Search Results (3,562)

The output quality of random-number sources directly affects the security of cryptographic systems. Physical-noise degradation, environmental disturbance, device aging, and fault injection may increase output bias, correlation, and predictability. This paper presents a resource-conscious on-chip entropy-quality supervisor for random-number source output streams. The design uses non-overlapping 1024-bit measurement windows and a shared feature engine for bit counts, directional transition counts, and run information. These features support repetition-count, adaptive-proportion, and low-toggle checks, together with two-bit pattern-concentration and first-order conditional-transition indicators, exponentially weighted moving-average trend monitoring, comprehensive scoring, and a seven-bit alarm bitmap. The RTL accepts a 32-bit valid-data interface, makes one decision every 32 valid words, and is integrated into an Artix-7 XC7A35T project configured with a 50 MHz system-clock constraint. The complete project includes a ring-oscillator TRNG, and controlled deterministic fault patterns are inserted into selected windows of the TRNG stream for fault-response verification. Deterministic RTL simulations show complete alarm mappings of 0111111 for fixed-value patterns, 0011000 for an isolated alternating window, and 1011000 for the fourth consecutive alternating window. A parameterized capture simulation also verifies pre-event, injection, and recovery sequencing. FPGA implementation results show that the entropy-supervisor core uses 1009 LUTs and 276 flip-flops without BRAM or DSP resources, while the complete project uses 1313 LUTs, 614 flip-flops, and one BRAM tile. The design meets the 50 MHz clock constraint with a WNS of 1.079 ns and no setup or hold violations. The total on-chip power reported by Vivado is 0.076 W; without simulation-derived switching activity, this value is approximate and is not a board measurement. The results establish the functional behavior of the monitoring and decision paths. The two-bit pattern-concentration and first-order conditional-transition indicators are empirical tools for online anomaly diagnosis; neither is a min-entropy estimator, and they do not replace source-specific entropy assessment under NIST SP 800-90B. Full article

Particle accelerators have become indispensable tools in fundamental charged-particle and beam-physics research, medical diagnostics and therapy, security screening, and many other applications. A small number of large, GeV-scale machines, often called “Big Science,” drive scientific discovery, while thousands of small accelerators serve everyday needs, primarily as X-ray sources when an electron beam is used. When an electron beam in the keV–MeV range strikes a high-Z (high-atomic-number) target material, it produces bremsstrahlung photons, whose interaction with matter is governed by the photoelectric effect, Compton scattering, and pair production, each dominant in a distinct energy regime that also depends on the atomic number of the absorbing material. A small accelerator can generate such significant effects if the energy and material parameters are properly matched, and these interactions can be exploited to generate material-specific signatures. Ion beams, on the other hand, deposit energy in materials and are used for gamma and neutron production, microstructure analysis, and many other applications. Here we review the operating principles of several small accelerators and their potential applications, offering a unified perspective on their role in contemporary science and technology. Full article

The rapid evolution of Large Language Models (LLMs) has opened new frontiers in cybersecurity automation, enabling intelligent agents capable of multi-step reasoning, tool invocation, and autonomous decision-making across complex security tasks. While individual applications have emerged across threat intelligence, vulnerability assessment, penetration testing, and security operations center (SOC) automation, a systematic understanding of the LLM-based agent paradigm in cybersecurity—encompassing both single-agent and multi-agent architectures—remains lacking. This paper presents a systematic literature review following PRISMA guidelines, identifying records through 59 structured web-search queries whose results resolve predominantly to arXiv, Semantic Scholar, the ACM Digital Library, IEEE Xplore, USENIX, MDPI, SpringerLink, and Elsevier ScienceDirect, supplemented by citation chaining, for works published between January 2022 and April 2026; the full query record is published with the paper. We applied structured inclusion and exclusion criteria and classified 59 primary studies along five dimensions: security function, agent architecture pattern, knowledge augmentation strategy, human-in-the-loop posture, and evaluation rigor. Our analysis reveals that penetration testing and threat intelligence are the most extensively studied domains, while incident response and compliance verification remain critically underrepresented. Penetration testing alone accounts for over half the corpus (50.8%). Single-agent tool-calling remains the most prevalent architecture (30.5% of studies), whereas centralized multi-agent orchestration—present in 18.6%—yields the strongest reported performance gains, up to 4.3× on zero-day exploitation; prevalence and performance therefore point in opposite directions. No included study achieves production-grade (E4) evaluation: the entire field currently rests on controlled laboratory assessments. An independent search of six bibliographic databases recovers 86.3% of the studies the primary search had surfaced (79.7% of the full corpus) while indicating a total eligible literature of roughly 400 studies, so the corpus is reported as a documented subset rather than an exhaustive census. We propose a unifying taxonomy, identify cross-cutting challenges including hallucination, prompt injection, and benchmark fragmentation, and outline open research directions with particular emphasis on multi-agent orchestration design. Financial sector applicability under DORA and the EU AI Act is treated as a documented evidence gap rather than a synthesis: the corpus’s only compliance and risk assessment study is also its only banking-specific system. Full article

