuke Kondo, Hironobu Tampo, Mayumi Chiba, Toshikatsu Kaburaki, Yasuo Yanagi, Hidenori Takahashi","doi":"10.3390/s25185862","DOIUrl":"10.3390/s25185862","url":null,"abstract":"<p><p>Retinal breaks are critical lesions that can cause retinal detachment and vision loss if not detected and treated early. Automated, accurate delineation of retinal breaks in ultra-widefield fundus (UWF) images remains challenging. In this study, we developed and validated a deep learning segmentation model based on the PraNet architecture to localize retinal breaks in break-positive cases. We trained and evaluated the model using a dataset comprising 34,867 UWF images of 8083 cases. Performance was assessed using image-level segmentation metrics, including accuracy, precision, recall, Intersection over Union (IoU), dice score, and centroid distance score. The model achieved an accuracy of 0.996, precision of 0.635, recall of 0.756, IoU of 0.539, dice score of 0.652, and centroid distance score of 0.081. To our knowledge, this is the first study to present pixel-level segmentation of retinal breaks in UWF images using deep learning. The proposed PraNet-based model showed high accuracy and robust segmentation performance, highlighting its potential for clinical application.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Unified GAN-Based Framework for Unsupervised Video Anomaly Detection Using Optical Flow and RGB Cues.","authors":"Seung-Hun Kang, Hyun-Soo Kang","doi":"10.3390/s25185869","DOIUrl":"10.3390/s25185869","url":null,"abstract":"<p><p>Video anomaly detection in unconstrained environments remains a fundamental challenge due to the scarcity of labeled anomalous data and the diversity of real-world scenarios. To address this, we propose a novel unsupervised framework that integrates RGB appearance and optical flow motion via a unified GAN-based architecture. The generator features a dual encoder and a GRU-attention temporal bottleneck, while the discriminator employs ConvLSTM layers and residual-enhanced MLPs to evaluate temporal coherence. To improve training stability and reconstruction quality, we introduce DASLoss-a composite loss that incorporates pixel, perceptual, temporal, and feature consistency terms. Experiments were conducted on three benchmark datasets. On XD-Violence, our model achieves an Average Precision (AP) of 80.5%, outperforming other unsupervised methods such as MGAFlow and Flashback. On Hockey Fight, it achieves an AUC of 0.92 and an F1-score of 0.85, demonstrating strong performance in detecting short-duration violent events. On UCSD Ped2, our model attains an AUC of 0.96, matching several state-of-the-art models despite using no supervision. These results confirm the effectiveness and generalizability of our approach in diverse anomaly detection settings.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Static Loads in Wharf Mooring Cables Using the Influence Coefficient Method.","authors":"Jia Zhou, Changshi Xiao, Langxiong Gan, Bo Jiao, Haojie Pan, Haiwen Yuan","doi":"10.3390/s25185867","DOIUrl":"10.3390/s25185867","url":null,"abstract":"<p><p>Directly measuring the mooring cable load while a ship is moored at a wharf poses significant practical challenges. This paper proposes an indirect load measurement method to identify mooring cable static loads based on the Influence Coefficient Matrix (ICM) method. First, a finite element analysis of the bollard is conducted to obtain the full-field strains under each unit load. A solution procedure based on the genetic algorithm (GA) is then implemented to determine the optimal placement and orientation of strain gauges, aiming to improve load identification accuracy. An optimal load coefficient matrix is derived to establish the correlation between cable loads and bollard strains. Subsequently, following the established measured point placement scheme, strain gauges are installed on the bollard surface to capture the strains, enabling inverse identification of mooring cable loads through the measured strains and the pre-established load-strain relationship. A numerical case study validated the feasibility of this method, demonstrating high identification accuracy. Furthermore, experimental verification was conducted to assess its reliability under different conditions. Results confirmed the effectiveness of this indirect approach for mooring cable static loads measurement. The research findings provide a technical framework for real-time monitoring of mooring cable loads.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ergo4Workers: A User-Centred App for Tracking Posture and Workload in Healthcare Professionals.","authors":"Inês Sabino, Maria do Carmo Fernandes, Ana Antunes, António Monteny, Bruno Mendes, Carlos Caldeira, Isabel Guimarães, Nidia Grazina, Phillip Probst, Cátia Cepeda, Cláudia Quaresma, Hugo Gamboa, Isabel L Nunes, Ana Teresa Gabriel","doi":"10.3390/s25185854","DOIUrl":"10.3390/s25185854","url":null,"abstract":"<p><p>Healthcare professionals (namely, occupational therapists) face ergonomic risk factors that may lead to work-related musculoskeletal disorders (WRMSD). Ergonomic assessments are crucial to mitigate this occupational issue. Wearable devices are a potential solution for such assessments, providing continuous measurement of biomechanical and physiological parameters. Ergo4workers (E4W) is a mobile application designed to integrate data from independent wearable sensors-motion capture system, surface