Search Results (4,684)

Named Data Networking (NDN) represents a promising Information-Centric Networking architecture that addresses limitations of traditional host-centric Internet protocols by emphasizing content names rather than host addresses for communication. While NDN offers advantages in content distribution, mobility support, and built-in security features, its stateful forwarding plane introduces significant vulnerabilities, particularly Interest Flooding Attacks (IFAs). These IFA attacks exploit the Pending Interest Table (PIT) by injecting malicious interest packets for non-existent or unsatisfiable content, leading to resource exhaustion and denial-of-service attacks against legitimate users. This survey examines research advances in IFA detection and mitigation from 2013 to 2024, analyzing seven relevant published detection and mitigation strategies to provide current insights into this evolving security challenge. We establish a taxonomy of attack variants, including Fake Interest, Unsatisfiable Interest, Interest Loop, and Collusive models, while examining their operational characteristics and network performance impacts. Our analysis categorizes defense mechanisms into five primary approaches: rate-limiting strategies, PIT management techniques, machine learning and artificial intelligence methods, reputation-based systems, and blockchain-enabled solutions. These approaches are evaluated for their effectiveness, computational requirements, and deployment feasibility. The survey extends to domain-specific implementations in resource-constrained environments, examining adaptations for Internet of Things deployments, wireless sensor networks, and high-mobility vehicular scenarios. Five critical research directions are proposed: adaptive defense mechanisms against sophisticated attackers, privacy-preserving detection techniques, real-time optimization for edge computing environments, standardized evaluation frameworks, and hybrid approaches combining multiple mitigation strategies. Full article

Efficient task offloading for delay-sensitive applications, such as autonomous driving, presents a significant challenge in multi-hop Vehicular Edge Computing (VEC) networks, primarily due to high vehicle mobility, dynamic network topologies, and complex end-to-end congestion problems. To address these issues, this paper proposes a graph attention-based reinforcement learning algorithm, named GAPO. The algorithm models the dynamic VEC network as an attributed graph and utilizes a graph neural network (GNN) to learn a network state representation that captures the global topological structure and node contextual information. Building on this foundation, an attention-based Actor–Critic framework makes joint offloading decisions by intelligently selecting the optimal destination and collaboratively determining the ratios for offloading and resource allocation. A multi-objective reward function, designed to minimize task latency and to alleviate link congestion, guides the entire learning process. Comprehensive simulation experiments and ablation studies show that, compared to traditional heuristic algorithms and standard deep reinforcement learning methods, GAPO significantly reduces average task completion latency and substantially decreases backbone link congestion. In conclusion, by deeply integrating the state-aware capabilities of GNNs with the decision-making abilities of DRL, GAPO provides an efficient, adaptive, and congestion-aware solution to the resource management problems in dynamic VEC environments. Full article

Background: Hip fractures represent a major clinical challenge, particularly in elderly and frail patients, where postoperative pain control must balance effective analgesia with motor preservation to facilitate early mobilization. Various regional anesthesia techniques are used in this setting, including the pericapsular nerve group (PENG) block, fascia iliaca compartment block (FICB), femoral nerve block (FNB), and quadratus lumborum block (QLB), yet optimal strategies remain debated. Objectives: To systematically review the efficacy, safety, and clinical applicability of major regional anesthesia techniques for pain management in hip fractures, including considerations of fracture type, surgical approach, and functional outcomes. Methods: A systematic literature search was conducted following PRISMA 2020 guidelines in PubMed, Scopus, Web of Science, and the virtual library of the Hospital Central de la Defensa “Gómez Ulla” up to March 2025. Inclusion criteria were RCTs, systematic reviews, and meta-analyses evaluating regional anesthesia for hip surgery in adults. Risk of bias in RCTs was assessed using RoB 2.0, and certainty of evidence was evaluated using the GRADE approach. Results: Twenty-nine studies were included, comprising RCTs, systematic reviews, and meta-analyses. PENG block demonstrated superior motor preservation and reduced opioid consumption compared to FICB and FNB, particularly in intracapsular fractures and anterior surgical approaches. FICB and combination strategies (PENG+LFCN or sciatic block) may provide broader analgesic coverage in extracapsular fractures or posterior approaches. The overall risk of bias across RCTs was predominantly low, and certainty of evidence ranged from moderate to high for key outcomes. No significant safety concerns were identified across techniques, although reporting of adverse events was inconsistent. Conclusions: PENG block appears to offer a favorable balance of analgesia and motor preservation in hip fracture surgery, particularly for intracapsular fractures. For extracapsular fractures or posterior approaches, combination strategies may enhance analgesic coverage. Selection of block technique should be tailored to fracture type, surgical approach, and patient-specific functional goals. Full article

