Search Results (11,088)

The Controller Area Network (CAN) remains the backbone of in-vehicle communication, but its lack of built-in security exposes safety-critical systems to cyberattacks. This paper presents a real-time, reconfigurable, specification-driven intrusion detection system (IDS) implemented on a custom test bench that emulates an EV powertrain. The CAN traffic captured from the four-ECU setup formed the dataset used in this study. The IDS enforces a compact, reconfigurable ruleset covering timing bounds, jitter envelopes, identifier whitelists, frame format, data length code (DLC) compliance, bus-load thresholds, application-level CRC, and alive-counter verification. The IDS achieves detection times below 2 ms with false positive rates under 1% for injection, denial of service (DoS), and fuzzy attacks, even at CAN bus loads up to 70%, while microcontroller resource usage remains within the constraints of automotive-grade devices, supporting deployment in embedded environments. The main contributions of this study are as follows: (i) a validated and reproducible EV powertrain test bench with millisecond-level timing, (ii) a deployable and easily reconfigurable ruleset with deterministic runtime, and (iii) a latency-oriented evaluation framework that is portable across automotive microcontroller platforms. The EV powertrain dataset v1.0 was released in a public GitHub repository to facilitate reproducible research and enable future benchmarking studies. Full article

This article presents a systematic survey of six prominent quantum computing applications in finance, unified under the paradigm of optimization as the foundational use case from which derivative applications are constructed. We formalize the transition from the classical Markowitz portfolio optimization framework to a quantum implementation via the Quantum Approximate Optimization Algorithm (QAOA), including explicit mathematical derivations, theoretical performance bounds, and convergence guarantees. Beyond algorithmic formalism, we critically assess prevailing hardware limitations, focusing on noise thresholds and coherence constraints that currently preclude a demonstrable quantum advantage over classical counterparts. Furthermore, we address the underexplored institutional prerequisites for financial deployment, including regulatory compliance, model validation protocols, and structural barriers to adoption. We conclude that despite ongoing hardware maturation, proactive engagement with quantum algorithm development is imperative for financial institutions to preempt technological obsolescence upon the achievement of hardware parity. Full article

This study systematically examines the intersection of emerging digital technologies and organizational accountability within the sustainability domain using the TCCM framework. Guided by the SPAR-4-SLR protocol, a final corpus of 67 high-impact peer-reviewed articles was analyzed to synthesize current knowledge and identify structural gaps in governance architectures. Findings indicate that traditional human-led narrative disclosures are increasingly supplemented or replaced by technology-embedded verification systems offering real-time data granularity. The analysis shows that while the field is largely grounded in Stakeholder Theory and the Resource-Based View, mid-range theorizing is needed to address algorithmic bias and the gap between technological capabilities and accountability practices. Empirical evidence is concentrated in Europe and East Asia, exposing a digital divide that limits the applicability of findings to resource-constrained enterprises. The study provides a conceptual synthesis of how AI, blockchain, and IoT reshape transparency, highlighting the need for governance approaches that prioritize ethical oversight, decentralized validation, and substantive rather than symbolic compliance. Full article

Background/Objectives: Schools may represent an ideal setting for increasing vaccine literacy and uptake. This quasi-experimental study took place between February and June 2025 with the aim of assessing the feasibility and effectiveness of a school-based educational intervention about vaccination among Italian adolescents. Methods: The European Commission’s e-Bug methodology was used to enhance vaccine knowledge in a sample of students attending four randomly chosen middle schools from Southern Italy. Pre and post-intervention vaccination knowledge was assessed through a questionnaire and compared through the Wilcoxon signed-rank test. Regression models were used to identify predictors of intervention-related outcomes. Results: A total of 262 students (mean age 12.3 ± 0.7 years, 52.3% female) participated in the study. A significant increase in vaccination knowledge score was registered from pre (5.6 ± 1.43) to post-intervention (6.79 ± 1.77). A significant improvement was found to be related to a lower number of cohabitants (OR = 0.61; 95% CI = 0.45–0.82), a lower score in the pre-test (OR = 0.60; 95% CI = 0.47–0.77), having considered the information provided completely clear (OR = 1.98; 95% CI = 1.05–3.74), and being willing to participate in similar future interventions (OR = 2.23; 95% CI = 1.12–4.42). Conclusions: These results show the effectiveness of school-based education strategies in increasing vaccine literacy within the targeted adolescent population. Similar interventions can be useful to increase compliance with vaccination in this age class. Randomized controlled studies are needed to confirm these findings. Full article

