Search Results (516)

Gait recognition, as a biometric technology with great potential, has been widely applied in numerous fields due to its unique advantages. Through in-depth research and the creation of in-the-wild gait datasets, gait recognition technology is progressively extending from laboratory settings to complex real-world scenarios, achieving notable advancements. However, the complexity of annotating gait data inevitably leads to labeling errors, known as noisy labels, which are one of the reasons for the suboptimal performance of in-the-wild gait recognition. To address these issues, this paper explores noisy label learning for in-the-wild gait recognition for the first time. We propose a plug-and-play gait recognition framework named Dynamic Noise Label Correction Network (DNLC). Specifically, it consists of two main parts: the dynamic class-center feature library and the label correction module, which can automatically identify and correct noisy labels based on the class-center feature library. In addition, we introduce the two-stage augmentation strategy to increase the diversity of the data and help reduce the impact of noisy labels. We integrated our proposed framework into five existing gait models and conducted extensive experiments on two widely used gait recognition datasets: Gait3D and CCPG. The results show that our framework increased the average Rank-1 accuracy of five methods by 10.03% and 6.45% on the Gait3D and CCPG datasets, respectively. These findings demonstrate the superior performance of our method. Full article

Hydrogen injection timing (HIT) plays a crucial role in the combustion and emission characteristics of a hydrogen/n-butanol dual-fuel engine with hydrogen direct injection. This study employed an integrated approach combining three-dimensional simulation modeling and engine test bench experiments to investigate the effects of HIT on engine performance. In order to have a more intuitive understanding of the physical and chemical change processes, such as the stratification state and combustion status of hydrogen in the cylinder, and to essentially explore the internal mechanism and fundamental reasons for the improvement in performance of n-butanol engines by hydrogen addition, a numerical study was conducted to examine the effects of HIT on hydrogen stratification and combustion behavior. The simulation results demonstrated that within the investigated range, at 100 °CA BTDC hydrogen injection time, hydrogen forms an ideal hydrogen stratification state in the cylinder; that is, a locally enriched hydrogen zone near the spark plug, while there is a certain distribution of hydrogen in the cylinder. Meanwhile, the combustion state also reaches the optimal level at this hydrogen injection moment. Consequently, 100 °CA BTDC is identified as the optimal HIT for a hydrogen/n-butanol dual-fuel engine. At the same time, an experimental study was performed to capture the actual complex processes and comprehensively evaluate combustion and emission characteristics. The experimental results indicate that both dynamic performance (torque) and combustion characteristics (cylinder pressure, flame development period, etc.) achieve optimal values at the HIT of 100 °CA BTDC. Notably, under lean-burn conditions, the combustion parameters exhibit greater sensitivity to HIT. Regarding emissions, the CO and HC emissions initially decreased slightly, then gradually increased with advanced injection timing. The 100 °CA BTDC timing effectively reduced the CO emissions at λ = 0.9 and 1.0. CO emissions at λ = 1.2, and showed minimal sensitivity to the injection timing variations. Therefore, optimized HIT facilitates enhanced combustion efficiency and emission performance in hydrogen-direct-injection n-butanol engines. Full article

Fabric defect detection is essential for quality assurance in textile manufacturing, where manual inspection is inefficient and error-prone. This paper presents a real-time deep learning-based system leveraging YOLOv11 for detecting defects such as holes, color bleeding and creases on solid-colored, patternless cotton and linen fabrics using edge computing. The system runs on an NVIDIA Jetson Orin Nano platform and supports real-time inference, Message Queuing Telemetry (MQTT)-based defect reporting, and optional Real-Time Messaging Protocol (RTMP) video streaming or local recording storage. Each detected defect is logged with class, confidence score, location and unique ID in a Comma Separated Values (CSV) file for further analysis. The proposed solution operates with two RealSense cameras placed approximately 1 m from the fabric under controlled lighting conditions, tested in a real industrial setting. The system achieves a mean Average Precision (mAP@0.5) exceeding 82% across multiple synchronized video sources while maintaining low latency and consistent performance. The architecture is designed to be modular and scalable, supporting plug-and-play deployment in industrial environments. Its flexibility in integrating different camera sources, deep learning models, and output configurations makes it a robust platform for further enhancements, such as adaptive learning mechanisms, real-time alerts, or integration with Manufacturing Execution System/Enterprise Resource Planning (MES/ERP) pipelines. This approach advances automated textile inspection and reduces dependency on manual processes. Full article

Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The rapid advancement of large language models (LLMs) in text analysis, summarization, and generation offers promising opportunities to improve construction risk identification. This study conducts a comprehensive benchmarking of natural language processing (NLP) and LLM techniques for automating the classification of risk items into a generic risk category. Twelve model configurations are evaluated, ranging from classical NLP pipelines using TF-IDF and Word2Vec to advanced transformer-based models such as BERT and GPT-4 with zero-shot, instruction, and few-shot prompting strategies. The results reveal that LLMs, particularly GPT-4 with few-shot prompts, achieve a competitive performance (F1 = 0.81) approaching that of the best classical model (BERT + SVM; F1 = 0.86), all without the need for training data. Moreover, LLMs exhibit a more balanced performance across imbalanced risk categories, showcasing their adaptability in data-sparse settings. These findings contribute theoretically by positioning LLMs as scalable plug-and-play alternatives to NLP pipelines, offering practical value by highlighting how LLMs can support early-stage project planning and risk assessment in contexts where labeled data and expert resources are limited. Full article

Platelets carry out their aggregatory and procoagulant roles in two distinct phenotypes. Aggregatory platelets initiate adhesion to the injured endothelium and extend the platelet plug, where procoagulant platelets accelerate thrombin formation and fibrinogen cleaving by exposing a procoagulant-rich outer membrane that facilitates coagulation factor assembly. Conventional anti-platelet therapies inhibit the aggregatory phenotype but fall short on restraining procoagulant platelets. Although procoagulant platelets are crucial for normal hemostasis, a shift toward excess procoagulant platelets is associated with human thrombotic disorders such as ischemic stroke. Although veterinary data is limited, recent studies show that feline and canine platelets display similar procoagulant phenotypes in response to potent agonists, suggesting that procoagulant platelets may play similar roles in the pathogenesis of thromboembolic disorders in veterinary species. Species-specific differences in platelet physiology and molecular structures, however, pose significant challenges. This review aims to (1) summarize cross-species evidence on the mechanisms driving procoagulant platelet formation, their defining features, and characteristics, (2) provide perspectives on procoagulant platelets as thrombotic biomarkers and outline the technical challenges of generating and detecting them in small animal medicine, and (3) summarize potential therapeutic targets and highlight priority research areas to advance the diagnosis and management of thromboembolic diseases in veterinary medicine. Full article

This study experimentally investigates the use of stainless-steel woven wire mesh (SSWWM) as a patch material for repairing damaged glass fibre-reinforced (GFR) composite laminates. The effects of several factors on the three-point bending (3PB) behaviour of the parent laminate were examined, including the repair method (the plugging of open hole and the external patch repair), the mesh count of the SSWWM, and the number of SSWWM layers. According to the findings, all parameters considered in this study play a pivotal role in 3PB behaviour. Employing SSWWM as a patch material can recover 66.02–129.2% of the undamaged 3PB failure load, depending on the repair method, mesh count of the SSWWM, and number of SSWWM layers. Overall, decreasing the mesh count and increasing the number of SSWWM layers and applying an external patch repair method yield better results in terms of failure load and patch efficiency. This can be attributed to the increased wire diameter, improved bending rigidity, and better load distribution over a wider area. The SSWWM bridges the damaged zone, ensuring effective load transfer between the patch and parent laminate while preventing crack propagation. Utilising SSWWM as a patch material provides a quick, reliable solution for damage scenarios in engineering applications. Full article

This paper presents ModuLab, a modular, low-cost sensor platform designed to simplify real-time environmental monitoring for laboratory research and educational settings. Centered on the APP-All MCU 2023 development board with an AVR128DA48 microcontroller (Microchip Technology Inc., Taiwan) ModuLab supports plug-and-play integration of multiple sensor types—including temperature, pH, light, and humidity—using a robust I²C communication protocol. The system features configurable sampling rates, built-in signal conditioning, and a Python-based interface for real-time data visualization. As a proof-of-concept, ModuLab was operated continuously for 48 h to evaluate system stability and filtering capabilities. However, due to institutional data ownership and confidentiality policies, the underlying datasets cannot be disclosed in this submission. The architecture and implementation details described herein are intended to guide future users and research groups seeking accessible alternatives to conventional data acquisition solutions. Comprehensive performance validation and open-access data sharing are planned as the next steps in this ongoing project. Full article