Lane Keeping Assistance (LKA) systems play a critical role in enhancing vehicular safety and driving comfort by maintaining lane alignment and mitigating risks associated with driver distraction or drowsiness. These systems rely on sensor data to execute corrective steering or braking actions, yet their dependence on interconnected electronic components exposes them to a range of safety and cybersecurity threats. Attackers can exploit vulnerabilities in sensors, communication protocols, and Electronic Control Units (ECUs), potentially triggering false interventions or disabling safety functions. This paper presents a comparative Threat Analysis and Risk Assessment (TARA) of two LKA system architectures using the Medini Analyze tool. The first architecture employs a hierarchical controller with driver-intention detection and Electronic Stability Control (ESC)-based actuation. The second architecture employs a Learning-Based Model Predictive Control (LBMPC) framework enabling situation-adaptive decision-making. Through systematic identification and evaluation of threats and vulnerabilities, the analysis assesses risk levels associated with each design. The comparative analysis reveals trade-offs among architectural complexity, system robustness, and exposure to potential vulnerabilities, offering practical insights to improve the safety and security of LKA system designs. Full article

Quantum communication is emerging as a foundation for next-generation networks, offering unprecedented capabilities in security, entanglement-based connectivity, and distributed computation. However, the classical Open Systems Interconnection (OSI) model, designed for deterministic, error-tolerant systems, is incompatible with quantum phenomena such as decoherence, probabilistic entanglement, and the no-cloning theorem. This paper surveys and redefines the OSI model for quantum networking in the context of 7G systems. We propose a Quantum-Converged OSI stack by extending the classical seven-layer model with two additional layers: (i) Layer 0, the Quantum Substrate, responsible for entanglement management, coherence preservation, and teleportation; and (ii) Layer 8, the Cognitive Intent Plane, which enables AI- and QML-driven orchestration. The survey synthesizes over 150 research works published between 2018 and 2025, classifying them by OSI layer, enabling technologies (e.g., Quantum Key Distribution, Quantum Error Correction, and Post-Quantum Cryptography), and application domains such as satellite quantum links, quantum IoT, and federated edge systems. We further provide a taxonomy of cross-layer enablers and discuss simulation tools, including NetSquid, QuNetSim, and QuISP. Finally, an evaluation framework with quantum-native metrics, such as entropy throughput, coherence latency, and entanglement fidelity, is introduced, along with open challenges for programmable stacks, digital twins, and AI-defined quantum agents. The specific and novel contribution of this work is a Quantum-Converged OSI stack that extends the classical seven-layer model with two additional layers: Layer 0, the Quantum Substrate, responsible for entanglement management, coherence preservation, and teleportation; and Layer 8, the Cognitive Intent Plane, which enables AI- and QML-driven orchestration. Unlike prior technology-centric surveys, the proposed framework classifies over 150 research works by OSI layer, maps enabling technologies (QKD, QEC, PQC) and application domains (satellite quantum links, quantum IoT, federated edge systems) to their functional layers, and introduces a quantum-native evaluation framework based on entropy throughput, coherence latency, and entanglement fidelity. This layer-resolved synthesis, together with the formal definition of cross-layer quantum-native metrics, constitutes the principal novelty distinguishing this survey from existing quantum-networking reviews. Full article