Traditional topology optimization methods often face challenges such as slow convergence, high sensitivity to initial structures, and limited exploration of the design space when dealing with multi-physics coupling problems. To address these challenges, this study proposes an efficient design framework integrating reinforcement learning and topology optimization. The framework first employs a Deep Q-Network (DQN) agent to dynamically adjust penalty factors, accelerating the convergence process, and uses its pre-optimization results as the initial conditions for the Bidirectional Evolutionary Structural Optimization (BESO) method, thereby enhancing optimization efficiency and structural performance. By introducing an anisotropic material model, the design space is expanded, further unlocking the potential for structural lightweighting. On this basis, a dual-objective optimization strategy for mechanical compliance and thermal compliance is adopted, enabling the final structure to adapt to various physical working conditions. Finally, the optimal design is extended from two-dimensional to three-dimensional, facilitating subsequent manufacturing and verification. Numerical examples demonstrate that compared with traditional methods, the proposed pre-optimization method achieves a 22.463% reduction in structural compliance and improves thermal management performance. The framework demonstrates robust convergence across different boundary conditions (MBB and cantilever beams) and expands the design space through anisotropic microstructures, offering a practical solution for multi-physics lightweight design. Full article

Methods examining eyewitness testimony and its identification of suspects have not received sufficient analysis internationally. In the face of growing empirical evidence of methodologic and judicial errors, Greece’s judicial process nonetheless prioritizes eyewitness testimony in gathering evidence and preparing cases for trial. Due to its pluralistic geographical and cultural position uniting European, Balkan, and Mediterranean influences, and its alignment with non-Napoleonic code nations, Greece is a useful example for studying witness interviewing in evolving judicial systems. Drawing on 87 semi-structured interviews with Greek legal professionals, this study identifies systemic variables affecting eyewitness interviews and suspect identification. Prominent barriers to robust witness interviewing included inappropriate questioning techniques and wording, frequent interruptions, scripted questions, and failure to develop a sense of trust. In identifying suspects, participants highlighted inadequate compliance with defined protocols, inadequate management of negative emotions, pressure on witnesses to make positive identifications, and introduction of improper guidance about the alleged perpetrator. Lengthy delays before the eyewitness interview and a lack of infrastructure were core influences alongside a lack of familiarity with best practices. The findings signal the need for authorities to adopt reliable methods and specific guidance for utilizing eyewitness testimony. Full article

Urban radio spectrum monitoring is becoming increasingly complex due to the rapid growth of wireless devices, unauthorized emissions, and dynamic electromagnetic environments in smart cities. Traditional spectrum analysis approaches, based on manual operation or static detection techniques, are no longer sufficient to ensure scalable, autonomous, and secure monitoring. The convergence of two emergent technologies—Large Language Models (LLMs) and the Model Context Protocol (MCP)—facilitates a fundamental shift in radio monitoring. We define this as the AICEBERG paradigm: a novel, stratified architecture where a high-level, intelligent agentic interface (the peak) abstracts the underlying complexity of SCPI-driven hardware integration and radio governance protocols (the foundational base). This autonomous framework provides the necessary objective rigor to audit the stochastic ‘ocean of electromagnetic waves’ characteristic of modern smart cities, ensuring a stable platform for regulatory enforcement amidst high-density signal interference. The proposed system implements a three-layer processing flow, enabling high-level natural language commands to be translated into validated and secure hardware actions on RF spectrum analyzers. A dual-server design separates operational execution from safety validation, ensuring controlled SCPI command handling, parameter verification, and instrument health monitoring. Experimental validation demonstrates the feasibility of autonomous measurement execution. The results show that the proposed architecture reduces human dependency, enhances reproducibility and lowers the expertise barrier required for RF spectrum surveillance. To the best of our knowledge, AICEBERG represents one of the first integrated frameworks to bridge LLMs with SCPI-compliant hardware through the MCP for autonomous radio governance. Full article

