Search Results (110)

The proliferation of cyber–physical systems in modern vehicles, characterized by densely interconnected Electronic Control Units (ECUs) and heterogeneous communication networks, has significantly expanded the automotive attack surface. Traditional Threat Analysis and Risk Assessment (TARA) methodologies remain predominantly manual processes that exhibit limitations in scalability, and comprehensive threat identification. This research addresses these limitations by developing a formalized framework for automating attack path analysis within the automotive architecture. While attack graph methodologies have demonstrated efficacy in conventional information technology domains, their application within automotive cybersecurity contexts presents unique challenges stemming from domain-specific architectural constraints. We propose a novel Graph-based Attack Path Prioritization (GAPP) methodology that integrates Extended Finite State Machine (EFSM) modeling. Our implementation employs the Neo4j property graph database architecture to establish the mappings between architectural components, security states, and exploitation vectors. This research contributes a systematic approach to automotive security assessment, enhancing vulnerability identification capabilities while reducing analytical complexity. Full article

The global financial sector’s accelerating digitalization, propelled by the growing demand for rapid and tailored services, is increasingly vulnerable to complex cyber threats. This vulnerability underscores the critical need for comprehensive and coordinated cybersecurity efforts across all organizational levels. In this context, this study examines the role of internal audit as the third line of defense, investigating its potential to improve the effectiveness of cybersecurity controls within Brazilian financial institutions. The research aims to bridge existing gaps in cyber risk management by employing a qualitative methodology centered on semi-structured interviews with internal auditing, risk management, and information security experts across ten financial institutions. The data collected were analyzed using content analysis, enabling the categorization and interpretation of current practices and challenges in cyber risk management. The results indicated two perspectives on the depth of assessments conducted by internal audit and reinforced the fundamental role of internal audit in strengthening cybersecurity defenses: whether through high-level assessments of governance and management or penetration testing in specific scenarios, it can validate and increase the effectiveness of implemented controls. In addition, the study highlights the usefulness of data analytics for continuous auditing, identifying it as a proactive approach for the early detection of emerging cyber risks. These insights contribute significantly to the scholarly discourse on internal auditing’s role in the improvement of a secure and resilient organizational environment. They also offer actionable strategies for financial institutions seeking to integrate effective cyber risk management practices, thus reinforcing the sector’s preparedness against increasingly sophisticated cyber threats. Full article

Caregiving for a patient with schizophrenia (PwS) imposes a high burden on caregivers and often affects their quality of life. This systematic review aims to synthesize the current evidence on the sociodemographic and psychological factors of caregivers, as well as patient-related sociodemographic and clinical factors, that may influence caregivers’ QoL. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was performed in three major databases—PubMed/Medline, SCOPUS, and Web of Science—to identify original studies examining informal caregivers of PwS and assessing the relationship between caregivers’ QoL and various sociodemographic, psychological, or clinical factors. Methodological quality appraisal was performed using the Joanna Briggs Institute checklist. In total, 31 studies were included in the review and discussed at length. Lower QoL was associated with unemployment, older age, female gender, financial difficulties, being unmarried, and lower education. Additionally, increased schizophrenia symptom severity, higher caregiver burden, and elevated levels of depression and anxiety may negatively influence caregivers’ QoL. Given these findings, future research should focus on developing tailored interventions to improve caregivers’ QoL. Addressing these modifiable risk factors through targeted support programs and policies could significantly enhance caregivers’ QoL. Full article