Agentic AI is changing enterprise cybersecurity as AI systems move beyond passive content generation toward autonomous planning, tool use, delegated execution, and operational action. As agents connect to email, code repositories, security operations center (SOC) platforms, finance workflows, cloud services, and enterprise application programming interfaces (APIs), they increasingly function as dynamic non-human identities rather than conventional software tools or service accounts. Existing identity and access management (IAM), Zero Trust, machine identity, and AI-governance approaches remain fragmented in their treatment of delegated authority, task intent, autonomy, runtime tool use, and auditable organizational consequences. This paper addresses these gaps by proposing the AIGATE (Agentic Identity Governance, Authority, Tool-Control and Evidence) Framework. AIGATE integrates eight governance layers: agent identity registration, lifecycle governance, delegated authority mapping, intent-bound access, least agency and least privilege, runtime tool-call control, audit evidence and accountability, and revocation and resilience. The framework treats agents as governed non-human enterprise identities whose actions remain attributable to designated human and organizational roles. AIGATE is developed through a structured critical synthesis of the recent literature on agentic AI security, machine identity, Zero Trust, runtime enforcement, and AI governance, with literature-derived governance requirements mapped explicitly to the eight framework layers. Three SOC, DevOps, and finance scenarios are used as illustrative applications rather than empirical validation. The contribution is an integrated governance architecture connecting identity, delegated authority, autonomy, runtime enforcement, evidence, and revocation across the agent lifecycle. Full article

As professionals increasingly encounter artificial intelligence (AI) in their workplaces, questions around adoption barriers, ethical concerns, and job security have grown in prominence. This paper reports an exploratory cross-sectional descriptive survey of self-reported perceptions among a convenience sample of 324 UK-based industry professionals. The study does not identify determinants, predictors, or causes of AI adoption. The survey examined eight binary items covering daily personal AI use, perceived strategic importance, cost as a barrier, ethical concern, perceived income change, subjective job-displacement anxiety, perceived work-performance change, and preference for conversational AI tools over traditional search engines. All eight items were completed by all 324 eligible respondents. Using Wald 95% confidence intervals, 47.2% (95% CI 41.8–52.7) reported using AI in their daily jobs, while 66.4% (95% CI 61.2–71.5) perceived AI as important for remaining competitive. Cost was identified as a barrier by 76.5% (95% CI 71.9–81.2), and 65.7% (95% CI 60.6–70.9) reported concern about ethical implications. These two figures are separate aggregate proportions and are not treated as an individual-level behavioural gap. Only 37.0% (95% CI 31.8–42.3) reported an income increase associated with AI at work, whereas 71.9% (95% CI 67.0–76.8) reported improved work performance. These two items were separate self-report questions; no respondent-level association was tested, and neither item measured objective productivity or pay. Job-automation concern was reported by 48.5% (95% CI 43.0–53.9). The same share as the performance item, 71.9% (95% CI 67.0–76.8) preferred ChatGPT-like tools over traditional search engines. The sample was recruited through professional networks, email, social media, and organisational mailing lists and is likely to over-represent professionals already interested in digital tools. Findings are therefore presented as descriptive perceptions from this respondent pool and are not generalised to UK professionals as a whole. Full article

Laboratory-acquired infections (LAIs) and accidental pathogen escape from laboratory settings (APELS) are still a significant concern, even with biosafety guidelines and manuals in place. The actual number of these events is probably underestimated because there are no mandatory reporting systems or standard surveillance frameworks. In this review, a clear overview is given about the evolution of biosafety, the worldwide distribution and governance of high-containment laboratories (BSL-3 and BSL-4), and the key factors that lead to laboratory incidents. The focus is specifically on human error, inadequate training, and differences in standard operating procedures as primary causes of biosafety failures. Furthermore, current regulatory frameworks are described, pointing out the lack of consistency in laboratory practices, infrastructure needs, and national policies, especially the variances in biosafety level recommendations for high-risk pathogens. Certification systems as potential tools to improve standardization and consistency in operations are discussed. Among these, ISO 35001 is a detailed standard for managing biorisks, including risk assessment, staff training, incident reporting, and processes for ongoing improvement. However, its use is still limited, and certification is not mandatory for laboratory licensing everywhere. Additionally, existing standards do not fully cover structural aspects and national differences in how pathogens are handled. This review also highlights the limitations of current certification systems and proposes recommendations to strengthen and harmonize standardization procedures across high-containment laboratories. Overall, this work highlights the need for greater global consistency in biosafety practices, suggesting the establishment of an independent international auditing body as an important step toward enhancing global health security and laboratory readiness. Full article

