Search Results (275)

Natural disasters can disrupt communication services, leading to severe consequences in emergencies. Maintaining connectivity and communication quality during crises is crucial for coordinating rescues, providing critical information, and ensuring reliable and secure service. This study proposes FedResilience, a Federated Learning (FL) system for classifying Long-Term Evolution (LTE) network coverage in both normal operation and natural disaster scenarios. A three-tier architecture is implemented: (i) edge nodes, (ii) a central aggregation server, and (iii) a batch processing interface. Five FL aggregation methods (FedAvg, FedProx, FedAdam, FedYogi, and FedAdagrad) were evaluated under normal conditions and disaster simulations. The results show that FedAdam outperforms the other methods under normal conditions, achieving an F1 score of 0.7271 and a Global System Adherence ($S{A}_{\mathrm{global}}$) of 91.51%. In disaster scenarios, FedProx was superior, with an F1 score of 0.7946 and $S{A}_{\mathrm{global}}$ of 61.73%. The innovation in this study is the introduction of the System Adherence (SA) metric to evaluate the predictive fidelity of the model. The system demonstrated robustness against Non-Independent and Identically Distributed (non-IID) data distributions and the ability to handle significant class imbalances. FedResilience serves as a tool for companies to implement automated corrective actions, contributing to the predictive maintenance of LTE networks through FL while preserving data privacy. Full article

High-resolution pushbroom satellites routinely acquire multi-tenskilometer-scale strips whose vendors’ rational polynomial coefficients (RPCs) exhibit systematic, direction-dependent biases that accumulate downstream when ground control is sparse. This study presents a physically interpretable stripwise extrapolation framework that predicts along- and across-track RPC correlation coefficients for inaccessible segments from an upstream calibration subset. Terrain-independent RPCs were regenerated and residual image-space errors were modeled with weighted least squares using elapsed time, off-nadir evolution, and morphometric descriptors of the target terrain. Gaussian kernel weights favor calibration scenes with a Jarque–Bera-indexed relief similar to the target. When applied to three KOMPSAT-3A panchromatic strips, the approach preserves native scene geometry while transporting calibrated coefficients downstream, reducing positional errors in two strips to <2.8 pixels (~2.0 m at 0.710 m Ground Sample Distance, GSD). The first strip with a stronger attitude drift retains 4.589 pixel along-track errors, indicating the need for wider predictor coverage under aggressive maneuvers. The results clarify the directional error structure with a near-constant across-track bias and low-frequency along-track drift and show that a compact predictor set can stabilize extrapolation without full-block adjustment or dense tie networks. This provides a GCP-efficient alternative to full-block adjustment and enables accurate georeferencing in controlled environments. Full article

Background/Objectives: Influenza A viruses—including highly pathogenic H5N1—remain a global threat due to rapid evolution, zoonoses, and pandemic potential. Strain-specific vaccines targeting variable antigens often yield limited, short-lived immunity. The HA receptor-binding domain (RBD), a functionally constrained and immunologically relevant region, is a promising target for broad and subtype-focused vaccines. We aimed to design multiepitope constructs targeting conserved HA-RBD and adjacent domains to elicit robust, durable, cross-protective responses. Methods: Extensive sequence analyses (>20,000 H5N1 and >190,000 influenza A sequences) were used to derive consensus sequences. Three HA-based candidates were developed: (i) EpitoCore-HA-VX, a multi-epitope construct containing CTL, HTL, and B-cell epitopes from the H5N1 HA-RBD; (ii) StructiRBD-HA-VX, incorporating a conformationally preserved RBD segment; and (iii) FusiCon-HA-VX, targeting the conserved HA fusion peptide shared across subtypes. Two external HA comparators—a 400-aa HA fragment and the literature-reported HA-13–263-Fd-His—were analyzed under the same pipeline. The workflow predicted epitopes; evaluated antigenicity, allergenicity, toxicity, conservation, and HLA coverage; generated AlphaFold models; performed TLR2/TLR4 docking with pyDockWEB; and carried out interface analysis with PDBsum; and C-ImmSim simulations. Results: Models suggested stable, energetically favorable TLR2/TLR4 interfaces supported by substantial binding surfaces and complementary electrostatic/desolvation profiles. Distinct docking patterns indicated receptor-binding flexibility. Immune simulations predicted strong humoral responses with modeled memory formation and, for the H5N1-focused designs, cytotoxic T-cell activity. All candidates and comparators were predicted to be antigenic, non-allergenic, and non-toxic, with combined HLA coverage approaching global breadth. Conclusions: This study compares three design strategies within a harmonized framework—epitope collation, structure-preserved RBD, and fusion-peptide targeting—while benchmarking against two HA comparators. EpitoCore-HA-VX and StructiRBD-HA-VX showed promise against diverse H5N1 isolates, whereas FusiCon-HA-VX supported cross-subtype coverage. As these findings are model-based, they should be interpreted qualitatively; nonetheless, the integrated, structure-guided approach provides an adaptable path for advancing targeted H5N1 and broader influenza A vaccine concepts. Full article

