Search Results (432)

The language used by attorneys at trial could influence case outcomes, impacting fairness and wrongful convictions. At trial, attorneys choose their words to manage impressions the jury forms of the defendant, thereby influencing case outcomes. This study examines whether the abstraction and emotional valence of attorneys’ language at trial influence jurors’ decision-making. In this 2 × 2 factorial experiment, 273 online participants read an attorney’s closing statement regarding a civil case, with the emotional valence of the attorney’s descriptions being either positive or negative, and the abstraction concrete or abstract (e.g., a negative–concrete description being “the cost of removing these cancer-causing chemicals is millions of dollars” vs. the corresponding abstract description, “the cost of removing these health-hazardous chemicals is enormous”). The results revealed that attorney language abstraction and emotional valence influenced jurors’ perceptions of the case: Participants judged the defendant as more liable when exposed to negative–concrete language than positive–concrete language—a difference not present with abstract language. Findings suggest that attorneys might benefit from tailoring their language in closing arguments and that jurors’ decisions can be influenced by how information is conveyed—highlighting implications for courtroom communication and legal outcomes. Full article

Ground deformation monitoring in mining areas is essential for hazard prevention and environmental protection. Although interferometric synthetic aperture radar (InSAR) provides detailed phase information for accurate deformation measurement, its performance is often compromised in regions experiencing rapid subsidence and strong noise, where phase aliasing and coherence loss lead to significant inaccuracies. To overcome these limitations, this paper proposes JOTGLNet, a guided learning network with joint offset tracking, for multiscale deformation monitoring. This method integrates pixel offset tracking (OT), which robustly captures large-gradient displacements, with interferometric phase data that offers high sensitivity in coherent regions. A dual-path deep learning architecture was designed where the interferometric phase serves as the primary branch and OT features act as complementary information, enhancing the network’s ability to handle varying deformation rates and coherence conditions. Additionally, a novel shape perception loss combining morphological similarity measurement and error learning was introduced to improve geometric fidelity and reduce unbalanced errors across deformation regions. The model was trained on 4000 simulated samples reflecting diverse real-world scenarios and validated on 1100 test samples with a maximum deformation up to 12.6 m, achieving an average prediction error of less than 0.15 m—outperforming state-of-the-art methods whose errors exceeded 0.19 m. Additionally, experiments on five real monitoring datasets further confirmed the superiority and consistency of the proposed approach. Full article

In addition to air temperature and personal factors, other weather quantities govern the outdoor human thermal perception. This study provides a new targeted approach for the evaluation of extreme events based on a specific multivariable bioclimate index. Heat waves (HWs) and outdoor human thermal stress (OHTS) events that occurred in downtown Rome (Italy) over the years 2018–2023 are identified, characterized, and compared through appropriate indices based on the air temperature for HWs and the Mediterranean Outdoor Comfort Index (MOCI) for OHTS events. The overlap between the two types of events is evaluated for each year through the hit (HR) and false alarm rates. The outcomes reveal severe traits for HWs and OHTS events and higher values of HR (minimum of 66%) with OHTS as a predictor of extreme conditions. This pilot investigation confirms that the use of air temperature threshold underestimates human physiological stress, revealing the importance of including multiple parameters, such as weather variables (temperature, wind speed, humidity, and solar radiation) and personal factors, in the assessment of hazards for the population living in a specific geographical region. This type of approach reveals increasingly critical facets and can provide key strategies to establish safe outdoor conditions for occupational and leisure activities. Full article