Large language models and AI agents are extending data-engineering automation beyond isolated artifact generation toward end-to-end processes in which agents interpret requirements, select data, generate transformations, invoke tools, validate results, and communicate analytical outputs. This shift introduces risks that conventional notions of data quality and execution success do not fully capture. A dataset may satisfy established quality standards, and a generated query may execute without technical errors, while the agent still selects an incorrect metric, combines incompatible analytical grains, accesses unauthorized data, or draws conclusions that are insufficiently supported by evidence. This paper develops a conceptual framework for Agentic Data Engineering centered on Quality of Agentic Data Use, defined as the extent to which an agent uses and communicates data in accordance with task, semantic, quality, security, governance, and provenance requirements. An evidence-informed analysis of Data Contracts, Semantic Layers, Data Quality, Guardrails, AI Governance, and Data Provenance shows that these foundations provide essential but fragmented capabilities. The proposed framework integrates and extends them through four core artifacts: Agentic Data Contracts, Agentic Expectations, Agentic Data Provenance, and Agentic Data Governance. It also introduces an execution lifecycle, a reference architecture, a failure taxonomy, and a multidimensional evaluation framework. A governed sales-analysis scenario illustrates how the proposed artifacts interact throughout an agent-mediated data process. In addition, a controlled Databricks prototype and a complementary benchmark comprising 10 cases and 40 executions demonstrate the framework’s technical feasibility and support the independent computation of enforcement indicators. The benchmark highlights the value of separating generation from validation while also showing that the current validation and automated-repair mechanisms require further calibration. These preliminary findings do not establish generalized improvements in safety, correctness, or reliability. Rather, they provide an operational foundation for broader empirical evaluation of trustworthy agent-mediated data-engineering processes. Full article

Unsustainable agricultural practices and overreliance on chemical fertilizers have led to severe environmental issues, such as soil and water pollution, loss of biodiversity, and risks to human and animal health. Moreover, plant diseases continuously decrease crop productivity and threaten global food security. Therefore, there is a strong need to focus on sustainable agricultural practices. Microbial biofertilizers emerge as environment-friendly alternatives to chemical fertilizers that help in nutrient solubilization and availability, soil fertility, and plant growth promotion, in addition to curbing the application of chemical fertilizers. Microbial inoculants enhance agricultural yield by performing complementary roles, such as facilitating nutrient uptake through biological nitrogen fixation and phosphate solubilization, promoting plant growth via phytohormone synthesis, and mitigating diseases by activating plant defense responses. A 2025 meta-analysis of 107 field studies in China reported mean yield increases of 22.3% in wheat, 13.6% in rice, 12.8% in maize, and 65.4% in millet, while a field study in saline soil reported a 25% reduction in NPK fertilizer use in barley without reducing the grain yield. This review provides an overview of the major types of microbial biofertilizers, their modes of action, and their use in important cropping systems. Special emphasis is placed on microbial consortia that can enhance nutrient cycling, plant productivity, and tolerance to abiotic stress factors. The application of nanotechnology, genetically engineered microorganisms, and combinations of microbial inoculants with organic waste are some strategies that could be adopted for next-generation biofertilizer development. The review also addresses the major hurdles in the formulation, field performance, and commercialization of microbial biofertilizers. Future perspectives revolve around optimizing microbial formulations, applying advanced biotechnological tools, and developing enabling policies for the rapid adoption of microbial biofertilizers for sustainable agriculture. Full article

