Search Results (141,766)

Global Navigation Satellite Systems are vulnerable to jamming and spoofing threats, compromising several critical applications. Existing detection methods based on hardware solutions (antenna array, spectrogram) are low-latency and accurate but require expensive hardware, while machine learning solutions are the most effective but require extensive training and lack adaptability. This work proposes an edge-based, statistical threat detector using crowdsourced GNSS data and fuzzy logic to integrate multiple anomaly indicators. A key feature is a C-/N₀-based crowdsourcing metric. Experiments show detection precision up to 88% for jamming and 97% for spoofing, with false positive rates around 1–2% and an average detection time of 10 s. Full article

Deep coalbed methane (CBM) is not only an important alternative resource but also plays a crucial role in ensuring energy security and optimizing energy structure in China. However, with the progress of exploration, the gas adsorption characteristics and heterogeneity of the coal reservoirs’ pore structure have become critical factors influencing the enhancement of CBM production capacity. This study investigates the micropore structure and fractal characteristics of medium-to-high rank coal samples from deep coal seams in the Benxi Formation, Ordos Basin. The primary objective is to examine the impact of thermal maturity on the evolution of micropore structures and their effect on methane adsorption. A series of coal samples with varying thermal maturities were analyzed using CO₂ adsorption. The results reveal that thermal maturity leads to a decrease in both specific surface area and pore volume, attributed to the collapse and compaction of pre-existing pores. Despite this, new micropores are formed at higher coal ranks, although their development is insufficient to fully offset the loss of pore volume. Fractal analysis demonstrates that thermal maturity reduces surface heterogeneity, rendering micropore structures more irregular and complex. These findings indicate that thermal maturation significantly influences the evolution of the pore structure, which, in turn, affects methane storage potential in deep coal seams. Full article

The development of energy-efficient conductors capable of operating reliably in harsh, cold climates is crucial for sustainable power infrastructure. High-mountain and cold regions are key research scenarios for energy-saving conductors, enabling the natural enhancement of conductor heat dissipation in low-temperature environments and improving the current carrying capacity and energy efficiency. These regions are rich in renewable energy and urgently need efficient transmission channels. However, the extremely complex working conditions create strict requirements for the thermal–mechanical coupling performance of conductors, and existing research has paid insufficient attention to this. This study evaluates the thermal and mechanical performance of three advanced energy-saving conductors (JLHA3-275, JL1/G1A-240/30, JL/LHA1-135/140) in comparison with a conventional conductor (JL/G1A-240/30) under cold-region operating conditions. A finite element analysis model, validated against theoretical calculations under combined meteorological factors, was employed to simulate radial temperature fields and stress distribution. The results demonstrate that the JLHA3 conductor exhibits superior heat dissipation and minimal resistive losses, maintaining a radial temperature of −23.35 °C under a 700 A load, approximately 1.6 °C lower than the conventional type. Its temperature further decreases significantly with increased wind speeds. Thermally, JLHA3 shows high stability across a broad temperature range (−28.85 °C to 29.03 °C). Mechanically, it displays uniform stress distribution and a notable decrease in stress from 79.53 MPa to 39.46 MPa with rising temperatures, indicating excellent flexibility and thermal adaptability. These findings confirm that the JLHA3 conductor offers an optimal combination of thermal performance, structural reliability, and energy efficiency for high-altitude, cold-region power transmission applications. Full article

Osteoarthritis (OA) in dogs is increasingly recognized as a condition with systemic inflammatory and metabolic components, potentially influenced by body condition and sex. This study aimed to characterize phenotypic differences in circulating inflammatory, metabolic, and biochemical biomarkers in dogs with OA according to body condition and sex. In this cross-sectional study, client-owned dogs were classified as healthy controls, thin dogs with OA (TOA), or obese dogs with OA (OOA). Circulating cytokines, adipokines, cartilage degradation markers, and routine biochemical parameters were measured in blood samples, including interleukin-1 beta, interleukin-4, interleukin-10, adiponectin, C-terminal telopeptide of type II collagen, and standard metabolic and hepatic markers. Data were analyzed using linear models fitted on log-transformed values, with group and sex as fixed effects, complemented by adjusted and sensitivity analyses. TOA dogs showed significantly higher interleukin-1 beta concentrations compared with controls (multiplicative effect 1.39, 95% confidence interval 1.05–1.82), indicating increased systemic inflammatory activity. In contrast, OOA dogs exhibited predominantly metabolic-associated alterations, including higher gamma-glutamyl transferase activity (multiplicative effect 1.22, 95% confidence interval 1.03–1.46) and higher cholesterol concentrations (multiplicative effect 1.22, 95% confidence interval 1.03–1.46). Several other biomarkers showed no clear group-related differences. Overall, these findings demonstrate that systemic biomarker profiles in canine OA vary primarily according to body condition, with secondary sex-related patterns, supporting the existence of biologically distinct OA phenotypes relevant for future diagnostic and therapeutic strategies. Full article