Colorectal cancer (CRC) is driven by a complex spectrum of somatic mutations and structural variants that contribute to tumor heterogeneity and therapy resistance. In this study, we performed a comparative analysis of short-read Illumina and long-read Nanopore sequencing technologies across multiple CRC sample groups, encompassing diverse tissue morphologies. Our evaluation included general base-level metrics—such as nucleotide ratios, sequence match rates, and coverage—as well as variant calling performance, including variant allele frequency (VAF) distributions and pathogenic mutation detection rates. Focusing on clinically relevant genes (KRAS, BRAF, TP53, APC, PIK3CA, and others), we characterized platform-specific detection profiles and completed the ground truth validation of somatic KRAS and BRAF mutations. Structural variant (SV) analysis revealed Nanopore’s enhanced ability to resolve large and complex rearrangements, with consistently high precision across SV types, though recall varied by variant class and size. To enable direct comparison with the Illumina exome panel, we applied an exonic position reference file. To assess the impact of depth and PCR amplification, we completed an additional high-coverage Nanopore sequencing run. This analysis confirmed that PCR-free protocols preserve methylation signals more accurately, reinforcing Nanopore’s utility for integrated genomic and epigenomic profiling. Together, these findings underscore the complementary strengths of short- and long-read sequencing platforms in high-resolution cancer genomics, and we highlight the importance of coverage normalization, epigenetic fidelity, and rigorous benchmarking in variant discovery. Full article

The kuruma shrimp (Penaeus japonicus), a globally high-value aquaculture species, faces critical challenges in sustainable development due to genetic diversity degradation and declining disease resistance. This study employed whole-genome resequencing (WGRS) to systematically assess genetic diversity, population structure, and core germplasm characteristics across 20 geographically distinct populations from Zhejiang, Fujian (China), and introduced Japanese stocks. Using 343.40 Gb of high-quality sequencing data (average depth: 12.44×), we identified 9,146,248 single nucleotide polymorphisms (SNPs), with 6.32% located in exon regions, while intergenic (56.75%) and intronic regions (30.99%) showed the highest polymorphism density. Principal component analysis (PCA) and phylogenetic tree construction revealed two major clades: Fujian (FJ) and Japan-introduced (RB) populations clustered closely due to shared artificial breeding backgrounds, whereas Zhejiang (XS) and Fujian (LS) populations displayed genetic heterogeneity driven by adaptive divergence. Core germplasm screening via the CoreHunter algorithm selected four representative individuals (FJ4-M, LS1-M, XS1-M, XS6-M), with the modified Rogers’ distance (0.34) and allele coverage (0.93) confirming effective preservation of original genetic diversity. This study provides genomic insights and technical frameworks for germplasm conservation, precision breeding, and genetic improvement in P. japonicus. Full article

Numerous imaging-based methods have been proposed for artifact monitoring and preservation, yet most rely on fixed-angle cameras or robotic platforms, leading to high cost and complexity. In this study, a portable monocular camera pose estimation and calibration framework is presented to capture artifact images from consistent viewpoints over time. The system is implemented on a Raspberry Pi integrated with a controllable three-axis gimbal, enabling untethered operation. Three methodological innovations are proposed. First, ORB feature extraction combined with a quadtree-based distribution strategy is employed to ensure uniform keypoint coverage and robustness under varying illumination conditions. Second, on-device processing is achieved using a Raspberry Pi, eliminating dependence on external power or high-performance hardware. Third, unlike traditional fixed setups or multi-degree-of-freedom robotic arms, real-time, low-cost calibration is provided, maintaining pose alignment accuracy consistently within three pixels. Through these innovations, a technically robust, computationally efficient, and highly portable solution for artifact preservation has been demonstrated, making it suitable for deployment in museums, exhibition halls, and other resource-constrained environments. Full article

