Search Results (2,119)

This paper investigates how corporate governance can impact the litigation risk of banks with reference to the board characteristics of the Jordanian banking industry. With a dataset of 14 of the banks listed on the Amman Stock Exchange between the years 2013–2023, the study examines how gender diversity on a board, board size, board independence, foreign board representation, and directors’ financial education affect litigation costs. The analysis is based on agency theory and upper echelons theory and uses pooled ordinary least squares regression. The findings indicate that board characteristics have an uneven impact on litigation risk. The presence of female board members is always related to reduced legal costs, which implies that gender diversity improves the quality of monitoring and control over risks. Conversely, an increased board size and increase in foreign directors and directors of financial education are both linked to increased legal expenses, suggesting coordination issues and unfamiliarity with regulations in the cross-border governance environment. Board independence, however, does not demonstrate any statistically significant correlation with litigation risk. The paper adds to the literature by offering new evidence based on a developing economy with a unique institutional environment. The findings have significant implications for regulators, policymakers, and practitioners seeking to design effective board structures that reduce legal and compliance risks in the banking sector. Full article

Early-stage structural design requires rapid exploration of large design spaces, where the initial sizing of reinforced concrete members shapes downstream material use, cost, and the number of design iterations. Conventional predimensioning relies on experience and simplified formulae, while finite element analysis remains too slow for iterative use. This study presents a multi-task graph neural network surrogate that predicts per-element Eurocode compliance together with the required reinforcement for reinforced concrete slab-and-column buildings in one pass. A shared GraphSAGE encoder, trained on 2562 synthetic building graphs from automated finite element simulations, feeds one head for a compliance probability and another for reinforcement quantities. Because the rule-based Eurocode check is a hard pass-or-fail decision that does not vary smoothly with the design, the surrogate learns a continuous, differentiable compliance probability in its place, demonstrated for two representative criteria, one per element type, namely the $l/250$ deflection limit for slabs and the 4% reinforcement-ratio limit for columns. Across five random seeds, cost-sensitive focal-loss training that weights missed non-compliance above false alarms reached 90.9% balanced accuracy and held the share of non-compliant elements wrongly passed as compliant at 6.1% for columns and 1.6% for slabs, with a mean reinforcement error near 2% of the normalised target range. Inference averaged approximately 0.5 ms per building, between five and six orders of magnitude faster than the finite element analyses. A differentiable, multi-task graph surrogate therefore supports fast, cost-sensitive compliance screening for early-stage predimensioning, serving as a seed for gradient-based design exploration and a starting point for finite element verification. Full article

This study aims identify plant-derived compounds that can inhibit glutathione peroxidase 4 (GPX4) enzyme and evaluate them through molecular docking, dynamics simulations, and ADMET analyses. The 3D structure of the GPX4 protein (PDB ID: 2OBI) was obtained from the Protein Data Bank. The plant-derived ligand library was compiled from the PubChem database and screened for compliance with Lipinski’s rules using ADMETLAB 2.0. Molecular docking simulations were performed using Autodock Vina. Molecular dynamics simulations of 100 nanoseconds were performed for the selected ligand–protein complexes using AMBER Tools and OpenMM software. The ADMET properties of the ligands were evaluated using the pKCSM web server. Compared to the reference inhibitor RSL3 (−7.2 kcal/mol), five plant compounds showed stronger binding affinity: withaferin A (−8.0 kcal/mol), mahanine (−7.9 kcal/mol), pseudobufarenogin (−7.8 kcal/mol), cucurbitacin I (−7.6 kcal/mol), and liquiritin (−7.5 kcal/mol). Molecular dynamics simulations showed that the complexes of withaferin A, mahanine, and liquiritin exhibited superior structural stability. ADMET analysis revealed that the compounds generally possess acceptable pharmacokinetic profiles but require some bioavailability optimization. The identified plant-derived compounds can be considered as potential therapeutic agents in cancer treatment by inducing ferroptosis via GPX4 inhibition. These findings provide an important basis for natural product-derived drug discovery studies. Full article

