Search Results (196)

As the core infrastructure for providing all-weather, full-coverage, high-speed, and diversified information services, spatial information networks (SINs) possess significant social, economic, and military value. However, due to the inherent characteristics of their network architecture, SINs are susceptible to core service paralysis and functional failure under large-scale targeted attacks or random disturbances, posing a critical bottleneck that constrains their stable operation. Current research on SIN vulnerability is predominantly confined to a single network topology perspective, lacking an integrated consideration of the task execution perspective. Consequently, it fails to accommodate the dual requirements of “network topology stability” and “task execution effectiveness”. To address the aforementioned research needs and challenges, this study adopts a “topology-task” dual-perspective fusion approach and proposes a vulnerability analysis framework for SINs that integrates multi-layer networks and hypernetworks. First, a two-layer SIN topology model encompassing the user layer and the satellite layer is constructed. Leveraging hypernetwork theory, information tasks involving multiple network entities are formally defined, and an integrated multi-layer hypernetwork model is established. Second, based on distinct task types, three categories of task efficiency evaluation metrics are defined, and corresponding quantitative methods for calculating SIN vulnerability are derived. Third, during the vulnerability analysis phase, a novel strategy for identifying and removing overlapping nodes in hypernetworks is introduced to enable precise localization of critical nodes within the network. Concurrently, a pre-attack node hardening strategy is designed to minimize the impact of attacks on network performance. Finally, through systematic analysis of vulnerability performance and critical node characteristics under different node removal strategies, the results demonstrate enhanced network performance. The effectiveness of the proposed method is validated by comparing the defense performance of the hardening strategy across various attack scenarios. To verify the feasibility and superiority of the proposed method, this study designs 5 × 5 groups of simulation experiments with varying network parameters. The results indicate that, compared with traditional methods, the proposed strategy can more accurately identify core nodes affecting the stable operation of SINs, significantly reducing network vulnerability and improving network survivability. In addition, a comprehensive sensitivity analysis of SIN vulnerability is conducted from three key influencing dimensions—mission scale, satellite count, and constellation configuration—clarifying the impact of each dimension on network invulnerability. Thus, this paper provides a reliable theoretical foundation and technical support for the planning, design, optimal deployment, and operation and maintenance management of SINs. Full article

Objective: Bone-modifying agents (BMA) are central to the prevention of skeletal-related events (SREs) in patients with cancer bone metastases, yet evidence guiding agent selection in very old patients remains limited. This study aimed to compare the effectiveness and safety of Denosumab versus bisphosphonates in patients aged ≥75 years with solid tumour-related bone metastases using a target trial emulation framework. Methods: We conducted a retrospective cohort study using the TriNetX Global Collaborative Network to emulate a hypothetical randomised trial. Patients aged ≥75 years with solid tumour-related bone metastases initiating Denosumab or bisphosphonates were included. After 1:1 propensity score matching (PSM), 10,662 patients were analysed in each treatment group. The primary outcome was time to first SRE. Secondary outcomes included individual SRE components, all-cause mortality, and safety events. Results: Among 21,324 matched patients (mean age, 75.6 years), bisphosphonate use was associated with a higher risk of SREs compared with Denosumab (hazard ratio [HR], 1.15; 95% CI, 1.06–1.25). The excess risk was driven by pathological fractures (HR, 1.28; 95% CI, 1.10–1.49), whereas other SRE components did not differ significantly. All-cause mortality was higher among bisphosphonate users (HR, 1.41; 95% CI, 1.33–1.49, p < 0.001). Hypocalcaemia occurred more frequently with Denosumab (5.7% vs. 2.4%), while risks of acute kidney injury and end-stage renal disease (ESRD) were similar. Findings were consistent across sensitivity and subgroup analyses. Conclusions: In patients aged ≥75 years with solid tumour-related bone metastases, Denosumab was associated with lower risks of skeletal-related events—particularly pathological fractures—and reduced all-cause mortality compared with bisphosphonates. These results extend randomised trial evidence to a clinically vulnerable population and support Denosumab as a preferred BMA in older adults. Full article