Feature map pooling in convolutional neural networks (CNNs) serves the dual purpose of reducing spatial dimensions and enhancing feature invariance. Current pooling approaches face a fundamental trade-off: deterministic methods (e.g., MaxPool and AvgPool) lack regularization benefits, while stochastic approaches introduce beneficial randomness but can suffer from sampling biases and may require careful hyperparameter tuning (e.g., S3Pool). To address these limitations, this paper introduces LDS3Pool, a novel pooling method that leverages low-discrepancy sequences (LDSs) for quasi-random spatial sampling. LDS3Pool first linearizes 2D feature maps to 1D sequences using Hilbert space-filling curves to preserve spatial locality, then applies LDS-based sampling to achieve quasi-random downsampling with mathematical guarantees of uniform coverage. This framework provides the regularization benefits of randomness while maintaining comprehensive feature representation, without requiring sensitive hyperparameter tuning. Extensive experiments demonstrate that LDS3Pool consistently outperforms baseline methods across multiple datasets and a range of architectures, from classic models like VGG11 to modern networks like ResNet18, achieving significant accuracy gains with moderate computational overhead. The method’s empirical success is supported by a rigorous theoretical analysis, including a quantitative evaluation of the Hilbert curve’s superior, size-independent locality preservation. In summary, LDS3Pool represents a theoretically sound and empirically effective pooling method that enhances CNN generalization through a principled, quasi-random sampling framework. Full article

Low-light image enhancement, especially for remote sensing images, remains a challenging task due to issues like low brightness, high noise, color distortion, and the unique complexities of remote sensing scenes, such as uneven illumination and large coverage. Existing methods often struggle to balance efficiency, accuracy, and robustness. Diffusion models have shown potential in image restoration, but they often rely on multi-step noise estimation, leading to inefficiency. To address these issues, this study proposes an enhancement framework based on a lightweight encoder–decoder and a physical-prior-guided end-to-end single-step residual diffusion model. The lightweight encoder–decoder, tailored for low-light scenarios, reduces computational redundancy while preserving key features, ensuring efficient mapping between pixel and latent spaces. Guided by physical priors, the end-to-end trained single-step residual diffusion model simplifies the process by eliminating multi-step noise estimation through end-to-end training, accelerating inference without sacrificing quality. Illumination-invariant priors guide the inference process, alleviating blurriness from missing details and ensuring structural consistency. Experimental results show that it not only demonstrates superiority over mainstream methods in quantitative metrics and visual effects but also achieves a 20× speedup compared with an advanced diffusion-based method. Full article

Background: The annular ligament is a key secondary stabilizer of the elbow, but its biomechanical behavior during forearm rotation has not been objectively quantified. This study aimed to assess interindividual variability in annular ligament tension, validate prior arthroscopic observations, and explore associations with chondral lesions in the lateral elbow compartment. Methods: In this cross-sectional anatomical study, 25 cadaveric upper limbs were analyzed following standardized dissection, preserving ligamentous and muscular integrity. Ligament displacement was measured using a custom mechanical apparatus and high-precision digital micrometer in neutral, 60° pronation, and 60° supination positions under axial tractions of 1, 2, and 3 kg. Ulnar length and presence of chondral lesions were also recorded. Results: Maximal ligament displacement occurred in supination in 80% of specimens (mean: 1.23 mm at 3 kg; range: 0.30–2.87 mm), indicating considerable interindividual variation. Significant displacement differences were observed between all forearm positions across load levels (p < 0.001). Chondral lesions were identified in three specimens with marked ligament laxity and reduced radial head coverage. Conclusions: This study provides the first objective evidence of annular ligament tension variability during forearm rotation. Ligament laxity may contribute to lateral elbow instability and cartilage degeneration, supporting the ligament’s role as a secondary stabilizer. Full article

