Search Results (2,158)

The rapid rise of social media has accelerated the evolution of public opinion, leading to frequent group polarization. Meanwhile, advancements in information science have enabled large-scale experiments, positioning dynamics as a crucial perspective for studying group polarization. This paper systematically reviews group polarization from a dynamics perspective. First, we outline its definitions and its explanatory theories. Then, we examine the role of dynamics in polarization research, summarize the current measurement methods of group polarization, and analyze intervention strategies based on elements of dynamics. Finally, we propose a logical framework for dynamics-based interventions. Our findings indicate that while research on group polarization from a dynamics perspective is relatively comprehensive, most intervention studies remain at the simulation level, requiring further validation for real-world applicability. This review provides a systematic overview of group polarization through a dynamics lens, offering insights for addressing challenges in network governance within the social media era. Full article

Drowning constitutes the leading cause of injury-related fatalities among adolescents. In swimming pool environments, traditional manual surveillance exhibits limitations, while existing technologies suffer from poor adaptability of wearable devices. Vision models based on YOLO still face challenges in edge deployment efficiency, robustness in complex water conditions, and multi-scale object detection. To address these issues, we propose YOLO11-LiB, a drowning object detection model based on YOLO11n, featuring three key enhancements. First, we design the Lightweight Feature Extraction Module (LGCBlock), which integrates the Lightweight Attention Encoding Block (LAE) and effectively combines Ghost Convolution (GhostConv) with dynamic convolution (DynamicConv). This optimizes the downsampling structure and the C3k2 module in the YOLO11n backbone network, significantly reducing model parameters and computational complexity. Second, we introduce the Cross-Channel Position-aware Spatial Attention Inverted Residual with Spatial–Channel Separate Attention module (C2PSAiSCSA) into the backbone. This module embeds the Spatial–Channel Separate Attention (SCSA) mechanism within the Inverted Residual Mobile Block (iRMB) framework, enabling more comprehensive and efficient feature extraction. Finally, we redesign the neck structure as the Bidirectional Feature Fusion Network (BiFF-Net), which integrates the Bidirectional Feature Pyramid Network (BiFPN) and Frequency-Aware Feature Fusion (FreqFusion). The enhanced YOLO11-LiB model was validated against mainstream algorithms through comparative experiments, and ablation studies were conducted. Experimental results demonstrate that YOLO11-LiB achieves a drowning class mean average precision (DmAP50) of 94.1%, with merely 2.02 M parameters and a model size of 4.25 MB. This represents an effective balance between accuracy and efficiency, providing a high-performance solution for real-time drowning detection in swimming pool scenarios. Full article

Pavement damage classification is crucial for road maintenance and driving safety. However, restricted to the varying scales, irregular shapes, small area ratios, and frequent overlap with background noise, traditional methods struggle to achieve accurate recognition. To address these challenges, a novel pavement damage classification model is designed based on the MambaOut named Multi-scale Damage Enhancement MambaOut (MDEM). The model incorporates two key modules to improve damage classification performance. The Multi-scale Dynamic Feature Fusion Block (MDFF) adaptively integrates multi-scale information to enhance feature extraction, effectively distinguishing visually similar cracks at different scales. The Damage Detail Enhancement Block (DDE) emphasizes fine structural details while suppressing background interference, thereby improving the representation of small-scale damage regions. Experiments were conducted on multiple datasets, including CQU-BPMDD, CQU-BPDD, and Crack500-PDD. On the CQU-BPMDD dataset, MDEM outperformed the baseline model with improvements of 2.01% in accuracy, 2.64% in precision, 2.7% in F1-score, and 4.2% in AUC. The extensive experimental results demonstrate that MDEM significantly surpasses MambaOut and other comparable methods in pavement damage classification tasks. It effectively addresses challenges such as varying scales, irregular shapes, small damage areas, and background noise, enhancing inspection accuracy in real-world road maintenance. Full article

