Search Results (528)

Interest in Natural Water Retention Measures (NWRMs) for large river basins is growing rapidly as a result of a wide range of benefits, including improved water retention capacity and regulation of ecosystem services. However, suitable site-specific NWRMs in small ungauged basins prone to flash floods and erosion, such as the Vrutci Reservoir Basin in Serbia, have yet to be evaluated and applied, primarily because of a lack of necessary data. The aim of this study was to design an easy-to-implement approach to evaluating the effects of NWRMs on peak discharge, tailored specifically to small basins with significant data gaps. The approach involves developing and analyzing a synthetic unit hydrograph (SUH) based on the available landscape geospatial data and evaluating the effects of NWRMs on the SUH before and after implementation of site-specific NWRMs. This methodological framework allows for quantification of the NWRMs’ effects on the basin and evaluates the proposed measures’ impact to secure better acceptance among stakeholders and informed decision-makers regarding their location in the basin. The results underscore a peak discharge rate reduction from 5% to 33% and hence indicate a positive impact on basin water retention potential. These results highlight the need for support for improved regulating ecosystem services in integrated water management. Full article

Unauthorised FM broadcasting poses significant challenges to spectrum regulators globally, contributing to interference, degraded service quality, and national security threats. While traditional spectrum monitoring relies primarily on carrier frequency and power measurements, this study demonstrates that FM baseband features—specifically the multiplex (MPX) signal structure, pilot tone, and Radio Data System (RDS) subcarrier—provide robust discriminative markers for detecting non-compliant transmissions. Using a real-world dataset of 3710 pre-processed records collected across Nigeria’s capital region between 2021 and 2024, we extracted and analysed six transmission parameters: assigned frequency, band occupancy (±100 kHz), MPX overshoot percentage, pilot tone presence, and RDS indicators. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel was trained to distinguish compliant licensed stations from regulatory non-compliant transmissions—encompassing both unlicensed transmitters and technically non-compliant licensed operators—achieving 99.96% accuracy, 99.38% precision, and 99.63% recall with a false alarm rate of 0.026%. A Comparative analysis against baseline feature sets confirmed that integrating MPX, pilot, and RDS significantly improved detection robustness compared with carrier-only approaches. Results demonstrate that feature-rich baseband analysis enables scalable, cost-effective regulatory enforcement, reducing manual monitoring burden while enhancing detection reliability. This framework offers practical applicability for spectrum management agencies in resource-constrained environments where unauthorised broadcasting remains prevalent. Full article

Ecologically fragile hilly areas are key regions for safeguarding national ecological security and advancing ecological civilization construction. Accurate assessment of ecosystem service value (ESV) and future scenario simulations in these regions is crucial for improving regional land use and attaining sustainable development. Based on high-resolution remote sensing data of the Longji Mountain area in Guangxi, China, from 2013 to 2023, this study systematically assesses the spatiotemporal evolution characteristics of ESV using the equivalent factor method with localized corrections. This study adopts spatial autocorrelation analysis, geographic modeling, and scenario simulation. It predicts the spatial patterns of ESV for 2028 and 2033 under three scenarios: ecological protection, natural development, and tourism development. The results reveal that: (1) from 2013 to 2023, the total ESV in the Longji Mountain area showed an overall fluctuating trend. It increased first, then declined and recovered slightly, with an average annual growth rate of −0.15%. Spatially, the ESV presented a heterogeneous pattern, characterized by “high-value agglomeration in forest land, medium-value transition in terraced fields, and low-value interpolation in constructed areas”, with distinct clustering features; (2) regional ecological functions are mainly dominated by regulating and supporting services. Climate regulation contributes the highest value. Water supply is the only service with negative value, indicating a persistent water ecological deficit that remains unaddressed; (3) scenario simulations reveal that the total ESV is highest and spatial connectivity is strongest under the ecological protection scenario. Furthermore, a consistent trend is observed across all three scenarios: high-value ESV areas tend to become dominant, while spatial connectivity shows progressive enhancement. The human–land system coupling framework for the ecologically fragile hilly region suggests that ecologically oriented decision-making is the core pathway to sustainably improve ecosystem services and realize regional sustainable development. This study offers scientific support for regional ecological conservation and sustainable advancement. Full article