Traffic safety is undergoing a profound transformation, driven by advances in data science, sensing technologies, and computational modeling. Proactive approaches are enabling the early identification of potential hazards, real-time decision-making, and the development of smarter, safer transportation systems. This Special Issue summarizes recent progress in traffic safety assessment, highlighting the application of emerging tools such as machine learning, explainable artificial intelligence, and computer vision. These innovations are used to predict crash risks, evaluate surrogate safety measures, and automate the analysis of behavioral data, contributing to more inclusive and adaptive safety frameworks, particularly for vulnerable road users such as pedestrians and cyclists. The research also addresses key challenges, including data integration across diverse sources, aligning safety metrics with human perception, and ensuring the scalability of models in complex environments. By advancing both technical methodologies and human-centered evaluation, these developments signal a shift toward more intelligent, transparent, and equitable approaches to traffic safety assessment and policy-making. Full article

Emerging technologies are revolutionizing power system operation and maintenance. Intelligent state perception is pivotal for stable grid operation, with small object detection technology being vital for identifying minor hazards in power facilities. However, challenges like small object size, low resolution, occlusion, and low confidence arise in small object detection for power operation and maintenance. This paper proposes PyraFAN, a feature fusion method designed for small object detection, and introduces a cloud-edge collaborative inference based smart detection method. This method boosts detection accuracy while ensuring real-time performance. Additionally, a graph-guided distillation method is developed for edge models. By quantifying model performance and task similarity, multi-model collaborative training is realized to improve detection accuracy. Experimental results show that compared with standalone edge models, the proposed method improves detection accuracy by 6.98% and reduces the false negative rate by 19.56%. The PyraFAN module can enhance edge model detection accuracy by approximately 12.2%. Updating edge models via cloud model distillation increases the mAP@0.5 of edge models by 2.7%. Compared to cloud models, the cloud-edge collaboration method reduces average inference latency by 0.8%. This research offers an effective solution for improving the accuracy of deep learning based small object detection in power operation and maintenance within cloud-edge computing environments. Full article

The construction industry in Pakistan faces persistent challenges due to uncertainties such as behavioral intention, risk identification, and stakeholder perception, which often lead to significant losses in construction activities and human resources. This study aims to quantitatively evaluate these critical factors within the theoretical framework of Building Information Modeling (BIM) and the Technology Acceptance Model (TAM). Specifically, key constructs—Behavioral Intention (BI), Hazard Identification (HI), and Stakeholder Perception (SP)—are analyzed to assess their influence on construction safety management practices. A structured questionnaire was distributed electronically to construction professionals across various ongoing projects in Pakistan. The questionnaire items were based on a five-point Likert scale, and reliability was confirmed with high Cronbach’s alpha values for BI (0.82), HI (0.92), and SP (0.91). To evaluate the relationships between constructs, descriptive statistics and multiple regression analysis were employed. The regression results showed strong model fit for BI and HI (R² = 0.945), and near-perfect fit for SP (R² = 0.998), demonstrating robust predictive power. Significant correlations were found among independent variables such as Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Use (ATU), and others. This study further identifies Trust (TR) and Organizational Culture (OC) as critical predictors of stakeholder perception in the BIM context. A conceptual framework was developed incorporating statistical parameters (e.g., p-values, R², t-stats) to categorize the effectiveness of BIM and TAM theoretical integration for safety risk management. This approach is novel in its use of TAM-based constructs to evaluate BIM-related safety outcomes in the Pakistani construction sector—a context where such empirical evidence is limited. The findings provide predictive insights into how behavioral, perceptual, and organizational variables influence construction safety performance, offering practical implications for BIM adoption and safety policy design. Full article

