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Animal phenotyping recognition is a pivotal component of precision livestock management, holding significant importance for intelligent farming practices and animal welfare assurance. In recent years, with the rapid advancement of deep learning technologies, the YOLO algorithm—as the pioneering single-stage detection framework—has revolutionized the field of object detection through its efficient and rapid approach and has been widely applied across various agricultural domains. This review focuses on animal phenotyping as the research target structured around four key aspects: (1) the evolution of YOLO algorithms, (2) datasets and preprocessing methodologies, (3) application domains of YOLO algorithms, and (4) future directions. This paper aims to offer readers fresh perspectives and insights into animal phenotyping research. Full article

Coordinate registration (CR) is the key technology for improving the target positioning accuracy of sky-wave over-the-horizon radar (OTHR). The CR parameters are derived by matching the sea–land clutter classification (SLCC) results with prior geographic information. However, the SLCC results often contain mixed clutter, leading to discrepancies between land and island contours and prior geographic information, which makes it challenging to calculate accurate CR parameters for OTHR. To address these challenges, we transform the sea–land clutter data from Euclidean space into graph data in non-Euclidean space, and the CR parameters are obtained by calculating the similarity between graph pairs. And then, we propose a similarity calculation via a graph neural network (SC-GNN) method for calculating the similarity between graph pairs, which involves subgraph-level interactions and node-level comparisons. By partitioning the graph into subgraphs, SC-GNN effectively captures the local features within the SLCC results, enhancing the model’s flexibility and improving its performance. For validation, we construct three datasets: an original sea–land clutter dataset, a sea–land clutter cluster dataset, and a sea–land clutter registration dataset, with the samples drawn from various seasons, times, and detection areas. Compared with the existing graph matching methods, the proposed SC-GNN achieves a Spearman’s rank correlation coefficient of at least 0.800, a Kendall’s rank correlation coefficient of at least 0.639, a p@10 of at least 0.706, and a p@20 of at least 0.845. Full article

Runner bean is an important food source worldwide, and effective disease prevention and control are crucial to ensuring food security. However, runner bean is vulnerable to various diseases during its growth, which significantly affect both yield and quality. Despite the continuous advancement of disease detection technologies, existing legume disease detection models still face significant challenges in identifying small-scale, irregular, and visually insignificant disease types, limiting their practical application. To address this issue, this study proposes an improved detection model, YOLOv8_RBean, based on the YOLOv8n object detection framework, specifically designed for runner bean leaf disease detection. The model enhances detection performance through three key innovations: (1) the BeanConv module, which integrates depthwise separable convolution and pointwise convolution to improve multi-scale feature extraction; (2) a lightweight LA attention mechanism that incorporates spatial, channel, and coordinate information to enhance feature representation; and (3) a lightweight BLBlock structure built upon DWConv and LA attention, which optimizes computational efficiency while maintaining high accuracy. Experimental results on the runner bean disease dataset demonstrate that the proposed model achieves a precision of 88.7%, with mAP50 and mAP50-95 reaching 83.5% and 71.3%, respectively. Moreover, the model reduces the number of parameters to 2.71 M and computational cost to 7.5 GFLOPs, representing reductions of 10% and 7.4% compared to the baseline model. Notably, the method shows clear advantages in detecting morphologically subtle diseases such as viral infections, providing an efficient and practical technical solution for intelligent monitoring and prevention of runner bean diseases. Full article

Sheep red blood cells (SRBC) has a long history as a classical T-cell dependent (TD) antigen. Due to its cost-effectiveness, easy accessibility, and ability to elicit a robust antibody immune response, SRBC continues to be widely used in studies related with humoral immunity modulation, vaccine development, and immunoactivity/immunotoxicity testing of bioactive agents. However, detecting the relative affinity levels of SRBC-specific antibodies in SRBC-immunized animal models remains challenging. Using flow cytometry, we established a detection system capable of quickly and accurately assessing the SRBC-specific antibody relative affinity levels in humoral samples (e.g., serum, tissue fluid) of SRBC-immunized mouse models. We further validated this method using affinity maturation-deficient mice, demonstrating that this method can distinguish affinity levels of the antibodies from different samples. This approach is simple and efficient, providing an accurate and effective technological solution for research on mechanisms of humoral immunity, antibody affinity maturation, vaccine response, and immunoactivity/immunotoxicity testing. Full article