Guaranteed annuity options (GAOs) allow policyholders to convert accumulated funds into life annuities at maturity at a guaranteed minimum rate. Thus, insurers are exposed to both investment and longevity risks. Accurate valuation of these long-term, survival-contingent contracts is essential for solvency assessment and risk management. Many existing approaches assume independence between interest rate and mortality risks. This paper develops a computationally efficient pricing framework for GAOs that jointly models interest and mortality rates as correlated stochastic processes with regime-switching dynamics governed by a finite-state continuous-time Markov chain. Model parameters are estimated using U.S. interest rates and cohort mortality data via quasi-maximum likelihood estimation. A semi-analytic valuation formula is derived based on the joint distribution of the underlying processes. Numerical results show that incorporating correlation and regime-switching materially increases GAO prices relative to conventional one-state models. The proposed semi-analytic approach delivers substantial computational advantages over standard Monte Carlo simulations. Sensitivity analysis further identifies the parameters most relevant for long-horizon pricing and solvency considerations. This highlights the practical relevance of the framework for managing longevity-linked guarantees under economic and demographic uncertainty. Full article

The Controller Area Network (CAN) protocol lacks native authentication mechanisms, exposing modern vehicles to critical security threats. While deep learning-based intrusion detection systems show promise, existing solutions require computational resources far exceeding automotive-grade microcontroller constraints, hindering practical embedded deployment. This paper proposes LCSMC-Net, an ultra-lightweight neural architecture for resource-constrained CAN intrusion detection. The framework integrates three innovations: (1) Separable Multiscale Convolution Lite (SMC-Lite) blocks capturing multitemporal attack patterns with minimal parameters; (2) Lightweight Channel-Temporal Attention (LCTA) achieving linear $\mathcal{O}\left(N\right)$ complexity through adaptive pruning; and (3) 6-dimensional CAN-optimized features exploiting protocol-specific characteristics for aggressive compression. The framework employs Bayesian hyperparameter optimization and knowledge distillation for systematic model compression. Extensive experiments on CAN and CAN-FD datasets demonstrate that LCSMC-Net achieves 99.89% accuracy with only 9401 parameters and 2.84M FLOPs, outperforming existing solutions while meeting real-time constraints of automotive embedded systems, providing a viable edge AI deployment solution. Full article

Background: Iron deficiency (ID) is common among people with chronic kidney disease (CKD) and/or heart failure (HF). Despite the additional burden ID causes among people with CKD and HF, there is considerable uncertainty surrounding the best way to diagnose it and, subsequently, identify who is most likely to benefit from receiving iron therapy. Methods: This manuscript reports the markers and thresholds used in ID diagnosis, treatment, and management in the UK by nephrologists and cardiologists who manage people with chronic kidney disease or heart failure, as well as investigating future challenges and questions that remain unanswered. The research involved three stages: an online questionnaire, individual interviews, and a panel meeting, which discussed the findings from the first two stages. Results: The panel concluded that there is no robust definition of iron deficiency that can be applied to chronic kidney disease and heart failure. Existing methods of diagnosing iron deficiency come with various problems; a transferrin saturation of <20% is the most popular, but it is not regarded as a perfect solution. Transferrin saturation is also the most popular way of assessing the success of iron deficiency treatment. Clinicians generally do not vary treatment regimens based on severity or subgroups. There are large variations in monitoring and the ability to administer iron therapy in secondary care. Conclusions: There is a clear need to consolidate current approaches to diagnosing and treating iron deficiency in people with chronic kidney disease and/or heart failure. Simple markers and thresholds, and simple strategies to implement them are required. Full article

