Search Results (3,192)

White Spot Syndrome Virus (WSSV) is one of the most devastating viral pathogens affecting shrimp, causing severe economic losses to the global farmed shrimp trade. The globalization of live shrimp trade and waterborne transmission have facilitated the rapid spread of WSSV across major shrimp-producing countries since its initial emergence. The present review gives an updated account of WSSV biology, pathology, transmission dynamics, and recent developments in control measures. The virus, a double-stranded DNA virus of the Nimaviridae family, utilizes advanced immune evasion strategies, resulting in severe mortality. Shrimp lack adaptive immunity and hence rely predominantly on innate immunity, which is insufficient to mount an effective response against severe infections. Traditional disease control measures such as augmented biosecurity, selective breeding, and immunostimulants have, despite extensive research, achieved only limited success. New biotechnological tools such as RNA interference, CRISPR-Cas gene editing, and nanotechnology offer tremendous potential for disease mitigation. In parallel, the development of DNA and RNA vaccines targeting WSSV structural proteins, such as VP28, holds significant promise for stimulating the shrimp immune system. This review highlights the urgent need for a convergent approach to sustainable disease management in global shrimp aquaculture, with interdisciplinarity playing a pivotal role in shaping the future of WSSV control. Full article

With the rapid development of autonomous vehicles and intelligent transportation systems, vehicle detection and distance estimation have become critical technologies for ensuring driving safety. However, real-world in-vehicle environments impose strict constraints on computational resources, making it impractical to deploy high-end GPUs. This implies that even highly accurate algorithms, if unable to run in real time on embedded platforms, cannot fully meet practical application demands. Although existing deep learning-based detection and stereo vision methods achieve state-of-the-art accuracy on public datasets, they often rely heavily on massive computational power and large-scale annotated data. Their high computational requirements and limited cross-scenario generalization capabilities restrict their feasibility in real-time vehicle-mounted applications. On the other hand, traditional algorithms such as Semi-Global Block Matching (SGBM) are advantageous in terms of computational efficiency and cross-scenario adaptability, but when used alone, their accuracy and robustness remain insufficient for safety-critical applications. Therefore, the motivation of this study is to develop a stereo vision-based collision warning system that achieves robustness, real-time performance, and computational efficiency. Our method is specifically designed for resource-constrained in-vehicle platforms, integrating a lightweight YOLOv8n detector with SGBM-based depth estimation. This approach enables real-time performance under limited resources, providing a more practical solution compared to conventional deep learning models and offering strong potential for real-world engineering applications. Full article

We present a photoacoustic spectroscopy (PAS)-based method using a laser Doppler vibrometer (LDV) for real-time detection of laser-induced damage (LID) in optical components. By measuring audible frequency surface vibrations, the method enables remote, non-contact, and sensitive detection. Experiments with various dielectric optics (slide glass and single-layer coatings) and pulse durations (7 ns and 360 ps) of an Nd:YAG laser (wavelength of 1064 nm) showed detection accuracy comparable to microscopy. Vibration spectra correlated with natural modes calculated by finite element modeling, and vibrations according to the detecting location were observed. The method remained effective under typical mounting conditions, demonstrating its practical applicability. This PAS-LDV approach offers a promising tool for in situ monitoring of LID in high-power laser systems. Full article

Oropouche virus (OROV) is an emerging arbovirus responsible for Oropouche fever, also known as sloth fever, a febrile illness that can lead to recurrent outbreaks in affected regions. Endemic to parts of South and Central America, OROV is primarily transmitted by biting midges (Culicoides paraensis), although mounting evidence implicates mosquitoes, particularly the Culex and Aedes species, as additional vectors. Recent ecological disturbances—such as deforestation, urbanization, and climate change—have driven significant shifts in vector population dynamics, contributing to the expanded geographic range and increased transmission of OROV. Notably, recent reports of OROV infections among American and European travelers to Cuba highlight the virus’s growing potential for international dissemination and underscore its significance as a global health concern. OROV is an enveloped orthobunyavirus within the Peribunyaviridae family, possessing a tripartite, single-stranded, negative-sense RNA genome composed of the S (small), M (medium), and L (large) segments. These segments encode the nucleocapsid (N) protein, glycoproteins (Gn and Gc), and RNA-dependent RNA polymerase, respectively. Despite increasing incidence and potential for global spread, no licensed vaccines or antiviral therapies currently exist, and effective diagnostic tools remain limited. Furthermore, although human-to-human transmission has not been observed, the absence of robust surveillance systems complicates timely outbreak detection and response. In this study, we present a comprehensive molecular characterization of OROV’s major structural proteins, with an emphasis on structural modeling and epitope prediction. By integrating bioinformatics approaches with available structural data, we identify key antigenic regions that could serve as targets for the development of serological diagnostics and vaccine candidates. Our findings contribute critical insights into the molecular virology of OROV and provide a foundational framework for future efforts aimed at the prevention, diagnosis, and control of this neglected tropical pathogen. These advancements are essential for mitigating the impact of OROV in endemic regions and reducing the risk of global emergence. Full article