Digital twins represent a transformative innovation for battery energy storage systems (BESS), offering real-time virtual replicas of physical batteries that enable accurate monitoring, predictive analytics, and advanced control strategies. These capabilities promise to significantly enhance system efficiency, reliability, and lifespan. Yet, despite the clear technical potential, large-scale deployment of digital twin-enabled battery systems faces critical governance barriers. This study identifies three major challenges: fragmented standards and lack of interoperability, weak or misaligned market incentives, and insufficient cybersecurity safeguards for interconnected systems. The central contribution of this research is the development of a comprehensive governance framework that aligns these three pillars—standards, market and regulatory incentives, and cybersecurity—into an integrated model. Findings indicate that harmonized standards reduce integration costs and build trust across vendors and operators, while supportive regulatory and market mechanisms can explicitly reward the benefits of digital twins, including improved reliability, extended battery life, and enhanced participation in energy markets. For example, simulation-based evidence suggests that digital twin-guided thermal and operational strategies can extend usable battery capacity by up to five percent, providing both technical and economic benefits. At the same time, embedding robust cybersecurity practices ensures that the adoption of digital twins does not introduce vulnerabilities that could threaten grid stability. Beyond identifying governance gaps, this study proposes an actionable implementation roadmap categorized into short-, medium-, and long-term strategies rather than fixed calendar dates, ensuring adaptability across different jurisdictions. Short-term actions include establishing terminology standards and piloting incentive programs. Medium-term measures involve mandating interoperability protocols and embedding digital twin requirements in market rules, and long-term strategies focus on achieving global harmonization and universal plug-and-play interoperability. International examples from Europe, North America, and Asia–Pacific illustrate how coordinated governance can accelerate adoption while safeguarding energy infrastructure. By combining technical analysis with policy and governance insights, this study advances both the scholarly and practical understanding of digital twin deployment in BESSs. The findings provide policymakers, regulators, industry leaders, and system operators with a clear framework to close governance gaps, maximize the value of digital twins, and enable more secure, reliable, and sustainable integration of energy storage into future power systems. Full article

The diversity of energy resources in distribution networks requires new strategies for planning and operation. In this context, microgrids are solutions that can integrate renewable energy sources, energy storage systems (ESSs), and demand response (DR), thereby decentralizing operations and utilizing digital technologies to create more proactive energy markets. Given the above, this work proposes a distributed optimal dispatch strategy for microgrids with multiple energy resources, with a focus on scalability. Simulations are performed using agent modeling on the Python Agent Development (PADE) platform, leveraging distributed computing resources and agent communication. A co-simulation environment, coordinated by Mosaik, synchronizes data exchange, while a plug-and-play system allows dynamic agent modification. The main contribution of the present study relies on a system integration approach, combining a multi-agent system (MAS) and Mosaik co-simulation framework with plug-and-play agent support for the very short-term (five-minute) dispatch of energy resources. Optimization algorithms, namely particle swarm optimization (PSO) and multi-agent particle swarm optimization (MAPSO), are framed as an incremental improvement tailored to this distributed architecture. Case studies show that distributed MAPSO performs better, with lower objective function values and a smaller relative standard deviation (15.6%), while distributed PSO had a higher deviation (33.9%). Although distributed MAPSO takes up to three times longer to provide a solution, with an average of 9.0 s, this timeframe is compatible with five-minute dispatch intervals. Full article

