Search Results (64)

The construction industry is inherently high-risk, with workers’ unsafe behaviors directly causing most safety incidents. As smart technologies are widely deployed on construction sites, new forms of risk-taking behavior have emerged, but their underlying mechanisms remain poorly understood. Grounded in Human–Technology–Organization (HTO) theory, this study establishes a multi-factor coupling analytical framework and employs a mixed NCA–fsQCA method to empirically analyze data from 312 workers across two smart construction sites in Beijing. The results show that no single antecedent variable acts as a necessary condition for either type of high-risk-taking behavior, though each variable exerts distinct bottleneck constraints. Five configurations driving high-risk behaviors are identified: smart technology adaptability serves as the core condition for automation trust bias behaviors, while individual risk-taking propensity and task situational pressure are universal core factors for both behavior types. These findings uncover the multi-dimensional coupling logic of risk-taking behaviors and offer theoretical and practical insights for targeted safety management in smart construction contexts. Full article

NAND flash-based solid state drives (SSDs) are increasingly common in computers, but they present a problem for forensic data recovery. SSDs use controller logic, flash translation layers, error correction, wear leveling, TRIM, garbage collection, and encryption to influence the recoverability of data after being written or erased, which is not the case for hard disk drives (HDDs). In this paper, we propose a machine learning-based method to determine the health of NAND SSDs and their data recoverability. The approach involves telemetry (SMART and NVMe) analysis, subsystems’ interpretation of NAND and controller health, and anomaly detection with the Isolation Forest machine learning algorithm. The task is formulated as a single-class learning problem that takes into account asymmetry, where telemetry from a healthy SSD represents the reference state and NAND degradation, controller instability, TRIM effects, and encryption-related limitations act as asymmetric deviations from this state. The presented method uses telemetry data, such as the temperature, wear level, spare blocks, media and data integrity errors, error logs, unsafe shutdowns, and uptime. This study shows that the potential for data recovery depends on the health of the NAND flash memory and controller, TRIM, encryption, and other anomalies but not necessarily any single SMART metric. The proposed approach provides explainable, data recovery-focused assessment and categorizes the SSD cases as recoverable, partially recoverable, and non-recoverable. The model was trained using a healthy SSD dataset consisting of 56,482 SATA SSD records and 82,665 NVMe SSD records, for a total of 139,147 healthy drive samples. Additionally, 20,000 synthetic training samples were generated for each SSD type to support controlled model training. The proposed platform was evaluated using 30 SSD recovery scenarios, including recovery, partial recovery, and no recovery cases. The results demonstrate that the proposed method can distinguish between healthy, warning, and abnormal SSD states and provide recovery recommendations based on NAND health, controller stability, TRIM status, and encryption limitations. Full article

To address the limitations of incomplete factor identification, insufficient cross-level coupling quantification, and inadequate causal path verification in traditional human factor analysis of road transportation of dangerous goods (RTDG) accidents, this study developed an integrated HFACS-Apriori-SEM analytical framework that enables full-process analysis from factor identification to causal mechanism exploration and hierarchical path validation. A five-level industry-specific Human Factors Analysis and Classification System (HFACS) framework with 85 causal indicators was established, and standardized coding was conducted for 58 fatal RTDG accidents in China from 2012 to 2022. Twelve core strong association rules were generated using the Apriori algorithm. Among these, the co-occurrence chain “organizational process failure → inadequate supervision → insufficient personnel readiness → routine violations” had the highest support of 0.621. Structural equation modelling (SEM) provided empirical support for a significant hierarchical chain transmission effect of the accident causation. The findings showed that preconditions for unsafe acts exerted the largest indirect effect on accident severity (total effect = 0.69, p < 0.001). Furthermore, unsafe acts were the only direct influencing factor (total effect = 0.85, p < 0.001). In addition, violations accounted for a significantly higher proportion of unsafe acts than errors. This study provides strong empirical evidence that catastrophic RTDG accidents stem from the chain failure of multi-level system defenses, offering a quantitative and targeted decision basis for hierarchical accident prevention and control in the RTDG industry. Full article