The selective harvesting of leaves from flexible-stem crops remains a major challenge in agricultural mechanization due to stem compliance, heterogeneous petiole strength, and unstable tool–crop interaction. To address these issues, a bio-inspired ring-cutting and compliant clamping harvesting mechanism is proposed for low-damage selective harvesting under complex terrain conditions. Inspired by the adaptive attachment behavior of octopus suckers, a flexible compliant clamping interface combined with a ring-shaped sliding cutting structure was developed to stabilize flexible stems during harvesting. A coupled kinematic–force analytical model was established to characterize the interaction between tool motion, stem feeding, and cutting behavior. In addition, a sliding cutting mechanics model was introduced to analyze the relationship between cutting force and sliding angle. Dynamic multibody simulations were performed using ADAMS to verify the motion feasibility and trajectory stability of the proposed harvesting mechanism. Bench-scale experiments were conducted using mulberry branches as a representative flexible-stem crop, and a response surface methodology based on a Box–Behnken experimental design was applied to optimize key operational parameters. The optimal parameter combination included a chain linear speed of 0.18 m·s⁻¹, a feeding speed of 0.30 m·s⁻¹, and an installation angle of 36°. Under these conditions, the missed harvest rate was reduced to 9.2–9.8%, demonstrating improved harvesting stability compared with conventional rigid cutting mechanisms. The results indicate that integrating compliant stabilization with sliding cutting provides an effective engineering strategy for selective harvesting of flexible-stem crops in complex agricultural environments. Full article

The realization of the value of health big data relies on the coordinated cooperation among patients, the government, and data users. Enhancing the symmetry and balance between patient participation and the compliant use of data by data users is a critical link. This paper constructs a tripartite evolutionary game model and employs MATLAB R2023a simulation to analyze the impact of factors such as initial willingness, compliance costs, and penalties for violations on the strategic choices of the game players and the evolution of the system. The findings reveal that: (1) Patient participation is a key condition for achieving an ideal equilibrium in the system. (2) The data service income from participating in data provision and the costs associated with privacy breaches are critical factors influencing patients’ strategic choices. (3) Penalties for violations are a crucial factor in ensuring that data users choose compliant utilization; however, when compliance costs are high, their constraining effect may be somewhat diminished. (4) Enhancing regulatory efficiency is the future direction for government departments. Based on these findings, countermeasures and suggestions are proposed, including trust building, technological innovation and differentiated supervision, and constructing trusted data spaces, to provide references for health big data governance. Full article