Most electrified railway networks are powered through a pantograph–overhead contact line (OCL) interface to ensure safe and reliable operation. The OCL is one of the most vulnerable components of the train traction power system as it is subjected to multiple impacts from the pantographs and to unpredictable environmental conditions. Wear, mounting imperfections, contact incidents, weather conditions, and inadequate maintenance lead to increased degradation of the pantograph–OCL current collection performance, causing degradation on contacting elements and assets failure. Incidents involving the pantograph–OCL system are significant sources of traffic disruption and train delays, e.g., Network Rail statistics show that, on average, delays due to OCL failures are 2500 h per year. In recent years, maintenance strategies have evolved significantly with improvements in technology and the increased interest in using real-time and historical data in decision support. This has led to an expansion in sensing systems for structures, vehicles, and machinery. The railway industry is currently investing in condition monitoring (CM) technologies in order to achieve lower failure rates and increase the availability, reliability, and safety of the railway service. This work presents a comprehensive review of the current CM systems for the pantograph–OCL, including their advantages and disadvantages, and outlines future trends in this area. Full article

Background: Effective asset tracking and monitoring are critical for modern warehouse management. Methods: In this paper, we present a real-time warehouse monitoring system that leverages ceiling-mounted cameras, computer vision-based object detection, a knowledge-graph based world model. The system is implemented in two architectural configurations: a distributed setup with edge processing and a centralized setup. Results: Experimental results demonstrate the system’s capability to accurately detect and continuously track common warehouse assets such as pallets, boxes, and forklifts. This work provides a detailed methodology, covering aspects from camera placement and neural network training to world model integration and real-world deployment. Conclusions: Our experiments show that the system achieves high detection accuracy and reliable real-time tracking across multiple viewpoints, and it is easily scalable to large-scale logistics and inventory applications. Full article

Malignant pleural effusion, commonly referred to as MPE, is a prevalent complication associated with individuals diagnosed with neoplastic disorders. The data acquired by pleural fluid cytology is beneficial for diagnostic objectives. Consequently, the initial step in the diagnostic procedure for lung cancer is the analysis of pleural effusion fluid. This research aims to provide a cutting-edge model for analyzing PE cytology images. This model utilizes a computer-aided diagnosis (CAD) system that integrates hyperspectral imaging (HSI) technology for the classification of spectral variations. Giemsa, which is one of the most popular microscopic stains, is employed to stain the samples, after which a sensitive CCD mounted on a microscope captures the images. Subsequently, the HSI model is tasked with obtaining the image spectra. Principal Component Analysis (PCA) constitutes the concluding phase in the classification procedure of various cell types. We expect that the suggested technique will enable medical professionals to stage lung cancer more rapidly. In the future, we aspire to develop an extensive data system that utilizes deep learning techniques to facilitate the automatic classification of cells, thereby ensuring the most precise diagnosis. Furthermore, enhancing accuracy and minimizing data dimensions are important priorities to accelerate diagnostics, conserve resources, and reduce computing time. Full article

The widespread adoption of lithium-ion batteries (LIBs) in electric vehicles (EVs) and energy-storage systems (ESSs) has raised growing concern about fire hazards caused by thermal runaway (TR). While many studies have examined cell-level TR mechanisms, investigations at the module level remain limited despite their importance for safety design. In this study, TR propagation was experimentally analyzed in a 12-cell (2p6s) pouch-type LIB module with EV-grade cells. The state of charge (SOC) and initiation location were the main variables. TR was initiated by a surface-mounted Kapton heating film, with power increased stepwise from 63 W to 141 W at 5-min intervals. Temperature, voltage, and heat release rate (HRR) were continuously monitored. Results showed that higher SOC led to earlier TR onset, shorter vent-to-ignition delay, and stronger combustion with jet flames. Center initiation produced rapid bidirectional propagation with a peak heat release rate (PHRR) of 590 kW and a propagation time of 107 s, whereas edge initiation caused slower unidirectional spread with a PHRR of 105 kW and a propagation time of 338 s. These results demonstrate that both SOC and initiation location critically control TR severity and propagation, providing essential data for EV fire safety evaluation and module design. Full article

