Search Results (10,683)

In recent years, the use of UAVs has become increasingly widespread, and the public safety risks posed by unauthorized UAV flights have become increasingly prominent, creating an urgent need for effective detection and identification of UAV targets. However, such targets are small in size, have low contrast, and exhibit an extremely low signal-to-noise ratio; conventional detection methods generally suffer from insufficient feature discrimination, missed detections, and false alarms in complex backgrounds. To address these challenges, this paper proposes a Frequency-Guided Cross-scale Refinement Network (FGCR-Net). Based on an encoder-decoder architecture, this network achieves end-to-end collaborative optimization through cross-layer feature fusion, side-channel prediction refinement, and frequency-domain background suppression. First, a multi-path selective cross-layer fusion module (SCFM) is designed. This module employs coordinated modeling via both channel and spatial paths, supplemented by adaptive weighting with learnable coefficients, to perform differentiated selective fusion of the encoder’s fine-grained features and the decoder’s semantic features, thereby bridging the semantic gap at jump connections; Second, we designed a Cross-Scale Adaptive Fusion Enhancement Attention Module (CAFEM), which cascades multi-receptive-field hollow convolutions, strip pooling, and a bidirectional semantic guidance mechanism to perform cross-scale refinement on the side outputs of each decoder layer, thereby alleviating the issues of blurred boundaries and false alarms caused by inconsistent quality of multi-scale prediction maps and insufficient cross-layer consistency; finally, we design a Frequency-Guided Semantic Enhancement Module (FGSEM), which uses the Fast Fourier Transform (FFT) to decouple encoder features into the frequency domain. By leveraging low-frequency energy to predict the background confidence map and applying spatially selective suppression to high-frequency components, this module distinguishes, from a frequency-domain perspective, the high-frequency responses of complex backgrounds and targets that are highly similar in the spatial domain. Experiments on MSDS-UAV, a self-built multi-scenario UAV dataset for small targets, demonstrate that our method consistently outperforms existing state-of-the-art methods across multiple performance metrics, with Pixel Accuracy, Mean Intersection over Union, and Probability of Detection reaching 92.76%, 70.91%, and 92.69%, respectively; Compared to the baseline model, these three metrics improved by 1.90, 3.20, and 3.76 percentage points, respectively, fully validating the effectiveness and superiority of the proposed method. Full article

A recurrent challenge in scaling ultra-wideband (UWB) motion-capture systems is interference management when many ranging transactions coexist in time and space. To address this, we study a two-layer localization architecture that separates field-level player localization from local on-body pose tracking, allowing the two tasks to operate with different communication regimes and spatial-reuse policies. A stochastic-geometry framework is used to map sport-dependent parameters, including player density, field size, tag count, anchor count, update rates, and ranging airtime, to reliability and update-rate tradeoffs. The analytical model is parameterized using controlled experiments that characterize ranging success under temporal overlap, player distance, and variable-delay scheduling. These measurements inform the design of a proximity-aware local coordination strategy. We apply our proposed approach to soccer, volleyball, and ice hockey as representative use cases. Our results show that proximity-aware coordination can provide a scalable and lightweight interference management mechanism. Coordination is activated only where local player clustering creates strong interference, while spatially separated players continue to share resources without coordination. For the highest-density scenario tested, this increases the local-layer ranging success from below $50\%$ without coordination to over $80\%$ in four- and eight-player congestion clusters, while avoiding network-wide coordination overhead. Full article

Objectives: We aimed to determine quantitative photon-counting CT (PCCT)-derived iodine map (IM) attenuation associated with visual detectability of parenchymal hyperattenuation on conventional brain window (BW) images after mechanical thrombectomy (MT) and to assess corresponding relative BW attenuation changes. Methods: In this retrospective single-center study, 12 patients with anterior circulation large vessel occlusion underwent MT followed by post-interventional PCCT. BW, IM, and virtual non-contrast reconstructions were generated. Follow-up non-contrast CT served as reference for final infarction extent. Of 54 ASPECTS regions with final infarction, 40 showed IM hyperattenuation and were included in the detectability analysis. Two blinded readers assessed visual detectability. IM attenuation and relative BW attenuation increase (%ΔHU) were evaluated using ROC analysis with clustered bootstrap resampling. Thresholds were determined using the Youden index. Results: Across all 54 regions with final infarction, median IM attenuation was significantly higher in infarcted than contralateral regions (6.39 vs. 2.18 HU, p < 0.001), while absolute BW attenuation did not differ significantly. Among the 40 regions with IM hyperattenuation, 15 were visually detectable on BW images. ROC analysis showed excellent performance for IM (AUC = 0.997) and %ΔHU (AUC = 0.984). The optimal IM threshold in the original cohort was 8.33 HU (sensitivity 100%, specificity 96.0%), and the optimal %ΔHU threshold was 3.06% (sensitivity 100%, specificity 92.0%). Bootstrap-derived median thresholds were 11.69 HU (95% interval 8.33–12.67) for IM and 3.06% (3.06–20.16%) for %ΔHU. Inter-reader agreement was excellent (κ = 0.81–0.86). Conclusions: PCCT-derived iodine quantification enables objective assessment of visual detectability of post-interventional parenchymal hyperattenuation. In this cohort, an internally derived IM cutoff of 8.33 HU was associated with BW visibility. These preliminary findings require validation in larger, independent multicenter cohorts. Full article