Surface plasmon resonance (SPR) aptasensors benefit from the SPR phenomenon in measuring aptamer interactions with specific targets. Integrating aptamers into SPR detection enables extensive applications in clinical analysis. Specifically, virus aptasensing platforms are highly desirable to face the ongoing challenges of virus outbreaks. This study systematically reviews the latest advances in SPR aptasensors for virus detection according to PRISMA guidelines. The literature search recovered 322 original articles from the Scopus (n = 152), Web of Science (n = 83), and PubMed (n = 87) databases. The selected articles (29) deal with the binding events between the aptamers immobilized on the sensor surface and their target molecule: virus proteins or intact viruses according to different SPR configurations. The methodological quality of each study was assessed using QUADAS-2, and a meta-analysis was conducted with the CochReview Manager (RevMan) Edition7.12.0 Data were analyzed, focusing on the types of viruses, the virus target, and the reference method. The pooled sensitivity was 1.89 (95%, CI 1.29, 2.78, I² = 49%). The analysis of different types of plasmonic sensors showed the best diagnostic results with the least heterogeneity for SPR conventional configurations: 3.23 (95% CI [1.80, 5.79]; I² = 0%, p = 0.65). These findings show that even though plasmonic biosensors effectively analyze viruses through aptamer approaches, there are still big challenges to using them regularly for diagnostics. Practical considerations for measuring label-free interactions revealed functional capabilities, technological boundaries, and future outlooks of SPR virus aptasensing. Full article

Salt lakes and the surrounding saline soils distributed across northwestern China and Inner Mongolia impose severe physicochemical corrosion on cement-based concrete. Understanding the corrosion products and mechanisms are crucial scientific and technological factors in ensuring the durability and service life of concrete structures in these regions. In this study, various analytical techniques—including X-ray diffraction, thermogravimetric–differential thermal analysis, X-ray fluorescence, and scanning electron microscopy coupled with energy-dispersive spectroscopy—were employed to systematically analyze the corrosion products of ordinary Portland cement (OPC) and high-performance concrete (HPC) specimens after eight years of field exposure in the Qarhan Salt Lake area of Qinghai. The study provided an in-depth understanding of the physicochemical corrosion mechanisms involved. The results showed that, after eight years of exposure, the corrosion products comprised both physical corrosion products (primarily sodium chloride crystals), and chemical corrosion products (associated with chloride, sulfate, and magnesium salt attacks). A strong correlation could be observed between the chemical corrosion products and the strength grade of the concrete. In C25 OPC, the detected corrosion products included gypsum, monosulfate-type calcium sulfoaluminate (AFm), Friedel’s salt, chloro-ettringite, brucite, magnesium oxychloride hydrate 318, calcium carbonate, potassium chloride, and sodium chloride. In C60 HPC, the identified corrosion products included Kuzel’s salt, Friedel’s salt, chloro-ettringite, brucite, calcium carbonate, potassium chloride, and sodium chloride. Among them, sulfate-induced corrosion led to the formation of gypsum and AFm, whereas chloride-induced corrosion resulted in chloro-ettringite and Friedel’s salt. Magnesium salt corrosion contributed to the formation of brucite and magnesium oxychloride hydrate 318, with Kuzel’s salt emerging as a co-corrosion product of chloride and sulfate attacks. Furthermore, a conversion phenomenon was evident between the sulfate and chloride corrosion products, which was closely linked to the internal chloride ion concentration in the concrete. As the chloride ion concentration increased, the transformation sequence of sulfate corrosion products occurred in the following order: AFm → Kuzel’s salt → Friedel’s salt → chloro-ettringite. There was a gradual increase in chloride ion content within these corrosion products. This investigation into concrete durability in salt-lake ecosystems offers technological guidance for infrastructure development and material specification in hyper-saline environments. Full article