Autonomous vehicles, submersible robotic systems and drones, and other human-carrying equipment consistently adhere to a safety perimeter, ensuring collision-free navigation amidst surrounding objects. In contrast, roller coaster vehicles, despite being constrained to a predetermined track, necessitate frequent safety distance detection owing to the variability introduced by trees and decorative installations. Passengers’ limbs may protrude beyond vehicle boundaries, posing a collision hazard. The motion range of limbs, influenced by vehicle-specific conditions, mismatches standardized safety volumes (cylindrical, cubic, and rectangular) designed for mobile entities. The roller coaster industry’s current practice involves a moving safety frame, which visually inspects for collisions to assess safety distances, which is cumbersome and prone to oversight in intricate settings. Therefore, this study introduces a novel safety envelope detector (SE-detector). It creates a customer-defined virtual safety envelope around the roller coaster vehicle and measures the safety distance based on LiDAR (Light Detection and Ranging) to detect the intrusions of obstacles. Meanwhile, this SE-detector also innovatively integrated an accelerometer to synchronously measure the acceleration of the vehicle. The measured acceleration will be aligned with simulated sequences by dynamic time warping (DTW) algorithms to pinpoint intrusion location. Additionally, a wide-angle camera is also deployed to enhance perception of the surrounding environment. The SE-detector developed in this study has the capability to record inspection results. It is expected to enhance the inspection capabilities of the safety envelope for roller coasters, thereby improving the efficiency of safety distance inspection. Full article

The visual performance and photobiological effects of white LED systems based on spectral compensation are discussed, specifically focusing on the total optical power, the ratio of scotopic vision luminous flux to photopic vision luminous flux (S/P), the blue light hazard (BLH), and the circadian action factor (CAF). Theoretical models are established by integrating the spectral power distribution (SPD) with spectral sensitivity functions associated with the human visual system, and meanwhile, the impacts of LEDs’ electro-thermal characteristics on the mixed spectral structure and optical properties are analyzed. As experimental results demonstrate, an excellent agreement is shown between the calculated and measured values of the total optical power, S/P, BLH, and CAF, in terms of both values and variation trends. These proposed models are expected to serve as effective tools for understanding the visual perception and non-visual biological effects in specific illumination environments. Moreover, they can offer valuable reference frameworks for the development of lighting solutions that are more human-centered and health-oriented. Full article

Underground mining operations face increasing challenges due to their complex and hazardous environments. One key difficulty is ensuring real-time safety monitoring and disaster prevention. Traditional monitoring systems often suffer from delayed data acquisition and rely heavily on cloud-based processing. These factors limit their responsiveness during emergencies. To address these limitations, this study presents an underground Internet of Things (IoT) monitoring system based on edge computing. The system architecture is composed of three layers: a perception layer for real-time sensing, an edge gateway layer for local data processing and decision-making, and a cloud service layer for storage and analytics. By shifting computation closer to the data source, the system significantly reduces latency and enhances response efficiency. The system is tailored to actual mine-site conditions. It integrates pressure monitoring for artificial expandable pillars and roof subsidence detection in stopes. It has been successfully deployed in a field environment, and the data collected during commissioning demonstrate the system’s feasibility and reliability. Results indicate that the proposed system meets real-world demands for underground safety monitoring. It enables timely warnings and improves the overall automation level. This approach offers a practical and scalable solution for enhancing mine safety and provides a valuable reference for future smart mining systems. Full article

