Search Results (1,075)

To enhance the ability to identify and analyze the construction safety risks of prefabricated building projects, this paper explores the risk factors affecting the construction safety of prefabricated buildings from the perspective of the construction stage. Based on the WSR theory, this paper identifies risk-influencing factors from five dimensions: personnel, materials, management, technology, and environment, and constructs a safety risk assessment index system. This paper establishes a risk assessment model based on the G1-CRITIC method and cloud model. Firstly, it quantitatively analyzes the weights of the risk indicators for prefabricated building construction, and then evaluates the specific degree of impact of each indicator on the construction risk of this type of project. The research results show that the project is at the low-risk level, but there are still some potential risks in terms of material and technical factors, which require close attention and targeted management. The evaluation results obtained by applying this model are consistent with the current actual situation of prefabricated building construction, further demonstrating the applicability of this model. The risk assessment model proposed in this paper, by focusing on a specific type of risk, comprehensively incorporates the fuzziness and randomness of risk factors, thereby more effectively capturing the dynamic characteristics of risk evolution. This model can effectively evaluate the level of safety risk management and plays a positive role in reducing the incidence of engineering accidents. Furthermore, it also provides practical experience that can be drawn upon by risk managers of similar projects which holds significant theoretical value and practical guiding significance. Full article

When measuring Stokes parameters using the rotating wave plate method, the angle error of the polarizer’s light transmission axis, the azimuth error of the wave plate’s fast axis, and the phase delay error are key factors restricting accuracy. To address the existing calibration methods’ insufficient accuracy and incomplete consideration of the error parameters, this study constructed an error-transfer analytical model for an in-depth analysis of the principle of measuring Stokes parameters using the rotating wave plate method. It also clarified the quantitative parameter relationship between the measurement, wave plate, and polarizer errors. A device parameter calibration scheme using multi-angle polarized light (horizontally linearly polarized, [1,1,0,0]^T, and 45° linearly polarized, [1,0,1,0]^T) was further proposed, and by using the deviation between the theoretical response of the standard incident light and the actual measurement data, an error equation was established to solve the device parameter error and precisely calibrate the polarization detection device. The experimental results show that after using this method, the calibration error of the Stokes parameters decreased from 4.83% to within 0.46%, significantly overcoming the traditional methods’ limitations regarding incomplete consideration of the error parameters and accuracy improvement, providing a more concise and reliable method for high-precision polarization measurement. Full article

The movement towards low-emission and sustainable building practices has driven increased use of natural, carbon-based materials such as wood. While these materials offer significant environmental advantages, their inherent flammability introduces new challenges for timber building safety. Despite advancements in fire protection standards and building regulations, the risk of fire incidents—whether from technical failure, human error, or intentional acts—remains. The rapid detection of fire onset is crucial for safeguarding human life, animal welfare, and valuable assets. This study investigates the potential of monitoring fire precursor gases emitted inside building structures during pre-ignition and early combustion stages. The research also examines the sensitivity and effectiveness of commercial smoke detectors compared with custom sensor arrays in detecting these emissions. A representative structural sample was constructed and subjected to a controlled fire scenario in a laboratory setting, providing insights into the integration of gas sensing technologies for enhanced fire resilience in sustainable building systems. Full article

The increasing reliance on Medical Internet of Things (MIoT) devices introduces critical cybersecurity vulnerabilities, necessitating advanced, adaptive defense mechanisms. Recent cyber incidents—such as compromised critical care systems, modified therapeutic device outputs, and fraudulent clinical data inputs—demonstrate that these threats now directly impact life-critical aspects of patient security. In this paper, we introduce a machine learning-enabled Cognitive Cyber-Physical System (ML-CCPS), which is designed to identify and respond to cyber threats in MIoT environments through a layered cognitive architecture. The system is constructed on a feedback-looped architecture integrating hybrid feature modeling, physical behavioral analysis, and Extreme Learning Machine (ELM)-based classification to provide adaptive access control, continuous monitoring, and reliable intrusion detection. ML-CCPS is capable of outperforming benchmark classifiers with an acceptable computational cost, as evidenced by its macro F1-score of 97.8% and an AUC of 99.1% when evaluated with the ToN-IoT dataset. Alongside classification accuracy, the framework has demonstrated reliable behaviour under noisy telemetry, maintained strong efficiency in resource-constrained settings, and scaled effectively with larger numbers of connected devices. Comparative evaluations, radar-style synthesis, and ablation studies further validate its effectiveness in real-time MIoT environments and its ability to detect novel attack types with high reliability. Full article