As the core component of photovoltaic (PV) power generation systems, PV cells are susceptible to subtle surface defects, including thick lines, cracks, and finger interruptions, primarily caused by stress and material brittleness during the manufacturing process. These defects substantially degrade energy conversion efficiency by inducing both optical and electrical losses, yet existing detection methods struggle to precisely identify and localize them. In addition, the complexity of background noise and other factors further increases the challenge of detecting these subtle defects. To address these challenges, we propose a novel PV Cell Surface Defect Detector (PSDD) that extracts subtle defects both within the backbone network and during feature fusion. In particular, we propose a plug-and-play Subtle Feature Refinement Module (SFRM) that integrates into the backbone to enhance fine-grained feature representation by rearranging local spatial features to the channel dimension, mitigating the loss of detail caused by downsampling. SFRM further employs a general attention mechanism to adaptively enhance key channels associated with subtle defects, improving the representation of fine defect features. In addition, we propose a Background Noise Suppression Block (BNSB) as a key component of the feature aggregation stage, which employs a dual-path strategy to fuse multiscale features, reducing background interference and improving defect saliency. Specifically, the first path uses a Background-Aware Module (BAM) to adaptively suppress noise and emphasize relevant features, while the second path adopts a residual structure to retain the original input features and prevent the loss of critical details. Experiments show that PSDD outperforms other methods, achieving the highest $mA{P}_{50}$ scores of 93.6% on the PVEL-AD. Full article

Pan-connected interlayers are widely present in oil reservoirs, forming flow channels at different positions. However, conventional profile control agents struggle to plug deep interlayer channels in reservoirs, limiting the swept volume of injected water. Additionally, a clear methodology for physically simulating pan-connected reservoirs with interlayer channels and calculating interchannel flow rates remains lacking. In this study, a physical model of pan-connected interlayer reservoirs was constructed to carry out deformable gel particles (DGPs) plugging experiments on interlayer channels. A mass conservation-based flow rate calculation method for interlayer channels with iterative solution was proposed, revealing the variation law of interlayer channel flow rates during DGP injection and subsequent water flooding. Finally, oil displacement and DGP profile control experiments in pan-connected interlayer reservoirs were conducted. The study shows that during DGP injection, injected water enters the potential layer through interlayer channels in the middle and front of the water-channeling layer and bypasses back to the water-channeling layer through channels near the production well. With the increase in DGP injection volume, the flow rate of each channel increases. During subsequent water flooding, DGP breakage leads to a rapid decline in its along-path plugging capability, so water bypasses back to the water-channeling layer from the potential layer through all interlayer channels. As the DGP injection volume increases, the flow rate of each channel decreases. Large-volume DGPs can regulate interlayer channeling reservoirs in the high water cut stage. Its effectiveness mechanism involves particle migration increasing the interlayer pressure difference, which drives injected water to sweep from the water-channeling layer to the potential layer through interlayer channels, improving oil recovery by 19.74%. The flow characteristics of interlayer channels during DGP injection play a positive role in oil displacement, so the oil recovery degree in this process is greater than that in the subsequent water flooding stage under each injection volume condition. The core objective of this study is to investigate the plugging mechanism of DGPs in pan-connected interlayer channels of high-water-cut reservoirs, establish a method to quantify interlayer flow rates, and reveal how DGPs regulate flow redistribution to enhance oil recovery. Full article

Polyurethane grouting materials are polymer materials formed through the reaction of polyisocyanates and polyols. They play important roles in underground engineering, tunnel construction, and mining due to their fast reaction rate, high bonding strength, and excellent impermeability. However, traditional polyurethane grouting materials have shortcomings such as high reaction heat release, high brittleness, and poor flame retardancy, which limit their applications in high-demand engineering projects. This paper systematically reviews the research progress on modified polyurethane grouting materials. Four major modification technologies are summarized: temperature reduction modification, flame retardant modification, mechanical enhancement, and environmental adaptability improvement. A multi-dimensional performance characterization system is established, covering slurry properties, solidified body performance, microstructure characteristics, thermal properties and flame retardancy, diffusion grouting performance, and environmental adaptability. The application effects of modified polyurethane grouting materials in grouting reinforcement, grouting water plugging, and grouting lifting are analyzed. Future development directions are projected. This review is particularly valuable for researchers and engineers working in tunneling, mining, geotechnical engineering, and infrastructure rehabilitation. Full article