Expanded newborn screening (NBS) programs are essential for early detection and treatment. This study highlights the implementation of an expanded NBS program for inborn errors of metabolism (IEMs) and congenital hypothyroidism (CH) in rural Thailand, focusing on Health Regions 7 and 8 as a model for resource-limited settings. Using the KKU-IEM web-based platform, the program streamlined workflows, integrating logistics, real-time sample tracking, and electronic data management. Regular training sessions, continuous feedback, and systematic monitoring improved outcomes. Starting from October 2022, the program covered 98.6% of 123,692 live births, identifying 101 CH cases (1 in 1208 live births) and 20 IEM cases (1 in 6100 live births). The CH incidence was slightly higher than Thailand’s national average, while the IEM incidence was double that found in a previous Bangkok pilot study. Six cases highlighted maternal conditions affecting outcomes. Process improvements reduced the average reporting time from 9.13 days in 2023 to 8.4 days in 2024, with a 19% reduction in Bueng Kan Province. Efficiencies were driven by electronic ordering, real-time tracking, and stakeholder collaboration. This program demonstrates a scalable model for rural settings, emphasizing technology integration, collaboration, and quality control. Future efforts should refine diagnostics, expand disease coverage, and enhance long-term outcomes. Full article

With the rapid advancement of artificial intelligence, particularly in fields such as autonomous driving, drone delivery, and logistics automation, the demand for high-precision and robust navigation services has become critical. In complex and dynamic urban environments, the navigation capabilities of single-sensor systems struggle to meet the practical requirements of autonomous driving technology. To address this issue, we propose a multi-source fusion navigation algorithm tailored for dynamic urban canyon scenarios, aiming to achieve reliable and continuous state estimation in complex environments. In our proposed method, we utilize independent threads on a graphics processing unit (GPU) to perform real-time detection and removal of dynamic objects in visual images, thereby enhancing the visual accuracy of multi-source fusion navigation in dynamic scenes. To tackle the challenges of significant Global Navigation Satellite System (GNSS) positioning errors and limited satellite availability in urban canyon environments, we introduce a specialized GNSS Real-Time Kinematic (RTK) stochastic model for such settings. The navigation performance of the proposed algorithm was evaluated using public datasets. The results demonstrate that our RTK/INS/Vision integrated navigation algorithm effectively improves both accuracy and availability in dynamic urban canyon environments. Full article

The need for real-time and scalable oceanographic monitoring has become crucial for coastal management, marine traffic control and environmental sustainability. This study investigates the integration of sensor technology into marine cables to enable real-time monitoring, focusing on tidal cycles and wave characteristics. A 2000 m cable demonstrator was deployed off the coast of Portugal, featuring three active repeater nodes equipped with pressure sensors at varying depths. The goal was to estimate hourly wave periods using fast Fourier transform and calculate significant wave height via a custom peak detection algorithm. The results showed strong coherence with tidal depth variations, with wave period estimates closely aligning with forecasts. The wave height estimations exhibited a clear relationship with tidal cycles, which demonstrates the system’s sensitivity to coastal hydrodynamics, a factor that numerical models designed for open waters often fail to capture. The study also highlights challenges in deep-water monitoring, such as signal attenuation and the need for high sampling rates. Overall, this research emphasises the scalability of sensor-integrated smart marine cables, offering a transformative opportunity to expand oceanographic monitoring capabilities. The findings open the door for future real-time ocean monitoring systems that can deliver valuable insights for coastal management, environmental monitoring and scientific research. Full article

Light detection and ranging (LiDAR) sensors are critical for autonomous vehicles that require unparalleled depth sensing. However, traditional LiDAR designs face significant challenges, including high costs and bulky configurations, limiting scalability and mass-market adoption. By uniquely combining patent and scientometric analysis, this study screened 188 recent LiDAR patents from a dataset of more than two million patents, uncovering strategies to enhance capability and reduce production costs. The key findings highlight the growing emphasis on solid-state architectures, modular designs, and integrated manufacturing processes as pathways to scalable and efficient LiDAR solutions. These insights bridge the gap between scientific advancements and practical implementation, providing stakeholders with a clear understanding of the technological landscape and emerging trends. By identifying future directions and actionable opportunities, this work supports the development of next-generation LiDAR systems, fostering innovation and enabling broader adoption across autonomous vehicles and other sectors. Full article

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia, associated with significant morbidity, mortality, and healthcare burden. Despite advances in AF management, challenges persist in early detection, risk stratification, and treatment optimization, necessitating innovative solutions. Artificial intelligence (AI) has emerged as a transformative tool in AF care, leveraging machine learning and deep learning algorithms to enhance diagnostic accuracy, improve risk prediction, and guide therapeutic interventions. AI-powered electrocardiographic screening has demonstrated the ability to detect asymptomatic AF, while wearable photoplethysmography-based technologies have expanded real-time rhythm monitoring beyond clinical settings. AI-driven predictive models integrate electronic health records and multimodal physiological data to refine AF risk stratification, stroke prediction, and anticoagulation decision making. In the realm of treatment, AI is revolutionizing individualized therapy and optimizing anticoagulation management and catheter ablation strategies. Notably, AI-enhanced electroanatomic mapping and real-time procedural guidance hold promise for improving ablation success rates and reducing AF recurrence. Despite these advancements, the clinical integration of AI in AF management remains an evolving field. Future research should focus on large-scale validation, model interpretability, and regulatory frameworks to ensure widespread adoption. This review explores the current and emerging applications of AI in AF, highlighting its potential to enhance precision medicine and patient outcomes. Full article