Agriculture in the European Union is a large energy user, dependent on fossil fuels and energy-intensive inputs. Farm incomes are vulnerable to volatile energy prices and climate risks, which threaten their economic resilience. The reformed Common Agricultural Policy (CAP) introduces eco-schemes as a central instrument that may reduce energy intensity and dependence on fossil-based resources. This review examines how CAP instruments—and eco-schemes in particular—are analyzed as drivers of farm energy use, energy intensity, and economic resilience. It maps the literature within a three-pillar framework (energy indicators, CAP instruments, income/resilience outcomes) and identifies where the intersection of these dimensions remains weakly exploredand income/resilience outcomes) and identifies where the intersection of these dimensions remains underexplored. We classify publications by combinations of these three dimensions and by the main groups of CAP instruments. The results reveal a narrow three-pillar core, a dominance of studies that link CAP to income and resilience without explicit energy indicators, and only fragmentary evidence on the energy effects of policy instruments. Research on eco-schemes focuses predominantly on environmental effects and institutional design, while the energy dimension is integrated only to a limited extent. Drawing on this evidence, we propose a conceptual framework linking eco-scheme design, the structure of on-farm energy costs, and the resilience of farm incomes. Full article

Pedestrian safety remains a major concern in rapidly urbanizing cities of developing countries, where road traffic crashes constantly involve vulnerable road users. In Lahore, Pakistan, pedestrian facilities such as signalized crossings often underperform due to limited awareness, inadequate design, poor maintenance, and weak enforcement. This study evaluates pedestrian awareness, perception, and compliance with pedestrian signals along the Mall Road Corridor, a busy urban arterial serving diverse socio-economic groups. Data were collected through a self-administered questionnaire survey, yielding 600 valid responses. Descriptive statistics, Pearson correlation analysis, ordinal logistic regression, and factor analysis were employed to examine the influence of socio-demographic characteristics and perceived infrastructural attributes on pedestrian behavior. Results indicate that gender, age, education, employment status, and income significantly affect compliance with pedestrian signals. Factor analysis identified seven latent constructs related to compliance behavior, safety perception, signal placement, traffic conditions, perceived importance, and user satisfaction. Only 43% of respondents demonstrated full awareness of pedestrian signals, and 54% reported regular or occasional use. The findings highlight that in this perception-based study, both infrastructural quality and perceived safety strongly shape pedestrian compliance, underscoring the need for targeted design improvements and enforcement measures to enhance pedestrian safety in developing urban contexts. Full article

Changing risk dynamics and the demand for more personalized, technology-driven services have spurred innovation in insurance through Insurtech, reshaping how insurance is supplied, purchased, and managed. This paper systematically reviews the impact of Insurtech on insurance inclusion, guided by the PRISMA-P protocol. The review finds strong evidence that Insurtech enhances insurance inclusion by lowering transaction costs, improving accessibility, and broadening market participation. These effects are most visible in short-term insurance, where digital platforms and tailored products reach previously underserved populations. Beyond this primary finding, the review highlights how insurance inclusion is conceptualized and measured in the literature. Quantitative measures typically include penetration rates, density, and the proportion of households with insurance coverage, while broader indices account for availability, usage, and accessibility of insurance services. Qualitative approaches often emphasize mismatches between the products offered and those needed, particularly for vulnerable groups. Similarly, studies of Insurtech adopt both demand-side indicators (such as product uptake and coverage per user) and supply-side measures (including patents, capital inflows, and innovation outputs). These insights suggest that fostering Insurtech development, while addressing regulatory, access, and equity concerns, can significantly improve insurance inclusion and narrow protection gaps. Full article

Real-time telecom scam detection is difficult without cloud AI, particularly for privacy-sensitive, low-resource environments. We developed a lightweight, offline voice scam detector using on-device audio segmentation, automatic speech recognition (ASR), and semantic similarity. Four detection strategies were implemented. We used Whisper ASR and DeepSeek to process 5 s speech chunks. An analysis of 120 synthetic and paraphrased Mandarin phone call dialogues reveals the A4 voting strategy’s superior performance in optimizing early detection and minimizing false positives, achieving an F1 score of 0.90, a 2.5% false positive rate, and a mean response time of under 4 s. The system is deployable on ESP32 for offline mobile inference. The proposed architecture provides a robust and scalable defense against threats targeting vulnerable user groups, such as older adults. It introduces a new method for real-time voice threat mitigation on devices through interpretable segment-level semantic analysis. Full article