Background/Objectives: The management of immature teeth with wide apical foramina presents significant challenges in endodontic treatment due to difficulties in achieving a hermetic seal. The aim of this study was to evaluate void formation in apical plugs created using three calcium silicate-based materials—Biodentine, NuSmile NeoPUTTY, and Well-Root PT—applied with the help of manual, ultrasonic, or rotary file condensation (XP-endo Shaper) in 3D-printed immature teeth. Methods: Micro-computed tomography analysis was used to assess the internal, external, and total void percentage of material volume. The statistical analysis was performed using two-way ANOVA and the post hoc Bonferroni test. Statistical significance was set at p < 0.05. Results: The materials and techniques used individually do not significantly influence the formation of internal voids, but their combination does (F(4, 99) = 2.717, p = 0.034). Both factors and their interaction are significant for external voids (F(4, 99) = 4.169, p = 0.004), and all have a notable effect on total void percentages (F(4, 99) = 3.456, p = 0.012). No significant differences were observed in internal voids across the groups (p > 0.05), ranging from 0.635% to 1.078%. External voids varied significantly, with Well-Root PT and ultrasonic condensation showing the highest values with a significant difference (p < 0.05), while NeoPUTTY and Biodentine with XP-endo Shaper exhibited the lowest. Total voids remained below 4%, with no significant differences among manual condensation groups. Neither material type nor application technique consistently influenced void formation, except for Well-Root PT with ultrasonic condensation. Conclusions: These findings suggest that modern bioceramic materials and application techniques produce comparable, low-void apical plugs, with XP-endo Shaper showing promise for minimizing external voids. The interaction between material and application technique plays a crucial role during the creation of apical plugs. Full article

Inspired by the human ability to learn continuously and adapt to changing environments, researchers have proposed Online Open-World Object Detection (OLOWOD). This emerging paradigm faces the challenges of detecting known categories, discovering unknown ones, continuously learning new categories, and mitigating catastrophic forgetting. To address these challenges, we propose Category Prototype-guided Streaming Multi-Level Perturbation, PSMP, a plug-and-play method for OLOWOD. PSMP, comprising semantic-level, enhanced data-level, and enhanced feature-level perturbations jointly guided by category prototypes, operates at different representational levels to collaboratively extract latent knowledge across tasks and improve adaptability. In addition, PSMP constructs the “contrastive tension” based on the relationships among category prototypes. This mechanism inherently leverages the symmetric structure formed by class prototypes in the latent space, where prototypes of semantically similar categories tend to align symmetrically or equidistantly. By guiding perturbations along these symmetric axes, the model can achieve more balanced generalization between known and unknown categories. PSMP requires no additional annotations, is lightweight in design, and can be seamlessly integrated into existing OWOD methods. Extensive experiments show that PSMP achieves an improvement of approximately 1.5% to 3% in mAP for known categories compared to conventional online training methods while significantly increasing the Unknown Recall (UR) by around 4.6%. Full article

Deep coalbed methane (CBM) reservoirs are characterized by high hydrocarbon content and are considered an important strategic resource. Due to their inherently low permeability and porosity, horizontal well drilling is commonly employed to enhance production, with the length of the horizontal section playing a critical role in determining CBM output. However, during extended horizontal drilling, wellbore instability frequently occurs as a result of drilling fluid invasion into the coal formation, posing significant safety challenges. This instability is primarily caused by the physical intrusion of drilling fluids and their interactions with the coal seam, which alter the mechanical integrity of the formation. To address these challenges, interpenetrating and semi-interpenetrating network (IPN/s-IPN) hydrogels have gained attention due to their superior physicochemical properties. This material offers enhanced sealing and support performance across fracture widths ranging from micrometers to millimeters, making it especially suited for plugging applications in deep CBM reservoirs. A self-degradable interpenetrating double-network hydrogel particle plugging agent (SSG) was developed in this study, using polyacrylamide (PAM) as the primary network and an ionic polymer as the secondary network. The SSG demonstrated excellent thermal stability, remaining intact for at least 40 h in simulated formation water at 120 °C with a degradation rate as high as 90.8%, thereby minimizing potential damage to the reservoir. After thermal aging at 120 °C, the SSG maintained strong plugging performance and favorable viscoelastic properties. A drilling fluid containing 2% SSG achieved an invasion depth of only 2.85 cm in an 80–100 mesh sand bed. The linear viscoelastic region (LVR) ranged from 0.1% to 0.98%, and the elastic modulus reached 2100 Pa, indicating robust mechanical support and deformation resistance. Full article