Search Results (194)

Safety-critical cyber-physical systems require strict verification methodologies explicitly taken to reference safety properties in their tests. Conventional methods of mutation testing simply lump all mutants together, regardless of their effects on product safety, resulting in ineffective resource allocation and insufficient understanding of the behaviors of crucial safety interest. The novel mutation-guided safety assurance framework presented by this paper is called MuGu and it combines mutation testing and formal safety property enforcement. It uses a hierarchical safety constraint analyzer that classifies mutants (temporally) according to their ability to break temporal safety specifications modeled in Signal Temporal Logic (STL). A graph attention network encodes program semantics and control-flow dependencies to forecast the probability of safety violations, enabling effective prioritization of safety-critical mutants. The proposed safety-conscious mutation delineators use vital areas of the code, such as sensor interfaces, actuator commands, and decision-making code in autonomous systems. Extensive testing on two publicly accessible benchmark sets, namely the Software-artifact Infrastructure Repository (SIR) and Defects4J, shows that MuGu achieves 96.3% safe property coverage and requires 71.8% fewer new tests than traditional methods. The scheme determines 2.4 times as many safety-violating methods as state-of-the-art methods and decreases wasted effort on equivalent mutants by 64.2%. The statistical analysis demonstrates overwhelming improvements across all primary metrics in 12 baseline comparisons $p<0.001$. MuGu provides a rational basis for applying safety-oriented next-generation mutation testing in autonomous systems. Full article

Unmanned Aerial Vehicle Path Planning (UAVPP) in obstacle-rich environments requires trajectories that are collision-free, threat-aware, and feasible under practical flight constraints. This study proposes an Intelligent Cooperative Differential Evolution approach, reffered to as MuCDEA, to improve the adaptability and robustness of conventional Differential Evolution (DE) for UAVPP. MuCDEA integrates complementary mechanisms from JADE, CoDE, EPSDE, SaDE, MIDE, and SHADE through adaptive strategy selection and cooperative evolution. The optimization model combines path-length (fuel) cost and threat exposure with explicit pitch and yaw constraints that enforce actuator-feasible maneuvering bounds. The proposed framework is evaluated on 20 benchmark UAVPP cases covering 2D and 3D scenarios with varying obstacle distributions and pathh discretization levels, and it is compared against 11 state-of-the-art DE variants and several widely used optimization methods using the CEC-2022 ranking methodology. Results show that the cooperative configuration MuCDEA24 achieves the best overall ranking and consistently produces feasible trajectories across the tested cases, indicating that cooperative DE strategies provide an effective and controller-compatible solution for constrained UAVPP. Full article

Sustainable agri-food value chains depend on the capacity of smallholders to participate in markets in stable, remunerative and upgrade-oriented ways, yet in conflict-affected and politically constrained settings this capacity is weakened by mobility restrictions, insecure logistics, payment disruption, volatile inputs, thin market information and uncertain enforcement. This study aims to explain how such constraints reshape smallholder market integration and under what conditions governance and institutional arrangements stabilize, rather than reproduce, market exclusion. An integrative review was conducted following established methodological guidance. A structured search of Scopus, Web of Science and Google Scholar, extended by citation tracing, returned 821 records; after de-duplication and two-stage screening against explicit eligibility criteria, 44 studies were retained, of which 35 empirical studies form the evidence corpus. Each study was coded with a five-field extraction matrix across seven diagnostic dimensions—inputs and supplies, production capacity and technology, end-markets and trade, value-chain governance, sustainable production and energy use, value-chain finance, and the business and socio-political environment—and appraised for methodological quality with the Mixed Methods Appraisal Tool, so that heterogeneous designs were weighted rather than treated as equivalent. The synthesis shows that the most binding constraints are transactional and institutional rather than purely technical, and that governance operates as a conditional bridge: cooperatives, contracting, trader coordination, market-support institutions and public enabling arrangements lower transaction costs and stabilize exchange only where transparency, credible enforcement, fair risk-sharing and protection against opportunism are present; where these are absent, the same arrangements shift risk onto farmers. The review derives a measurement-focused, equity-sensitive research agenda for resilient and sustainable value-chain integration under constraints. Full article