The proliferation of Internet of Things (IoT) deployments demands Authentication and Key Agreement (AKA) protocols that scale from one initiator to many devices while preserving strong security guarantees on constrained hardware. Prior lightweight one-to-many designs often rely on a network-wide secret, reuse a single group session key across devices, or omit Perfect Forward Secrecy (PFS), leaving systems vulnerable to compromise and traffic exposure. To this end, we present in this paper a lightweight protocol, named Lightweight One-To-many User-to-Sensors Authentication and Key Agreement (LOTUS-AKA), that achieves mutual authentication, PFS, and per-sensor key isolation while keeping devices free of public-key costs. The user and gateway perform an ephemeral elliptic-curve Diffie–Hellman exchange to derive a short-lived group key, from which independent per-sensor session keys are expanded via Hashed Message Authentication Code HMAC-based Key Derivation Function (HKDF). Each sensor receives its key through a compact Authenticated Encryption with associated data (AEAD) wrap under its long-term secret; sensors perform only hashing and AEAD, with no elliptic-curve operations. The login path uses an augmented Password-Authenticated Key Exchange (PAKE) to eliminate offline password guessing in the smart-card theft setting, and a stateless cookie gates expensive work to mitigate denial-of-service. We provide a game-based security argument and a symbolic verification model, and we report microbenchmarks on Cortex-M–class platforms showing reduced device computation and linear low-constant communication overhead with the number of sensors. The design offers a practical path to secure, scalable multi-sensor sessions in resource-constrained IoT. Full article

The increasing sophistication of social bots demands advanced simulation frameworks to model potential vulnerabilities in detection systems and probe their robustness.While existing studies have explored aspects of social bot simulation, they often fall short in capturing key adversarial behaviors. To address this gap, we propose a simulation framework that jointly incorporates both realistic behavioral mimicry and adaptive inter-bot coordination. Our approach introduces a human-like behavior module that reduces detectable divergence from genuine user activity patterns through distributional matching, combined with a coordination module that enables strategic cooperation while maintaining structural stealth. The effectiveness of the proposed framework is validated through adversarial simulations against both feature-based (Random Forest) and graph-based (BotRGCN) detectors on a real-world dataset. Experimental results demonstrate that our approach enables bots to achieve remarkable evasion capabilities, with the human-like behavior module reaching up to a 100% survival rate against RF-based detectors and 99.1% against the BotRGCN detector. This study yields two key findings: (1) The integration of human-like behavior and target-aware coordination establishes a new paradigm for simulating botnets that are resilient to both feature-based and graph-based detectors; (2) The proposed likelihood-based reward and group-state optimization mechanism effectively align botnet activities with the social context, achieving concealment through integration rather than mere avoidance. The framework provides valuable insights into the complex interplay between evasion strategies and detector effectiveness, offering a robust foundation for future research on social bot threats. Full article