Infrared imaging technology demonstrates significant advantages in aviation safety monitoring due to its exceptional all-weather operational capability and anti-interference characteristics, particularly in scenarios requiring real-time detection of aerial objects such as airport airspace management. However, traditional infrared target detection algorithms face critical challenges in complex sky backgrounds, including low signal-to-noise ratio (SNR), small target dimensions, and strong background clutter, leading to insufficient detection accuracy and reliability. To address these issues, this paper proposes the AFK-YOLO model based on the YOLO11 framework: it integrates an ADown downsampling module, which utilizes a dual-branch strategy combining average pooling and max pooling to effectively minimize feature information loss during spatial resolution reduction; introduces the KernelWarehouse dynamic convolution approach, which adopts kernel partitioning and a contrastive attention-based cross-layer shared kernel repository to address the challenge of linear parameter growth in conventional dynamic convolution methods; and establishes a feature decoupling pyramid network (FDPN) that replaces static feature pyramids with a dynamic multi-scale fusion architecture, utilizing parallel multi-granularity convolutions and an EMA attention mechanism to achieve adaptive feature enhancement. Experiments demonstrate that the AFK-YOLO model achieves 78.6% mAP on a self-constructed aerial infrared dataset—a 2.4 percentage point improvement over the baseline YOLO11—while meeting real-time requirements for aviation safety monitoring (416.7 FPS), reducing parameters by 6.9%, and compressing weight size by 21.8%. The results demonstrate the effectiveness of dynamic optimization methods in improving the accuracy and robustness of infrared target detection under complex aerial environments, thereby providing reliable technical support for the prevention of mid-air collisions. Full article

The purpose of the study is to research emotional labor and cognitive effort in radar-based collision avoidance tasks within a nautical simulator. By assessing participants’ emotional responses and mental strain, the research aimed to identify negative emotional states associated with a lack of experience, which, in the worst-case scenario, could contribute to navigational incidents. Fifteen participants engaged in multiple sessions simulating typical maritime conditions and navigation challenges. Emotional and cognitive effort were evaluated using three primary methods: heart rate monitoring, a Likert-scale questionnaire, and real-time facial expression recognition software. Heart rate data provided physiological indicators of stress, while the questionnaire and facial expressions captured subjective perceptions of difficulty and emotional strain. By correlating the measurements, the study aimed to uncover emotional patterns linked to task difficulty with insight into engagement, attention, and blink rate levels during the simulation, revealing how a lack of experience contributes to negative emotions and human factor errors. The understanding of the emotional labor and effort in maritime navigation training contributes to strategies for reducing incident risk through improved simulation training practices. Full article

In modern industry, the spring full-lift safety valve is a key device for safe pressure relief of pressure-bearing systems. Its valve seat sealing surface is easily damaged after long-term use, causing internal leakage, resulting in safety hazards and economic losses. Therefore, it is of great significance to quickly and accurately diagnose its internal leakage state. Among the current methods for identifying fluid machinery faults, model-based methods have difficulties in parameter determination. Although the data-driven convolutional neural network (CNN) has great potential in the field of fault diagnosis, it has problems such as hyperparameter selection relying on experience, insufficient capture of time series and multi-scale features, and lack of research on valve internal leakage type identification. To this end, this study proposes a safety valve internal leakage identification method based on high-frequency FPGA data acquisition and improved CNN. The acoustic emission signals of different internal leakage states are obtained through the high-frequency FPGA acquisition system, and the two-dimensional time–frequency diagram is obtained by short-time Fourier transform and input into the improved model. The model uses the leaky rectified linear unit (LReLU) activation function to enhance nonlinear expression, introduces random pooling to prevent overfitting, optimizes hyperparameters with the help of horned lizard optimization algorithm (HLOA), and integrates the bidirectional gated recurrent unit (BiGRU) and selective kernel attention module (SKAM) to enhance temporal feature extraction and multi-scale feature capture. Experiments show that the average recognition accuracy of the model for the internal leakage state of the safety valve is 99.7%, which is better than the comparison model such as ResNet-18. This method provides an effective solution for the diagnosis of internal leakage of safety valves, and the signal conversion method can be extended to the fault diagnosis of other mechanical equipment. In the future, we will explore the fusion of lightweight networks and multi-source data to improve real-time and robustness. Full article