Background/Objectives: The integration of artificial intelligence (AI) and digital technologies into dental practice is reshaping clinical workflows, administrative processes, and, increasingly, the patient experience and the doctor–patient relationship. While prior research has documented the attitudes of clinicians and practice managers, the perspective of the patient remains comparatively underexplored. This study examined how dental patients perceive AI integration and digital tools across the dental care pathway, together with the associated implications for data security, cost, and the human dimension of care. Methods: A cross-sectional, questionnaire-based study was conducted among 200 dental patients in Bucharest, Romania, and the surrounding region. The instrument assessed perceived difficulty and availability regarding digital technology, current use of digital tools, demographic and educational characteristics (age, gender, practice environment, educational level), and two attitudinal dimensions, namely digital prudence and concern for technological sustainability, across five subdomains of the dental care pathway: scheduling, diagnosis, treatment planning, feedback, and follow-up (dispensarization). Responses were analyzed using non-parametric tests and exploratory principal component analysis with internal-consistency validation. Results: Patients expressed moderate-to-high interest in AI support during the diagnostic (median = 3.3, IQR = 2.7–3.9) and feedback (median = 3.11, IQR = 2.78–3.67) stages and the lowest interest in scheduling (median = 2.7, IQR = 2.0–3.3). A marked level of digital prudence was observed (median = 3.24, IQR = 2.82–3.61), reflecting concerns about data security, automation, and a possible weakening of the clinician–patient bond. Younger and academically educated patients reported lower perceived difficulty, higher availability, and greater current use of digital tools (all p ≤ 0.001); counterintuitively, the same patients scored significantly higher on digital prudence (Spearman’s ρ = −0.260, p < 0.001). Greater familiarity with digital tools was therefore accompanied by a more critical awareness of their informational risks rather than by uncritical acceptance. Conclusions: Dental patients approach AI through a dual lens of openness and informed caution, welcoming efficiency gains in the clinical and continuity-of-care stages while voicing measured concerns about data security, affordability, and the preservation of human contact. To interpret this profile, we propose two conceptual contributions: a mapping of patient needs onto Maslow’s hierarchy in the context of AI-mediated care and the Informational VUCA framework, which characterizes the volatility, uncertainty, complexity, and ambiguity that patients face when navigating AI-generated information. The findings point to a clear practical agenda of transparent communication, robust data governance, and education strategies adapted to patients’ educational and demographic profiles, so that AI-enhanced workflows strengthen rather than erode the doctor–patient relationship. Full article

Robotic waste sorting requires end-effectors capable of handling objects with highly variable geometry, stiffness, surface condition and leakage characteristics. In such applications, the performance of a vacuum suction cup cannot be evaluated only on the basis of nominal holding force, since grasp initiation, contact compliance, airflow conditions and cyclic durability are strongly coupled. This paper presents a combined numerical and experimental assessment of elastomeric vacuum suction cups intended for robotic waste-sorting applications. Seven suction cup configurations were compared using computational fluid dynamics, finite element analysis and functional gripping tests performed on representative plastic waste objects. The CFD analysis was used to evaluate airflow velocity and generated vacuum level, while FEM simulations provided information on stress distribution and deformation under axial compression. For the selected suction cup, a durability-oriented wear prediction was additionally performed using stress data exported from the FEM model and processed with a dedicated Python tool. The complete vacuum system supplied by a side-channel blower was also analysed in Hopsan to assess leakage sensitivity and pressure margins for grasp initiation and secure holding. The results showed that the best flow performance was obtained for suction cups 1 and 2, but suction cup 2 provided the most favourable overall balance, combining the highest vacuum level, limited deformation, stable gripping performance and acceptable durability indicators. The study demonstrates that multi-criteria evaluation is necessary for selecting vacuum end-effectors for robotic waste sorting, especially when objects are deformable, contaminated or imperfectly sealed. Full article