Recently, the usage of mobile phone-based games has increased due to the growing accessibility and convenience they provide. Using a descriptive-quantitative design, a disaster-based mobile application was developed in this study to enhance disaster literacy among the private senior high schools in science, technology, engineering, and mathematics (STEM) education in Davao City, the Philippines. The developed application was provided together with survey questionnaires to 364 students randomly selected from different schools in Davao City usingF a simple random sampling method. The technology acceptance (TAM) model was used to explain how users accepted the new technology. The mobile application was designed with features in four disaster scenarios—fire, flood, volcano, and earthquake. The results revealed a high acceptance, with an average score of the perceived usefulness (PE) of 4.52, perceived ease of use (PEOU) of 4.44, and a behavioral intention (BI) of 4.12. The students accepted the application to enhance disaster risk reduction and management. Aligned with SDG 4 and SDG 11, the application can be used to equip users with the knowledge to respond to disasters and ensure community resilience. Full article

Background/Objectives: Frailty is a prevalent geriatric syndrome associated with impaired postural control and elevated fall risk. Although conventional exercise is a core strategy for frailty management, adherence remains limited. Virtual reality (VR)-based interventions have emerged as potentially engaging alternatives, but their effects on objective postural control and task-specific confidence in frail populations remain understudied. This study aimed to evaluate the effectiveness of a supervised VR training program using the Nintendo Ring Fit Plus™ on postural control, functional mobility, and balance confidence among frail community-dwelling older adults. Methods: Fifty-one adults aged ≥65 years classified as frail or prefrail were enrolled in a four-week trial. Participants were assigned to either a VR intervention group (n = 28) or control group (n = 23). Participants were non-randomly assigned based on availability and preference. Outcome measures were collected at baseline and post-intervention. Primary outcomes included center of pressure (CoP) metrics—sway area, mean velocity, and sway path. Secondary outcomes were the Timed Up and Go (TUG), Berg Balance Scale (BBS), Activities-specific Balance Confidence (ABC), and Falls Efficacy Scale–International (FES-I). Results: After adjusting for baseline values, age, and BMI, the intervention group showed significantly greater improvements than the control group across all postural control outcomes. Notably, reductions in sway area, mean velocity, and sway path were observed under both eyes-open and eyes-closed conditions, with effect sizes ranging from moderate to very large (Cohen’s d = 0.57 to 1.61). For secondary outcomes, significant between-group differences were found in functional mobility (TUG), balance performance (BBS), and balance confidence (ABC), with moderate-to-large effect sizes (Cohen’s d = 0.53 to 0.73). However, no significant improvement was observed in fear of falling (FES-I), despite a small-to-moderate effect size. Conclusions: A supervised VR program significantly enhanced postural control, mobility, and task-specific balance confidence in frail older adults. These findings support the feasibility and efficacy of VR-based training as a scalable strategy for mitigating frailty-related mobility impairments. Full article