Mongolian rangelands face interacting ecological and livelihood pressures, including livestock pressure, vegetation change, soil-carbon dynamics, household income variability, and inefficiencies in livestock by-product recovery. This paper examines whether observed administrative and household data, field-observed pilot-area audit evidence, satellite-derived/backcast vegetation indicators, model-reconstructed ecological trajectories, econometric associations, machine-learning diagnostics, Monte Carlo uncertainty outputs, and scenario-based carbon-finance calculations are consistent with a study-specific ecological–economic feedback framework in Mongolian pastoral rangelands. The analysis combines observed livestock and household data, satellite-derived vegetation indicators, field-anchored soil organic carbon (SOC) information, climate controls, and pilot-area by-product audit evidence in a dual-scale framework comprising nine pasture-user groups in Öndörshireet Soum, Töv Aimag, and a national soum-level panel for 2002–2024. SOC, above-ground biomass (AGB), and below-ground biomass (BGB) trajectories are treated as model-reconstructed series rather than independently observed annual field measurements. Fixed-effects panel models are used to estimate conditional associations, while machine-learning models assess predictive consistency within reconstructed data structures. Under the fitted full specification, the best-performing national-panel model reports an out-of-sample R² of 0.942 for model-reconstructed SOC; this value is interpreted as high internal predictive consistency within the reconstructed SOC panel, not as independent validation of observed annual SOC change. Because the SU/SOC ratio mechanically contains SOC, the full-specification predictive results are subject to leakage risk, and leakage-free validation is needed for a more conservative assessment of predictive performance. Panel estimates suggest that vegetation condition is positively associated with ln(household income), while the by-product waste ratio is negatively associated with ln(income), conditional on fixed effects and model specification. Scenario-based carbon-finance outputs, framed with reference to Verra’s VM0042 Improved Agricultural Land Management methodology, vary materially with compliance, carbon price, weighted average cost of capital, and revenue-sharing assumptions; these outputs are illustrative sensitivity calculations and do not demonstrate VM0042 compliance, project eligibility, project-registration readiness, verified emission reductions, or credit-issuance readiness. The findings are associational, reconstruction-dependent, and scenario-based. They support an analytical framework rather than establish a closed causal loop. Full article

The increasing prevalence of multidrug-resistant Staphylococcus aureus (MRSA) has necessitated the development of alternative therapeutic strategies targeting bacterial virulence factors. This study employed an integrated in silico approach to identifying potential inhibitors of the iron-regulated surface determinant B Near-iron Transporter domain, a key protein involved in heme acquisition and pathogenicity. Virtual screening and molecular docking identified certain similarity-selected small molecules possessing strong binding affinities, with (4-(1-oxoisoindolin-2-yl)benzoic acid (TOP1) and (4-(2-oxochromen-3-yl)benzoic acid (TOP2) exhibiting the most favorable binding energies at −12.0 and −11.8 kcal/mol, respectively. Molecular dynamics simulations over 200 ns confirmed stable protein–ligand interactions that yielded reduced structural fluctuations in ligand-bound complexes when compared with the apo form. Molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) analysis revealed that van der Waals interactions were the primary contributors to binding, with TOP1 showing a more favorable overall binding energy. Drug-likeness and pharmacokinetic predictions indicated compliance with Lipinski’s rule of five and moderate bioavailability, although limited intestinal absorption was observed. Toxicity predictions indicated that both compounds are non-mutagenic but may exhibit hepatotoxicity. Notably, TOP1 exhibited potential nephrotoxicity, cardiotoxicity, and carcinogenicity, whereas TOP2 demonstrated a more favorable safety profile. These findings highlight a trade-off between binding affinity and safety, suggesting that TOP2 emerged as a computationally prioritized candidate for future experimental validation. Because the present findings represent computational predictions only, further orthogonal computational analyses and experimental studies are required to confirm the proposed binding modes, biological activity, and therapeutic potential of the identified compounds. Full article

Cybersecurity relies heavily on warning systems to regulate user behavior under uncertainty. These warnings (ranging from browser security dialogs to phishing alerts and enterprise security notifications) do more than convey information: they may alter the conditions under which users select, avoid, verify, report, or override security-related actions. When combined with feedback, they may also contribute to calibrated reliance and safer behavior over time. However, existing research remains fragmented across usable security, human–computer interaction, and safety-related decision-making, and is largely focused on short-term outcomes. As a result, limited attention has been given to how cybersecurity warnings function as risk-control and learning mechanisms within safety-relevant socio-technical systems. This scoping review maps how empirical studies have examined cybersecurity warning systems in relation to behavioral adaptation, trust calibration, and risk regulation, and whether they assess persistence, transfer, or learning over time, identifying recurring design patterns, critical trade-offs, and structural gaps. Following PRISMA-ScR 2018 guidelines, we searched major multidisciplinary and domain-specific databases, with no time frame limits, for empirical studies that examined cybersecurity warnings in relation to learning-relevant behavioral, cognitive, or performance outcomes. Seventeen studies met the inclusion criteria; this number reflects the review’s focused conceptual scope rather than the size of the cybersecurity-warning literature as a whole. Eligible studies included experimental, quasi-experimental, field, and mixed-method designs, but no included study assessed persistence or transfer over time. Data extraction focused on warning characteristics, learning and trust mechanisms, user behavior, and security outcomes. Across the included studies, research primarily evaluates immediate responses, such as clicks, choices, response time, and classification accuracy, whereas comprehension and corrective feedback are infrequently assessed, trust calibration is never formally measured, and persistence or transfer over time is assessed in none of the included studies. On this basis, the review proposes a learning-oriented framework for evaluating cybersecurity warnings beyond short-term compliance, emphasizing feedback, calibrated reliance, risk-regulation responses, and direct tests of maintenance and transfer over time. Full article