The fragmentation of clinical information across health systems, community pharmacies, and specialty providers continues to undermine medication safety and emergency care, particularly when patients are unconscious or otherwise unable to communicate their history. The dominant response to this fragmentation has been the construction of a centralized data infrastructure—health information exchanges, prescription drug monitoring programs (PDMPs), and federated electronic health record (EHR) networks—that aggregates clinical information into institutional databases that are queryable by providers, insurers, regulators, and, in many jurisdictions, law enforcement. This article argues that the same care-coordination problems can be addressed through an architecturally different approach in which the patient, not the institution, holds the integrative artifact. The proposed design, here labeled the Guardian Card (a conceptual architecture, not a commercial product), pairs an HL7 Fast Healthcare Interoperability Resources (FHIR) clinical payload with the SMART Health Cards verifiable-credential framework and a dual-modality (QR code plus near-field communication) physical carrier. After describing the technical architecture, hardware options, and a five-phase deployment roadmap, the design is situated within the surveillance-critical scholarship that has documented PDMP function creep, third-party doctrine erosion, racial disparities in algorithmic prescribing oversight, and the surveillance-instrumentarian repackaging of nominally de-identified prescription data. The Guardian Card is offered as one operational implementation of a patient-controlled medication-record architecture, with community pharmacy and long-term post-acute care, where the Pharmacist eCare Plan integration is most feasible as a recommended first-deployment venue. Full article

Background/Objectives: Elder abuse remains a significantly underreported public health issue. The study examines how elder abuse is detected through a passive, suspicion-based case-finding pathway in an Italian university hospital emergency department (ED) and what the findings imply for improving systematic screening. Methods: This retrospective study analyzed elder abuse cases accessed at Careggi University Hospital ED (Florence, Italy) from 2017 to 2022. Eligible patients were aged ≥65 years and had suspected or confirmed elder abuse identified through Rosa Code protocol activation, abuse-related ICD-10 codes, and forensic consultation records. Two investigators independently reviewed eligible charts using predefined inclusion criteria and a standardized data-extraction form. Missing or unclear documentation was quantified descriptively, and no imputation was performed. Results: Sixty-seven elder abuse cases were identified during the six-year period, corresponding to a reported detection rate of 0.8% among the records screened for this study (mean: 11.2 cases/year). All eligible cases were captured through Rosa Code activation; ICD-10 and forensic-record searches did not identify additional cases. The majority of victims were women (76.1%), with a mean age of 75.5 years, and 76.1% had documented comorbidities. Physical abuse was the most common form (61.2%), predominantly perpetrated by family members (93.8%) within the victim’s home (64.2%). Head and neck injuries were most frequent (43.3%). A notable 50% decline in reported cases occurred during the COVID-19 pandemic. Despite law enforcement notification in 78% of cases, 65.7% of patients were discharged home. Conclusions: The study’s detection rate (<1%) falls critically short of international benchmarks (3–5%), underscoring urgent need for systematic screening using validated tools and staff training and multidisciplinary safeguarding pathways in Italian emergency departments. Full article

Tool-calling LLM agents are vulnerable to indirect prompt injection: externally retrieved data can redirect tool calls without system-prompt access, and prompt-level defences leave three harm classes undefended (path traversal, user-guided exfiltration, high-frequency tool abuse). We present a Policy Enforcement Point (PEP) that intercepts at the tool-call boundary with declarative rules over a cross-step information-flow label system (source integrity, data sensitivity) and a synchronous SHA-256 hash-chained audit log. On a controlled dataset across four attack classes, the full system cuts the attack success rate (ASR) from 40.0% to 5.0% (deepseek-v4-pro, five repeats) versus 35.0% for the strongest prompt-only baseline; disabling cross-step label propagation raises the call-level false-negative rate by 26.4 points. The 30.0% task-level false-positive rate is dominated by by-design least-privilege capability-token denials, not rule false positives—an expanded 30-task benign set yields 0/30 rule false positives under scripted isolation. A conservative-DS mitigation (intent-taint) closes the constructed denied-read reconstruction blind-spot variant (ASR 100% to 0%) at no cost on standard workflows. The audit log detects all three tested tamper classes; the in-process enforcement overhead is sub-millisecond per call. Across four further backends, ASR drops under the full system, though LLaMA-3.3-70B retains 16.7% (a rule-coverage gap). A preliminary run over a real MCP stdio transport (an official filesystem server) shows the mechanism operates at a real boundary with a sub-millisecond execution-path increment. We frame these as mechanism-coverage evidence on a controlled benchmark, not a deployability claim for production MCP workloads. Code, data, and metrics are openly available in the replication repository. Full article