Vulnerable road users in informal urban environments confront a distinct set of hazards that standard computer vision datasets are ill-equipped to represent: artisanal speed bumps constructed without regulatory compliance, deteriorated road markings, and the mototaxi—a three-wheeled motorized vehicle that constitutes the primary informal transport mode in intermediate Andean cities yet is absent from all major international repositories. This paper presents QHAWAY—from Quechua qhaway, a transitive verb meaning “to look; to observe”—an Advanced Driver Assistance System (ADAS) predicated on instance segmentation, monocular distance estimation via the pinhole camera model, and Time-to-Collision (TTC) computation, developed for the road environment of Ayacucho, Peru (2761 m a.s.l.), a city recognised by UNESCO as a Creative City of Crafts and Folk Art since 2019. A hybrid dataset comprising 25,602 images with 127,525 annotated instances across 12 classes was assembled by combining an original local collection of 4598 images (10,701 instances) captured through four complementary acquisition methods across the five urban districts of the Huamanga province with three established international datasets (BDD100K, BSTLD, RLMD; 21,004 images, 116,824 instances). A three-phase progressive training strategy with monotonically increasing resolution (640, 800, and 1024 pixels) was evaluated as an ablation study. A multi-architecture comparison spanning YOLOv8L-seg and the YOLO26 family (nano, small, large) identified YOLO26L-seg as the best-performing model, attaining mAP50 Box of 0.829 and mAP50 Mask of 0.788 at epoch 179. The integration of ByteTrack multi-object tracking with the pinhole equation $D=(H_{\mathrm{real}}\times f)/h_{\mathrm{px}}$ delineates operational risk zones aligned with the NHTSA forward collision warning standard (danger: <3 m; caution: 3–7 m; TTC threshold ≤ 2.4 s). The system sustains processing rates of 19.2–25.4 FPS on an NVIDIA RTX 5080 GPU. A systematic field survey established that 96% of the audited speed bumps fail to comply with MTC Directive No. 01-2011-MTC/14, constituting the first quantitative record of informal road infrastructure non-compliance in the Andean region. Validation was conducted under naturalistic driving conditions without staged scenarios. Grad-CAM explainability analysis, encompassing three complementary visualisation algorithms (Grad-CAM, Grad-CAM++, and EigenCAM), confirmed that model attention concentrates consistently on safety-critical objects. Full article

Global decarbonization and the rapid growth of distributed energy resources increase the need for information-centric mechanisms that can support secure, scalable, cross-border coordination under heterogeneous technical and regulatory conditions. This paper proposes a Global Virtual Prosumer (GVP) framework that integrates IoT sensing, multi-agent coordination, and permissioned blockchain smart contracts to operationalize cross-border energy services as auditable service commitments rather than physical power exchange. Building on prior work that validated MAS-based power management and blockchain-secured operation within individual Virtual Prosumers, the present contribution lies in the cross-border coordination layer and its associated contractual and evaluation mechanisms, not in the constituent technologies themselves. A layered IoT–AI–blockchain architecture is introduced, where off-chain optimization produces allocations and admissibility indicators and on-chain contracts enforce identity, feasibility guards, delegation and partner-assignment rules, oracle verification, and settlement time compliance outcomes. The contractual lifecycle is formalized through four smart-contract algorithms covering trade registration, conditional delegation, cooperative fulfillment, and cross-border settlement with explicit failure semantics and event-based audit trails. The framework is evaluated on a global case study with seven Virtual Prosumers and quantified using contract-centric KPIs that capture registration time rejections, settlement success versus non-compliance, oracle-driven failure attribution, and full lifecycle traceability. The results demonstrate internal consistency of the proposed lifecycle and the practical value of KPI-driven accountability for cross-border energy service coordination. At the same time, the evaluation is based on synthetic parameterization and an emulated contract environment; realistic deployment constraints—including consensus latency, cross-region communication reliability, and regulatory overlap—are discussed as explicit limitations and directions for future empirical validation. Full article

The globalization of the food supply chain presents complex challenges for safety assurance within a highly fragmented regulatory landscape. This review synthesizes the frameworks of eight influential jurisdictions—including the European Union (EU), the United States, China, and Codex Alimentarius—to evaluate how legal mandates function as regulatory drivers that guide the evolution of analytical chemistry. By examining legislation on Maximum Residue Limits (MRLs), positive list systems, and method validation guidelines (e.g., SANTE), we demonstrate that strict preventive controls have established chromatography coupled with tandem mass spectrometry (LC/GC-MS/MS) as the universal standard for multi-residue screening. We show that global regulatory fragmentation is not merely an administrative artifact, but is rooted in divergent toxicological philosophies and localized dietary exposure models. This regulatory heterogeneity requires analytical laboratories to adopt a posture of “defensive technological redundancy,” forcing them to continuously optimize targeted methods against the strictest global default limits (e.g., 0.01 mg/kg). We establish that this continuous methodological escalation for ultra-trace quantification has reached practical and operational limits. Consequently, we conclude that the future of food safety testing must transition from static target-list compliance toward adaptable, non-targeted chemical profiling using High-Resolution Mass Spectrometry (HRMS), enabling laboratories to proactively address emerging contaminants, food fraud, and the complexities of modern food matrices. Full article