Stroke is a significant global health concern characterized by the abrupt disruption of cerebral blood flow, leading to neurological impairment. Accurate and timely diagnosis—enabled by imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI)—is essential for differentiating stroke types and initiating interventions like thrombolysis, thrombectomy, or surgical management. In parallel, recent advancements in wearable technology, particularly smart clothing, offer new opportunities for stroke prevention, real-time monitoring, and rehabilitation. These garments integrate various sensors, including electrocardiogram (ECG) electrodes, electroencephalography (EEG) caps, electromyography (EMG) sensors, and motion or pressure sensors, to continuously track physiological and functional parameters. For example, ECG shirts monitor cardiac rhythm to detect atrial fibrillation, smart socks assess gait asymmetry for early mobility decline, and EEG caps provide data on neurocognitive recovery during rehabilitation. These technologies support personalized care across the stroke continuum, from early risk detection and acute event monitoring to long-term recovery. Integration with AI-driven analytics further enhances diagnostic accuracy and therapy optimization. This narrative review explores the application of smart clothing in conjunction with traditional imaging to improve stroke management and patient outcomes through a more proactive, connected, and patient-centered approach. Full article

The shift from Internal Combustion Engine Vehicles (ICEVs) to Battery Electric Vehicles (BEVs) has accelerated global efforts to decarbonize transportation. However, battery degradation, high costs, and limited lifespan remain critical barriers. This review synthesizes recent innovations to extend Li-ion battery life in BEVs by exploring advances in degradation modeling, adaptive Battery Management Systems (BMSs), electronic component simulations, and real-world usage profiling. The authors have systematically analyzed over 80 recent studies using a PRISMA-guided review protocol. A novel comparative framework highlights gaps in current literature, particularly regarding real-world driving impacts, ripple current effects, and second-life battery applications. This review article critically compares model-driven, data-driven, and hybrid model approaches, emphasizing trade-offs in interpretability, accuracy, and deployment feasibility. Finally, the review links battery life extension to broader sustainability metrics, including circular economy models and predictive maintenance algorithms. This review offers actionable insights for researchers, engineers, and policymakers aiming to design longer-lasting and more sustainable electric mobility systems. Full article

Background/Objective: Sepsis-associated acute kidney injury (SA-AKI) is a substantial contributor to mortality in critically ill patients. This study aimed to investigate the impact of gum acacia (GA) and dexamethasone (DEX) combination on lipopolysaccharide (LPS)-induced SA-AKI in rats. Methods: Thirty-six male Sprague Dawley rats were separated into six groups, including the control, GA group, LPS-induced AKI group, DEX + LPS group, GA + LPS group, and GA + DEX + LPS group. AKI was induced in rats using LPS (10 mg/kg, i.p.). GA was administered orally (7.5 g/kg) for 14 days before LPS injection, and DEX was injected (1 mg/kg, i.p.) 2 h after LPS injection. Results: LPS injection significantly (p < 0.05, vs. control group) impaired renal function, as evidenced through increased levels of kidney function biomarkers, decreased creatinine clearance, and histopathological alterations in the kidneys. LPS also significantly (p < 0.05, vs. control group) elevated levels of oxidative stress markers, while it reduced levels of antioxidant enzymes. Furthermore, LPS triggered an inflammatory response, manifested by significant (p < 0.05, vs. control group) upregulation of Toll-like receptor 4, myeloid differentiation primary response 88, interleukin-1β, tumor necrosis factor-α, and nuclear factor-κB, along with increased expression of high-mobility group box 1. Administration of GA significantly ameliorated LPS-induced renal impairment by enhancing antioxidant defenses and suppressing inflammatory pathways (p < 0.05, vs. LPS group). Furthermore, GA-DEX-treated rats showed improved kidney function, reduced oxidative stress, and attenuated inflammatory markers (p < 0.05, vs. LPS group). Conclusions: The GA-DEX combination exhibited potent renoprotective effects against LPS-induced SA-AKI, possibly due to their antioxidant and anti-inflammatory properties. These results suggest that the GA-DEX combination could be a promising and effective therapeutic agent for managing SA-AKI. Full article