With the continuous advancement of on-orbit services and space intelligence sensing technologies, the efficient and accurate identification of spacecraft components has become increasingly critical. However, complex lighting conditions, background interference, and limited onboard computing resources present significant challenges to existing segmentation algorithms. To address these challenges, this paper proposes a lightweight spacecraft component segmentation framework for on-orbit applications, termed M4MLF-YOLO. Based on the YOLOv5 architecture, we propose a refined lightweight design strategy that aims to balance segmentation accuracy and resource consumption in satellite-based scenarios. MobileNetV4 is adopted as the backbone network to minimize computational overhead. Additionally, a Multi-Scale Fourier Adaptive Calibration Module (MFAC) is designed to enhance multi-scale feature modeling and boundary discrimination capabilities in the frequency domain. We also introduce a Linear Deformable Convolution (LDConv) to explicitly control the spatial sampling span and distribution of the convolution kernel, thereby linearly adjusting the receptive field coverage range to improve feature extraction capabilities while effectively reducing computational costs. Furthermore, the efficient C3-Faster module is integrated to enhance channel interaction and feature fusion efficiency. A high-quality spacecraft image dataset, comprising both real and synthetic images, was constructed, covering various backgrounds and component types, including solar panels, antennas, payload instruments, thrusters, and optical payloads. Environment-aware preprocessing and enhancement strategies were applied to improve model robustness. Experimental results demonstrate that M4MLF-YOLO achieves excellent segmentation performance while maintaining low model complexity, with precision reaching 95.1% and recall reaching 88.3%, representing improvements of 1.9% and 3.9% over YOLOv5s, respectively. The mAP@0.5 also reached 93.4%. In terms of lightweight design, the model parameter count and computational complexity were reduced by 36.5% and 24.6%, respectively. These results validate that the proposed method significantly enhances deployment efficiency while preserving segmentation accuracy, showcasing promising potential for satellite-based visual perception applications. Full article

Cloud coverage and obstruction significantly affect the usability of remote sensing images, making cloud detection a key prerequisite for optical remote sensing applications. In existing cloud detection methods, using U-shaped convolutional networks alone has limitations in modeling long-range contexts, while Vision Transformers fall short in capturing local spatial features. To address these issues, this study proposes a dual-branch framework, TransCNet, which combines Transformer and CNN architectures to enhance the accuracy and effectiveness of cloud detection. TransCNet addresses this by designing dual encoder branches: a Transformer branch capturing global dependencies and a CNN branch extracting local details. A novel feature aggregation module enables the complementary fusion of multi-level features from both branches at each encoder stage, enhanced by channel attention mechanisms. To mitigate feature dilution during decoding, aggregated features compensate for information loss from sampling operations. Evaluations on 38-Cloud, SPARCS, and a high-resolution Landsat-8 dataset demonstrate TransCNet’s competitive performance across metrics, effectively balancing global semantic understanding and local edge preservation for clearer cloud boundary detection. The approach resolves key limitations in existing cloud detection frameworks through synergistic multi-branch feature integration. Full article

Clean water security demands autonomous systems that sense, reason, and act at scale. We introduce AquaSurveil, a unified multi-agent platform coupling mobile robots, fixed IoT nodes, and privacy-preserving machine learning for continent-scale water health surveillance. The architecture blends Gaussian-process mapping with distributed particle filtering, multi-agent deep-reinforcement Voronoi coverage, GAN/LSTM anomaly detection, and sheaf-theoretic data fusion; components are tuned by Bayesian optimization and governed by Age-of-Information-aware power control. Evaluated on a 2.82-million-record dataset (1940–2023; five countries), AquaSurveil achieves up to 96% spatial-coverage efficiency, an ROC-AUC of 0.96 for anomaly detection, ≈95% state-estimation accuracy, and reduced energy consumption versus randomized patrols. These results demonstrate scalable, robust, and energy-aware water quality surveillance that unifies robotics, the IoT, and modern AI. Full article