Counterfeit and unsafe medicines pose significant risks to patient safety and undermine trust in healthcare systems. This paper presents ACTMeds, a blockchain-supported pharmaceutical traceability and recall platform that considers pharmaceutical supply chain requirements and public health operational needs relevant to the Australian Capital Territory (ACT). The system integrates Ethereum smart contracts, developed using Ganache, with a React-based web application providing regulator, operator, pharmacy, and auditor interfaces, alongside a public verification portal leveraging QR and GS1 barcodes. In addition, role-based access control is enforced across the medicine lifecycle, including manufacture, custody transfer, dispensing, and recall, with immutable on-chain events generated to support auditability and accountability. To balance transparency with confidentiality, the platform prototypes a zero-knowledge (ZK) recall mechanism in which regulators can cryptographically prove that recall conditions meet predefined policy requirements without disclosing sensitive incident details. Threat modeling was conducted using the STRIDE framework, and security evaluation combined static application security testing (Solhint and ESLint) and dynamic testing. The paper further discusses deployment options, cost considerations, ZK recall performance analysis, ethical implications, and future enhancements. Security testing validated the platform’s resilience, with no high-severity vulnerabilities identified and medium-severity issues related to HTTP security headers addressed. The results indicate that a regulator-led, privacy-preserving, tamper-evident ledger can improve medicine authenticity verification and recall responsiveness while maintaining compliance and data protection obligations. Full article

Text-to-image diffusion models such as Stable Diffusion enable high-quality image synthesis from text and are widely deployed due to their open-source nature and low computational requirements. However, this accessibility also makes them attractive targets for misuse, including the generation of not-safe-for-work and otherwise restricted content. In this paper, we propose EvilPrompt, a jailbreak attack that exploits the cross-attention mechanism in Stable Diffusion. The attack operates purely at inference time using plain-text prompts and does not require fine-tuning or modification of model parameters. By selectively reweighting cross-attention for specific tokens, EvilPrompt preserves the overall semantic structure of the prompt while steering the generation toward prohibited content. This enables fine-grained control over malicious semantics without introducing explicit unsafe keywords. We evaluate EvilPrompt on two real-world prompt sets, 4chan and Lexica, each containing 500 prompts. The attack achieves an Attack Success Rate (ASR) of 97.4% on 4chan and 98.0% on Lexica, yielding an overall average ASR of 97.7%. The attack maintains high semantic alignment between prompts and generated images. Bootstrapping Language-Image Pre-training (BLIP) similarity consistently exceeds 0.75 across all categories on both datasets. Human evaluation further confirms high visual realism, with mean scores above 7.0 on a 10-point scale, and strong semantic consistency, with mean scores above 7.3. These results demonstrate that cross-attention manipulation provides an effective and practical jailbreak pathway. We further analyze how commonly used text-level moderation affects the success of such attacks. Although the strongest defense configuration (HateCoT with GPT-4) reduces the ASR to 5.9%, it introduces 21.5 s of additional latency and a cost of $0.01182 per query. Lighter-weight alternatives such as Perspective API leave nearly half (45.0%) of attacks successful. These observations indicate that safeguards acting only on the input or final output are insufficient to capture attention-level manipulations. Overall, our results reveal a fundamental limitation of post-generation safety pipelines when confronted with inference-time control of cross-attention. Full article

Tanker cargo operations involve hazardous cargo environments, complex technical systems and stringent operational procedures. These conditions make accident analysis particularly demanding and require analytical approaches that consider the specific operational context of tanker cargo handling. Existing Human Factors Analysis and Classification System (HFACS) adaptations used in maritime safety research provide a useful framework for analysing human and organisational factors, but they do not fully capture the operational characteristics of tanker cargo operations. As a result, some factors specific to tanker cargo handling remain insufficiently represented in existing HFACS-based analyses. Therefore, this study develops and validates a domain-specific HFACS framework for tanker cargo operations (HFACS-TCO) and applies it to the analysis of accident investigation reports. The framework was developed through an iterative process based on accident report analysis, expert evaluation and the development of structured coding guidelines. The reliability of the coding procedure was assessed using Fleiss’s kappa coefficient to evaluate inter-rater agreement. The proposed framework extends existing HFACS adaptations by incorporating cargo operation-specific organisational, operational and environmental factors. A total of 27 accident investigation reports related to tanker cargo operations were analysed. From these reports, 333 causal factors were identified and classified using the HFACS-TCO framework. The results show that tanker cargo accidents rarely arise from a single cause and usually involve multiple interacting organisational, operational and human factors. Most factors were identified at the levels of Preconditions for Unsafe Acts, Organisational Influences and External Factors, indicating that many accident conditions are established before unsafe acts occur at the operational level. The analysis also shows that most accidents involve factors across several HFACS levels, indicating that tanker cargo incidents develop through interactions between different system levels. The proposed HFACS-TCO framework provides a structured, domain-specific approach to analysing tanker cargo accidents and supports a more systematic identification of organisational and human factors in tanker cargo-related operations. Full article