As an important traffic and transportation roadway, tunnel engineering is widely used in important fields such as highways, railways, water conservancy, subways and mining. It is limited by complex geological conditions, harsh construction environments and poor robustness of the monitoring system. If the construction process and monitoring method are not properly designed, it will often directly induce disasters such as tunnel deformation, collapse, leakage and rockburst. This seriously threatens the safety of tunnel construction and operation and the protection of the regional ecological environment. Therefore, based on distributed fiber optic sensing technology, the full–cycle spatiotemporally continuous sensing information of the tunnel structure is obtained in real time. Accordingly, the health status of the tunnel is dynamically grasped, which is of great significance to ensure the intrinsic safety of the whole life cycle for the tunnel project. Firstly, this manuscript systematically sorts out the development and evolution process of the theory and technology of structural health monitoring in tunnel engineering. The scope of application, advantages and disadvantages of mainstream tunnel engineering monitoring equipment and main optical fiber technology are compared and analyzed from the two dimensions of equipment and technology. This provides a new path for clarifying the key points and difficulties of tunnel engineering monitoring. Secondly, the mechanism of action of four typical optical fiber sensing technologies and their application in tunnel engineering are introduced in detail. On this basis, a spatiotemporal continuous perception method for tunnel engineering based on DFOS is proposed. It provides new ideas for safety monitoring and early warning of tunnel engineering structures throughout the life cycle. Finally, a high–speed rail tunnel in northern China is used as the research object to carry out tunnel structure health monitoring. The dynamic changes in the average strain of the tunnel section measurement points during the pouring and curing period and the backfilling period are compared. The force deformation characteristics of different positions of tunnels in different periods have been mastered. Accordingly, scientific guidance is provided for the dynamic adjustment of tunnel engineering construction plans and disaster emergency prevention and control. At the same time, in view of the development and upgrading of new sensors, large models and support processes, an innovative tunnel engineering monitoring method integrating “acoustic, optical and electromagnetic” model is proposed, combining with various machine learning algorithms to train the long–term monitoring data of tunnel engineering. Based on this, a risk assessment model for potential hazards in tunnel engineering is developed. Thus, the potential and disaster effects of future disasters in tunnel engineering are predicted, and the level of disaster prevention, mitigation and relief of tunnel engineering is continuously improved. Full article

Plant-based diets are increasingly attracting attention as they play a significant role in human health and environmental sustainability and are believed to be key components of sustainable food systems. In the present study, both pros and cons of the adoption of plant-based diets are analyzed using a bibliometric method integrated with a qualitative examination of the scientific literature. For the bibliometric study, Bibliometrix software was utilized, examining 3245 scientific articles, downloaded from the Scopus database, and printed between the years 1957 and 2025. The analyses were conducted using R software, version 4.4.1, with access to the Bibliometrix package, version 4.1. The results indicate a remarkable rise, in the last two decades, in the scholarly focus on the influence of plant-based diets on the individual’s health condition as well as the environment. Keyword co-occurrence studies and international collaborations demonstrate a dominance of research focus in both the United States and Europe, with significant contributions from the Asia–Pacific region. Furthermore, the current work offers qualitative identification of the benefits of plant diets from various perspectives like nutritional, economic, ecological, and cultural. It also explores the main dissuaders from adhering to these diets, including perceived nutritional hazards, cost perceptions, low availability, and social prohibitions. Findings emphasize that, in spite of all the barriers, plant food-based diets have a wide-ranging ability to provide tangible benefits at both the individual and population levels, and documented in the scientific literature are recommendations of expert-led education programs, economic incentives, and judiciously framed public policies to overcome these barriers and to make this transition possible towards sustainable food choices. Findings provide a comprehensive understanding of the current lines of inquiry and stage the subsequent work on how to motivate sustainability among the general population. Full article

This study presents a smartphone-based dual-index framework for evaluating bicycle pavement conditions, aimed at supporting sustainable urban mobility and cyclist safety. Conventional assessment methods, such as the International Roughness Index (IRI), often overlook short-range discontinuities and are impractical for micromobility-scale infrastructure monitoring. To address these limitations, two perception-aligned indices were developed: the Bicycle Road Roughness Index (BRI), reflecting sustained surface discomfort, and the Faulting Impact Index (FII), quantifying acute vertical shocks. Both indices were calibrated through structured panel surveys involving 40 experienced cyclists and validated using high-frequency tri-axial acceleration data collected in both experimental and field settings. Regression analysis confirmed strong alignment between sensor signals and user perception (R² = 0.74 for BRI; R² = 0.76 for FII). A five-grade classification system was proposed, with critical FII thresholds at 87.3 m/s² for “risky” and 119.4 m/s² for “not rideable” conditions. Field validation across four diverse sites revealed over 380 hazard segments requiring attention, demonstrating the framework’s ability to identify localized risks that may be masked by traditional metrics. By leveraging off-the-shelf smartphones and open-source sensing tools, the proposed approach enables scalable, low-cost, and cyclist-centered diagnostics. The dual-index system not only enhances rideability evaluation but also supports targeted maintenance planning, real-time hazard detection, and broader efforts toward data-driven, sustainable micromobility management. Full article