Fall armyworm, Spodoptera frugiperda (J.E. Smith), has emerged as one of the most damaging invasive pests affecting maize production and household food security across sub-Saharan Africa since its first detection in 2016. This systematic review synthesizes empirical evidence published between 2016 and 2025 to assess the agronomic, livelihood, and food security impacts of FAW on smallholder farming systems across Eastern, Southern, Western, and Central Africa. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Population, Intervention, Comparison, Outcome, Time, and Setting (PICOTS) framework, 20 studies (17 empirical and 3 contextual) were identified through comprehensive searches of academic databases and institutional repositories and were included in the final synthesis after methodological screening. The evidence indicates that FAW invasion causes substantial maize yield losses ranging from approximately 20% to 50%, with the greatest reductions reported in rain-fed systems with limited access to pest management technologies. Infestation rates frequently exceeded 50%, particularly during early invasion phases. Beyond agronomic losses, several studies reported reduced household income, constrained food availability, and livelihood disruptions, including increased labor requirements, higher production costs, and reliance on short-term coping strategies. Only a small proportion of studies (n = 4) directly assessed nutrition-related indicators, but the available evidence indicates declines in dietary diversity in severely affected communities. Overall, the agronomic impacts of FAW are consistently documented across regions, whereas the socioeconomic and nutrition outcomes remain comparatively underreported, indicating a significant evidence gap. These findings highlight FAW as both an agronomic and livelihood challenge, underscoring the need for integrated pest management strategies, strengthened extension services, and coordinated policy responses to safeguard food and income security among smallholder farmers in Africa. Full article

This paper presents a Threat Analysis and Risk Assessment (TARA) of the takeover request (TOR) component in Advanced Driver Assistance Systems (ADAS) for SAE Level 2–3 automation. A TOR prompts the human driver to retake control when the system approaches its Operational Design Domain limits or when risk increases; late, false, or muted requests directly impact safety. The study models the TOR pipeline (perception, driver monitoring, decision logic, in-vehicle networks, and Human–Machine Interface) as assets and data flows, applies STRIDE-based threat identification using Microsoft Threat Modeling Tool and Ansys Medini Analyze, and rates risks under ISO/SAE 21434 with traceability to ISO 26262, ISO 21448, and UNECE R155/R157. The assessment produces 165 threat rows, with an initial risk distribution of 1 Critical, 113 High, 34 Medium, and 17 Low. Results show that tampering, denial of service, and spoofing dominate the TOR threat landscape, with the central processing unit, sensor-to-CPU links, and HMI channels as primary trust anchors. After applying mitigation measures including secure boot, message authentication, intrusion detection, redundancy checks, and encrypted communication, the residual post-mitigation security levels were reduced to 0 Critical, 0 High, 13 Medium, 101 Low, and 51 Negligible. Unlike other ADAS TARA studies, this TOR-focused analysis shows that cybersecurity risk is shaped by the interaction between cyber compromise, driver-readiness estimation, HMI delivery, fallback execution, and the limited handover time budget. The results support a defence-in-depth mitigation strategy for secure TOR operation in SAE Level 2–3 vehicles. 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

Ultra-reliable low-latency communication (URLLC) is a fundamental technology that plays a crucial role in enabling fifth-generation new radio (5G-NR) communication. URLLC aims to provide a highly reliable connection with strict block error probability requirements and extremely low latency for mission-critical and remote operations. Meanwhile, the advent of sixth-generation (6G) communication, marked by its novel, immersive, and high-stakes control applications, imposes notably more stringent demands on reliability and latency, alongside the added requirements of high data rates, scalability, precision, security, and real-time operation. This scenario introduces unparalleled challenges for both system architecture and the solutions it entails. Several previously proposed solutions, such as retransmission schemes, error correction techniques, and grant-free access, have been insufficient for emerging requirements, as most of these solutions primarily facilitate either low latency or high reliability, but not both. Latency and reliability are conflicting objectives of URLLC. Therefore, an in-depth understanding of the associated issues and careful mitigation of these challenges are essential. This article provides an extensive review of 5G URLLC, emphasizing its technical evolution from 3GPP Release 15 through 19, while also detailing its inherent shortcomings and the potential solutions required for 6G and beyond. We investigate the prerequisites and enabling technologies necessary for URLLC services, exploring related issues across various network components, including frame structure, propagation, processing, retransmission, scheduling, fading, and interference. An important discussion is provided on the fundamental trade-off between latency and reliability, particularly due to retransmission mechanisms. Furthermore, we examine the practical limitations of 5G URLLC when coexisting with other 5G application use cases, such as enhanced mobile broadband (eMBB) and massive machine-type communication (mMTC). Finally, we discuss the future trajectory of URLLC in 6G, identifying key research challenges and opportunities to meet the escalating demands of future mission-critical applications. Full article