When emergency incidents, such as bridge damage, abruptly occur on highways and lead to traffic disruptions, the multidimensionality and complexity of driver behaviors present significant challenges to the design of effective emergency response mechanisms. This study introduces a multi-level collaborative emergency mechanism grounded in driver behavior characteristics, aiming to enhance both traffic safety and emergency response efficiency through hierarchical collaboration and dynamic optimization strategies. By capitalizing on human drivers’ perception and decision-making attributes, a driver behavior classification model is developed to quantitatively assess the risk response capabilities of distinct behavioral patterns (conservative, risk-taking, and conformist) under emergency scenarios. A multi-tiered collaborative framework, comprising an early warning layer, a guidance layer, and an interception layer, is devised to implement tailored emergency strategies. Additionally, a rear-end collision risk propagation model is constructed by integrating the risk field model with probabilistic risk assessment, enabling dynamic adjustments to interception range thresholds for precise and real-time emergency management. The efficacy of this mechanism is substantiated through empirical case studies, which underscore its capacity to substantially reduce the occurrence of secondary accidents and furnish scientific evidence and technical underpinnings for emergency management pertaining to highway bridge damage. Full article

In petroleum exploration and production, lost circulation not only significantly increases exploration and development costs and operational cycles but may also lead to major incidents such as wellbore instability or even project abandonment. This paper constructs a polymer gel plugging system by optimizing high-molecular-weight polymers, crosslinker systems, and resin hardeners. The optimized system composition was determined as 1% polymer J-1, 0.3% catechol, 0.6% hexamethylenetetramine (HMTA), and 15% urea–formaldehyde resin. Experimental studies demonstrated that during the initial stage (0–3 days) at 120 °C, the optimized gel system maintained a storage modulus (G′) of 17.5 Pa and a loss modulus (G″) of 4.3 Pa. When the aging period was extended to 9 days, G′ and G″ decreased to 16 Pa and 4 Pa, respectively. The insignificant reduction in gel strength indicates excellent thermal stability of the gel system. The gel exhibited superior self-filling capacity during migration, enabling complete filling of fractures of varying sizes. After aging for 1 day at 120 °C, the plugging capacity of the gel system under water flooding and gas flooding conditions was 166 kPa/m and 122 kPa/m, respectively. Furthermore, a complete gel barrier layer formed within a 6 mm wide vertical fracture, demonstrating a pressure-bearing capacity of 105.6 kPa. This system shows good effectiveness for wellbore isolation and fracture plugging. The polymer gel plugging system studied in this paper can simplify lost circulation treatment procedures while enhancing plugging strength, providing theoretical support and technical solutions for addressing lost circulation challenges. Full article

Despite extensive prevention strategies in Spain since the 1980s, gender-based violence, including among adolescents, remains prevalent, as observed in the Romance SUCC-ED Project (R&D&I Operating Programme ERDF Andalusia 2014–2020). This research study investigates the dimensions, meanings, relationships, and practices shaping the culture of romance in digital Andalusian adolescence (12–16 years) and its potential impact on school trajectories in Compulsory Secondary Education. Based on the premise that equality-focused relationship education is key to preventing gender violence, the study employs an ethnographic methodology with 12 Andalusian school case studies (4 out of them are located in rural areas) and 220 in-depth interviews (126 girls, 57.3%; 94 boys, 42.7%). This article aims to empirically explain gender violence in early adolescence by analysing the culture of romance as an explanatory factor. Findings reveal an interconnected model where dimensions (love, couple, sexuality, pornography, social networks, and cultural references), meanings (constructed by adolescents within each of them), relationships (partner), and practices (control and jealousy) reinforce romanticised femininity and dominant masculinity, thus explaining the high incidence of gender-based violence among students in the study. Full article