Train fires in railway bridge–tunnel connection sections (BTCSs) create severe evacuation challenges because tunnel–bridge spatial transitions interact with heat, smoke, visibility loss, and constrained rescue conditions. Existing evacuation management methods remain limited in coupling quantitative risk assessment with adaptive route guidance under evolving fire hazards. To address this issue, this paper proposes a large language model (LLM)-enhanced intelligent decision-support system (IDSS) framework for quantitative risk assessment and dynamic evacuation routing in BTCS fire scenarios. First, a multi-dimensional risk assessment model is established using the analytic hierarchy process and fuzzy comprehensive evaluation to quantify post-stop evacuation risk from the perspectives of evacuation organization, structural damage, and line recovery. Second, a dynamic topology-based routing method is developed to prune fire-threatened nodes and identify safer evacuation paths under evolving hazard conditions. The risk assessment model and routing algorithm are further embedded as callable tools into an LLM-enhanced evacuation IDSS under a perception–reasoning–recommendation architecture, in which an LLM orchestrates tool invocation, situational reasoning, and recommendation generation, thereby enabling autonomous risk interpretation, dynamic route replanning, and cross-regional collaborative decision support. The proposed framework is validated through a representative real-world railway engineering case. The results show that the IDSS-recommended routes achieved higher comprehensive safety scores (80.44 and 79.56) than routes involving fire-affected areas did (77.00 and 77.88). Workflow analysis further indicates that the proposed IDSS reduces the manual route-derivation workload by integrating risk assessment, topology pruning, and route allocation into structured, human-reviewable evacuation recommendations. Expert evaluations further confirm the rationality and compliance of the outputs, with review scores ranging from 1.76 to 1.92 out of 2.00. Overall, the proposed framework offers a feasible decision-support approach for intelligent evacuation management in complex railway fire emergencies. Full article

A recurring challenge in the development of information systems (ISs) across complex organizational domains is the lack of integration and alignment between strategic, tactical, and operational levels, resulting in methodological fragmentation that constrains traceability, innovation, and organizational value generation. This study proposes and applies to the Integrated Innovation Framework for Information System Development (IIF-ISD) to overcome this gap. The research was structured through a systematic literature review, following the PRISMA and ROSES protocols, and validated through an exploratory single-case study involving the development of an IS supporting the Selo Casa Azul (SCA) environmental certification process in a Brazilian construction company, a context chosen for its multi-level organizational complexity and ESG compliance requirements, representative of broader certification IS development challenges. The framework integrates DSRM, agile methodologies, Design Thinking, and Lean Startup through three governing principles—Hierarchical Embedding, Functional Complementarity, and Traceability by Design—achieving cross-level alignment between strategic objectives, tactical performance monitoring, and operational execution. Empirical evaluation (n = 9; 14 weeks) yielded SUS scores of 76.8–82.1/100, a 76% reduction in data entry error rates, and a 78% stakeholder engagement rate, providing initial support for the framework’s practical effectiveness. Full article

This study addresses the absence of a structured and reproducible framework for evaluating road infrastructure project quality under decentralized procurement systems. A quantitative approach was employed using project-level data from 96 completed road infrastructure projects and questionnaire responses from 50 stakeholders. Infrastructure quality was measured using an Infrastructure Quality Index (IQI) derived from objective technical inspection data, while perceived risk factors were assessed through Likert-scale questionnaires. Multiple regression analysis was used to estimate the relationship between risk factors and infrastructure quality, and statistically significant predictors were incorporated into a risk-based evaluation model following ISO 31000 principles. The results indicate that Procurement Governance Risk, Contract Compliance Risk, Project Supervision Risk, Resource Availability Risk, and Sociocultural Risk significantly influence infrastructure quality, whereas Contractor Capacity Risk and Contractor Workload Risk were not statistically significant. A Composite Risk Index was subsequently developed by integrating likelihood and consequence dimensions through weighted aggregation and explicit classification thresholds. The proposed framework provides a transparent and reproducible tool for project evaluation, contractor performance assessment, and risk-informed decision-making. The findings highlight the importance of strengthening governance, supervision, and contract compliance to improve infrastructure quality in decentralized construction environments. Full article

