Search Results (7,835)

Quadrotor motion planning in cluttered environments presents significant challenges in achieving both computational efficiency and trajectory smoothness, particularly in low-altitude economy and intelligent energy system applications where autonomous aerial vehicles perform infrastructure inspection and power line monitoring. Many existing methods either rely on sampling-based algorithms that suffer from long computation times and suboptimal paths, or employ trajectory representations that produce high-order derivative discontinuities unsuitable for agile flight. In this work, we propose an efficient hierarchical motion planning framework that integrates a JPS–Bresenham-based path search with safe flight corridor construction and Bézier curve optimization. Our approach addresses trajectory generation through a two-stage process: a front-end path search that efficiently identifies collision-free paths with reduced waypoints, followed by a back-end optimization that leverages convex safe corridors with overlapping regions to expand the solution space. Through comprehensive benchmark experiments across six different map scenarios, we demonstrate that our method outperforms RRT* and PRM in both path quality and computational efficiency. Monte Carlo experiments across varying map sizes and obstacle densities confirm robustness and scalability advantages. Comparative studies with state-of-the-art planners demonstrate superior success rates and cost efficiency while maintaining strict kinodynamic feasibility. The Bézier-based optimization reduces snap integral by up to 55% compared to ordinary polynomial approaches, demonstrating its superiority for fast quadrotor trajectory planning in complex environments. Full article

Background/Objectives: Early autism screening needs to be both accurate and privacy-preserving, but single-source assessments can miss clinically important context. We therefore study a preliminary integrated framework that combines privacy-preserving questionnaire-based risk estimation with a second reasoning component based on a large language model (LLM) that evaluates symptom narratives. The objective is to test whether structured screening outputs can be translated into uncertainty-aware narrative reasoning within one privacy-conscious workflow. Methods: The proposed pipeline links a PATE-style AQ-10 screening stage to a MedPrompt-style consensus reasoning stage that operates on behavioral summaries and transcript-style inputs. Evaluation includes component-wise testing on AQ-10 data, an end-to-end controlled setting, synthetic stress testing, and transcript-only analysis on 26 examples. Results: In component-wise evaluation, the combined pipeline reaches ceiling performance on a controlled AQ-10 split, synthetic stress testing reduces accuracy to 97.2%, and transcript-only testing shows that contextual factors such as age substantially improve sensitivity. Conclusions: These findings support only a highly preliminary proof-of-concept under constrained evaluation conditions and should be interpreted as motivation for broader external validation rather than as evidence of practical decision-support readiness across settings. Full article

Accurate absolute radiometric calibration is critical for ensuring the data quality of spaceborne Synthetic Aperture Radar (SAR) systems and supporting quantitative remote sensing applications. Absolute radiometric calibration generally relies on ground reference targets with known radar cross-section (RCS) deployed at dedicated calibration sites. Such ground-based calibration methods are costly and time-consuming, and calibration frequency is constrained by the distribution of calibration sites and the satellite revisit cycles. Additionally, for specialized SAR missions, such as deep space exploration, deploying calibration equipment on the observed extraterrestrial surface is infeasible. This study proposes a space-based absolute calibration concept using a small calibration satellite carrying a well-characterized reference (e.g., a passive reflector or an active transponder) and flying in formation with the SAR satellite. The relative motion ensures a side-looking acquisition geometry, enabling the SAR to image the accompanying target and derive calibration factors. The overall calibration process is divided into two stages: determination of an in-orbit calibration factor using the calibration satellite, followed by its transformation to accommodate ground imaging conditions. This method effectively isolates the radar system gain to characterize the intrinsic hardware response. Furthermore, by operating entirely in space, it avoids atmospheric and ground-clutter distortions, ensuring a fully space-based, end-to-end calibration process dominated primarily by sensor systematic errors. Moreover, it allows for more frequent and flexible calibration, eliminating reliance on ground calibration sites and infrastructure. The feasibility and advantages of the proposed concept are demonstrated through comprehensive simulations, covering orbit analysis, echo simulation, and image processing. Full article