In contemporary manned–unmanned teaming (MUM-T) systems, the accurate detection of aircraft in Synthetic Aperture Radar (SAR) imagery is crucial for battlefield surveillance and target identification. However, challenges such as background clutter, significant scale variations, and limitations in existing feature extraction methods hinder detection accuracy. To address these issues, this study proposes CCS-Net, a lightweight Cooperative Context-aware Sensing Network designed specifically for SAR aircraft detection. CCS-Net enhances image contrast through Contrast-Limited Adaptive Histogram Equalization (CLAHE) preprocessing and employs a novel C2F_LK module combined with a Multi-scale Context Aggregation (MSCA) module to improve multi-scale feature representation with minimal parameters. An FPN-PAN structure adaptively fuses these features, while the Spatial Coordinate Attention Head (SCA-Head) integrates spatial and coordinate attention mechanisms to emphasize key aircraft regions. Optimized with label smoothing, our model achieves a 96.6% mAP@0.5 and 93.5% precision on the SAR-Aircraft-1.0 dataset with only 0.97M parameters. It generalizes well on the SADD aircraft dataset and cross-domain HRSID and SSDD ship benchmarks, outperforming state-of-the-art methods in both accuracy and lightweight design. Full article

Long-term exposure to polystyrene nanoplastics (PS-NPs) causes neurotoxicity, but the underlying mechanisms remain unclear. We combined network toxicology, molecular docking, and in vivo experiments to investigate the role of MTOR-TFEB-regulated autophagy in PS-NP-induced neurotoxicity. Potential targets related to PS-NPs and neurodegenerative diseases were screened from public databases. Enrichment analysis indicated involvement of neurodegenerative and autophagy pathways. Protein–protein interaction and docking simulations prioritized MTOR as a candidate target. Sprague–Dawley rats were gavaged with PS-NPs (0.15 or 1.5 mg/kg) for 60 days. Morris water maze tests showed impaired spatial learning and memory. Western blotting of hippocampal tissues revealed increased p-MTOR/MTOR ratios, decreased total cytoplasmic and nuclear TFEB, reduced lysosomal proteins (LAMP2, CTSD, and CTSB), elevated autophagy markers SQSTM1 and MAP1LC3B-II, and altered apoptosis regulators (BAX up and BCL2 down). Collectively, PS-NPs disrupt the MTOR-TFEB axis, impair lysosomal function and autophagic clearance, and promote apoptosis, leading to neurocognitive deficits. These findings provide mechanistic insights into the MTOR-TFEB axis and highlight it as a candidate pathway warranting further evaluation as a potential intervention target. Full article

Background and Objectives: Non-invasive mechanical ventilation is the first-line treatment for managing acute respiratory failure. However, patient variability and complex pulmonary mechanics complicate therapy adjustments, frequently leading to ventilator-induced lung injuries. This study aims to propose and define a simulation platform and decision support prototype, named VENTILA2, to optimize pressure-controlled ventilation strategies. Methods: The system integrates a bicompartmental series model of the respiratory system incorporating severity-stratified physiological profiles of chronic obstructive pulmonary disease and acute respiratory distress syndrome, and it is coupled with a Mamdani fuzzy inference system. This architecture maps inspiratory time adjustments based on pressure errors and their derivatives across predefined clinical profiles within a scenario-based feedforward parameter-mapping framework. Results: Evaluated through quantitative operational verification across all profiles and proof-of-concept case studies, the platform successfully recreates complex clinical scenarios, accurately simulating phenomena such as accelerated lung emptying in severe acute respiratory distress syndrome and air trapping in moderate chronic obstructive pulmonary disease. Conclusions: VENTILA2 provides a controlled simulation environment for evaluating pathology-specific ventilatory configurations across simulated profiles prior to clinical implementation, though it remains an early-stage prototype whose clinical effectiveness, safety, and robustness remain to be rigorously evaluated. Full article