Background/Objectives: Workplace conflict and aggression pose significant psychosocial risks across diverse professional sectors. This protocol outlines a novel, university-based educational intervention. Developed by a multidisciplinary team from the University Hospital of Pisa, Italy, including occupational physicians and a psychiatrist specializing in work and organizational psychology, its primary purpose is to enhance conflict prevention and crisis management skills. While initially developed and tested within the veterinary sector due to its identified vulnerabilities, the intervention is inherently generalizable to any high-stress professional environment characterized by intense client, customer, or public interactions. Methods: The intervention integrates didactic instruction with active, immersive learning through tailored role-playing scenarios simulating real-world challenging encounters. This study protocol details the structured methodology for evaluating the immediate effectiveness of this training. We are using a specifically developed efficacy scale to assess outcomes. Results: The results demonstrate a significant improvement in all assessed skills from the pre-training to the post-training evaluation. For every item on the scale, the median scores increased, indicating a positive shift in overall group performance. The p-value for each item was <0.001, confirming that the observed improvements were statistically significant. These results demonstrate enhanced conflict resolution skills, improved communication, and an increased sense of self-efficacy among participants. Conclusions: This protocol offers a comprehensive and generalizable approach to addressing workplace psychosocial risks through an innovative educational intervention. A key future goal involves advancing this training methodology by integrating virtual reality (VR) environments with AI-driven avatars for role-playing, aiming to achieve a more realistic and impactful learning experience and sustained behavioral change. Full article

Underwater imaging is affected by spatially varying blur caused by water flow turbulence, light scattering, and camera motion, resulting in severe visual quality loss and diminished performance in downstream vision tasks. Although numerous underwater image enhancement methods have been proposed, the issue of addressing non-uniform blur under realistic underwater conditions remains largely underexplored. To bridge this gap, we propose PMSPNet, a Progressive Multi-Scale Perception Network, designed to handle underwater non-uniform blur. The network integrates a Hybrid Interaction Attention Module to enable precise modeling of feature ambiguity directions and regional disparities. In addition, a Progressive Motion-Aware Perception Branch is employed to capture spatial orientation variations in blurred regions, progressively refining the localization of blur-related features. A Progressive Feature Feedback Block is incorporated to enhance reconstruction quality by leveraging iterative feature feedback across scales. To facilitate robust evaluation, we construct the Non-uniform Underwater Blur Benchmark, which comprises diverse real-world blur patterns. Extensive experiments on multiple real-world underwater datasets demonstrate that PMSPNet consistently surpasses state-of-the-art methods, achieving on average 25.51 dB PSNR and an inference speed of 0.01 s, which provides high-quality visual perception and downstream application input from underwater sensors for underwater robots, marine ecological monitoring, and inspection tasks. Full article

Inverse Synthetic Aperture Radar (ISAR) imaging plays a crucial role in reconnaissance and target monitoring. However, the presence of uncertain factors often leads to indistinct component visualization and significant noise contamination in imaging results, where weak scattering components are frequently submerged by noise. To address these challenges, this paper proposes a Phase-Aware Multi-Scale Transformer network (PA-MSFormer) that simultaneously enhances weak component regions and suppresses noise. Unlike existing methods that struggled with this fundamental trade-off, our approach achieved 70.93 dB PSNR on electromagnetic simulation data, surpassing the previous best method by 0.6 dB, while maintaining only 1.59 million parameters. Specifically, we introduce a phase-aware attention mechanism that separates noise from weak scattering features through complex-domain modulation, a dual-branch fusion network that establishes frequency-domain separability criteria, and a progressive gate fuser that achieves pixel-level alignment between high- and low-frequency features. Extensive experiments on electromagnetic simulation and real-measured datasets demonstrate that PA-MSFormer effectively suppresses noise while significantly enhancing target visualization, establishing a solid foundation for subsequent interpretation tasks. Full article