Electroencephalography (EEG)-based motor imagery (MI) brain–computer interfaces (BCIs) hold considerable potential for applications in neuro-rehabilitation and assistive technologies. Yet, their development remains constrained by challenges such as low spatial resolution, vulnerability to noise and artifacts, and pronounced inter-subject variability. Conventional approaches, including common spatial patterns (CSP) and convolutional neural networks (CNNs), often exhibit limited robustness, weak generalization, and reduced interpretability. To overcome these limitations, we introduce EEG-GCIRNet, a Gaussian connectivity-driven EEG imaging representation network coupled with a regularized LeNet architecture for MI classification. Our method integrates raw EEG signals with topographic maps derived from functional connectivity into a unified variational autoencoder framework. The network is trained with a multi-objective loss that jointly optimizes reconstruction fidelity, classification accuracy, and latent space regularization. The model’s interpretability is enhanced through its variational autoencoder design, allowing for qualitative validation of its learned representations. Experimental evaluations demonstrate that EEG-GCIRNet outperforms state-of-the-art methods, achieving the highest average accuracy (81.82%) and lowest variability (±10.15) in binary classification. Most notably, it effectively mitigates BCI illiteracy by completely eliminating the “Bad” performance group (<60% accuracy), yielding substantial gains of ∼22% for these challenging users. Furthermore, the framework demonstrates good scalability in complex 5-class scenarios, performing competitive classification accuracy (75.20% ± 4.63) with notable statistical superiority (p = 0.002) against advanced baselines. Extensive interpretability analyses, including analysis of the reconstructed connectivity maps, latent space visualizations, Grad-CAM++ and functional connectivity patterns, confirm that the model captures genuine neurophysiological mechanisms, correctly identifying integrated fronto-centro-parietal networks in high performers and compensatory midline circuits in mid-performers. These findings suggest that EEG-GCIRNet provides a robust and interpretable end-to-end framework for EEG-based BCIs, advancing the development of reliable neurotechnology for rehabilitation and assistive applications. Full article

Background: Heart failure (HF) remains a major global health challenge, with orthotopic heart transplantation (OHT) serving as the gold-standard therapy for end-stage disease. Chronic immunosuppression required to prevent graft rejection increases the risk of infections and malignancies. The COVID-19 pandemic underscored the particular vulnerability of transplant recipients to severe SARS-CoV-2 infection. Specific immunosuppressive agents used in OHT patients may differentially affect SARS-CoV-2 infection. In particular, mTOR inhibitors may modulate viral replication and immune responses, potentially influencing disease severity. Objectives: This study evaluated the impact of immunosuppressive regimens—particularly mTOR inhibitors—on COVID-19 outcomes in heart transplant recipients, comparing mTOR-based therapy (with or without calcineurin inhibitors, CNIs) to non-mTOR-based regimens. Methods: This single-center retrospective observational study included 556 orthotopic heart transplant recipients (76.3% male; median age, 58 years) followed from March 2020 to March 2024. To compare patients receiving mTOR inhibitors with similar non-mTOR recipients, 3:1 propensity score matching was performed based on age, sex, and body mass index. Among the study population, 88 patients (15.8%) received mTOR inhibitors (everolimus or sirolimus), of whom 66 were concomitantly treated with calcineurin inhibitors and 22 without. Data were obtained from the National Health Fund database and clinical follow-ups. Results: Overall mortality was 13.5%, and COVID-19-related mortality 3.2%. COVID-19 incidence was 33% in the mTOR group versus 36.7% in the non-mTOR group (p = 0.52). Hospitalization rates were 3.4% and 6.4% (p = 0.29), respectively. All-cause mortality was higher among mTOR users (21.6% vs. 11.7%, p = 0.02), especially in the mTOR+CNI subgroup. Notably, no COVID-19-related deaths occurred in the mTOR CNI-free group. Conclusions: mTOR-based immunosuppression was non-inferior to standard therapy for COVID-19 outcomes. The absence of COVID-19-related deaths in patients on mTOR CNI-free regimens suggests potential protective effects that merit further investigation. Full article

Background: Vulnerable road users (VRUs), including children and older adults, face a high risk of fatal road traffic accidents (RTAs) due to limited protection and greater injury susceptibility. Romania reports some of the highest child and elderly RTA mortality rates in the European Union. This study analyzed medico-legal autopsies from the Timisoara Institute of Legal Medicine (TILM) between 2017 and 2021 to compare fatalities in these two groups and identify key risk factors. Methods: A retrospective analysis was conducted on autopsy records of children (0–17 years) and older adults (>70 years) who died in RTAs during the study period. Data on demographics, type of road user, traumatic injuries, cause of death, and accident circumstances were extracted and supplemented by police reports. Comparative statistical analyses were performed for categorical and continuous variables. Results: Among 395 RTA autopsies, 23 (5.8%) involved children and 51 (12.9%) older adults. Most child victims were passengers (56.5%), whereas elderly fatalities occurred mainly among pedestrians (33.3%) and cyclists (25.5%), with statistically significant differences between age groups. Polytrauma was the leading cause of death in both categories, though isolated cranio-cerebral trauma was proportionally more frequent in children. Crash circumstances also showed age-related patterns, with children more involved in high-energy collisions and older adults more frequently struck as pedestrians. Survival intervals showed a similar distribution across groups. Conclusions: Child and elderly RTA fatalities in Romania share common determinants, primarily driver-related behaviors and insufficient safety measures, while also exhibiting distinct age-related vulnerabilities. Autopsy-based data highlights these patterns and can guide targeted interventions such as stricter law enforcement, public education, and infrastructure improvements. Full article