electromyography, force platform, and smartwatch-to provide an overview of the posture and workload of occupational therapists. It can help identify poor work practices and raise awareness about ergonomic risk factors. This paper describes the development of E4W by following a User-Centred Design (UCD) approach. The initial stage focused on specifying the context of use in collaboration with six occupational therapists. Then the app was implemented using WordPress. Three iterations of the UCD cycle were performed. The usability test of prototype 1 was carried out in a laboratory environment, while the others were tested in a real healthcare work environment. The Cognitive Walkthrough was applied in the usability tests of prototypes 1 and 2. The System Usability Scale evaluated prototype 3. Results evidenced positive feedback, reflecting an easy-to-use and intuitive smartphone app that does not interfere with daily work activities.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473953/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time Auto-Monitoring of Livestock: Quantitative Framework and Challenges.","authors":"Sarah Brocklehurst, Zhou Fang, Adam Butler","doi":"10.3390/s25185871","DOIUrl":"10.3390/s25185871","url":null,"abstract":"<p><p>The use of automated sensors has grown rapidly in recent years, with sensor data now routinely used for monitoring in a wide range of situations, including human health and behaviour, the environment, wildlife, and agriculture. Livestock farming is a key area of application, and our primary focus here, but the issues discussed are widely applicable. There is the potential to massively increase the use of empirical data for decision-making in real time, and a range of quantitative methods, including machine learning and statistical methods, have been proposed for this purpose within the literature. In many areas, however, development and validation of quantitative approaches are still needed in order for these methods to effectively inform decision-making. Within the context of livestock farming, for example, it must be practically feasible to repeatedly apply the method dynamically in real time on farms in order to optimise decision-making, and we discuss the challenges in using quantitative approaches for this purpose. It is also crucial to evaluate and compare the applied performance of methods in a fair and robust way-such comparisons are currently lacking within the literature on livestock farming, and we outline approaches to addressing this key gap.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145177905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Environment-Adaptive Multi-Channel Ranging Optimization Algorithm Based on a Multi-Objective Evolutionary Model for Multipath Wireless Sensor Networks.","authors":"Xuming Fang, Zuqin Ji","doi":"10.3390/s25185851","DOIUrl":"10.3390/s25185851","url":null,"abstract":"<p><p>Recently, high-precision WSN (wireless sensor network) ranging and positioning algorithms based on RSSI (Received Signal Strength Indicator) in complex indoor environments have become a popular research topic. This is because RSSI is easy to obtain and more suitable for the large-scale deployment of WSNs. However, WSN ranging and positioning algorithms using RSSI are severely affected by the presence of noise and multipath effects in complex indoor environments. To reduce multipath effects, a multi-channel ranging algorithm was developed. This algorithm must obtain accurate initial parameter values or the target-reference distance in advance; otherwise, it will fall into local optima. We propose an environment-adaptive algorithm for multi-channel ranging optimization based on an innovative evolutionary model with multiple objectives and an existing adaptive extended Kalman filter. This novel model includes a newly created objective function of the relationship between weighted multi-channel RSSI and node distance, which allows it to achieve globally optimal results without requiring extensive training to obtain accurate initial parameter values or the target-reference distance beforehand. Extensive simulations and experiments show that our proposed algorithm always has much higher ranging accuracy than the existing algorithm, regardless of whether the multi-channel RSSI is regular or the number of paths matches.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473460/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of Uneven Road Surface Information Perception Methods for Suspension Preview Control.","authors":"Yujie Shen, Kai Jing, Kecheng Sun, Changning Liu, Yi Yang, Yanling Liu","doi":"10.3390/s25185884","DOIUrl":"10.3390/s25185884","url":null,"abstract":"<p><p>Accurate detection of road surface information is crucial for enhancing vehicle driving safety and ride comfort. To overcome the limitation that traditional suspension systems struggle to respond to road excitations in real time due to time delays in signal acquisition and control, suspension preview control technology has attracted significant attention for its proactive adjustment capability, with efficient road surface information perception being a critical prerequisite for its implementation. This paper systematically reviews road surface information detection technologies for suspension preview, focusing on the identification of potholes and speed bumps. Firstly, it summarizes relevant publicly available datasets. Secondly, it sorts