In developing countries, indoor air pollution in rural areas is often attributed to the use of solid biomass fuels for cooking. Such fuels generate particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO₂), polyaromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs). PM created from biomass combustion is a pollutant particularly damaging to health. This rigorous study employed a personal sampling device and multi-stage cascade impactor to collect airborne PM (including PM_2.5) and deposited ash from 20 real-world kitchen microenvironments. A robust analysis of the PM was undertaken using a range of morphological, physical, and chemical techniques, the results of which were then compared to a controlled burn experiment. Results revealed that airborne PM was predominantly carbon (~85%), with the OC/EC ratio varying between 1.17 and 11.5. Particles were primarily spherical nanoparticles (50–100 nm) capable of deep penetration into the human respiratory tract (HRT). This is the first systematic characterisation of biomass cooking emissions in authentic rural kitchen settings, linking particle morphology, chemistry and toxicology at health-relevant scales. Toxic heavy metals like Cr, Pb, Cd, Zn, and Hg were detected in PM, while ash was dominated by crustal elements such as Ca, Mg and P. VOCs comprised benzene derivatives, esters, ethers, ketones, tetramethysilanes (TMS), and nitrogen-, phosphorus- and sulphur-containing compounds. This research showcases a unique collection technique that gathered particles indicative of their potential for penetration and deposition in the HRT. Impact stems from the close link between the physico-chemical properties of particle emissions and their environmental and epidemiological effects. By providing a critical evidence base for exposure modelling, risk assessment and clean cooking interventions, this study delivers internationally significant insights. Our methodological innovation, capturing respirable nanoparticles under real-world conditions, offers a transferable framework for indoor air quality research across low- and middle-income countries. The findings therefore advance both fundamental understanding of combustion-derived nanoparticle behaviour and practical knowledge to inform public health, environmental policy, and the UN Sustainable Development Goals. Full article

To ensure the safety and quality of grain during storage requires distributed monitoring of temperature and relative humidity within the bulk material, where hundreds of sensors may be needed. Conventional multi-channel systems are often constrained by the limited number of sensors connectable to a single acquisition unit, high hardware cost, and poor scalability. To address these challenges, this study proposes a novel design method for a multi-channel temperature and relative humidity acquisition system (MTRHAS). The system integrates sequential sampling control and a time-division multiplexing mechanism, enabling efficient data acquisition from multiple sensors while reducing hardware requirements and cost. This system employs sequential sampling control using a single complex programmable logic device (CPLD), and uses multiple CPLDs for multi-channel sensor expansion with a shared address and data bus for communication with a microcontroller unit (MCU). A prototype was developed using two CPLDs and one MCU, achieving data collection from 80 sensors. To validate the approach, a simulated grain silo experiment was conducted, with nine sensors deployed to monitor temperature and relative humidity during aeration. Calibration ensured sensor accuracy, and real-time monitoring results revealed that the system effectively captured spatial and temporal variation patterns of intergranular air conditions. Compared with conventional designs, the proposed system shortens the sampling cycle, decreases the number of acquisition units required, and enhances scalability through the shared bus architecture. These findings demonstrate that the MTRHAS provides an efficient and practical solution for large-scale monitoring of grain storage environments. Full article

As the safety requirements of elevator systems continue to rise, the detection of loose bolts and the high-precision segmentation of anti-loosening lines have become critical challenges in elevator landing door inspection. Traditional manual inspection and conventional visual detection often fail to meet the requirements of high precision and robustness under real-world conditions such as oil contamination and low illumination. This paper proposes two improved algorithms for detecting loose bolts and segmenting anti-loosening lines in elevator landing doors. For small-bolt detection, we introduce the DS-EMA model, an enhanced YOLOv8 variant that integrates depthwise-separable convolutions and an Efficient Multi-scale Attention (EMA) module. The DS-EMA model achieves a 2.8 percentage point improvement in mAP over the YOLOv8n baseline on our self-collected dataset, while reducing parameters from 3.0 M to 2.8 M and maintaining real-time throughput at 126 FPS. For anti-loosening-line segmentation, we develop an improved DeepLabv3+ by adopting a MobileViT backbone, incorporating a Global Attention Mechanism (GAM) and optimizing the ASPP dilation rate. The revised model increases the mean IoU to 85.8% (a gain of 5.4 percentage points) while reducing parameters from 57.6 M to 38.5 M. Comparative experiments against mainstream lightweight models, including YOLOv5n, YOLOv6n, YOLOv7-tiny, and DeepLabv3, demonstrate that the proposed methods achieve superior accuracy while balancing efficiency and model complexity. Moreover, compared with recent lightweight variants such as YOLOv9-tiny and YOLOv11n, DS-EMA achieves comparable mAP while delivering notably higher recall, which is crucial for safety inspection. Overall, the enhanced YOLOv8 and DeepLabv3+ provide robust and efficient solutions for elevator landing door safety inspection, delivering clear practical application value. Full article