This paper investigates secure and low-latency communications in UAV-mounted simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted urban vehicular networks, where severe blockage, high vehicle mobility, eavesdropping threats, and delay-sensitive traffic services coexist. In the considered system, the UAV is used not only as an aerial carrier for the STAR-RIS but also as a mobile intelligent control node that can dynamically adjust its horizontal aerial position according to vehicle distribution, blockage conditions, and eavesdropping threats. First, a UAV-STAR-RIS-assisted vehicular communication system model is developed by jointly considering urban blockage, vehicle mobility, passive eavesdropping attacks, queueing dynamics, and UAV flight constraints. Then, a high-dimensional, non-convex, and strongly coupled dynamic optimization problem is formulated to maximize the long-term average secure and low-latency utility through the joint optimization of the UAV trajectory, the STAR-RIS transmission–reflection partition ratio, the phase-shift matrices, and the transmit power allocation. Furthermore, the problem is modeled as a Markov decision process with continuous state and action spaces, and a hierarchical constrained soft actor–critic (HC-SAC)-based joint control algorithm is proposed to enable adaptive UAV movement, STAR-RIS configuration, and power control in complex dynamic environments. Simulation results demonstrate that the proposed method outperforms DDPG and several structural benchmark schemes. In the representative evaluation, the proposed HC-SAC achieves an average delay of 10.85 slots and a secrecy outage probability of 0.7160, compared with 11.72 slots and 0.8501 for PPO, and 11.94 slots and 0.8599 for DDPG. Although PPO provides the highest average secrecy rate and successful service ratio, the proposed method still maintains a competitive secure communication capability and service reliability. A normalized composite utility analysis further shows that HC-SAC attains the highest utility value of 0.9254, indicating a more favorable security–latency trade-off in complex urban vehicular scenarios. Full article

Background: Epidemiological alerts about the possible spread of different pathogens have highlighted the risk of international travelers contracting infectious diseases when visiting endemic areas. The role of travelers in disease transmission underscores the importance of pre-travel consultations, which provide critical information on health risks, vaccinations, and preventive measures. Understanding travelers’ risk perceptions and behaviors is essential for enhancing global health security in the post-pandemic era. Methods: A cross-sectional study (June 2023–January 2024) was conducted by administering an anonymous questionnaire at the Rome-Fiumicino Airport International Prophylaxis Clinic (USMAF-SASN). The questionnaire explored demographics, travel patterns, risk perceptions, vaccination behaviors, and sources of health information. Descriptive statistics and a multivariable logistic regression analysis were performed to identify low-risk perception predictors. Results: Among 217 participants, 89.8% were Italian, with a balanced representation of genders. The primary purpose of travel was tourism (61.6%), followed by work-related trip (23.1%). While 77.1% rated preventive measures as effective, 23.2% evaluated infection risk as low. Being male (aOR 3.63, 95% CI 1.37–9.61), and being a hotel user (aOR 6.27, 95% CI 2.43–16.15), was significantly associated with a lower risk perception. As expected, healthcare professionals and individuals using institutional healthcare sources showed a higher risk awareness. Vaccination uptake at the Airport Clinic was motivated by self-protection, vaccine confidence, and poor time flexibility to access local vaccination services, and last-minute plans, making the airport a more convenient option. Conclusions: Travelers’ risk perception is influenced by gender, profession, accommodation type, and information sources. Public health strategies should enhance health literacy, promote pre-travel consultations, and improve access to preventive services. Strengthening collaborations between health authorities, educational institutions, and the travel sector is key to mitigating health risks and ensuring global health security. Future interventions should address structural vaccination barriers and improve outreach to under-informed travelers. Full article