This work presents a practical C++23 implementation of a physics-informed neural network (PINN) for a fractional-order damped oscillator. A fully connected network outputs displacement and velocity, so the governing dynamics are enforced through a compact state-space residual involving first and second time derivatives. Integer-order derivatives are obtained via automatic differentiation, which removes finite-difference noise and preserves smooth, consistent gradients during training. The history-dependent fractional damping term is incorporated using the classical L1 discretization on a uniform time grid, which makes each residual evaluation depend on the entire predicted solution history and naturally captures memory effects. The training objective combines the squared residual norms at collocation points with a strongly weighted initial-condition penalty to control drift and stabilize early iterations. Gradients of the complete objective with respect to all network parameters are computed using reverse-mode automatic differentiation in CppAD (20260000.0) by constructing a scalar loss function of a flat parameter vector, enabling efficient gradient-based optimization. Parameters are updated with the Adam algorithm using bias correction and double-precision moment accumulation for numerical robustness. This implementation includes deterministic parameter packing, explicit size checks, and lightweight diagnostics of boundary values during training, improving reproducibility and debuggability. Overall, the code provides an end-to-end baseline for PINN-based simulation of fractional-order oscillatory systems and can be readily extended to include external forcing, alternative loss weight schedules, and parameter identification from measurement data. Full article

The integrity of halal supply chains is increasingly threatened by fragmented paper-based records, certificate fraud, and the absence of real-time traceability. This paper presents HalalChain, a blockchain-based halal product traceability system that enforces role-based access control (RBAC) through three Solidity smart contracts deployed on an Ethereum-compatible blockchain. HalalChain is designed for production deployment on an EVM-compatible Layer-2 or sidechain such as Polygon or BNB Chain, on which the contracts run without code changes. A dual-storage architecture synchronises every supply chain event to both a PostgreSQL relational database and the blockchain, balancing on-chain immutability with off-chain query performance. The system supports five stakeholder roles, namely administrator, supplier, manufacturer, logistics, and retailer, each restricted to specific supply chain event types enforced at the smart contract level. Consumers can verify product halal status and full supply chain history by scanning a QR code linked to a public verification endpoint that cross-checks database records against on-chain event counts, producing a chain-integrity indicator. As the current chain-integrity check is count-base, it can detect missing or extra database rows, but it cannot detect content-level modification if the row count remains unchanged. A total of 107 automated test cases were executed covering functional correctness, edge cases, end-to-end integration, and gas performance benchmarks. Core smart contract operations consume between 25,365 and 213,684 gas units, indicating feasible deployability on Ethereum-compatible networks. An exploratory analysis was carried out with a preliminary survey of 40 respondents (mean = 4.10 on a 5-point Likert scale), suggesting that consumer demand for blockchain-verified halal certification is encouraging. The results demonstrate that HalalChain provides a tamper-evident, role-enforced traceability foundation for the halal food industry. The system secures the digital chain of custody cryptographically and the physical–digital binding between the QR code, and the product remains a separate trust assumption requiring complementary anti-tamper mechanisms. Full article