This paper examines how Generative AI (GenAI) reshapes digital inclusion in e-government. We develop the E-Government Divide Measurement Indicator (EGDMI) across three dimensions: D1—Breadth of the Divide (foundational access, affordability, and basic skills), D2—Sectoral/Specific Divide (actual use, experience, and trust in e-government), and D3—GenAI Gap (access, task use, and competence). The index architecture specifies indicator lists, sources, units, transformations, uniform normalization, and a documented weighting strategy with sensitivity and basic uncertainty checks. Using official statistics and qualitative evidence for Serbia, we report D1 and D2 as composite indices and treat D3 as an exploratory, non-aggregated layer given current data maturity. Results show strong foundational readiness (D1 = 73.6) but very low e-government uptake (D2 = 19.9), indicating a shift of the divide from access to meaningful use, usability, and trust. GenAI capabilities are emergent and uneven (D3 sub-dimensions: access 47.8; task use 39.4; competence/verification 43.6). Cluster analysis identifies four user profiles—from “Digitally Excluded” to “GenAI-Augmented Citizens”— that support differentiated interventions. The initial hypothesis—that GenAI can widen disparities in the short run—receives partial confirmation: GenAI may lower interaction costs but raises verification and ethics thresholds for vulnerable groups. We outline a policy roadmap prioritizing human-centered service redesign, transparency, and GenAI literacy before automation, and provide reporting templates to support comparable monitoring and cross-country learning. Full article

Emergency evacuation planning in high-rise buildings presents complex optimization challenges critical to achieving sustainable and inclusive urban development. Traditional evacuation models inadequately address vulnerable groups’ needs—particularly persons with disabilities—while neglecting fire spread dynamics, congestion effects, and real-time risk assessment. This neglect undermines both human safety and social equity—core dimensions of sustainable communities. Sustainable cities must integrate inclusive design and emergency preparedness into high-rise development. This paper develops a comprehensive mathematical optimization framework for disability-inclusive emergency evacuation that integrates dynamic fire spread modeling, congestion-aware routing mechanisms, and explicit accessibility constraints within a unified formulation. The proposed approach balances evacuation efficiency, safety, and fairness across diverse population groups through a multi-objective optimization model that incorporates time-varying risk assessments, elevator priority systems for wheelchair users, and group-specific mobility coefficients. To address the computational scalability challenges inherent in large-scale mixed-integer nonlinear programming problems, we introduce an innovative solution methodology that combines Graph Neural Networks (GNN) with Proximal Policy Optimization (PPO) algorithms. The graph neural network component captures spatial-temporal feature representations of building geometry, occupant distributions, and hazard dynamics, while the reinforcement learning algorithm develops adaptive routing policies that respond to evolving emergency conditions. Experimental results on a representative high-rise building scenario demonstrate that the proposed GNN-PPO method achieves substantial improvements in safety, efficiency, and equity. The dynamic policy successfully prioritizes vulnerable populations, utilizes elevator systems effectively for persons with disabilities, and adapts to real-time emergency conditions, providing a robust framework for inclusive emergency evacuation planning in complex building environments. This work demonstrates how advanced computational methods can advance sustainability objectives by ensuring equitable safety outcomes across diverse populations—a prerequisite for truly sustainable cities. Full article