Background: Temporomandibular disorders (TMDs) are common musculoskeletal conditions associated with pain, functional limitation, and reduced quality of life (QoL). Despite the widespread use of conservative and minimally invasive treatments, the available evidence remains fragmented across heterogeneous interventions, diagnostic criteria, and outcome measures, limiting comparative interpretation and clinical applicability. Objectives: The primary objective of this systematic review is to evaluate the effectiveness of conservative and minimally invasive interventions for pain reduction in adult patients with temporomandibular disorders. Secondary objectives include assessing effects on mandibular function and QoL and exploring differences across intervention categories, TMD subtypes, diagnostic criteria, and follow-up durations. Methods: This protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251250251) and adheres to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. A systematic search will be conducted in PubMed/MEDLINE, Web of Science, Scopus, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) for randomized controlled trials (RCTs) published from 1 January 2015, up to the date of study initiation, using controlled vocabulary terms and free-text keywords combined with Boolean operators. Eligible studies will include adult patients (≥18 years) diagnosed with temporomandibular disorders using validated diagnostic criteria and treated with conservative or minimally invasive interventions, compared with placebo/sham, no treatment or usual care, or active comparators, in accordance with the PICOS framework. Two reviewers will independently screen studies and extract data, with disagreements resolved by consensus or consultation with a third reviewer; the study selection process will be documented using a PRISMA 2020 flow diagram. Interventions will be synthesized within predefined clusters (e.g., physical and manual therapies, occlusal splint therapy, physical agent modalities, and minimally invasive joint procedures). Risk of bias will be assessed using the revised Cochrane Risk of Bias tool (RoB 2). The primary outcome will be pain intensity, while secondary outcomes will include mandibular function and QoL. Where appropriate, meta-analysis using a random-effects model will be performed; otherwise, a structured narrative synthesis will be provided. Expected Impact: The systematic review is expected to deliver an updated and methodologically rigorous synthesis of evidence on conservative and minimally invasive interventions for TMDs. By addressing existing research gaps such as the fragmentation of evidence across intervention types, heterogeneity in diagnostic criteria, and variability in outcome measures, this review will support evidence-based clinical decision-making and identify priorities for future research. Full article

Yersinia pestis and Francisella tularensis are Tier-1 pathogens with high interest for biodefense and public health. Evaluating the antibacterial activity of repurposed drugs against these high-priority pathogens is a key element in the ongoing effort to develop diversified antimicrobial strategies. Drug repurposing offers a cost-effective and time-efficient approach to address antibiotic resistance by identifying new applications for existing therapeutics. In this study, we demonstrate in vitro antibacterial effect of the antifungal agent ciclopirox and offer this drug as a potential antibacterial treatment. Ciclopirox in vitro activity was previously reported against various Gram-negative bacteria, including resistant strains, primarily through iron chelation that disrupts key metabolic pathways and virulence mechanisms. Additionally, it exhibits antibiofilm activity and can potentiate the efficacy of certain antibiotics. Our findings reveal that ciclopirox effectively inhibits the in vitro growth of fully virulent strains of Y. pestis and F. tularensis, as well as avirulent isolates, including avirulent mutants that their wild-type susceptibility was reduced through selection to MIC levels defining them as “nonsusceptible” to ciprofloxacin (Y. pestis Kim53Δ70Δ10 and F. tularensis LVS) and doxycycline (LVS), or resistant to doxycycline (Kim53Δ70Δ10) according to CLSI interpretive criteria. Additionally, prolonged exposure of Y. pestis and F. tularensis to sub-MIC and MIC concentrations of ciclopirox did not lead to an increase in observed MIC during the study period. These results highlight ciclopirox as a potential candidate for treatment alternative, combined with other antibiotic substances or repurposed drugs against these bacterial threats. Full article