As software systems increasingly combine machine learning, deep learning, and generative AI components with classical deterministic logic, the systematic detection of AI-based algorithmic elements in application code is becoming essential for software audit, compliance with the EU AI Act (Regulation (EU) 2024/1689), and quality assurance. This paper introduces Quality-Informed Neural Networks (QINN), an architecture in which the structured knowledge encoded in the Quality Function Deployment (QFD) House of Quality is embedded into the network topology and weight initialisation through QFD-derived binary structural masks and knowledge-calibrated initialisation—in direct analogy with Physics-Informed Neural Networks (PINNs). The QFD relationship matrices act as structural priors that constrain the hypothesis space toward quality-consistent solutions by enforcing domain-expert-validated sparsity on network connectivity, while an optional QFD-regularised loss term provides an additional soft constraint on the learned weight structure. As a proof of concept, QINN is instantiated in its masked-architecture configuration for the binary classification of software repositories as AI-enabled or classical. On the AIC-199 proof-of-concept dataset, the proposed QINN attains a cross-validated AUC of 99.47% (±1.18%), recall of 100.00% (±0.00%), and F1-score of 99.02% (±1.34%) under QFD-informed structural masking, outperforming a non-learned QFD scoring baseline by 37.37 percentage points in recall and exceeding a cross-validated Random Forest ensemble on AUC by 2.47 percentage points (W = 0, p < 0.05), while producing explanations at three QFD-grounded levels—feature salience, named Technical-Evidence activations, and per-criterion quality requirement scores—that align directly with the EU AI Act documentation obligations. Validation on larger, independently curated datasets and sensitivity analysis of the QFD elicitation process are identified as priorities for future work. A domain-general seven-phase application protocol is provided. Full article

Digital evidence management systems are designed to ensure that the digital evidence is genuine and effectively handle its complexity. In this work, blockchain technology is applied to handle the digital evidence by introducing several layers of security to ensure its protection, data integrity, and confidentiality, as well as trace the evidence throughout all its phases. To store the evidence files and their metadata, the proposed system uses a decentralized storage architecture that utilizes the InterPlanetary File System (IPFS) and Google Drive. Moreover, the proposed system ensures the chain of custody of the digital evidence through the use of Hyperledger Fabric technology. In addition, smart contracts (chaincode) are used in this work to validate the digital evidence, enforce strong access controls, and protect evidence metadata integrity. To ensure reliable transaction sequencing and consistency across the distributed ledger, an ordering service is used. At last, we combine two hash algorithms, symmetric encryption, file fragmentation, and metadata logging to protect the digital evidence from unauthorized access. The proposed framework is integrated with modern forensic tools, including Autopsy. The procedure of acquiring and analyzing digital evidence is made straightforward by the application of a set of forensic procedures. Moreover, the system’s modular design allows users to perform preprocessing operations, administer the decentralized storage, administer the evidence retrieval, test system performance, and enhance the system scalability. Moreover, we implemented secure coding practices and applied large language models to mitigate identified vulnerabilities, including weak system input validation, concurrent access to the system, and an insecure logging system. The experimental results indicate that the proposed framework preserves the digital evidence’s integrity, ensures chain of custody, and records all transactions. Results also indicate that the digital evidence is protected from unauthorized access and change attempts. Finally, by following local relevant regulations and established standards, the digital evidence should be admissible in court. Full article

Internet of Things (IoT), sensor-network, and cyber-physical system (CPS) software increasingly relies on large language models (LLMs) and autonomous agents for code generation, maintenance, and vulnerability repair. However, LLM-generated edge services, telemetry APIs, configuration handlers, and data-aggregation routines can introduce SQL injection, path traversal, command injection, hard-coded credentials, and unsafe device-control logic, which may compromise sensing data integrity and system safety. Existing approaches largely rely on static post hoc analysis and lack a unified modeling of the generation process, making it difficult to achieve a principled trade-off between functionality and security. To address this challenge, we propose SafeCodeRL, a framework that integrates multi-agent collaboration with constrained reinforcement learning for trustworthy LLM-generated IoT/CPS software. SafeCodeRL models code generation as a security-aware sequential decision process, where Planner, Code, Security, Test, and Critic agents jointly optimize task decomposition, code synthesis, vulnerability auditing, and sandbox-based validation. We design a constraint-aware policy based on Proximal Policy Optimization, augmented with a Lagrangian mechanism and a shielding strategy to explicitly enforce security constraints. Experiments on real-world engineering and security benchmarks, including SWE-bench, SecurityEval, and CyberSecEval, show that SafeCodeRL reduces high-risk vulnerabilities by over 60% while maintaining high functional correctness. A scenario-level IoT/CPS case study further demonstrates that SafeCodeRL substantially improves secure pass rates for sensor telemetry, edge gateway, configuration-management, and data-aggregation tasks, providing a practical path toward trustworthy AI-assisted software development for sensor-driven systems. Full article