Digital image steganography aims to imperceptibly embed secret information into a cover image to enable covert communication. This paper focuses on image-level imperceptibility and recovery quality, and proposes a cost-guided joint mask–perturbation optimization with attentive decoding for image steganography method (CMAD). In an end-to-end differentiable framework, CMAD jointly optimizes the embedding mask, perturbation magnitude, and decoding-network parameters, thereby improving recovery accuracy while preserving imperceptibility. During optimization, the proposed AniCost cost-map guidance mechanism computes pixel-level embedding costs through wavelet-based anisotropy analysis, and uses a probability-map guidance loss to directly encourage the mask to activate in complex-texture regions and deactivate in smooth regions. The channel-attention-based decoding network is fine-tuned for each image pair during optimization to adapt to the current pair. Experimental results show that the stego images generated by CMAD achieve PSNR values of 55–59 dB, while the recovered secret images achieve PSNR values of 35–40 dB. Full article

When a water treatment plant, power grid, or pipeline is compromised, the consequences extend beyond data loss: a manipulated sensor reading can trigger physical damage, and a disabled safety interlock can endanger lives. Cyber–physical systems (CPSs) sit at this intersection of digital control and physical process, yet existing security reviews treat threats and defences in separate silos, leaving practitioners without a clear picture of which defences fail against which attacks, and why. This paper fills that gap with a structured narrative review of 82 sources (70 from the primary window January 2020 to April 2026, plus 12 foundational pre-2020 works), organised through the CPS Defence-Gap Taxonomy (CPS-DGT)—a framework that classifies 14 attack mechanisms by architectural layer and physical impact, evaluates six defensive technology categories against documented failure modes, and maps five governance dimensions to the institutional conditions required for deployment. Across five intrusion detection system (IDS) studies that differ in dataset, attack selection, training regime and evaluation scope, reported F1 scores lie between 0.796 and 0.969 under each study’s own standard conditions; these values are not a controlled comparison and are reported descriptively. For the one architecture evaluated under adversarial evasion, F1 falls by 37.4 percentage points in absolute terms, a relative reduction of 38.6%. The defence-gap matrix identifies seven entries with insufficient coverage. Five of the 14 attack mechanisms are uncovered: A03, A09, A10, A11 and A14. Two further mechanisms, A01 and A12, have only partial defences. Adversarial evasion of learned detectors is reported separately as a transversal failure mode of one defensive category rather than as an attack mechanism. The uncovered mechanisms cluster at the cyber–physical boundary and in supply-chain channels. We conclude with five concrete research challenges, each with a direct path from the identified gap to a tractable research agenda. Full article

The reproductive stage is a critical time in a plant’s life history when dealing with heat stress as the specific processes of meiosis, gametogenesis, anthesis, pollination, fertilization, and early seed development all occur within comparatively narrow temperature limits. Much of the importance of flowering is tied directly to temperature, both for the timing of reproductive transition and the ability for male and female reproductive tissues to survive exposure to damaging heat. Ambient-temperature sensing is linked to flowering via regulatory modules that involve phytochrome B, EARLY FLOWERING 3 (ELF3), PHYTOCHROME INTERACTING FACTOR 4 (PIF4), FLOWERING LOCUS T (FT), FLOWERING LOCUS M (FLM), SHORT VEGETATIVE PHASE (SVP), and the light–circadian components. Conversely, when temperature is harmful, protective responses involve other cellular mechanisms such as activation of heat-shock transcription factors (HSFs), heat-shock proteins (HSPs), endoplasmic-reticulum protein quality control, calcium and reactive oxygen species (ROS) signaling, antioxidant systems, hormone regulation, metabolic reprogramming, autophagy and DNA-repair pathways. Male reproductive development is often very sensitive, especially at meiosis, during formation of the tetrad, microspore development, during the maturation of pollen, and during the growth of the pollen tubes, although injury to pistils, ovules and the post-fertilization tissues may solely have an effect on the restriction of fertilization and seed set. Phenological heat escape and intrinsic reproductive thermotolerance are genetically separable but can be complementary aspects of adaptation, as revealed by natural allelic variation, QTL mapping, genomic prediction and marker assisted selection. This review summarizes molecular, genetic and physiological evidence, to propose that, to ensure stable yields at high temperatures, there is a need to coordinate optimization of reproductive timing, cell and development thermotolerance, and post-fertilization sink stability. Full article