Satellite–terrestrial spectrum sensing plays a crucial role in enhancing spectrum efficiency through reusing spectra. However, in a satellite–terrestrial converged system, the large SNR range, non-Gaussian signal characteristics and noise uncertainty pose significant challenges for spectrum sensing. In this paper, we investigate a downlink spectrum sensing framework where multi-terrestrial BSs act as a secondary system to sense idle satellite spectra through a multi-domain feature-level sensing signal fusion. To enhance the characterization of signal/noise features, we provide a fusion strategy of multi-features including energy, power spectral density, cyclic autocorrelation function, higher-order moments, sparse ratio, and I/Q samples, constructing two feature tensors of statistical features and an I/Q component. Then, we propose a deep-learning-enabled multi-feature fusion spectrum sensing method (DL-MFFSSnet) based on a dual-branch deep neural network architecture with the constructed two feature tensors as inputs. In the statistical feature processing branch, CNN and channel self-attention are incorporated to capture intra-channel correlations and inter-channel relative contributions of different feature modalities. In the I/Q branch, multi-scale dilated convolutions and spatial self-attention are introduced to analyze dependencies across different temporal positions and multi-scale spatial features. The feature map extracted from both branches passed through fully connected layers for deepwise feature fusion, achieving accurate spectrum sensing. Extensive simulation results demonstrate that the DL-MFFSSnet method outperforms the existing state-of-the-art algorithms. Full article

For nations heavily dependent on fossil-fuel exports, hydrogen is emerging as a promising solution to reduce carbon emissions while preserving economic stability and promoting countries’ energy independence. This research study examines hydrogen potential as a renewable energy source to facilitate the transition toward a sustainable economy with a special focus on Middle East and North Africa (MENA) countries. The analysis delves into policy frameworks, technological advancements, and infrastructure adaptations to build a reliable green hydrogen supply chain for a scalable and bankable future. The role played by other renewable energies like solar and wind, together with the risk related to the high demand for water resources to achieve the green hydrogen transition, has also been assessed. Furthermore, key challenges have been highlighted, including the repurposing of the existing pipelines into the energy networks, public–private partnerships to secure investment, and legislation requirements to encourage the adoption of novel hydrogen applications. In order to do that, a SWOT-PESTEL analysis has been carried out to identify the main decarbonization strategies for achieving a replicable framework. Moreover, a multi-criteria decision analysis was performed, applying 11 indicators across supply-side (e.g., solar/wind potential, LCOE, and water stress), demand-pull/logistics (e.g., maritime connectivity, steel production, and LNG export capacity), and risk/regulation dimensions (e.g., governance effectiveness, regulatory quality, and fossil rent dependence). The Analytic Hierarchy Process (AHP) was used for weighting, the entropy method for weighting variability (hybrid 50/50 combined weights), min–max normalization for costs, 5% Winsorization for outliers, and TOPSIS for aggregation following OECD-JRC composite indicator guidelines. Results have been validated through a multiple scenario analysis (base, supply-led, and risk-aware) and sensitivity testing via Dirichlet bootstrapping (5000 iterations) with ±20% weight perturbations. Six countries of the MENA region have been studied. The multi-criteria decision analysis outcomes rank Egypt (composite score 0.518), Algeria (0.482), and Oman (0.479) as the most suitable countries for large-scale green hydrogen and ammonia production/export, while Saudi Arabia, Qatar, and Kuwait achieved lower supply scores in the base case due to higher perceived risks. Full article

Caregivers of children with Autism Spectrum Disorder (ASD) frequently experience chronic psychological stress, thereby necessitating accessible support. Although artificial intelligence (AI)-based assisted technologies have the potential to reduce caregiver workload, most existing solutions lack robust privacy control and clinical interoperability, which significantly limits their adoption in regulated healthcare environments. To address these challenges, this paper proposes a Privacy-by-Design (PbD) multi-agent architecture that enables consent-aware, auditable, and privacy-preserving AI-assisted support for caregivers of children with ASD. The effectiveness of the proposed architecture was evaluated using two datasets: one focusing on clinically grounded autism-related knowledge and another reflecting naturalistic caregiver observation language. System performance was assessed using a Retrieval-Augmented Generation Assessment (RAGAs)-based framework with a Large Language Model (LLM)-as-a-Judge approach implemented via a locally deployed Llama 3 8B model. The system achieved answer relevancy scores of 0.767 for the clinical dataset and 0.750 for the observational dataset, with corresponding Recall@K values of 0.400 and 0.742, respectively. Context precision ranged from 0.599 to 0.631, and no harmful content was detected. Overall, the proposed architecture demonstrates secure caregiver–specialist collaboration through consent-aware routing, anonymised data storage, and controlled data reconstruction, providing a regulation-aligned design option for privacy-preserving AI integration in assisted care platforms. Full article