This paper presents an advanced framework for real-time monitoring and analysis of network traffic and endpoint security in large-scale enterprises by addressing the increasing complexity and frequency of cyber-attacks. Our Network Security Traffic Analysis Platform employs a comprehensive technology stack including the Elastic Stack, ZEEK, Osquery, Kafka, and GeoLocation data. By integrating supervised machine learning models trained on the UNSW-NB15 dataset, we evaluate Random Forest (RF), Decision Trees (DT), and Support Vector Machines (SVM), with the Random Forest classifier achieving a notable accuracy of 99.32%. Leveraging Artificial Intelligence and Natural Language Processing, we apply the BERT model with a Byte-level Byte-pair tokenizer to enhance network-based attack detection in IoT systems. Experiments on UNSW-NB15, TON-IoT, and Edge-IIoT datasets demonstrate our platform’s superiority over traditional methods in multi-class classification tasks, achieving near-perfect accuracy on the Edge-IIoT dataset. Furthermore, Network Security Traffic Analysis Platform’s ability to produce actionable insights through charts, tables, histograms, and other visualizations underscores its capability in static analysis of traffic data. This dual approach of real-time and static analysis provides a robust foundation for developing scalable, efficient, and automated security solutions, essential for managing the evolving threats in modern networks. Full article

Background: antimicrobial resistance represents a critical issue leading to delayed wound healing; hence, it is necessary to develop novel strategies to address this phenomenon. Objectives: this study aimed to explore the antimicrobial/anti-virulence action of Methylglyoxal-MGO alone or combined with novel technologies such as Light-Emitting Diodes-LED and Complex Magnetic Fields-CMFs against resistant clinical strains isolated from chronic wounds. Methods: characterized planktonic Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans isolates were used. Antimicrobial activity was evaluated by measuring optical density, Colony Forming Units-CFU, and synergy between MGO/LED or CMFs. Cellular membrane permeability by propidium iodide fluorescence and fluidity by Laurdan generalized polarization measurements were performed. P. aeruginosa motility was tested using the soft agar method. A docking study was performed to evaluate the possible interaction between MGO and urease in P. aeruginosa. Results: single/combined treatments showed significant antimicrobial activity. Major CFU reduction was detected after CMFs/MGO+CMFs application on C. albicans. Treatments exhibited significant changes in membrane permeability and fluidity. The treatments decreased P. aeruginosa motility with a major reduction after LED application. Docking analysis showed that MGO could bind with P. aeruginosa urease leading to defective folding and functional alterations. Conclusions: the results suggest that these treatments could represent promising and green therapeutic solutions against resistant isolates from chronic wounds. Full article

This article explores the integration of Maintenance 4.0 into HVAC (heating, ventilation, and air conditioning) systems, highlighting its essential role within the framework of Industry 4.0. Maintenance 4.0 utilizes advanced technologies such as artificial intelligence and IoT sensing technologies. It also incorporates sophisticated data management techniques to transform maintenance strategies into HVAC and indoor ventilation systems. These innovations work together to enhance energy efficiency, air quality, and overall system performance. The paper provides an overview of various Maintenance 4.0 frameworks, discussing the role of IoT sensors in real-time monitoring of environmental conditions, equipment health, and energy consumption. It highlights how AI-driven analytics, supported by IoT data, enable predictive maintenance and fault detection. Additionally, the paper identifies key research gaps and challenges that hinder the widespread implementation of Maintenance 4.0, including issues related to data quality, model interpretability, system integration, and scalability. This paper also proposes solutions to address these challenges, such as advanced data management techniques, explainable AI models, robust system integration strategies, and user-centered design approaches. By addressing these research gaps, this paper aims to accelerate the adoption of Maintenance 4.0 in HVAC systems, contributing to more sustainable, efficient, and intelligent built environments. Full article