Supraventricular tachycardias (SVTs), including atrial fibrillation (AF), atrioventricular nodal re-entrant tachycardia (AVNRT), atrioventricular re-entrant tachycardia (AVRT), and focal atrial tachycardias, can possibly arise from the interaction of genetic predisposition and environmental exposures. While genome-wide association studies (GWASs) have identified 525 loci for atrial fibrillation and a small number of loci for AVNRT and accessory pathway-mediated tachycardia, the contribution of air pollution, lifestyle factors and psychosocial stress to epigenetic remodeling of atrial tissue remains insufficiently integrated into current mechanistic models of arrhythmogenesis. This review aims to synthesize evidence on how environmental exposures, including PM_2.5, NO₂, ozone, tobacco smoke, obesity, alcohol use, and physical inactivity, modulate epigenetic pathways relevant to SVT susceptibility, and to evaluate whether AF-derived epigenetic insights can be extrapolated to other SVTs. Thus, a narrative synthesis was conducted across studies examining environmental determinants, epigenetic mechanisms (DNA methylation, histone modifications, non-coding RNAs), and genetic susceptibility in SVTs. Literature from cardiac tissue studies, circulating epigenetic biomarker analyses, and mechanistic AF models was integrated to construct a unified gene–environment–epigenome framework. In atrial fibrillation, environmental exposures are consistently associated with epigenetic alterations affecting atrial electrophysiology, inflammation, oxidative stress and structural remodeling, and air pollutants and lifestyle factors modulate methylation signatures, histone-modifying enzymes and microRNA networks implicated in atrial conduction and re-entry. For AVNRT, AVRT and focal atrial tachycardia the evidential position is different. Large prospective cohort and case-crossover analyses now link air pollution to incident and acute supraventricular tachycardia, and genome-wide association studies have identified susceptibility loci for AVNRT and for accessory-pathway-mediated tachycardia, including one gene encoding a cardiac chromatin-remodeling protein; but no epigenomic profiling of nodal or accessory-pathway tissue has been reported, and no study has measured an environmental exposure, an atrial epigenetic mark and a non-AF SVT endpoint in the same participants. Twin data indicate that approximately 35% of SVT risk is attributable to genetic and 65% to unique environmental factors, which motivates a gene–environment–epigenome framework without validating its mechanistic detail outside AF. Mapping GWAS-identified loci onto environmentally responsive regulatory pathways identifies candidate convergence points between inherited risk and exposure-driven remodeling; for non-AF SVT, these are designated working hypotheses rather than established mechanisms. Air pollution and lifestyle factors are associated with supraventricular arrhythmia across the spectrum, and in atrial fibrillation there is direct evidence that they act, in part, through epigenetic reprogramming of atrial tissue. No epigenetic panel has been prospectively validated for the prediction of any supraventricular arrhythmia, and precision risk stratification therefore remains a research objective rather than a near-term clinical horizon. Integrating environmental exposure data with genetic and epigenomic profiling is nonetheless the most plausible route toward it. Future priorities include exposure-stratified, cell-resolved epigenomic profiling of atrial and nodal tissue, prospective validation of candidate circulating markers against incident arrhythmia, and replication in non-European populations and in both sexes. Full article

The climate crisis exacerbates structural inequalities and disproportionately affects women, particularly in rural and low-income contexts where limited access to resources, education and financing undermines their adaptive capacity. This article examines how gender considerations are integrated into European Union (EU) climate adaptation governance, combining doctrinal analysis of EU and international legal frameworks with a bibliometric analysis of the scientific literature. The bibliometric component maps research trends and thematic gaps on gender-responsive climate adaptation. The doctrinal analysis focuses on the European Climate Law, the EU Gender Equality Strategies and selected national instruments, with Spain’s 2021–2030 PNIEC and PNACC as illustrative case studies. These frameworks are assessed against five human-rights-based criteria: substantive equality and intersectionality, participation and empowerment, accountability and access to justice, data and monitoring, and financing and institutional arrangements. The findings reveal a structural “transformation deficit” between normative commitments and operational reality: core climate instruments lack robust requirements on gender impact assessments, sex-disaggregated data, women’s participation and gender-responsive budgeting. The study argues that a truly transformative Gender-Responsive Climate Adaptation (GRCA) in the EU requires moving beyond symbolic references toward binding gender-responsive governance mechanisms derived from CEDAW and EU primary law, capable of dismantling structural barriers and ensuring an environmentally effective and substantively just transition. Full article