Safe and efficient building evacuation for heterogeneous populations, particularly individuals with disabilities, remains a critical challenge in emergency management. This study proposes a hybrid evacuation framework that integrates Floor Field Cellular Automaton (FFCA) with reinforcement learning, specifically a Deep Q-Network (DQN), to enhance adaptive decision-making in dynamic and complex environments. The model incorporates velocity heterogeneity, friction-based conflict resolution, and real-time path planning to capture diverse mobility capabilities and interactions among evacuees. Simulation experiments were conducted under varying population densities, walking speeds, and exit configurations, considering four types of occupant groups: able-bodied individuals, wheelchair users, and people with visual or hearing impairments. The results demonstrate that the DQN-enhanced model consistently outperforms the conventional SFF + DFF approach, achieving significant reductions in evacuation time, particularly under high-density and reduced-speed scenarios. Notably, the DQN dynamically adapts evacuation paths to mitigate congestion, thereby improving both system efficiency and the safety of vulnerable groups. These findings highlight the potential of combining CA-based environmental modeling with reinforcement learning to develop adaptive and inclusive evacuation strategies. The proposed framework provides practical insights for designing evacuation protocols and intelligent navigation systems in public buildings. Future work will extend the proposed FFCA + DQN framework to more complex and realistic environments, including multi-exit and multi-level buildings, and further integrate multi-agent reinforcement learning (MARL) architectures to enable decentralized adaptation among heterogeneous evacuees. Furthermore, lightweight DQN variants and distributed training schemes will be explored to enhance computational scalability, while empirical data from evacuation drills and real-world case studies will be used for model calibration and validation, thereby improving predictive accuracy and generalizability. Full article

Introduction: The COVID-19 pandemic posed additional challenges for this vulnerable population, such as Sjögren’s disease (SjD), underscoring the need for effective and safe vaccination strategies. Objective: To evaluate the immunogenicity and safety of COVID-19 vaccines in patients with SjD. Methods: This prospective, observational, longitudinal study included SjD patients from the SAFER cohort. Immunogenicity was assessed via anti-spike IgG (IgG-S) titers using chemiluminescence reported as geometric mean titers (GMT) and fold increase in GMT (FI-GMT). Disease activity was evaluated using the ESSDAI score. Adverse events and COVID-19 infections were also monitored. Assessments were conducted at four time points: pre-first dose (T1), pre-second dose (T2), pre-booster (T3), and four weeks post-booster (T4). Primary vaccination involved ChAdOx1 nCoV-19 or inactivated vaccine (CoronaVac), and boosters were either homologous (ChAdOx1 nCoV-19) or heterologous (BNT162b2). Results: Among 51 participants (mean age 46 years; 90% female), 41% had comorbidities and 27% (n = 14/51) were highly immunosuppressed. Among those 73% (n = 37/51) under low immunosuppression, n = 8/51 (13%) were not using any medication. At baseline, 11% (n = 4/35) showed moderate/high disease activity, which decreased to 6.5% (n = 2/31) at T4. Primary vaccination was ChAdOx1 in 94% (n = 48/51) and CoronaVac in 6% (n = 3/51); 73% (n = 37/51) received heterologous and 27% (n = 14/51) homologous boosters. COVID-19 infection post-booster occurred in 20% (n = 10/51). Seroconversion rates reached nearly 100% across all medication subgroups except for biologic users, who showed delayed but stable seroconversion by T4. IgG-S titers increased progressively through T4. Primary immunization induced an ascending GMT in both vaccine types. At T4, the GMT was significantly higher in the BNT162b2 group (2148.03 [1452.05–3155.84]; p < 0.001; 95% CI) than in the ChAdOx1 group (324.29 [107.92–974.48]; p < 0.001; 95% CI); the fold-increase in immune response was six times greater with BNT162b2 (5.98 [2.97–12.03]; p = 0.001; 95% CI). Seroconversion was 100% in the heterologous group versus 83% in the homologous group (p > 0.01). Those with prior infection showed significantly higher titers, particularly at T2 and T3 (p < 0.001 for T1–T3). Adverse events were mild and not statistically significant. Multivariate regression confirmed BNT162b2 as an independent factor for higher antibody titers. Conclusion: COVID-19 vaccination in patients with SjD was safe and induced high anti-spike antibody titers and seropositivity. Heterologous boosting, particularly with BNT162b2, demonstrated superior immunogenicity. No association was found between vaccination and SjD disease flares or worsening activity. Full article