To prevent the occurrence of excavation face instability incidents during shield tunneling, this study takes the Bailuyuan tunnel of the ‘Hanjiang-to-Weihe River Water Diversion Project’ as the engineering background. A three-dimensional discrete element method simulation was employed to analyze the tunneling process, revealing the displacement response of the excavation face to various tunneling parameters. This led to the development of a risk assessment method that considers both tunneling parameters and geological conditions for deep-buried shield tunnels. The above method effectively overcomes the limitations of finite element method (FEM) studies on shield tunneling parameters and, combined with the Analytic Hierarchy Process (AHP), enables rapid tunnel analysis and assessment. The results demonstrate that the displacement of the excavation face in shield tunnel engineering is significantly influenced by factors such as the chamber earth pressure ratio, cutterhead opening rate, cutterhead rotation speed, and tunneling speed. Specifically, variations in the chamber earth pressure ratio have the greatest impact on horizontal displacement, occurring predominantly near the upper center of the tunnel. As the chamber earth pressure ratio decreases, horizontal displacement increases sharply from 12.9 mm to 267.3 mm. Conversely, an increase in the cutterhead opening rate leads to displacement that first rises gradually and then rapidly, from 32.1 mm to 121.1 mm. A weighted index assessment model based on AHP yields a risk level of Grade II, whereas methods from other scholars result in Grade III. By implementing measures such as adjusting the grouting range, cutterhead rotation speed, and tunneling speed, field applications confirm that the risk level remains within acceptable limits, thereby verifying the feasibility of the constructed assessment method. Construction site strategies are proposed, including maintaining a chamber earth pressure ratio greater than 1, tunneling speed not exceeding 30 mm/min, cutterhead rotation speed not exceeding 1.5 rpm, and a synchronous grouting range of 0.15 m. Following implementation, the tunnel construction successfully passed the high-risk section without any incidents. This research offers a decision-making framework for shield TBM operation safety in complex geological environments. Full article

In cybersecurity, constructing an accurate knowledge graph is vital for discovering key entities and relationships in security incidents buried in vast unstructured threat reports. Traditional knowledge-graph construction pipelines based on handcrafted rules or conventional machine learning models falter when the data scale and linguistic variety grow. GraphRAG, a retrieval-augmented generation (RAG) framework that splits documents into fixed-length chunks and then retrieves the most relevant ones for generation, offers a scalable alternative yet still suffers from fragmentation and semantic gaps that erode graph integrity. To resolve these issues, this paper proposes SC-LKM, a cybersecurity knowledge-graph construction method that couples the GraphRAG backbone with hierarchical semantic chunking. SC-LKM applies semantic chunking to build a cybersecurity knowledge graph that avoids the fragmentation and inconsistency seen in prior work. The semantic chunking method first respects the native document hierarchy and then refines boundaries with topic similarity and named-entity continuity, maintaining logical coherence while limiting information loss during the fine-grained processing of unstructured text. SC-LKM further integrates the semantic comprehension capacity of Qwen2.5-14B-Instruct, markedly boosting extraction accuracy and reasoning quality. Experimental results show that SC-LKM surpasses baseline systems in entity-recognition coverage, topology density, and semantic consistency. Full article

As shared micromobility grows quickly in metropolitan settings, e-scooter safety issues have become more urgent. This paper uses a Bayesian hierarchical model applied to census block groups in several Texas metropolitan areas to construct a spatial risk assessment methodology for e-scooter crashes. Based on crash statistics from 2018 to 2024, we develop a severity-weighted crash risk index and combine it with variables related to land use, transportation, demographics, economics, and other factors. The model comprises a geographically structured random effect based on a Conditional Autoregressive (CAR) model, which accounts for residual spatial clustering after capture. It also includes fixed effects for covariates such as car ownership and nightlife density, as well as regional random intercepts to account for city-level heterogeneity. Markov Chain Monte Carlo is used for model fitting; evaluation reveals robust spatial calibration and predictive ability. The following key predictors are statistically significant: a higher share of working-age residents shows a positive association with crash frequency (incidence rate ratio (IRR): ≈1.55 per +10% population aged 18–64), as does a greater proportion of car-free households (IRR ≈ 1.20). In the built environment, entertainment-related employment density is strongly linked to elevated risk (IRR ≈ 1.37), and high intersection density similarly increases crash risk (IRR ≈ 1.32). In contrast, higher residential housing density has a protective effect (IRR ≈ 0.78), correlating with fewer crashes. Additionally, a sensitivity study reveals that the risk index is responsive to policy scenarios, including reducing car ownership or increasing employment density, and is sensitive to varying crash intensity weights. Results show notable collision hotspots near entertainment venues and central areas, as well as increased baseline risk in car-oriented urban environments. The results provide practical information for targeted initiatives to lower e-scooter collision risk and safety planning. Full article