Background/Objectives: Fluoropyrimidines are widely used chemotherapeutic agents in various solid tumors. Germline variants in the DPYD gene, which encodes the enzyme dihydropyrimidine dehydrogenase (DPD), are known to impair drug metabolism and increase the risk of severe toxicity. This umbrella review aims to synthesize the current evidence from systematic reviews on the association between DPYD variants and fluoropyrimidine-induced toxicity. Methods: A comprehensive search was conducted in PubMed, Web of Science, Scopus, and the Cochrane Library from inception to 2023, including gray literature. Systematic reviews assessing fluoropyrimidine toxicity in oncologic patients with DPYD variants were included. Study quality was assessed using the AMSTAR-2 tool. Registration number in PROSPERO: CRD42023401226. Results: Two independent investigators performed the study selection, quality assessment, and data collection. Eight systematic reviews met the inclusion criteria. Methodological confidence was rated as critically low in six, low in one, and medium in another one. The reviews included 125 primary studies, most of them focused on four key DPYD variants (DPYD2*A, DPYD*13, c.2846A>T, and HapB3), all of which showed consistent associations with an increased risk of severe toxicity. Rare variants such as DPYD*4, *5, and *6 were also examined, though evidence remains limited. Pharmacogenetics-guided dosing of fluoropyrimidines significantly reduced toxicity rates in several studies. The integration of DPYD genotyping with phenotyping approaches faces limitations; these tests should complement rather than replace genotyping information. Conclusions: This umbrella review confirms the clinical relevance of DPYD genotyping to predict and mitigate fluoropyrimidine toxicity. Incorporating genotyping into clinical practice, potentially alongside phenotyping and therapeutic drug monitoring, may enhance patient safety and treatment efficacy. Full article

This study focuses on Huangshi Town, Longchuan County, Guangdong Province, as the research area. By utilizing existing data and field surveys, the study identifies geological hazards and risks in the area, deeply analyzes the formation mechanisms and disaster-causing patterns, and systematically summarizes the developmental characteristics and spatial distribution of these hazards. Methodologically, the research combines regular grids and slope units, selecting ten evaluation factors for correlation analysis. The information theory model is used to assess disaster susceptibility. The study further evaluates the impact of three rainfall scenarios—100 mm of rainfall over 24 h, 250 mm of rainfall over 24 h, and 240 mm of effective rainfall over 72 h—on geological disasters in Huangshi Town. As a result, a comprehensive hazard assessment under multiple rainfall scenarios is produced. The findings show that the Receiver Operating Characteristic (ROC) accuracy of the disaster-susceptibility evaluation reaches 0.8739, ensuring high data quality for the geological hazard assessment in Huangshi Town. The zoning results align closely with field survey observations. In conclusion, incorporating rainfall as a triggering factor enhances the accuracy of the susceptibility analysis by better capturing the temporal and spatial patterns of landslide occurrences, thereby offering a more comprehensive understanding of the geological hazard development in the study area. Full article

Landing requires precise coordination among lower limb joints to absorb impact forces and maintain dynamic stability. Coordination and its variability during landing are influenced by factors such as injury status, training, sex, age, fatigue, and task complexity. Altered coordination patterns may compromise impact absorption and increase injury risk, highlighting the importance of understanding these movement strategies across populations and conditions. This scoping review aimed to map and synthesize the existing literature on lower limb joint coordination and coordination variability during landing tasks across different populations and task conditions. A comprehensive search was conducted across four databases (PubMed, Web of Science, Scopus, SPORTDiscus) through November 2024, with additional articles identified through reference screening. Peer-reviewed studies were included if they assessed joint or segmental coordination and/or coordination variability using time-series analyses (such as vector coding, continuous relative phase, and discrete relative phase) during landing tasks in human participants. Formal critical appraisal was not performed, consistent with PRISMA-ScR guidelines. Eighteen studies were thematically grouped into five focus areas: injured/at-risk individuals, training/fatigue interventions, gender differences, age differences, and healthy populations under varied landing conditions. Injured individuals exhibited altered coordination patterns, often showing either rigid or erratic strategies with excessive or reduced variability. Training interventions generally improved coordination stability, whereas fatigue increased variability and disrupted control. Females displayed more constrained patterns and lower coordination variability compared to males, particularly at the knee joint. Children demonstrated greater variability and less refined coordination than adults. Healthy individuals typically showed symmetric adaptable variability. Lower limb joint coordination and its variability during landing are shaped by injury status, fatigue, training, sex, age, and task complexity. These findings highlight the need for consistent methodologies and suggest that coordination analysis can inform injury prevention, rehabilitation, and targeted training strategies to optimize landing performance and safety. Full article