The increasing electrification of urban transportation has formulated a tightly coupled energy-mobility nexus. Under extreme disaster events or grid faults, rapidly restoring power supply capacity and re-dispatching shared electric vehicle (EV) fleets are critical for enhancing system resilience. Existing co-optimization methods face the curse of dimensionality when dealing with high-dimensional discrete grid reconfigurations and continuous spatio-temporal EV queuing dynamics. While multi-agent deep reinforcement learning (MADRL) offers real-time responsiveness, it inherently struggles to satisfy strict physical constraints, frequently generating infeasible and unsafe actions. To bridge this gap, this paper proposes a heuristic-guided safe multi-agent reinforcement learning (Safe-MADRL) framework for the resilient dispatch of the energy-mobility nexus. Instead of relying solely on black-box neural networks, the framework structurally embeds physical models and heuristic solvers into the learning loop. A quantum particle swarm optimization (QPSO) algorithm acts as a heuristic action refiner to ensure that grid topology actions strictly comply with non-linear power flow and voltage constraints. Simultaneously, a mixed-integer linear programming (MILP) model coupled with a single-queue multi-server (SQMS) model serves as a safety projection layer. This layer mathematically guarantees EV battery energy continuity and accurately quantifies spatio-temporal queuing delays at charging stations. Case studies on a coupled IEEE 33-node distribution system and a regional transportation network demonstrate that the proposed Safe-MADRL framework achieves zero physical violations during training and significantly outperforms traditional mathematical optimization and pure learning-based methods in computational efficiency, system power loss reduction, and overall operational economy. Full article

Occupational safety analytics is increasingly moving toward data-driven methodologies; however, existing models often struggle to capture the multidimensional nature of accident causation. This study presents a multimodal Hybrid Transformer-LSTM framework for classifying occupational fatalities by jointly modeling unstructured narratives, cyclical temporal features, and regional spatial indicators. Utilizing a large-scale dataset of 14,914 OSHA fatality records, the proposed architecture leverages BERT-based embeddings for semantic extraction and Bidirectional LSTMs as non-linear pattern encoders for spatiotemporal context. Conceptually grounded in the Swiss Cheese Model, the framework treats different data modalities as proxies for distinct layers of system risk, ranging from proximal unsafe acts to environmental preconditions. Experimental results show that the multimodal architecture achieves an accuracy of 84.56%, representing a 5.33% gain over unimodal BERT baselines. To address the inherent “black-box” nature of deep learning, a SHAP-based explainability framework is incorporated to quantify the contributions of both textual tokens and environmental features to the model’s decision-making process. The results indicate that integrating narrative semantics with temporal and spatial context enhances discriminative performance and enables context-aware classification within a weakly supervised setting. By providing a scalable and interpretable classification framework, this study offers a data-driven decision-support approach for safety professionals and regulatory bodies seeking to implement evidence-based risk management strategies in high-risk industrial sectors. Full article

The sustainable development of university safety governance is an important component of the national security management system and also serves as a fundamental safeguard for protecting the life and health of students and staff on campus. The improvement of university safety risk governance relies on analyzing the identification of various safety risks and maintaining an effective crisis management process for potential sudden safety risks. The 24Model and the 4R model have respectively demonstrated strong analytical advantages in the fields of accident causation analysis and emergency crisis management; however, few studies have examined the internal relationship between them. This study attempts to integrate the 24Model and the 4R crisis management framework to propose and analyze a 2-4-4R model for university safety risk management. Through a case study, the model is applied to analyze a laboratory explosion accident at a university. The results show that the risk factors leading to campus safety accidents can be analyzed from four aspects: safety culture, safety management system, individual factors, and unsafe acts and physical conditions. University safety management should comprehensively identify these four types of factors and propose governance measures sequentially from the four stages of reduction, readiness, response, and recovery in order to improve safety management capacity. The case analysis confirms that the 2-4-4R model has applicability and practical value in the identification and governance analysis of university safety risks. It provides a systematic research perspective for the identification and management of safety risks in universities, and is of great significance for promoting the sustainable development of universities. Full article