Background/objectives: Falls are a leading cause of traumatic injury and hospitalization for adults over the age of 65. While common, bed-related falls are relatively understudied when compared to ambulatory falls. The aim of this study is to characterize the risk factors for the hospitalization of older adults presenting to U.S. emergency departments (EDs) after a fall from bed. Methods: This was a cross-sectional study using publicly available data from the U.S. Consumer Product Safety Commission’s National Electronic Injury Surveillance System (NEISS) from 2014 to 2023, including all adults over the age of 65 presenting to the NEISS’s participating EDs with bed-related fall injuries. We identified fall injuries using a keyword search of the NEISS narratives and determined how the fall occurred by manually reviewing a randomized 3% sample of the narratives. We summarized demographics and injury patterns with descriptive statistics. We constructed a multivariable logistic regression model to identify risk factors for hospitalization and used Poisson regression to assess temporal trends in fall incidence and hospital admissions. Results: An estimated average of 320,751 bed-related fall injuries presented to EDs annually from 2014 to 2023. ED visits increased by 2.85% per year, while hospital admissions rose by 5.67% per year (p < 0.001). The most common injury patterns were superficial injuries (contusions, abrasions, lacerations, avulsions, and punctures) (28.6%), fractures (21.7%), and internal injuries (including concussions) (21.6%). Most of the falls occurred while transitioning into or out of bed (34.4%) or falling out of bed (56.8%). Hospitalization was required in 34.1% of cases and was associated with male sex, medication use at time of injury, and fracture injuries. Conclusions: Bed-related falls and associated hospitalizations are increasing among older adults. ED providers should understand risk factors for hospitalization in these common injuries such as male sex, medication use at time of injury, and high-risk injury patterns. Additionally, prevention efforts should focus on helping older adults remain safely in bed and then assisting with transitions into or out of bed. Full article

Chemical leakage accidents in chemical industrial parks pose significant threats to personnel safety, particularly during evacuation processes, where individual behavior and evacuation strategies have a considerable impact on overall efficiency. This study takes a leakage incident at an alkylation unit as a case study. First, ALOHA5.4.7 software was used to simulate the influence of meteorological conditions across different seasons on the dispersion range of toxic gases, thereby generating an annual comprehensive risk zone distribution map. Subsequently, different evacuation scenarios were constructed in Pathfinder2024.1.0605, with the integration of trigger mechanisms to simulate individual behaviors during evacuation, such as variations in risk perception and peer influence. Furthermore, this study expanded the conventional application scope of Pathfinder—typically limited to small-scale building evacuations—by successfully adapting it for large-scale evacuation simulations in chemical industrial parks. The feasibility of such simulations was thereby demonstrated, highlighting the software’s potential. According to the simulation results, exit configuration, shelter placement, and individual behavior modeling significantly affect the total evacuation time. This study provides both theoretical insights and practical guidance for emergency response planning in chemical industrial parks. Full article

Background/Objectives: Cutaneous melanoma is one of the aggressive forms of skin cancer originating from melanocytes. The high incidence of melanoma metastasis continues to rise, partly due to the complex nature of the molecular mechanisms driving its progression. While melanomas generally arise from melanocytes, we investigated whether aberrant keratinocyte differentiation pathways—like cornified envelope formation—discriminate primary melanoma from metastatic melanoma, revealing novel biomarkers in progression. Methods: In the present study, we retrieved four datasets (GSE15605, GSE46517, GSE8401, and GSE7553) associated with primary and metastatic melanoma tissues and identified differentially expressed genes (DEGs). Thereafter, an integrated meta-analysis and functional enrichment analysis of the DEGs were performed to evaluate the molecular mechanisms involved in melanoma metastasis, such as immune cell deconvolution and protein-protein interaction (PPI) network construction. Hub genes were identified based on four topological methods, including ‘Betweenness’, ‘MCC’, ‘Degree’, and ‘Bottleneck’. We validated the findings using the TCGA-SKCM cohort. Drug-gene interactions were evaluated using the DGIdb, whereas structural druggability was assessed using the ProteinPlus and AlphaFold databases. Results: We identified a total of eleven hub genes associated with melanoma progression. These included members of the keratin gene family (e.g., KRT5, KRT6A, KRT6B, etc.). Except for the gene CDH1, all the hub genes were downregulated in metastatic melanoma tissues. From a prognostic perspective, these hub genes were associated with poor prognosis (i.e., unfavorable). Using the Human Protein Atlas (HPA), immunohistochemistry evaluation revealed mostly undetected levels in metastatic melanoma. Additionally, the cornified envelope formation was the most enriched pathway, with a gene ratio of 17/33. The tumor microenvironment (TME) of metastatic melanomas was predominantly enriched in NK cell–associated signatures. Finally, several hub genes demonstrated favorable druggable potential for immunotherapy. Conclusions: Through integrated meta-analysis, this study identifies transcriptional, immunological, and structural pathways to melanoma metastasis and highlights keratin family genes as promising biomarkers for therapeutic targeting. Full article