Infrastructure projects that transform ecosystems and landscapes, such as hydropower developments, are essential for economic growth but pose significant socio-environmental challenges. Addressing these complexities requires advanced, dynamic management strategies. This study presents the Bayesian integrated risk mitigation model (BIRMM), a novel probabilistic framework designed to augment traditional environmental impact assessments. BIRMM enables comprehensive risk evaluation, scenario-based analysis, and mitigation planning, empowering stakeholders to make informed decisions throughout project lifecycles. BIRMM integrates socio-environmental and economic risks using a three-dimensional risk assessment approach grounded in a Bayesian belief network influence diagram. It provides a holistic view of risk interactions by capturing interdependencies across spatial, temporal, and magnitude dimensions. Through simulation of risk dynamics and adaptive evaluation of mitigation strategies, BIRMM offers actionable insights for resource allocation, enhancing project resilience, and minimizing socio-environmental disruptions. The framework was validated using the Balakot Hydropower Project in Pakistan. BIRMM successfully simulated proposed risks and assessed mitigation strategies under varying scenarios, demonstrating its reliability in navigating complex socio-environmental challenges. The case study highlighted its potential to support adaptive decision-making across all project phases. With its versatility and practical ease, BIRMM is particularly suited for large-scale energy, transportation, and urban development projects. By bridging gaps in traditional methodologies, BIRMM advances sustainable development practices, promotes equitable stakeholder outcomes, and establishes itself as an indispensable decision-support tool for modern infrastructure projects. Full article

Background/Objectives: Although cultural heritage conservators and restorers face consistent exposure to a multifaceted range of occupational hazards, research on their health remains limited. This systematic review aims to explore and synthesize the prevalence and types of occupational diseases among conservators and restorers of cultural heritage. It also intends to map populations, interventions, contexts and other relevant information to assess the current state of knowledge and identify gaps in the literature on the occupational health of conservation and restoration professionals. Methods: The systematic review followed PRISMA 2020 guidelines and the Cochrane handbook. Eligible studies were identified through comprehensive searches of databases, and inclusion criteria were applied to select relevant articles. The protocol was designed according to PRISMA 2020, Prisma-ScR guidelines and the Cochrane handbook. The searches were conducted on 23 May 2024 in PubMed, Scopus and Web of Science (core collection). The risk-of-bias assessment was performed using the Cochrane method for non-randomized studies (RoBANS). Results: Respiratory symptoms were the most prevalent occupational health issue, affecting 28% of cases. General symptoms and abdominal issues each accounted for 20% and 18%, respectively, while musculoskeletal disorders were reported in 14% of cases, primarily affecting the neck, back, shoulders and wrists due to prolonged static postures and repetitive movements. Dermatological and irritation manifestations were reported in 10% of cases. Additionally, 10% of cases involved specific diseases such as pneumonia and cancer. The risk-of-bias assessment revealed significant methodological heterogeneity, with notable gaps in exposure assessment and disease outcome reporting across studies. Conclusions: This analysis highlights the different health risks faced by conservators and restorers of cultural heritage, underscoring the need for standardized methodologies and prospective studies to increase the data on occupational risks. Full article

Eye-tracking technology provides high-resolution information about a user’s visual behavior and interests. Combined with advances in machine learning, it has become possible to recognize user traits and states using eye-tracking data. Despite increasing research interest, a comprehensive systematic review of eye-based recognition approaches has been lacking. This study aimed to fill this gap by systematically reviewing and synthesizing the existing literature on the machine-learning-based recognition of user traits and states using eye-tracking data following PRISMA 2020 guidelines. The inclusion criteria focused on studies that applied eye-tracking data to recognize user traits and states with machine learning or deep learning approaches. Searches were performed in the ACM Digital Library and IEEE Xplore and the found studies were assessed for the risk of bias using standard methodological criteria. The data synthesis included a conceptual framework that covered the task, context, technology and data processing, and recognition targets. A total of 90 studies were included that encompassed a variety of tasks (e.g., visual, driving, learning) and contexts (e.g., computer screen, simulator, wild). The recognition targets included cognitive and affective states (e.g., emotions, cognitive workload) and user traits (e.g., personality, working memory). A set of various machine learning techniques, such as Support Vector Machines (SVMs), Random Forests, and deep learning models were applied to recognize user states and traits. This review identified state-of-the-art approaches and gaps, which highlighted the need for building up best practices, larger-scale datasets, and diversifying tasks and contexts. Future research should focus on improving the ecological validity, multi-modal approaches for robust user modeling, and developing gaze-adaptive systems. Full article