Food safety training is a cornerstone of foodborne disease prevention in collective catering, particularly in settings serving vulnerable populations. This study aimed to assess baseline food safety knowledge and evaluate the effectiveness of a food safety training session among food handlers employed in school cafeterias and residential care facilities (RSAs) collective catering. A pre–post design was applied to 168 participants who completed a structured knowledge questionnaire before and after training. At baseline, only 31% of participants achieved a passing score. Knowledge levels were significantly associated with primary job role (p < 0.001): food preparers and managers were more likely to pass compared with food service workers involved mainly in meal distribution. In multivariate analysis, both job role and catering setting remained independently associated with test performance. Following the training session, the proportion of participants passing the test increased to 74% (p < 0.001), and differences between professional categories were reduced. These findings indicate that food safety knowledge in collective catering could vary according to occupational role and organizational context, but can be improved through training. Role-tailored, HACCP-based educational programs could be essential to strengthen compliance and protect vulnerable populations in institutional catering settings. Full article

The CONCERTO project intends to enable innovation, while maintaining the safety performance of current configurations, by drafting suggestions for new means of compliance within the scope of the three Clean Aviation branches. This paper is drafted within the high-voltage distribution branch of the CONCERTO consortium. It provides an overview of existing considerations about the certifiability of (hybrid-)electric aircraft configurations and presents a structured and methodical approach that has been established while conducting CS-23 regulatory gap analysis for the CS-25-derived CS-23 CONCERTO HVD architecture. A limiting factor when incorporating existing insights into a new regulatory gap analysis is that most studies are based on a specific configuration, limiting their applicability. One key feature of the presented methodical approach is the property-based similarity approach that overcomes this limitation by enabling systematic incorporation of existing project-specific findings into the analysis to be conducted, taking into account the specific properties of different system design and sizing. The structured comparison, based on relevant technological properties, enhances the applicability of the identified critical areas and regulatory gaps of available gap analyses by enabling a systematic transfer of gap findings to other HVD system designs, even with different properties. Full article

Background: Costal cartilage is the preferred structural material for augmentation rhinoplasty when robust and durable tip support is required. However, conventional full-thickness harvest is associated with significant donor-site morbidity, and commonly employed rigid fixation strategies—such as the septal extension graft—substantially restrict postoperative nasal tip compliance. The present study introduces a novel two-component technique combining a half-harvest costal cartilage procurement method with a pyramidal-shaped columellar strut graft anchored on the floating-tip principle, with the objective of maintaining postoperative nasal tip flexibility while providing structural support following augmentation rhinoplasty. Methods: A retrospective review was performed of consecutive patients who underwent primary or revision augmentation rhinoplasty using the pyramidal costal cartilage columellar strut graft technique by a single surgeon between June 2018 and February 2026. The medial half of the conjoined costal cartilage at the seventh, eighth, or ninth rib was procured via a half-harvest approach, preserving the lateral cortex and perichondrium to minimize donor-site morbidity and potential cartilage regeneration was considered a theoretical benefit. The harvested cartilage was carved into a pyramidal columellar strut and secured to the anterior nasal spine using a floating fixation construct; the inferior base of the strut was rigidly fixed to the nasal septum and anterior nasal spine with a minimum of three PDS 5-0 sutures, while the superior portion remained free to preserve physiologic nasal tip mobility. Adjunctive cap and shield grafts, perichondrial wrapping, and dermal fat grafts were employed as indicated. Primary outcomes included nasal tip projection, postoperative tip mobility, donor-site morbidity, and surgical complication rates. Results: Favorable clinical observations of maintained tip projection were noted throughout follow-up. Manual postoperative examination suggested preservation of tip flexibility in most patients; however, no validated objective mobility assessment tool was available. The revision rate for clinically significant tip deviation was low. No major donor-site adverse events—including pneumothorax or rib fracture—were encountered. Postoperative chest wall pain was minimal and transient, with most patients resuming daily activities within one week of surgery. Conclusions: The pyramidal-shaped costal cartilage columellar strut graft with half-harvest technique is a novel, biomechanically informed, and technically reproducible approach to augmentation rhinoplasty that was developed to address donor-site morbidity and postoperative tip rigidity, two commonly recognized limitations of conventional costal cartilage rhinoplasty: donor-site morbidity and postoperative nasal tip rigidity. Preservation of the lateral cortex and perichondrium during procurement may contribute to reduced postoperative donor-site discomfort, accelerates functional recovery, and may promote endogenous cartilage regeneration over time. The anatomically derived pyramidal strut geometry, combined with floating fixation to the anterior nasal spine, was designed to approximate the native columellar architecture, enabling consistent preservation of physiologic nasal tip mobility. The present series demonstrated a favorable safety profile with a low overall complication rate and an absence of major donor-site adverse events. Prospective studies with validated objective outcome measures are required to confirm these findings, to delineate the optimal patient selection criteria, and to establish evidence-based long-term outcome benchmarks for this technique. Full article