Artificial intelligence (AI) is embedded in healthcare through decision support, imaging, documentation, monitoring, digital twins, and smart-hospital infrastructures. This scoping review mapped technologies, healthcare contexts, socio-technical dimensions, governance mechanisms, and implementation conditions of AI-enabled healthcare systems. The review followed PRISMA-ScR. Scopus, Web of Science Core Collection, PubMed, and IEEE Xplore were searched on 1 July 2026 for English-language sources published from 2021 to 2026. All four authors participated in source selection; each record was assessed by two reviewers, and disagreements were resolved by consensus. Data were charted in matrices and synthesized descriptively and thematically. Of 2422 records, 426 duplicates were removed and 1996 were screened. Among 185 full-text reports, 124 were excluded, including 18 for insufficient methodological or empirical information, and 61 were included. Included sources then underwent a complementary seven-criterion cross-design appraisal scored from 1 to 3, without altering the final corpus. Technologies included machine learning, deep learning, decision support, explainable AI, natural language processing, large language models, interoperability frameworks, blockchain/IoMT, and digital twins. Challenges involved validation, data quality, interoperability, accountability, privacy, security, explainability, trust, bias, equity, and workforce readiness. Reported implementation facilitators included interoperable infrastructure, participatory design, lifecycle governance, continuous validation, and context-sensitive implementation. Full article

This scoping review maps the available literature aimed at identifying the most appropriate technique for suturing pelvic flexure enterotomies in horses. A comprehensive search of Web of Science, Google Scholar, and the National Center for Biotechnology Information was conducted for studies published between January 1996 and February 2026, using terms related to pelvic flexure enterotomy and equine patients. From 22,146 records, only five ex vivo studies on healthy equine colon specimens met the inclusion criteria. The studies compared variables such as suturing time, suture pattern, material, bursting pressure, luminal diameter reduction, and cost. Double-layer hand-sewn closure generally achieved the highest bursting pressures, although it required longer operative time, while TA90 stapling was the most expensive option. Because ex vivo models cannot reproduce tissue healing or host–construct interactions, the clinical applicability of the available evidence remains limited. Nonetheless, a full-thickness simple continuous pattern oversewn with a continuous Cushing pattern appears to offer the most dependable mechanical security until stronger in vivo evidence becomes available. Full article

Karst piedmont fault overflow springs are widely developed in structurally controlled, basin-margin settings, where basin-bounding faults obstruct regional groundwater flow. However, the coupled fault blocking, fault conduction, and caprock sealing mechanisms governing their genesis remain insufficiently quantified. This study investigates the Shentou Spring system in Shanxi Province, North China—a typical piedmont fault overflow spring—to develop a three-dimensional genetic model characterised by coupled blocking, conduction, and overflow processes. Integrating borehole datasets, multi-year groundwater level monitoring records, hydrochemical and isotopic measurements, and detailed structural mapping, this study identifies three key controlling mechanisms. First, spatial variations in the throw of the Mayi Fault control fault blocking efficiency, partitioning the fault zone into complete barrier and semi-permeable segments, which underpins the incomplete drainage behaviour of the spring system. Second, the Gengzhuang Fault intersects the high-permeability Qilihe and Yuanzihe groundwater flow zones, acting as the primary conduit that transports groundwater from distant recharge areas to the discharge zone. Third, the Quaternary caprock in the discharge area features a critical thickness threshold of approximately 30 m; confined karst groundwater breaches the overlying caprock and forms spring outlets where caprock thickness falls below this threshold. The proposed tripartite coupled model provides a semi-quantitative framework for interpreting the genesis of piedmont fault overflow springs. In practical terms, it supports the delineation of fault-conduit protection zones and the design of long-term water-quality monitoring networks along fault-controlled flow paths. Full article

Accurate and automated segmentation of ischemic stroke lesions from magnetic resonance imaging (MRI) data is crucial for rapid clinical diagnosis, treatment planning, and patient prognosis. This study presents a comprehensive evaluation of various deep learning architectures for segmenting acute and subacute ischemic lesions using the multicenter ISLES 2022 dataset. We compare traditional 3D U-Net models, attention mechanisms, and modern hybrid architectures, specifically Swin-UNETR (Swin Transformer-based UNet TRansformer). Our experiments utilize multimodal MRI data, including diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) maps. Results demonstrate that the Swin-UNETR architecture, combined with a specialized pad or crop preprocessing pipeline, advanced data augmentation using the TorchIO library, and a combined Dice–Cross Entropy (DiceCE) loss function, provides competitive performance. The best Swin-UNETR model achieved a Dice of 0.717, precision of 0.793, recall of 0.708, and HD95 of 12.24 mm on the validation set, with a loss of 0.26. The baseline 3D U-Net achieved a comparable Dice of 0.726, while Swin-UNETR showed higher precision, recall, and lower HD95. This study highlights the effectiveness of integrating hierarchical transformers with convolutional decoders for extracting both local and global contextual information in complex medical imaging tasks. Full article