Data-centric operational systems, machine learning (ML), and other analytical and artificial intelligence (AI) pipelines are becoming increasingly imperative for organizations seeking to increase the protection of sensitive data while satisfying customer expectations. This paper proposes a novel methodology to assess the level of vulnerability assigned to each of the data storage components in complex multilayered data ecosystems through a nuanced assessment of data persistence and content metrics. The suggested methodology introduces a new and effective way to address the issues of determining perceived privacy risk across data storage layers and informing necessary security measures for an ecosystem by calculating an ecosystem vulnerability score. This offers a comprehensive overview of data vulnerability, aiding in the identification of high-risk components and guiding strategic decisions for enhancing data privacy and security measures. With consistent and generalized assessment of risk, the methodology can properly pinpoint the most vulnerable storage systems and assist in directing efforts to mitigate them. Full article

This study introduces the Audit Risk Sentiment Value (ARSV), a novel audit risk proxy that leverages sentiment analysis to address limitations in traditional audit risk measures such as audit fees (LNFEE), audit hours (LNHOUR), and discretionary accruals (|MJDA|). Traditional proxies primarily capture quantitative dimensions, overlooking qualitative insights embedded in audit report narratives. By systematically analyzing sentiment and tone, ARSV captures nuanced audit risk dimensions that reflect the auditor’s risk perception. The study validates ARSV using a dataset of South Korean firms listed on the KOSPI from 2018 to 2023. The results demonstrate the ARSV’s superior explanatory power, as confirmed through the Vuong test, showing consistent performance across binary and continuous measures of explanatory language. ARSV bridges the gap between qualitative and quantitative audit risk assessments, offering significant benefits to auditors, regulators, and investors. Its ability to enhance the interpretability of audit reports improves transparency and trust in financial reporting, addressing stakeholder demands for actionable, forward-looking information. Furthermore, ARSV aligns with global trends emphasizing sustainability and accountability by integrating qualitative insights into audit practices. While this study provides robust evidence supporting ARSV effectiveness, its focus on South Korean firms may limit its generalizability. Future research should explore ARSV application in diverse regulatory and cultural contexts and refine the sentiment analysis tools using advanced machine learning techniques. Expanding ARSV to include other unstructured data, such as management commentary, could further enhance its applicability. This study marks a significant step toward modernizing audit methodologies, aligning them with evolving demands for comprehensive and transparent financial reporting. The empirical analysis reveals that ARSV outperforms traditional audit risk proxies with significantly higher explanatory power. Specifically, ARSV achieved a pseudo R² of 0.786, compared to 0.608 for LNFEE, 0.604 for LNHOUR, and 0.578 for |MJDA|. The Vuong test results further validate ARSV superiority, with Z-statistics of −12.168, −12.492, and −9.775 when compared against LNFEE, LNHOUR, and |MJDA|, respectively. The model incorporating ARSV demonstrated a 62.454 F-value and an Adjusted R² of 0.599, highlighting its robustness and reliability in audit risk assessment. These quantitative metrics underscore ARSV’s effectiveness in capturing qualitative audit risk dimensions, offering a more precise and informative measure for stakeholders. Full article

The integration of Internet of Things (IoT) technologies into solar energy systems has transformed them into smart solar energy systems, enabling advanced real-time monitoring, control, and optimization. However, this connectivity also expands the attack surface, exposing critical components to cybersecurity threats that could compromise system reliability and long-term sustainability. This study presents a comprehensive cybersecurity threat modeling analysis for IoT-based smart solar energy systems using the STRIDE threat model to systematically identify, categorize, and assess potential security risks. These risks, if unmitigated, could disrupt operations and hinder large-scale adoption of solar energy. The methodology begins with a system use case outlining the architecture and key components, including sensors, PV modules, IoT nodes, gateways, cloud infrastructure, and remote-access interfaces. A Data Flow Diagram (DFD) was developed to visualize the data flow and identify the critical trust boundaries. The STRIDE model was applied to classify threats, such as spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege across components and their interactions. The DREAD risk assessment model was then used to prioritize threats based on the Damage Potential, Reproducibility, Exploitability, Affected Users, and Disability. The results indicate that most threats fall into the high-risk category, with scores ranging from 2.6 to 2.8, emphasizing the need for targeted mitigation. This study proposes security recommendations to address the identified threats and enhance the resilience of IoT-enabled solar energy systems. By securing these infrastructures, this research supports the transition to sustainable energy by ensuring system integrity and protection against cyber threats. The combined use of STRIDE and DREAD provides a robust framework for identifying, categorizing, and prioritizing risks, enabling effective resource allocation and targeted security measures. These findings offer critical insights into safeguarding renewable energy systems against evolving cyber threats, contributing to global energy sustainability goals in an increasingly interconnected world. Full article