Urban cycling faces the challenge of cyclist vulnerability due to infrastructural deficiencies and complex traffic environments. This study evaluates the accuracy of risk perception among 153 urban cyclists in Loja, Ecuador, using a mixed-methods design that integrates self-reported behaviors (Cycling Behavior Questionnaire—CBQ), visual assessments of 12 road segments, and objective risk classifications derived from the CycleRAP methodology. Results show a notable misalignment between perceived and actual risk, with consistent underestimation of extreme-risk scenarios and overestimation of low-risk ones. The combined CBQ score (violations + errors) emerged as the strongest predictor of inaccurate risk perception in decision tree models, explaining 28.75% of the model’s predictive power. Interestingly, cycling experience did not improve accuracy; frequent cyclists with high violation/error scores and older age showed the poorest perception, while young cyclists with moderate behavior scores exhibited higher accuracy. These results suggest that the relationship between cycling experience and risk assessment is more complex than commonly assumed. Findings highlight the need for behavioral interventions to correct misperceptions, alongside infrastructural measures that address objective hazards. Given the limited number of road segments and moderate sample size, subgroup analyses may be underpowered and should be interpreted with caution. Full article

Working in hyperthermal environments can lead to heat-related illnesses. Evaluating and predicting high-temperature environments can effectively reduce heat risks and hazards. However, there is still a lack of corresponding high-temperature environment assessment methods and indicators in existing research. Moreover, traditional evaluation indicators and prediction methods have shortcomings in objectivity, accuracy, and practicality. To fill these gaps, a climate chamber was constructed to simulate different environmental conditions, and human labor experiments with 98 subjects were conducted. The ambient temperatures were set to 34 °C, 36 °C, 38 °C, and 40 °C, and the relative humidity was set to 60%, 70%, 80%, and 90%, respectively. During the experiments, the subjects’ oral temperatures, heart rates, skin temperatures, and subjective perceptions were recorded. Based on the obtained parameters of the subjects, two principal components with an explained variance of 92.131% were extracted by principal component analysis, and with the determination of weightings, a comprehensive evaluation index (F) was established and the F-score was calculated. According to the F-score, 16 different hyperthermal environments were classified into three categories through hierarchical clustering analysis and discriminant analysis, with the corresponding F-score ranges of 28.14–39.76, 39.17–45.21, and 44.13–52.39. An analysis was conducted on the value of physiological and subjective indicators to test the nature of classification. Full article

Robotic waste sorting systems offer a scalable and consistent alternative to manual sorting for Construction and Demolition Waste (CDW) by reducing labor-intensive tasks and exposure to hazardous conditions, while enabling the extraction of high-purity materials (e.g., polymers) from the waste streams. Despite advancements in perception systems, manipulation and planning remain significant bottlenecks, limiting widespread adoption due to high complexity and cost. This paper introduces RaapWaste, a robot- and application-agnostic planning framework specifically designed for waste sorting, addressing challenges in motion planning, scheduling, and real-world integration. Built on open-source resources, RaapWaste employs a modular and flexible architecture, enabling integration of diverse planning techniques and scheduling strategies. The framework aims to simulate the performance of real-world sorting equipment (e.g., robots, grippers). To evaluate its effectiveness, we conducted simulations with articulated and delta robots, as well as real-world tests on CDW sorting. Metrics such as the Sorting Throughput (ST) and Sorting Ratio (SR) reveal the RaapWaste’s capability across different waste sorting cases. In simulation, the delta robot achieved an SR exceeding 95%, while the UR5e showed consistent performance. In real-world CDW experiments, the system achieved a peak SR of 99% and maintained 80% using the SPT scheduler. Full article