Connected and autonomous vehicles (CAVs) increasingly rely on vehicle-to-everything (V2X) communication and distributed sensing infrastructures to support cooperative driving and intelligent transportation services. While these capabilities improve traffic efficiency and safety, they also expand the attack surface of vehicular networks and expose in-vehicle communication systems such as the Controller Area Network (CAN) bus to a wide range of cyber threats. Machine learning-based anomaly detection has emerged as a promising approach for identifying malicious CAN traffic patterns; however, conventional centralized learning requires large-scale data aggregation from vehicles, which raises privacy and scalability concerns. Federated learning (FL) enables collaborative model training across distributed vehicles without requiring the exchange of raw in-vehicle data, making it attractive for privacy-preserving vehicular security applications. Nevertheless, FL systems remain vulnerable to adversarial participants that manipulate local training data or model updates to poison the global model during aggregation. In this work, we present a systematic robustness evaluation of federated anomaly detection in connected vehicular networks under adversarial conditions. The study compares six aggregation strategies, including Federated Averaging (FedAvg), coordinate-wise Median, Trimmed Mean, Krum, Multi-Krum, and Geometric Median (GeoMed), within a non-IID federated CAN bus anomaly detection setting. The evaluation covers label-flipping attacks, gradient-scaling attacks, and a feature-triggered backdoor attack. In addition, the analysis examines malicious client participation, attack-strength variation, learning-rate sensitivity, Trimmed Mean beta sensitivity, multi-seed reliability, and server-side aggregation time. The results show that FedAvg is vulnerable under strong adversarial manipulation, while Trimmed Mean is sensitive to the selected trimming fraction. Median and GeoMed provide strong robustness against gradient-scaling attacks, whereas Multi-Krum achieves the strongest resistance to label-flipping and backdoor attacks. These findings demonstrate that no single aggregation strategy is optimal across all threat models. Instead, robust aggregation for federated CAV anomaly detection should be selected according to the expected attack type, reliability requirement, and computational overhead. Full article

Background: Food insecurity is an important but under-recognised issue in cancer patients. It is linked to malnutrition and contributes to inequities in care. As there is minimal national population data available, this study aimed to assess the food security status of people receiving treatment for cancer in the state of Victoria, Australia. Methods: A multi-site point prevalence study was conducted in Victorian acute health services in July 2024. Adults receiving ambulatory treatment and multi-day stay inpatients were included. Patients were screened and assessed for malnutrition (using the Malnutrition Screening Tool and Global Leadership Initiative on Malnutrition criteria) and assessed for their food security status (using the Household Food Security Survey Module for Adults). Results: A total of 24 health services recruited 2121 adults with cancer. Overall, 6.9% experienced food insecurity, with the majority (52.4%) experiencing marginal food insecurity. No differences in food security status were observed between admitted and ambulatory patients, nor between metropolitan and regional/rural locations. Culturally and linguistically diverse (CALD) patients recorded higher rates of food insecurity compared to non-CALD patients (10.4% vs. 6.0%; p = 0.001). Patients who were food insecure had a higher prevalence of malnutrition compared to food secure patients (37.4% vs. 27.5%; p = 0.014). Conclusions: Although the prevalence of food security was low overall among patients with cancer, it was more pronounced in patients with malnutrition or from CALD backgrounds. To effectively address the issue of malnutrition in patients with cancer, food security must be considered as part of a multi-modal intervention. Full article

Grain production resilience forms a critical foundation for national food security, and the ongoing development of land transfer provides essential momentum for establishing a more resilient grain production system. Using panel data from 30 provincial-level regions from 2013 to 2024, this study constructs a multi-dimensional evaluation system for grain production resilience and calculates the comprehensive grain production resilience index using the entropy value method. This study applies two-way fixed effects and mediation models to empirically analyze the impact of land transfer on grain production resilience and its underlying mechanisms. The results show the following: (1) Land transfer significantly enhances grain production resilience: a 1 percentage point increase in the land transfer rate leads to a 0.0014-point increase in the resilience index, equivalent to 0.64% of the sample mean, and this finding remains robust after model replacement, extreme value trimming, and variable substitution. (2) Land transfer exerts its positive effect through three mediating pathways: agricultural insurance (scale dimension), specialized farmer cooperation, and agricultural mechanization. (3) Heterogeneity analysis reveals significant regional differences: the enhancing effect is more pronounced in non-major grain-producing regions and areas with underdeveloped agricultural service systems; while in major grain-producing regions and high-service-level regions, the relationship presents an inverted U-shape, with turning points at 66.794% and 71.921% of the land transfer rate respectively. Accordingly, this study proposes that China should further improve the institutional design of land transfer to systematically support the development of grain production resilience, optimize relevant policy pathways, and implement region-specific measures for targeted and effective intervention. Full article