Research into Remotely Piloted Aircraft Systems (RPASs) has expanded rapidly, yet the competencies, knowledge, skills, and other attributes (KSaOs) required of RPAS pilots remain comparatively underexamined. This review consolidates existing studies addressing human performance, subject matter expertise, training practices, and accident causation to provide a comprehensive account of the KSaOs underpinning safe civilian and commercial drone operations. Prior research demonstrates that early work drew heavily on military contexts, which may not generalize to contemporary civilian operations characterized by smaller platforms, single-pilot tasks, and diverse industry applications. Studies employing subject matter experts highlight cognitive demands in areas such as situational awareness, workload management, planning, fatigue recognition, perceptual acuity, and decision-making. Accident analyses, predominantly using the human factors accident classification system and related taxonomies, show that skill errors and preconditions for unsafe acts are the most frequent contributors to RPAS occurrences, with limited evidence of higher-level latent organizational factors in civilian contexts. Emerging research emphasizes that RPAS pilots increasingly perform data-collection tasks integral to professional workflows, requiring competencies beyond aircraft handling alone. The review identifies significant gaps in training specificity, selection processes, and taxonomy suitability, indicating opportunities for future research to refine RPAS competency frameworks and support improved operational safety. Full article

Accident investigations in high-risk industries frequently focus on attributing unsafe acts to individual operators, often neglecting the organizational conditions that shape such behaviors. This study adopts an exploratory perspective to examine how communication, resource adequacy, and procedural design influence the potential for unsafe acts in refinery maintenance operations within the oil and gas sector. Building on the HFACS-OGI framework, unsafe acts were classified into perception errors, decoding errors, model errors, decision errors, and violations. Data were collected through a survey (n = 46) and analyzed using ordinal logistic regression with 10,000 bootstrap replications, complemented by partial correlation analysis to capture indirect associations. The results provide preliminary evidence that organizational factors operate both as direct predictors of unsafe acts and as systemic pathways linking broader contextual conditions with operator behavior. In particular, deficiencies in communication emerged as a transversal determinant, partially explaining the relationship between organizational context and both perception and decision errors. While limited by sample size and exploratory design, the study contributes to safety science by extending the empirical application of HFACS-OGI beyond post-accident analysis and offering actionable insights for safety governance. The findings underscore the need for proactive organizational interventions that enhance communication systems, ensure resource adequacy, and promote the usability of procedures in order to mitigate the potential for unsafe acts. Full article

UAV accident investigation is essential for safeguarding the fast-growing low-altitude airspace. While near-daily incidents are reported, they were rarely analyzed in depth as current inquiries remain expert-dependent and time-consuming. Because most jurisdictions mandate formal reporting only for serious injury or substantial property damage, a large proportion of minor occurrences receive no systematic investigation, resulting in persistent data gaps and hindering proactive risk management. This study explores the potential of using large language models (LLMs) to expedite UAV accident investigations by extracting human-factor insights from unstructured narrative incident reports. Despite their promise, the off-the-shelf LLMs still struggle with domain-specific reasoning in the UAV context. To address this, we developed a human factors analysis and classification system (HFACS)-guided analytical framework, which blends structured prompting with lightweight post-processing. This framework systematically guides the model through a two-stage procedure to infer operators’ unsafe acts, their latent preconditions, and the associated organizational influences and regulatory risk factors. A HFACS-labelled UAV accident corpus comprising 200 abnormal event reports with 3600 coded instances has been compiled to support evaluation. Across seven LLMs and 18 HFACS categories, macro-F1 ranged 0.58–0.76; our best configuration achieved macro-F1 0.76 (precision 0.71, recall 0.82), with representative category accuracies > 93%. Comparative assessments indicate that the prompted LLM can match, and in certain tasks surpass, human experts. The findings highlight the promise of automated human factor analysis for conducting rapid and systematic UAV accident investigations. Full article