Multi-sensor fusion provides richer information for bearing fault diagnosis. However, under variable working conditions, the coupling relationships among signals from different sensors exhibit significant non-stationarity and directionality, posing challenges for modeling and practical deployment. Existing methods often rely on fixed or symmetric graph structures or construct correlation relationships entirely based on data-driven approaches; this makes balancing physical consistency, robustness, and computational efficiency difficult. To address these issues, we propose a Physics-guided Adaptive Graph Transformer Network (AGTN) for multi-modal bearing fault diagnosis under variable working conditions. More specifically, we offer innovative improvements across three aspects. Firstly, we introduce domain knowledge priors into the graph structure learning process to adaptively construct sparse and asymmetric dynamic graph structures that capture physically meaningful directional dependencies among different sensor signals. Secondly, we combine a graph-aware transformer to jointly model the temporal features and structural correlations of multi-source signals. Finally, we further introduce a hierarchical subgraph training strategy that significantly reduces memory usage and training time while ensuring diagnostic performance. Experimental results on a self-built multi-condition bearing dataset show that AGTN achieves an average diagnostic accuracy of 99.42% under the same distribution conditions and demonstrates good generalization and robustness, e.g., variable speed and load and sensor failure. In particular, when using only 25% of the nodes for training, the model can still maintain a diagnostic accuracy of 97.9%, while reducing the peak memory usage to about 19% of that of full-graph training. The above results validate the effectiveness of the proposed method under complex industrial conditions, as well as its practical application potential in resource-constrained scenarios. Full article

When solving partial differential equations (PDEs), traditional Physics-Informed Neural Networks (PINNs) often encounter difficulties in capturing critical physical features and addressing information bias between subdomains. To overcome these limitations, this paper proposes a Dual Adaptive Domain Decomposition Physics-Informed Neural Network (DADD-PINN). The core of this method lies in the construction of a dual-driven architecture that facilitates intra-subdomain feature extraction and inter-subdomain feature coordination. Within each subdomain, the solver’s precision is significantly enhanced by integrating a multi-criterion adaptive sampling strategy with a dynamic weighting mechanism. Experimental results demonstrate that DADD-PINN reduces the optimal $L^2$ error by 1–2 orders of magnitude compared to existing baselines. The model exhibits superior generalization and robustness across various physical fields, offering a new route toward accurate and efficient solutions for complex PDEs. Full article

Background: The outbreak and spreading of antimicrobial resistance (AMR) in a very short time has made most of the old-fashioned antibiotics ineffective, and thus new therapeutic substances have to be developed. The traditional methods of antibiotics discovery are defined by long periods of time, high levels of expenditure, and high rates of failure, which contributes to the necessity of new approaches. Artificial intelligence (AI) has become a disruptive technology that can be used to accelerate and optimize various steps of antibiotic discovery, such as target detection and virtual screening, new molecular design, and early-stage testing. Methods: This review provides an in-depth discussion of the role of AI methodologies in the form of machine learning, deep learning, natural language processing, and generative models in the discovery of small-molecule antibiotics and antimicrobial peptides (AMPs). The major areas that are discussed include virtual screening, pharmacokinetics optimization, resistance mechanism prediction, and AMPs design, which is accompanied by relevant case studies, including the AI-based discovery of Abaucin. Results: The article highlights how AI can be used in a synergistic relationship with synthetic biology, nanotechnology, and multi-omics data as a core component in the next generation of antimicrobial approaches, such as personalized therapy and predictive stewardship. The existing issues, i.e., the lack of data, bias in algorithms, and the translational divide between research and clinical use, are addressed, as well as suggested measures of responsible, collaborative, and ethical AI use. Conclusions: The combination of computational innovation with experimentation validation, AI-driven antibiotic discovery paves the way for a potent and scalable approach in addressing the rising threat of AMR. Full article