With the advance of digital transformation, the assessment of the environmental impact of digital tools and technologies grows more relevant. Considering the inflated expectations of environmental responsibility in higher education, this study analyses how websites of Polish universities conform to sustainable web design criteria. The sustainability audit employed a methodology encompassing carbon emissions measurement, technical website analysis, and SEO evaluation. The author analysed 63 websites of public universities in Poland using seven independent audit tools, including an original AI Custom GPT agent preconfigured in the ChatGPT ecosystem. The results revealed a substantial differentiation in CO₂ emissions and website optimisation, with an average EcoImpact Score of 66.41/100. Nearly every fourth website exhibited a significant carbon footprint and excessive component sizes, which indicates poor asset optimisation and energy-intensive design techniques. The measurements exposed considerable variability in emission intensities and resource intensity among the university websites, suggesting the need for standardised digital sustainability practices. Regulations on the carbon footprint of public institutions’ websites and mobile applications could become vital strategic components for digital climate neutrality. Promoting green hosting, “Green SEO” practices, and sustainability audits could help mitigate the environmental impact of digital technologies and advance sustainable design standards for the public sector. The proposed auditing methodology can effectively support the institutional transition towards sustainable management of digital infrastructure by integrating technical, sustainability, and organisational aspects. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

This study addresses critical safety challenges in sustainable urban mobility by developing a probabilistic framework for lane-change risk prediction on congested expressways. Utilizing unmanned aerial vehicle (UAV)-captured trajectory data from 784 validated lane-change events, we construct a Bayesian network model integrated with an I-CH scoring-enhanced MMHC algorithm. This approach quantifies risk probabilities while accounting for driver decision dynamics and input data uncertainties—key gaps in conventional methods like time-to-collision metrics. Validation via the Asia network paradigm demonstrates 80.5% reliability in forecasting high-risk maneuvers. Crucially, we identify two sustainability-oriented operational thresholds: (1) optimal lane-change success occurs when trailing-vehicle speeds in target lanes are maintained at 1.0–3.0 m/s (following-gap < 4.0 m) or 3.0–6.0 m/s (gap ≥ 4.0 m), and (2) insertion-angle change rates exceeding 3.0°/unit-time significantly elevate transition probability. These evidence-based parameters enable traffic management systems to proactively mitigate collision risks by 13.26% while optimizing flow continuity. By converting behavioral insights into adaptive control strategies, this research advances resilient transportation infrastructure and low-carbon mobility through congestion reduction. Full article

Based on a comprehensive review of the current research status of ecological compensation both domestically and internationally, combined with field survey data, this study delves into the issue of multi-stakeholder participation in the ecological compensation mechanisms of the Xin’an River Basin. This research reveals that the joint participation of multiple stakeholders is crucial to achieving the goals of ecological compensation in river basins. The government plays a significant role in macro-guidance, financial support, policy guarantees, supervision, and management. It promotes the comprehensive implementation of ecological environmental protection by formulating relevant laws and regulations, guiding the public to participate in ecological conservation, and supervising and punishing pollution behaviors. The public, serving as the main force, forms strong awareness and behavioral habits of ecological protection through active participation in environmental protection, monitoring, and feedback. As participants, enterprises contribute to industrial transformation and green development by improving resource utilization efficiency, reducing pollution emissions, promoting green industries, and participating in ecological restoration projects. Scientific research institutions, as technology enablers, have effectively enhanced governance efficiency through technological research and innovation, ecosystem value accounting to provide decision-making support, and public education. Social organizations, as facilitators, have injected vitality and innovation into watershed governance by extensively mobilizing social forces and building multi-party collaboration platforms. Communities, as supporters, have transformed ecological value into economic benefits by developing characteristic industries such as eco-agriculture and eco-tourism. Based on the above findings, further recommendations are proposed to mobilize the enthusiasm of upstream communities and encourage their participation in ecological compensation, promote the market-oriented operation of ecological compensation mechanisms, strengthen cross-regional cooperation to establish joint mechanisms, enhance supervision and evaluation, and establish a sound benefit-sharing mechanism. These recommendations provide theoretical support and practical references for ecological compensation worldwide. Full article