Electrical measurements of emerging materials such as thin films, two-dimensional materials, and fragile porous systems are often hindered by damage and contamination caused by conventional contact methods, including metal electrode deposition. In this study, we demonstrate the novelty and advantages of a mechanically compliant “soft probe” over conventional methods and conductive AFM. The non-destructive soft probe achieves stable electrical contact in the repulsive-force regime using a hairpin-shaped spring structure, allowing consistent measurements without active force control nor electrode fabrication. Case studies demonstrate that the soft probe prevents metal penetration and preserves intrinsic properties, as demonstrated in NiO resistive switching devices, and improves interface quality compared to deposited electrodes in ferroelectric measurements. It also enables electrical characterization of fragile materials such as metal–organic frameworks without inducing structural degradation. Furthermore, its mechanical compliance ensures stable operation under vibration and thermal stress, enabling measurements in vacuum and low-temperature environments. These results indicate that the soft probe provides a simple, versatile, and contamination-free platform for reliable electrical measurements, and represents a promising approach for the characterization of a wide range of emerging material systems. Full article

Cybersecurity risk management is often complicated by fragmented solutions for threat identification and detection, vulnerability assessment, and control selection across multiple frameworks. This paper presents a unified, dynamically updated, threat-based cybersecurity control platform that addresses this challenge by integrating Information Technology (IT), Operational Technology (OT), and Internet of Things (IoT) standards, including ISO/IEC 27001:2022, National Institute of Standards and Technology Cybersecurity Framework (NIST CSF) 2.0, and IEC 62443-3-3. The platform enables (1) querying a selected threat to identify associated vulnerabilities, (2) recommending applicable security controls across multiple frameworks, and (3) identifying overlapping or unique controls to avoid redundant implementation. Automated integration of Common Vulnerabilities and Exposures (CVEs) from the NIST National Vulnerability Database (NVD) links vulnerabilities to mapped threats and controls, supporting proactive risk management. A structured evaluation was conducted across 100 threat scenarios spanning IT, OT, and IoT domains, producing approximately 1000 threat–control relationships across 3 integrated frameworks. Performance evaluation demonstrates that the platform is scalable. While integrating additional frameworks, it maintains an average query latency of 0.40 s to 0.43 s, which implies an insignificant incremental latency increase of 0.03 s, while its web-based interface provides dynamic querying and visualization in a user-friendly manner for technical and non-technical users. By unifying threat, vulnerability, and control data, the platform streamlines compliance, reduces control retrieval time, and ensures traceable, consistent, and cross-framework mitigation strategies, enhancing informed cybersecurity decision making. Full article

Precision tilt stages capable of high angular resolution and low cross-axis coupling are essential for applications such as free-space optical communication, adaptive optics, and micro/nano-positioning. In this study, a two-degree-of-freedom compliant tilt stage based on differential actuation of positive and negative Poisson’s ratio folded-beam structures is proposed. The stage incorporates four circumferential compliant motion units, each consisting of a W-shaped positive Poisson’s ratio folded beam, an M-shaped negative Poisson’s ratio folded beam, a lever amplification mechanism, and compliant decoupling leaf springs. By exploiting the opposite out-of-plane deformation tendencies of the two folded-beam types under identical input forces, a push–pull differential driving effect is generated, enabling independent tilting motion about two orthogonal axes with enhanced angular output. The lever amplification mechanisms enlarge the small displacement of the piezoelectric actuators, while the decoupling leaf springs suppress parasitic motion and reduce cross-axis coupling. A static analytical model is established based on compliance analysis and force–moment equilibrium. The model predictions are validated through finite element analysis, with errors of 4.77% and 4.80% for the two axes, respectively. Experimental results obtained from a stereolithography-fabricated prototype demonstrate maximum tilt angles of 9.19 mrad and 8.80 mrad about the x- and y-axes under a 150 V driving voltage, while the corresponding coupling angles are only 0.043 mrad and 0.040 mrad, yielding coupling ratios below 0.5%. The proposed design achieves a favorable combination of compact monolithic structure, effective displacement amplification, and excellent decoupling performance, offering a practical solution for precision optical adjustment, beam steering, and micro/nano-positioning systems. Full article