out mainstream detection methods, including traditional dynamic methods, 2D image processing, 3D point cloud analysis, machine/deep learning methods, and multi-sensor fusion methods, while comparing their applicable scenarios and evaluation metrics. Furthermore, it emphasizes the core role of elevation information (e.g., pothole depth, speed bump height) in suspension preview control and summarizes elevation reconstruction technologies based on LiDAR, stereo vision, and multi-modal fusion. Finally, it prospects future research directions such as optimizing robustness, improving real-time performance, and reducing labeling costs. This review provides technical references for enhancing the accuracy of road surface information detection and the control efficiency of suspension preview systems, and it is of great significance for promoting the development of intelligent chassis.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging FMMEA for Digital Twin Development: A Case Study on Intelligent Completion in Oil and Gas.","authors":"Nelson Victor Costa da Silva, Flavia Albuquerque Pontes, Mariana Santos da Silva, Breno Cagide Fialho, Jamile Eleutério Delesposte, Dalton Garcia Borges de Souza, Luiz Antônio de Oliveira Chaves, Rodolfo Cardoso","doi":"10.3390/s25185846","DOIUrl":"10.3390/s25185846","url":null,"abstract":"<p><p>The implementation of Digital Twins (DTs) represents a significant advancement for the Oil and Gas (O&G) industry. A DT virtually replicates a physical asset, enabling the monitoring, diagnosis, prediction, and optimization of its outcomes. Since failures are undesirable outcomes, investigations into potential failure modes are often integrated into the development. Traditional methods, such as Failure Modes and Effects Analysis (FMEA) and Failure Mode, Effects, and Criticality Analysis (FMECA), are widely used to identify, assess, and mitigate risks. However, there is still a lack of specific guidelines for studying potential failures in complex systems. This article introduces a framework for Failure Modes, Mechanisms, and Effects Analysis (FMMEA) as a tool for identifying and assessing failures in early DT development. Exploring failure mechanisms is highlighted as essential for effective prediction and management We also propose adjustments to FMMEA for complex, predictable systems, such as using a DPR (Detectable Priority Risk) instead of RPN (Risk Priority Number) for prioritizing risks. A comprehensive case illustrates the framework's application in developing a DT for an intelligent completion system in a major O&G company. The approach enables mechanism-oriented failure analysis and more detailed prognostic health management, providing greater transparency in the failure identification process.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12474008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Sensor-Based Mobility Assessment for Older Adults: A Multimodal Framework Integrating PoseNet Gait Dynamics and InBody Composition.","authors":"Sinan Chen, Lingqi Kong, Zhaozhen Tong, Yuko Yamaguchi, Masahide Nakamura","doi":"10.3390/s25185878","DOIUrl":"10.3390/s25185878","url":null,"abstract":"<p><p>The acceleration of global population aging has driven a surge in demand for health monitoring among older adults. However, traditional mobility assessment methods mostly rely on invasive measurements or laboratory-grade equipment, making it difficult to achieve continuous monitoring in daily scenarios. This study investigated the correlation between dynamic gait characteristics and static body metrics to enhance the understanding of elderly mobility and overall health. A sensor-based framework was implemented, which utilizes the <i>Short Physical Performance Battery (SPPB)</i>, combined with <i>PoseNet</i> (a vision-based sensor) for dynamic gait analysis, and the <i>InBody</i> bioelectrical impedance device for static body composition assessment. Key variables comprised the dynamic metric <i>mean directional shift</i> and static metrics, including <i>skeletal muscle index (SMI)</i>, <i>skeletal muscle mass (SMM)</i>, <i>body fat percentage (PBF)</i>, <i>visceral fat area (VFA)</i>, and <i>intracellular water</i>. Nineteen elderly participants aged 60-89 years underwent assessments; among them, 16 were males (84.21%), and 3 were females (15.79%), 50% were in the 80-89 age group, 95% did not live alone, and 90% were married. Dynamic gait data were analyzed for center displacement and horizontal directional shifts. A Pearson correlation analysis revealed that the mean directional shift positively correlated with <i>SMI</i> (ρ=0.561, p<0.01), <i>SMM</i> (ρ=0.496, p<0.01), and <i>intracellular water</i> (ρ=0.497, p<0.01), highlighting the role of muscle strength in movement adaptability. Conversely, negative correlations were found with <i>PBF</i> (ρ=-0.256) and <i>VFA</i> (ρ=-0.342, p<0.05), suggesting that greater fat mass impedes dynamic mobility. This multimodal integration of dynamic movement patterns and static physiological metrics may enhance health monitoring comprehensiveness, particularly for early sarcopenia risk detection. The findings demonstrate the framework's potential, indicating mean directional shift as a valuable dynamic health indicator.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"25 18","pages":""},"PeriodicalIF":3.5,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145178421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}