Credit risk prediction is central to financial stability and regulatory compliance, guiding lending decisions and portfolio risk management. While traditional approaches such as logistic regression and tree-based models have long been the industry standard, recent advances in deep learning (DL) have introduced architectures capable of capturing complex nonlinearities, temporal dynamics, and relational dependencies in borrower data. This study provides a comprehensive review of DL methods applied to credit risk prediction, covering multi-layer perceptron, recurrent and convolutional neural networks, transformer, and graph neural networks. We examine benchmark and large-scale datasets, highlight peer-reviewed applications across corporate, consumer, and peer-to-peer lending, and evaluate the benefits of DL relative to classical machine learning. In addition, we critically assess key challenges and identify emerging opportunities. By synthesising methods, applications, and open challenges, this paper offers a roadmap for advancing trustworthy deep learning in credit risk modelling and bridging the gap between academic research and industry deployment. Full article

This study proposes and analyzes an integrated framework for monitoring potable water quality and laboratory effluent management in universities, with emphasis on its practical application in a Brazilian public institution. Adopting a qualitative and documentary approach, the research was based on high-impact scientific publications, institutional reports, and environmental databases. The results demonstrate that effective water and effluent governance depends on the interaction of three core dimensions: regulatory compliance, technological innovation, and institutional governance. These elements operate synergistically to ensure transparency, risk prevention, and environmental accountability. The proposed University Laboratory Water Monitoring Framework (UL-WMF) illustrates how universities can transform water control into a managerial and educational tool aligned with sustainability goals. The illustrative institutional application revealed potential for integrating Internet of Things (IoT) and Laboratory Information Management System (LIMS) technologies into environmental management routines, reinforcing universities’ strategic role in achieving global sustainability objectives. Despite relying on secondary data, this study provides a scalable foundation for decision support systems and future empirical validation. The novelty of the University Laboratory Water Management Framework (UL-WMF) lies in its integration of potable water monitoring and laboratory effluent governance into a single operational framework, addressing a gap in the existing literature and offering a model specifically tailored to the context of universities in developing countries. The applied component of the study consists of an illustrative institutional case constructed exclusively from publicly available environmental and governance reports. This illustration serves to demonstrate the operational relevance of the proposed framework, without implying field measurements or primary data collection. Full article

Amorphous metallic materials have emerged as a promising class of functional materials for energy storage and conversion owing to their disordered atomic structure and unique interfacial properties. This review focuses on amorphous metals and alloys, including metallic glasses and high-entropy amorphous systems, with particular emphasis on their surface- and interface-driven behavior in electrochemical environments. This review analyzes how structural disorder influences key properties such as electronic structure, ion transport, catalytic activity, and mechanical compliance and how these factors govern performance in batteries, supercapacitors, electrolyzers, and fuel cells. Special attention is given to interfacial phenomena, including charge-transfer kinetics, corrosion and passivation processes, and structural evolution during long-term operation. In addition, recent advances in fabrication strategies such as rapid solidification, thin-film deposition, mechanical alloying, thermoplastic forming, and electrodeposition are discussed in relation to their ability to tailor amorphous structures and interfaces. This review also highlights critical failure mechanisms and discusses some strategies to mitigate these effects. Overall, this work provides a focused perspective on the role of amorphous metallic surfaces and interfaces in electrochemical systems, identifying current challenges in scalability, durability, and compositional control, and outlining future directions for their integration into next-generation energy technologies. Full article