Background: Propeller perforator flaps (PPFs) have gained increasing popularity in lower limb reconstruction. While their use in elective settings is well described, their role in acute post-traumatic reconstruction remains less defined. Methods: A systematic review was conducted following PRISMA 2020 guidelines. PubMed, Scopus, and Cochrane Library were searched on 2 June 2025, for studies reporting on the use of propeller flaps in lower limb reconstruction after trauma. Only studies rated as “good” quality using the NIH quality assessment tool were included. Data on anatomical location, flap survival, complications, reinterventions, and functional and patient-reported outcomes were extracted and analyzed descriptively. Results: Twenty-eight studies published between 2008 and 2024 were included, accounting for 619 propeller flaps in a population of 838 patients. The majority of flaps were fasciocutaneous, with the posterior tibial artery being the most commonly used source vessel. Among the flaps included, 422 (68.2%) achieved complete survival without necrosis, 84 (13.6%) developed partial necrosis, and 23 (3.7%) failed completely. Considering all flaps that remained viable after any required revisions or conservative management, the overall survival rate was 97%. Venous congestion was the leading cause of flap compromise. The overall complication rate was 21.8%, increasing to 35.1% in acute trauma cases. A statistically significant correlation was found between wide rotation angles (≥150°) and higher complication rates (p = 0.015). The mean follow-up duration was 12.5 months. Functional and aesthetic outcomes were poorly reported, but when available, they were generally favorable. Conclusions: PPFs represent a valuable option for lower limb reconstruction, providing reliable coverage while preserving major vascular axes. Their application in acute trauma settings appears promising, although current evidence is limited by small verified cohorts and predominantly retrospective study designs. Despite higher complication rates in acute cases, flap survival remains consistently high, supporting their use in carefully selected patients. Further prospective studies with standardized outcome reporting are needed to clarify long-term functional results and refine selection strategies. Full article

With the evolution of 6G technology toward global coverage and multidimensional integration, OTFS modulation has become a research focus due to its advantages in high-mobility scenarios. However, existing OTFS signal detection algorithms face challenges such as pilot contamination, Doppler spread degradation, and diverse interference in complex environments. This paper proposes the SPARSE-OTFS-Net algorithm, which establishes a comprehensive signal detection solution by innovatively integrating sparse random pilot design, compressive sensing-based frequency offset estimation with closed-loop cancellation, and joint denoising techniques combining an autoencoder, residual learning, and multi-scale feature fusion. The algorithm employs deep learning to dynamically generate non-uniform pilot distributions, reducing pilot contamination by 60%. Through orthogonal matching pursuit algorithms, it achieves super-resolution frequency offset estimation with tracking errors controlled within 20 Hz, effectively addressing Doppler spread degradation. The multi-stage denoising mechanism of deep neural networks suppresses various interferences while preserving time-frequency domain signal sparsity. Simulation results demonstrate: Under large frequency offset, multipath, and low SNR conditions, multi-kernel convolution technology achieves significant computational complexity reduction while exhibiting outstanding performance in tracking error and weak multipath detection. In 1000 km/h high-speed mobility scenarios, Doppler error estimation accuracy reaches ±25 Hz (approaching the Cramér-Rao bound), with BER performance of 5.0 × 10⁻⁶ (7× improvement over single-Gaussian CNN’s 3.5 × 10⁻⁵). In 1024-user interference scenarios with BER = 10⁻⁵ requirements, SNR demand decreases from 11.4 dB to 9.2 dB (2.2 dB reduction), while maintaining EVM at 6.5% under 1024-user concurrency (compared to 16.5% for conventional MMSE), effectively increasing concurrent user capacity in 6G ultra-massive connectivity scenarios. These results validate the superior performance of SPARSE-OTFS-Net in 6G ultra-massive connectivity applications and provide critical technical support for realizing integrated space–air–ground networks. Full article

Wireless sensor networks (WSNs) are fundamental to applications in the Internet of Things, smart cities, and environmental monitoring, where coverage optimization is critical for maximizing monitoring efficacy under constrained resources. Conventional approaches often suffer from low global coverage efficiency, high computational overhead, and a tendency to converge to local optima. To address these challenges, this study proposes the fusion multi-strategy gray wolf optimizer (FMGWO), an advanced variant of the Gray Wolf Optimizer (GWO). FMGWO integrates various strategies: electrostatic field initialization for uniform population distribution, dynamic parameter adjustment with nonlinear convergence and differential evolution scaling, an elder council mechanism to preserve historical elite solutions, alpha wolf tenure inspection and rotation to maintain population vitality, and a hybrid mutation strategy combining differential evolution and Cauchy perturbations to enhance diversity and global search capability. Ablation studies validate the efficacy of each strategy, while simulation experiments demonstrate FMGWO’s superior performance in WSN coverage optimization. Compared to established algorithms such as PSO, GWO, CSA, DE, GA, FA, OGWO, DGWO1, and DGWO2, FMGWO achieves higher coverage rates with fewer nodes—up to 98.63% with 30 nodes—alongside improved convergence speed and stability. These results underscore FMGWO’s potential as an effective solution for efficient WSN deployment, offering significant implications for resource-constrained optimization in IoT and edge computing systems. Full article