The geological and environmental conditions of the northern Shaanxi Loess Plateau are highly fragile, with frequent landslides and collapse disasters triggered by rainfall and human engineering activities. This research addresses the limitations of current landslide hazard assessment models, considers Zhuanyaowan Town in northern Shaanxi Province as a case study, and proposes an integrated model combining the information value model (IVM) with ensemble learning models (RF, XGBoost, and LightGBM) employed to derive the spatial probability of landslide occurrences. Adopting Pearson’s type-III distribution with the Bayesian theorem, we calculated rainfall-induced landslide hazard probabilities across multiple temporal scales and established a comprehensive regional landslide hazard assessment framework. The results indicated that the IVM coupled with the extreme gradient boosting (XGBoost) model achieved the highest prediction performance. The rainfall-induced hazard probabilities for the study area under 5-, 10-, 20-, and 50-year rainfall return periods are 0.31081, 0.34146, 0.4, and 0.53846, respectively. The quantitative calculation of regional landslide hazards revealed the variation trends in hazard values across different areas of the study region under varying rainfall conditions. The high-hazard zones were primarily distributed in a belt-like pattern along the Xichuan River and major transportation routes, progressively expanding outward as the rainfall return periods increased. This study presents a novel and robust methodology for regional landslide hazard assessment, demonstrating significant improvements in both the computational efficiency and predictive accuracy. These findings provide critical insights into regional landslide risk mitigation strategies and contribute substantially to the establishment of sustainable development practices in geologically vulnerable regions. Full article

In the current operational landscape, organizations face a growing and diverse array of cybersecurity challenges, necessitating the development and implementation of innovative and effective security solutions. This paper presents a novel methodology for dynamic risk assessment and mitigation suggestions aimed at assessing and reducing cyber risks. The proposed approach gathers information from publicly available cybersecurity-related open sources and integrates it with environment-specific data to generate a comprehensive understanding of potential risks. It creates multiple distinct risk scenarios based on the identification of vulnerabilities, network topology, and the attacker’s perspective. The methodology employs Bayesian networks to proactively and dynamically estimate the probability of threats and Fuzzy Cognitive Maps to dynamically update vulnerability severity values for each risk scenario. These elements are combined with impact estimations to provide dynamic risk assessments. Furthermore, the methodology offers mitigation suggestions for each identified vulnerability across all risk scenarios, enabling organizations to effectively address the assessed cybersecurity risks. To validate the effectiveness of the proposed methodology, a case study is presented, demonstrating its practical application and efficacy. Full article

In an era of increasingly abundant and granular spatial and temporal data, the traditional divide between environmental GIS and building-centric BIM scales is diminishing, offering an opportunity to enhance natural hazard assessment by bridging the gap between territorial impacts and the effects on individual structures. This study addresses the challenge of integrating disparate data formats by establishing a centralised database as the foundation for a comprehensive risk assessment approach. A use case focusing on flood risk assessment for a public building in northwest Italy demonstrates the practical implications of this integrated methodology. The proposed TErritorial RIsk Management & Analysis Across Scale (TERIMAAS) framework utilises this centralised repository to store, process, and dynamically update diverse BIM and GIS datasets, incorporating real-time IoT-derived information. The GIS spatial analysis assesses risk scores for each hazard type, providing insights into vulnerability and potential impacts. BIM data further refine this assessment by incorporating building and functional characteristics, enabling a comprehensive evaluation of resilience and risk mitigation strategies tailored to dynamic environmental conditions across scales. The results of the proposed scalable approach could provide a valuable understanding of the territory for policymakers, urban planners, and any stakeholder involved in disaster risk management and infrastructure resilience planning. Full article