Objective: Tumor progression is regulated by systemic immune status, nutritional metabolism, and the inflammatory microenvironment. This study aims to investigate inflammatory–nutritional biomarkers associated with metachronous liver metastasis (MLM) in colorectal cancer (CRC) and develop a machine learning model for accurate prediction. Methods: This study enrolled 680 patients with CRC who underwent curative resection, randomly allocated into a training set (n = 477) and a validation set (n = 203) in a 7:3 ratio. Feature selection was performed using Boruta and Lasso algorithms, identifying nine core prognostic factors through variable intersection. Seven machine learning (ML) models were constructed using the training set, with the optimal predictive model selected based on comprehensive evaluation metrics. An interactive visualization tool was developed to interpret the dynamic impact of key features on individual predictions. The partial dependence plots (PDPs) revealed a potential dose–response relationship between inflammatory–nutritional markers and MLM risk. Results: Among 680 patients with CRC, the cumulative incidence of MLM at 6 months postoperatively was 39.1%. Multimodal feature selection identified nine key predictors, including the N stage, vascular invasion, carcinoembryonic antigen (CEA), systemic immune–inflammation index (SII), albumin–bilirubin index (ALBI), differentiation grade, prognostic nutritional index (PNI), fatty liver, and T stage. The gradient boosting machine (GBM) demonstrated the best overall performance (AUROC: 0.916, sensitivity: 0.772, specificity: 0.871). The generalized additive model (GAM)-fitted SHAP analysis established, for the first time, risk thresholds for four continuous variables (CEA > 8.14 μg/L, PNI < 44.46, SII > 856.36, ALBI > −2.67), confirming their significant association with MLM development. Conclusions: This study developed a GBM model incorporating inflammatory-nutritional biomarkers and clinical features to accurately predict MLM in colorectal cancer. Integrated with dynamic visualization tools, the model enables real-time risk stratification via a freely accessible web calculator, guiding individualized surveillance planning and optimizing clinical decision-making for precision postoperative care. Full article

Firmware vulnerabilities in embedded devices have caused serious security incidents, necessitating similarity analysis of binary program instruction embeddings to identify vulnerabilities. However, existing instruction embedding methods neglect program execution semantics, resulting in accuracy limitations. Furthermore, current embedding approaches utilize independent computation across models, where the lack of standardized interaction information between models makes it difficult for embedding models to efficiently detect firmware vulnerabilities. To address these challenges, this paper proposes a firmware vulnerability detection scheme based on statistical inference and code similarity fuzzy matching analysis for resource-constrained vehicular network environments, helping to balance both accuracy and efficiency. First, through dynamic programming and neighborhood search techniques, binary code is systematically partitioned into normalized segment collections according to specific rules. The binary code is then analyzed in segments to construct semantic equivalence mappings, thereby extracting similarity metrics for function execution semantics. Subsequently, Google Protocol Buffers (ProtoBuf) is introduced as a serialization format for inter-model data transmission, serving as a “translation layer” and “bridging technology” within the firmware vulnerability detection framework. Additionally, a ProtoBuf-based certificate authentication scheme is proposed to enhance vehicular network communication reliability, improve data serialization efficiency, and increase the efficiency and accuracy of the detection model. Finally, a vehicular network simulation environment is established through secondary development on the NS-3 network simulator, and the functionality and performance of this architecture were thoroughly tested. Results demonstrate that the algorithm possesses resistance capabilities against common security threats while minimizing performance impact. Experimental results show that FirmPB delivers superior accuracy with 0.044 s inference time and 0.932 AUC, outperforming current SOTA in detection performance. Full article