We present the eyeglass-type switch, an eyeglass-mounted eye/blink switch designed for nurse-call operation by people with severe motor impairments, with a particular focus on amyotrophic lateral sclerosis (ALS). The system targets real-world bedside constraints—low illumination at night, supine posture, and network-independent operation—by combining near-infrared (NIR) LED illumination with an NIR eye camera and executing all processing on a small, GPU-free computer. A two-stage convolutional pipeline estimates eight periocular landmarks and the pupil center; eye-closure is detected either by a binary classifier or by an angle criterion derived from landmarks, which also skips pupil estimation during closure. User intent is determined by crossing a caregiver-tunable “off-area” around neutral gaze, implemented as rectangular or sector shapes. Four output modes—single, continuous, long-press, and hold-to-activate—are supported for both oculomotor and eyelid inputs. Safety is addressed via relay-based electrical isolation from the nurse-call circuit and audio feedback for state indication. The prototype runs at 18 fps on commodity hardware. In feature-point evaluation, mean errors were 2.84 pixels for landmarks and 1.33 pixels for the pupil center. In a bedside task with 12 healthy participants, the system achieved $F=0.965$ in single mode and $F=0.983$ in hold-to-activate mode; blink-only input yielded $F=0.993$. Performance was uniformly high for right/left/up and eye-closure cues, with lower recall for downward gaze due to eyelid occlusion, suggesting camera placement or threshold tuning as remedies. The results indicate that the proposed switch provides reliable, low-burden nurse-call control under nighttime conditions and offers a practical input option for emergency alerts and augmentative and alternative communication (AAC) workflows. Full article

Deaf-blindness, particularly in progressive conditions such as Usher syndrome, presents profound challenges to communication, independence, and access to information. Existing tactile communication technologies for individuals with Usher syndrome are often limited by the need for close physical proximity to trained interpreters, typically requiring hand-to-hand contact. In this study, we introduce a novel, cloud-based, AI-assisted gesture recognition and haptic communication system designed for long-term use by individuals with Usher syndrome, whose auditory and visual abilities deteriorate with age. Central to our approach is a wearable haptic interface that relocates tactile input and output from the hands to an arm-mounted sleeve, thereby preserving manual dexterity and enabling continuous, bidirectional tactile interaction. The system uses surface electromyography (sEMG) to capture user-specific muscle activations in the hand and forearm and employs lightweight, personalized convolutional neural networks (CNNs), hosted on a centralized server, to perform real-time gesture classification. A key innovation of the system is its ability to adapt over time to each user’s evolving physiological condition, including the progressive loss of vision and hearing. Experimental validation using a public dataset, along with real-time testing involving seven participants, demonstrates that personalized models consistently outperform cross-user models in terms of accuracy, adaptability, and usability. This platform offers a scalable, longitudinally adaptable solution for non-visual communication and holds significant promise for advancing assistive technologies for the deaf-blind community. Full article

The integration of neural interfaces with assistive robotics has transformed the field of prosthetics, rehabilitation, and brain–computer interfaces (BCIs). From brain-controlled wheelchairs to Artificial Intelligence (AI)-synchronized robotic arms, the innovations offer autonomy and improved quality of life for people with mobility disorders. This article discusses recent trends in brain–computer interfaces and their application in robotic assistive devices, such as wheelchair-mounted arms, drone control systems, and robotic limbs for activities of daily living (ADLs). It also discusses the incorporation of AI systems, including ChatGPT-4, into BCIs, with an emphasis on new innovations in shared autonomy, cognitive assistance, and ethical considerations. Full article