Large language model (LLM)-based agents are evolving into agents for computer use (ACUs) that read files, invoke applications, communicate over networks, and operate graphical interfaces, moving the effective security boundary from model inputs and outputs to autonomous actions that alter endpoint state. Conventional identity and access controls remain applicable and necessary, but they are authorized based on identity, resource, and network policy rather than on the semantic scope of the active task or the provenance of the instruction that triggered an action. This paper presents ARES (Agent Resource Enforcement and Security), an action-centric framework that inserts enforceable authorization between agent-generated tool calls and protected resources, combining a Resource Proxy Layer for interception, a Behavioral Guardrail Engine for task- and context-aware authorization, and a Multi-Agent Trust Boundary Manager for provenance and taint propagation. We implement ARES-lite and evaluate it against file exfiltration through indirect prompt injection, prompt infection propagation, and internal-network access abuse. Under the controlled replay-based evaluation, integrated ARES-lite reduced the observed attack-success rate from 100% under the permissive baseline to 0% across the three evaluated scenarios, while preserving the predefined handling of eight benign and ambiguous tasks with no false positives. A closed-loop evaluation in which the agent re-plans after an intervention, an adversarial boundary-case suite covering path aliasing, address encoding, look-alike destinations, and taint laundering, and a comparison against representative prompt-filtering and tool-allowlist defenses further characterize the enforcement path; a model and temperature sweep show that baseline exposure varies with model capability, whereas the mediated outcome does not. Full article

Although large-scale wind and solar power provide green electricity, their intermittency and reverse-peak characteristics pose severe challenges to the secure operation of power systems. Pumped storage hydropower (PSH), as the most mature and economically attractive large-scale energy storage technology, enables temporal energy shifting and serves as a core flexible resource for smoothing renewable fluctuations and peak load shaving. In a new power system dominated by renewables, the reverse distribution between resources and loads gives rise to a typical “three-zone coexistence” pattern, i.e., renewable-rich zones, load centers, and hub zones coexist. However, existing research lacks in-depth modeling of zonal functional differences and fails to reveal the coupling mechanism between inter-zonal section constraints and the temporal energy behavior of pumped storage plants (PSPs). To address these gaps, this paper proposes a zonal-differentiated optimal dispatch model for multiple PSPs considering inter-zonal section constraints. The model establishes a “zonal role–PSP behavior” matching mechanism, assigning differentiated objectives and operational constraints to PSPs located in different zones, and thereby automatically generating charging/discharging strategies that match each zone’s functional positioning. It integrates section power flow constraints with the energy balance equations of PSPs in each zone into a unified framework, quantifying how section congestion restricts the “cross-zone energy shifting” efficiency of PSPs. Furthermore, a congestion-driven adaptive rule is derived from the above coupling framework. Case studies on a three-zone test system demonstrate that the proposed model effectively reduces wind and solar curtailment, alleviates section overloading, and lowers total operating costs, while the adaptive rule provides real-time decision support for dispatchers. The proposed model is applicable to power grids at various levels exhibiting the “three-zone coexistence” characteristic, offering theoretical support and a practical tool for the joint dispatch of multiple PSPs under high-penetration renewable energy integration. Full article

This research examines the structural evolution of environmental assessment in Spain over a 35-year period (1991–2025), serving as a longitudinal case study for the transition of mid-sized developed economies from industrial-age infrastructure to a decarbonized energy model. The study introduces a novel approach by utilizing Environmental Assessment records to evaluate national strategic directions and quantify developmental intent via administrative proxies. The analysis utilizes a comparative quantitative study of Strategic Environmental Assessments and Environmental Impact Assessments of national and supranational projects. Administrative trends were contextualized within major socio-economic and geopolitical events, including global financial crises, public health emergencies, and energy security developments, to identify potential external influences. The data reveals a paradigm shift. While the period 1991–2010 was dominated by grey infrastructure (transport and civil engineering), the post-2018 era shows an unprecedented increase in renewable energy dossiers. Between 2021 and 2025, energy projects dominated the regulatory workload, accounting for 73.86% of total processed dossiers, and reaching a single-year peak of 84.56% in 2022. National and international legal, economic, and social events, such as European Green Deal mandates and the NextGenerationEU recovery funds, have an apparent alignment with a country’s development strategy. We highlight the challenges and risks of such an exponential change in the energy model. For mid-sized developed economies, these findings suggest that Environmental Assessment data is an essential tool for navigating the complexities of rapid decarbonization and identifying systemic gaps in strategic planning. Full article