The global elimination of hepatitis C virus (HCV) has been prioritized by the World Health Organization (WHO) as a key public health target, with a deadline set for 2030. This initiative aims to significantly reduce both new infection rates and HCV-associated mortality. A major breakthrough in achieving this goal has been the development of direct-acting antiviral agents (DAAs), which offer cure rates exceeding 95%, along with excellent safety and tolerability. Nevertheless, transmission via parenteral routes continues to be the dominant pathway, particularly among high-risk groups, such as individuals who inject drugs, incarcerated populations, those exposed to unsafe medical practices, and healthcare professionals. Identifying, monitoring, and delivering tailored interventions to these groups is crucial to interrupt ongoing transmission and to reduce the burden of chronic liver disease. On a global scale, several nations have demonstrated measurable progress toward HCV elimination, with some nearing the targets set by WHO. These achievements have largely resulted from context-adapted policies that enhanced diagnostic and therapeutic access while emphasizing outreach to vulnerable communities. This review synthesizes current advancements in HCV prevention and control and proposes strategic frameworks to expedite global elimination efforts. Full article

Neglecting preventive healthcare policies has contributed to the global surge in chronic diseases, increased hospitalizations, declining quality of care, and escalating costs. Non-communicable diseases (NCDs)—notably cardiovascular conditions, diabetes, and cancer—consume over 80% of healthcare expenditure and account for more than 60% of global deaths, which are projected to exceed 75% by 2030. Poor diets, sedentary lifestyles, regulatory loopholes, and underfunded public health initiatives are driving this crisis. Compounding the issue are flawed policies, congressional lobbying, and conflicts of interest that prioritize costly, hospital-based, symptom-driven care over identifying and treating to eliminate root causes and disease prevention. Regulatory agencies are failing to deliver their intended functions. For instance, the U.S. Food and Drug Administration’s (FDA) broad oversight across drugs, devices, food, and supplements has resulted in inefficiencies, reduced transparency, and public safety risks. This broad mandate has allowed the release of unsafe drugs, food additives, and supplements, contributing to the rising childhood diseases, the burden of chronic illness, and over-medicalization. The author proposes separating oversight responsibilities: transferring authority over food, supplements, and OTC products to a new Food and Nutraceutical Agency (FNA), allowing the FDA to be restructured as the Drug and Device Agency (DDA), to refocus on pharmaceuticals and medical devices. While complete reform requires Congressional action, interim policy shifts are urgently needed to improve public health. Broader structural changes—including overhauling the Affordable Care Act, eliminating waste and fraud, redesigning regulatory and insurance systems, and eliminating intermediaries are essential to reducing costs, improving care, and transforming national and global health outcomes. The information provided herein can serve as a White Paper to help reform health agencies and healthcare systems for greater efficiency and lower costs in the USA and globally. Full article

Many rural communities in Malawi use groundwater from boreholes and shallow wells for drinking and cooking with limited or no treatment because it is considered as a safe source of water. The contamination of groundwater sources by antimicrobial resistant bacteria renders the water unsafe to use. This study investigated the antibiotic susceptibility of pathogenic micro-organisms isolated from groundwater sources in T/A Makhwira, Chikwawa. Water samples were collected from 13 boreholes and 7 protected shallow wells from T/A Makhwira, Chikwawa. E. coli, Salmonella enterica ssp. Arizona, K. pneumoniae, ESBL E. coli, and ESBL K. pneumoniae were detected in some water samples. Antibiotic susceptibility tests showed that the isolates had a high resistance to Ampicillin (42%), followed by Trimethoprim-sulfamethoxazole (26%), Ciprofloxacin (21%), Doxycycline, and Amoxicillin/clavulanic acid (16%). The isolates had a very high sensitivity to Gentamicin (89%). The study revealed that the water from some boreholes and shallow wells in T/A Makhwira is highly contaminated and needs to be treated before consumption. Drinking untreated water from these sources could transfer antibiotic-resistant bacteria to humans because the groundwater may act as a vehicle for the transmission of these antibiotic-resistant bacteria. Full article