Simultaneous measurement of force and position often relies on delicate tactile sensing systems that only measure small forces at discrete positions. This study proposes a compact, durable sensor which can provide simultaneous and continuous measurements of force and position using multiple strain gauges mounted on a cantilever beam. When a point force is applied to the cantilever, the strain gauges are used to determine the magnitude of the applied force and its position along the beam. A major advantage of the force-position sensor concept is its compact electronics and durable sensing surface. We designed, tested, and evaluated three different prototypes for the force-position sensor concept. The prototypes achieved an average percent error of 1.71% and were highly linear. We also conducted a thorough analysis of design variables and their effects on performance. The force and position measurement ranges can be adjusted by tuning the material and geometric properties of the beam and the spacing of the strain gauges. The accuracy of force measurements is dependent upon applied load, but insensitive to the location of the applied load. Accuracy of position measurements is also dependent upon applied load and weakly dependent upon position of the applied load. Full article

Climate change is fundamentally transforming global water systems, affecting the availability, quality, and ecological dynamics of water resources. This review synthesizes current scientific understanding of climate change impacts on hydrological systems, with a focus on freshwater ecosystems, and regional water availability. Rising global temperatures are disrupting thermal regimes in rivers, lakes, and ponds; intensifying the frequency and severity of extreme weather events; and altering precipitation and snowmelt patterns. These changes place mounting stress on aquatic ecosystems, threaten water security, and challenge conventional water management practices. The paper also identifies key vulnerabilities across diverse geographic regions and evaluates adaptation strategies such as integrated water resource management (IWRM), the water, energy and food (WEF) nexus, ecosystem-based approaches (EbA), the role of advanced technology and infrastructure enhancements. By adopting these strategies, stakeholders can strengthen the resilience of water systems and safeguard critical resources for both ecosystems and human well-being. Full article

This study seeks to determine the efficacy of a novel, virtual reality (VR)-based sensorimotor assessment tool, VIST Neuro-ID, in comparison to the gold standard. This was achieved through computing common postural sway metrics, as well as comparing these metrics across population groups including sex and age (50–60 vs. 61–75). Sensorimotor assessments were conducted within the VIST Neuro-ID VR software while participants stood on a force plate. A proxy for center-of-pressure measurement was developed using the six-degree-of-freedom data collected from the head-mounted display used with the VR system. Moderate-to-high (r = 0.542–0.906) Pearson’s correlations were found between VIST Neuro-ID and the force plate for all eight postural sway metrics that were computed. Both systems detected significant differences (p < 0.05) across age groups for all metrics, except for two-dimensional path length from the force plate. Several significant differences were found across sexes, including AP and resultant sway velocity from the force plate, and resultant and AP root-mean-square from the HTC Vive Pro Eye. This indicates potential for VR to be used to collect vital postural sway metrics needed for assessing patient function, while also highlighting potential to identify balance patterns related to aging. Full article

This study focuses on human awareness, a critical component in human–robot interaction, particularly within agricultural environments where interactions are enriched by complex contextual information. The main objective is identifying human activities occurring during collaborative harvesting tasks involving humans and robots. To achieve this, we propose a novel and lightweight deep learning model, named 1D-ResNeXt, designed explicitly for recognizing activities in agriculture-related human–robot collaboration. The model is built as an end-to-end architecture incorporating feature fusion and a multi-kernel convolutional block strategy. It utilizes residual connections and a split–transform–merge mechanism to mitigate performance degradation and reduce model complexity by limiting the number of trainable parameters. Sensor data were collected from twenty individuals with five wearable devices placed on different body parts. Each sensor was embedded with tri-axial accelerometers, gyroscopes, and magnetometers. Under real field conditions, the participants performed several sub-tasks commonly associated with agricultural labor, such as lifting and carrying loads. Before classification, the raw sensor signals were pre-processed to eliminate noise. The cleaned time-series data were then input into the proposed deep learning network for sequential pattern recognition. Experimental results showed that the chest-mounted sensor achieved the highest F1-score of 99.86%, outperforming other sensor placements and combinations. An analysis of temporal window sizes (0.5, 1.0, 1.5, and 2.0 s) demonstrated that the 0.5 s window provided the best recognition performance, indicating that key activity features in agriculture can be captured over short intervals. Moreover, a comprehensive evaluation of sensor modalities revealed that multimodal fusion of accelerometer, gyroscope, and magnetometer data yielded the best accuracy at 99.92%. The combination of accelerometer and gyroscope data offered an optimal compromise, achieving 99.49% accuracy while maintaining lower system complexity. These findings highlight the importance of strategic sensor placement and data fusion in enhancing activity recognition performance while reducing the need for extensive data and computational resources. This work contributes to developing intelligent, efficient, and adaptive collaborative systems, offering promising applications in agriculture and beyond, with improved safety, cost-efficiency, and real-time operational capability. Full article