The rapid growth of publicly available digital services increases the need for scalable security assessment. This is particularly important for software directly used by end users, such as Android applications. Due to staff shortages and financial constraints, small and medium-sized enterprises are often unable to test their applications for vulnerabilities. One possible support mechanism is the use of large language models (LLMs) to assist testers during such assessments. The aim of this study was to investigate the possibility of using an LLM as an interactive guide for dynamic application security testing (DAST) of Android applications. For this purpose, five LLM-based systems were compared: Gemini 2.5 Flash, GPT-oss 120B, Llama 3.3 70B, Qwen 3 32B, and Trinity Large Preview accessed via OpenRouter. The models were evaluated on intentionally vulnerable Android applications using weighted step-level scoring and three selected exploit guidance scenarios. In the main guidance experiment, Gemini achieved the highest combined Fully Discovered and Partially Discovered (FD + PD) detection rate of 79.1% in the representative run, while repeated runs for selected models showed limited aggregate variability. The results also indicate that more detailed prompts improve the quality of generated guidance. The findings suggest that LLMs can serve as interactive guides for DAST testing of Android applications, although they should be treated as supporting tools rather than standalone security-testing systems. Full article

With the rapid development of the Internet, open-source graph datasets are increasingly shared and reused in intelligent networked services, making robust infringement verification increasingly important. Backdoor-based watermarking for graph neural networks (GNNs) can be used to check whether a suspicious model has been trained on protected data without authorization. However, existing dataset infringement verification methods have limited applicability and are mainly designed for private datasets. Directly applying them to open-source datasets would cause models trained by legitimate users to learn backdoor behavior, which would expose them to security risks. In this paper, we propose a new infringement verification method for open-source graph datasets, which reduces backdoor-related security risks in models trained by legitimate users. The core idea is to introduce an additional expansion-class and re-label watermarked samples as belonging to this class. This design completely separates the learning of watermark patterns from the original feature-label mappings during training. As a result, only trigger-bearing samples are directly involved in infringement verification, which helps prevent watermark patterns from being associated with existing classes in the original task. The proposed method provides a practical solution for trustworthy graph data sharing and infringement verification in Internet environments. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves a high verification success rate while largely preserving the model’s clean accuracy. Full article

The demands of competitiveness in global markets require the integration of Industry 4.0 (I4.0) digital technologies for any manufacturing company, regardless of size. Industrial operations require complete supply chain visibility to ensure data protection and authenticity throughout the process. This document presents a distributed architecture based on RAMI 4.0, designed for product traceability in industrial environments. It integrates automation tools, IIoT communication, cloud storage, artificial intelligence, and secure data transmission using encrypted communication protocols. The system consists of a hybrid architecture; only the first, lower-level layer corresponds to a simulated manufacturing plant with deterministic and stochastic dynamics within the production line. In the second part, the middle and upper layers are implemented, where plant data is transmitted to a cloud instance, stored in a PostgreSQL database, and subsequently analyzed using automated scripts. Reporting capabilities are incorporated with ChatGPT-3.5 Turbo, and visualization is provided through Odoo. Experimental tests demonstrated an average end-to-end communication latency of less than 200 ms, a packet loss rate of 2.67%, and 100% reliability in verifying requested reports when using the cognitive computing service. Furthermore, the results of the systematic vulnerability identification process for the architecture show a significant reduction in overall risk for most assets, with a predominant shift from high or moderate to low or moderate. The proposed architecture is validated in a simulated industrial environment under controlled conditions, demonstrating its viability as a prototype rather than as a fully implemented industrial solution. Full article