Earthquakes pose a serious threat to urban areas located in seismically active regions, particularly in developing countries where rapid urbanization and weak enforcement of building regulations increase the vulnerability of the built environment. Pakistan is highly exposed to seismic hazards due to its tectonic setting, and many residential buildings are constructed without adequate seismic design considerations. Therefore, assessing building vulnerability and understanding community perception of earthquake risk are essential for effective disaster risk reduction. This study investigates the relationship between the structural vulnerability of residential buildings and earthquake risk perception among residents in Peshawar, Pakistan. Two contrasting urban settlements were selected as case studies: WAPDA Town, representing a planned residential area, and Hashtnagri, representing an older unplanned settlement. A total of 400 buildings were surveyed through field investigations. Seismic vulnerability was assessed using the Rapid Visual Screening (RVS) method based on structural characteristics such as building age, number of floors, construction materials, structural irregularities, construction quality, and presence of seismic reinforcement features. A Physical Vulnerability Index (PVI) was developed to categorize buildings into different vulnerability levels. In addition, a questionnaire survey was conducted to evaluate earthquake risk perception among residents, and a risk perception index (RPI) was calculated. The results indicate that buildings located in the unplanned settlement exhibit significantly higher seismic vulnerability compared to those in the planned residential area due to poor construction practices, irregular structural configurations, and the absence of seismic-resistant features. Statistical analysis further reveals a positive relationship between physical vulnerability and earthquake risk perception, suggesting that residents living in structurally vulnerable environments tend to perceive higher earthquake risk. The findings highlight the importance of integrating structural vulnerability assessment with community awareness and preparedness programs. Implementation of seismic design provisions and improved enforcement of construction regulations, such as those specified in the Building Code of Pakistan 2022, can significantly reduce earthquake risk in rapidly growing urban areas. However, the present study did not directly evaluate the level of enforcement or compliance with the Building Code of Pakistan 2022 in either WAPDA Town or Hashtnagri. Therefore, the policy recommendations are intended as general implications derived from the observed vulnerability patterns. Full article

Despite extensive research on corporate governance compliance and firm-level outcomes, limited attention has been paid to how internal organizational structures, particularly business complexity and decentralization, shape governance effectiveness across institutionally differentiated emerging markets. This study examines these relationships within the Palestinian and Jordanian capital markets, which provide a relevant comparative setting due to differences in governance enforcement, institutional maturity, and sustainable governance adaptation. Grounded in agency theory, transaction cost theory, and contingency theory, the study adopts a comparative cross-sectional design using documentary data from non-financial firms listed on the Palestine Exchange (PEX) and the Amman Stock Exchange (ASE) for the 2023 fiscal year. Composite indices for governance effectiveness, decentralization, and business complexity were constructed using binary-coded governance disclosures. The empirical analysis employs descriptive statistics, correlation analysis, regression models, and moderation testing. The findings reveal substantial cross-market heterogeneity. In the Palestinian market, decentralization and business complexity are positively associated with governance effectiveness when examined independently, whereas the interaction effect is not supported. In the Jordanian market, business complexity emerges as the primary determinant of governance effectiveness, while decentralization shows no significant effect. Across both markets, the hypothesized moderating role of business complexity is not supported. The study contributes to the sustainable corporate governance literature by demonstrating that governance effectiveness in emerging markets is not merely a compliance issue, but also a sustainability-related organizational capability that supports transparency, accountability, institutional resilience, and responsible long-term decision-making. The findings provide context-sensitive implications for regulators and firms seeking to strengthen sustainable corporate governance practices within institutionally heterogeneous emerging-market environments. Full article