Forest fires are commonly regarded as negative for ecosystems; however, they also represent a major ecological force shaping the biodiversity of invertebrates and many other organisms. The aim of this study was to better understand how multiple groups of invertebrates respond to wildfire across different forest types in Central Europe. The research was conducted following a large forest fire (ca. 1200 ha) that occurred in 2022. Data were collected over two years (2023 and 2024), from April to September. The research was conducted in coniferous forests and included six pairwise study types: burnt and unburnt dead spruce (bark beetle affected), burnt and unburnt clear-cuts, and burnt and unburnt healthy stands. In total, 96 traps were deployed each year. Across both years, 220,348 invertebrates were recorded (1.Y: 128,323; 2.Y: 92,025), representing 24 taxonomic groups. A general negative trend in abundance following forest fire was observed in the groups Acari, Auchenorhyncha, Blattodea, Dermaptera, Formicidae, Chilopoda, Isopoda, Opiliones, and Pseudoscorionida. Groups showing a neutral response included Araneae, Coleoptera, Collembola, Diplopoda, Heteroptera, Psocoptera, Raphidioptera, Thysanoptera, and Trichoptera. Positive responses, indicated by an increase in abundance, were recorded in Hymenoptera, Orthoptera, Lepidoptera, and Diptera. However, considerable differences among management types (clear-cut, dead spruce, and healthy) were evident, as their distinct characteristics largely influenced invertebrate abundance in both unburnt and burnt variants of the types across all groups studied. Forest fire primarily creates favorable conditions for heliophilous, open-landscape, and floricolous invertebrate groups, while less mobile epigeic groups are strongly negatively affected. In the second year post-fire, the total invertebrate abundance in burnt sites decreased to 59% of the first year’s levels. Conclusion: Forest fire generates a highly heterogeneous landscape from a regional perspective, creating unique ecological niches that persist more than two years after fire. For many invertebrates, successional return toward pre-fire conditions is delayed or incomplete. Full article

Smart cities are technologically advanced urban environments where interconnected systems and data-driven technologies enhance public service delivery and quality of life. These cities rely on information and communication technologies, the Internet of Things, big data, cloud computing, and other Industry 4.0 tools to support efficient city management and foster citizen engagement. Often referred to as digital cities, they integrate intelligent infrastructures and real-time data analytics to improve mobility, security, and sustainability. Ubiquitous sensors, paired with Artificial Intelligence, enable cities to monitor infrastructure, respond to residents’ needs, and optimize urban conditions dynamically. Given the increasing significance of Industry 4.0 in urban development, this study adopts a bibliometric approach to systematically review the application of these technologies within smart cities. Utilizing major academic databases such as Scopus and Web of Science the research aims to identify the primary Industry 4.0 technologies implemented in smart cities, assess their impact on infrastructure, economic systems, and urban communities, and explore the challenges and benefits associated with their integration. The bibliometric analysis included publications from 2016 to 2023, since the emergence of urban researchers’ interest in the technologies of the new industrial revolution. The task is to contribute to a deeper understanding of how smart cities evolve through the adoption of advanced technological frameworks. Research indicates that IoT and AI are the most commonly used tools in urban spaces, particularly in smart mobility and smart environments. Full article

Rice blast caused by Magnaporthe oryzae is a major cause of yield reductions and quality deterioration in rice. Therefore, early detection of the disease is necessary for controlling the spread of rice blast. This study proposed a dual-channel feature fusion model (DCFM) to achieve effective identification of rice blast. The DCFM model extracted spectral features using successive projection algorithm (SPA), random frog (RFrog), and competitive adaptive reweighted sampling (CARS), and extracted spatial features from spectral images using MobileNetV2 combined with the convolutional block attention module (CBAM). Then, these features were fused using the feature fusion adaptive conditioning module in DCFM and input into the fully connected layer for disease identification. The results show that the model combining spectral and spatial features was superior to the classification models based on single features for rice blast detection, with OA and Kappa higher than 90% and 88%, respectively. The DCFM model based on SPA screening obtained the best results, with an OA of 96.72% and a Kappa of 95.97%. Overall, this study enables the early and accurate identification of rice blast, providing a rapid and reliable method for rice disease monitoring and management. It also offers a valuable reference for the detection of other crop diseases. Full article