Educational games often fail to effectively merge game mechanics with educational goals, lacking adaptive feedback and real-time performance monitoring. This study explores how Human–Computer Interaction principles and adaptive feedback can enhance educational game design to improve learning outcomes and user experience. Four educational games were analyzed using a mixed-methods approach and evaluated through established frameworks, such as the Serious Educational Games Evaluation Framework, the Assessment of Learning and Motivation Software, the Learning Object Evaluation Scale for Students, and Universal Design for Learning guidelines. In addition, a novel Human–Machine Pair Inspection protocol was employed to gather real-time data on adaptive feedback, cognitive load, and interactive behavior. Findings suggest that Human–Machine Pair Inspection-based adaptive mechanisms significantly boost personalized learning, knowledge retention, and student motivation by better aligning games with learning objectives. Although the sample size is small, this research provides practical insights for educators and designers, highlighting the effectiveness of adaptive Game-Based Learning. The study proposes the Human–Machine Pair Inspection methodology as a valuable tool for creating educational games that successfully balance user experience with learning goals, warranting further empirical validation with larger groups. Full article

This study investigates the formation mechanism and stress response characteristics of normal faults in coal-bearing strata through large-scale physical simulation experiments. A multi-layer heterogeneous model with a geometric similarity ratio of 1:300 was constructed using similar materials that were tailored to match the mechanical properties of real strata. Real-time monitoring techniques, including fiber Bragg grating strain sensors and a DH3816 static strain system, were employed to record the evolution of deformation, strain, and displacement fields during the fault development. The results show that the normal fault formation process includes five distinct stages: initial compaction, fault initiation, crack propagation, fault slip, and structural stabilization. Quantitatively, the vertical displacement of the hanging wall reached up to 5.6 cm, equivalent to a prototype value of 16.8 m, and peak horizontal stress increments near the fault exceeded 0.07 MPa. The experimental data reveal that stress concentration during the fault slip stage causes severe damage to the upper coal seam roof, with localized vertical stress fluctuations exceeding 35%. Structural planes were found to control crack nucleation and slip paths, conforming to the Mohr–Coulomb shear failure criterion. This research provides new insights into the dynamic coupling of tectonic stress and fault mechanics, offering novel experimental evidence for understanding fault-induced disasters. The findings contribute to the predictive modeling of stress redistribution in fault zones and support safer deep mining practices in structurally complex coalfields, which has potential implications for petroleum geomechanics and energy resource extraction in similar tectonic settings. Full article

Covering problems constitute a central theme in facility location research. This study extends the classical Maximal Covering Location Problem (MCLP) and Partial Set Covering Location Problem (PSCLP) to their generalized variants, in which each demand point must be simultaneously served by multiple facilities. This generalization captures reliability requirements inherent in applications such as emergency response and robust communication networks. We first present integer programming formulations for both generalized problems, followed by equivalent reformulations that facilitate algorithmic development. Building on these, we design exact Benders decomposition algorithms that exploit structural properties of the problems to achieve enhanced scalability and computational efficiency. Computational experiments on large-scale synthetic instances with up to 200,000 demand points demonstrate that our method attains more than a threefold speedup over CPLEX. We further validate the effectiveness of the proposed approach through experiments on a real-world dataset. In addition, we compare our method with a tabu search heuristic, and the numerical results show that within a fixed time limit, our method is generally able to identify higher-quality feasible solutions. These results collectively demonstrate both the effectiveness and the practical applicability of our approach for large-scale generalized covering problems. Full article