Background: Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) depends on the fusion of multiple complementary modalities. However, clinical practice often faces incomplete modality sets due to acquisition failures, patient contraindications, or protocol variations. Current methods either treat each modality feature extractor in isolation or depend on computationally expensive teacher networks for cross-modal knowledge transfer. Objective: This paper presents Hierarchical Adaptive Group Self-Support Learning with Boundary-Aware Calibration (HAGSS), a framework that overcomes three key limitations of existing group self-support methods: static group formation that ignores temporal prediction quality, uniform treatment of boundary and interior voxels, and distribution mismatch across heterogeneous modality logits. Methods: We propose a hierarchical adaptive group formation mechanism that reassigns group leader roles at each epoch based on voxel-level prediction confidence scores instead of fixed sensitivity priors. We also introduce a boundary-aware calibration module that applies spatially varied distillation weights with greater emphasis on tumor boundary regions. In addition, we design a cross-scale consistency regularization term that enforces agreement between multi-resolution predictions to stabilize the self-support target. Results: Experiments on BraTS2020, BraTS2018, and BraTS2021 datasets show that HAGSS achieves consistent improvements over state-of-the-art baselines. The average Dice gains across the whole tumor, tumor core, and enhancing tumor regions reach 1.30% on BraTS2020 and 1.61% on BraTS2021 compared to existing methods. All improvements are statistically significant ($p<0.05$). Conclusions: HAGSS operates exclusively during training, adds no parameters or inference cost, and can be applied as a plug-in module to any multi-encoder incomplete multi-modal segmentation architecture. Code is publicly available at GitHub. Full article

Post-disaster reconstruction programmes create an irreversible window for embedding or foreclosing residential energy efficiency at scale. This study examines the structural determinants of per capita residential electricity consumption (K_MES) across all 81 provinces of Türkiye over 2013–2022 using a balanced province-year panel. We develop two complementary panel models, both estimated by two-way fixed effects (province + year) with cluster-robust standard errors, and supported by GLS-AR(1) and random-effects GLS robustness checks. Note that K_MES measures the electricity component of residential energy use only; we, therefore, also estimate the building-stock model with a constructed total-energy dependent variable that combines residential electricity (H_MES) and natural-gas consumption (X_DG) in kWh-equivalent units. Model 1 isolates the macroeconomic transmission channel through which exchange-rate volatility shapes residential electricity demand. Because the USD/TRY rate has no cross-sectional variation, its identifying power in two-way fixed effects comes from its interaction with province-level natural-gas-heating exposure (sh_gas × EV_DA). The interaction is robustly negative across all full-sample specifications (β ≈ −0.022, p < 0.01), indicating that provinces with greater gas-heating penetration are buffered against currency-depreciation pass-through into electricity demand. Provincial GDP carries the dominant direct macro coefficient (β ≈ 0.27–0.29, p < 0.01), establishing income elasticity rather than the exchange rate as the headline aggregate driver. Model 2 decomposes the building stock by structural system, filler material, heating system, and heating fuel. The dominant predictors are the share of electric heating (β ≈ 1.16–1.27, p < 0.01) and the share of AC-only heating (β ≈ −1.0 to −1.13, p < 0.05), with a total-energy specification reaching R² = 0.92. In the comparative subsample of the eleven Kahramanmaraş-affected provinces, masonry construction emerges as the dominant pre-disaster predictor of per capita electricity consumption (β = 14.04, p < 0.05), revealing structurally distinct stock characteristics that pre-date the February 2023 earthquake. Two re-framings are required. First, since the panel covers 2013–2022, the disaster-province estimates capture pre-disaster structural heterogeneity rather than post-disaster market rupture. Second, the macroeconomic mechanism that prior work attributed to the exchange-rate level is more accurately understood as a fuel-mix-mediated exposure channel. The combined evidence implies that mandatory building-code enforcement and natural-gas grid extension are complementary policy levers in the 488,000-unit Turkish Housing Development Administration reconstruction programme: gas grid expansion reduces the macroeconomic vulnerability of residential energy demand, while masonry-replacement construction standards address the largest pre-disaster structural determinant of energy intensity in the affected region. Full article