Background: Elevated anticardiolipin IgG (aCL IgG) has been reported in end-stage renal disease (ESRD), but its association with specific etiologies of kidney failure remains unexplored. The unique pathophysiology of diabetic–hypertensive nephropathy may be associated with a microenvironment that could potentially contribute to antiphospholipid antibody production and thrombotic complications. This study aimed to investigate whether aCL IgG elevation in hemodialysis (HD) patients is associated with combined diabetes–hypertension (DM + HTN) etiology and thrombocytopenia, thereby identifying a clinically distinct potential high-risk subgroup. In this hypothesis-generating study, we focused on within-HD patient comparisons rather than healthy controls. Methods: We enrolled 242 participants: 150 healthy controls (included only to establish local reference ranges) and 92 patients with maintenance HD. The study was conducted from 01 September to 20 November 2025 in Madinah, Saudi Arabia. Serum aCL IgG was measured by chemiluminescence immunoassay (positive ≥ 12 GPL units). Comprehensive hematological and biochemical parameters were analyzed. Multivariable logistic regression identified predictors of aCL positivity. Results: In the HD cohort, 21% demonstrated aCL positivity; this represents a substantially higher rate than the 2% observed in local healthy controls (p < 0.001). This elevation was not uniform across etiologies. Strikingly, 94.7% (18/19) of aCL-positive HD patients had DM + HTN aetiology, compared with only 17.8% of aCL-negative patients (p < 0.001). Thrombocytopenia was significantly more severe in aCL-positive patients (median platelets: 100 vs. 191 × 10⁹/L, p < 0.001). In multivariable analysis, DM + HTN etiology (HTN-alone vs. DM + HTN odds ratio [OR]: 0.0013, 95% confidence interval [CI]: 0.00002–0.0999, p = 0.003; confirmed by Firth’s penalized logistic regression sensitivity analysis, and lower platelet count (OR: 0.92 per 1 × 10⁹/L increase, 95% CI: 0.87–0.98, p = 0.006) independently predicted aCL positivity. Conclusions: These hypothesis-generating findings suggest a potential association between metabolic–vascular disease and antiphospholipid immunity in ESRD. Causality cannot be inferred from this cross-sectional design. At present, routine aCL screening is not recommended outside of research protocols; prospective studies are needed to confirm these associations. Full article

Ship target detection is a prerequisite for achieving automated monitoring in ship detection systems. To address the challenge of accurately detecting ship targets in complex water environments, this study proposes a ship target detection method based on an improved YOLOv11 framework. To enhance the model’s ability to perceive and fuse features across multiple scales and in complex backgrounds, an Iterative Attention Feature Fusion (iAFF) module and a Biformer module are integrated at the end of the backbone network. The iAFF module iteratively optimizes multi-scale features through a two-stage attention mechanism, effectively focusing on key target regions, thereby improving the model’s detection capability for small, medium-sized, and occluded ships. The Biformer module leverages its innovative Bi-level Routing Attention (BRA) mechanism to enhance the modeling of global semantic information while reducing computational complexity, mitigating false detections caused by occlusions among ship targets, and consequently improving detection precision. This study employs the Minimum Point Distance Intersection over Union (MPDIoU) loss function, which more comprehensively measures the similarity between predicted and ground-truth bounding boxes by optimizing the distances of their key geometric points, effectively enhancing the accuracy of bounding box regression. Experimental results show that the proposed model achieved 93.96% mAP, 92.93% recall, and 94.97% precision on a self-built ship dataset, surpassing mainstream detection algorithms including YOLOv11 in multiple metrics. The model has only 2.90 M parameters, achieving a good balance between accuracy and efficiency. This provides an accurate and efficient solution for intelligent ship supervision. Full article

To address severe strata pressure induced by large end-area hanging spans and poor caving of thick, hard roofs in western coal mines, this study takes the 1302 working face of Zhujiamao Coal Mine as a case study. A multiscale mechanical model is developed to describe the progressive evolution of a stratified hard roof from a continuous beam to a cantilever beam and finally to an arched triangular hanging roof. Limit criteria for the maximum hanging length under bending and shear failure are derived, indicating that bending governs end-area roof instability. The theoretical results show good agreement with field observations and numerical simulations, providing guidance for liquid nitrogen fracturing target selection. Coupled FLAC3D-3DEC simulations reveal the staged deformation of overlying strata and clarify the spatial correspondence between the “O-X” fracture pattern and the arched triangular hanging roof. Based on these findings, a collaborative weakening strategy integrating directional drilling, hydraulic pre-cracking, and deep liquid nitrogen fracturing is proposed. Field observations and comparative tests confirm that this method effectively forms a three-dimensional fracture network, reduces roof stiffness and strength, shortens the caving interval, lowers peak shield resistance, and promotes stable caving of the end-area hanging roof. Full article

Coal preparation plants pursue maximum economic benefit, yet product structure optimization under fluctuating coal quality and changing market demand is a coupled decision-making problem involving the organization of primary products such as lump clean coal, clean coal, raw fine coal, coal slime, and gangue, together with commercial coal blending and process-scheme selection. Conventional optimization methods that focus on a single stage are often insufficient to address such complex coordinated decisions. To this end, a GPSOM–WOA nested optimization model was developed to achieve the coordinated optimization of primary product separation, commercial coal blending, and process-scheme selection under the objective of economic benefit maximization. In the outer layer, where process-scheme selection and primary product structure adjustment involve both discrete decisions and continuous variables, a simplified Group-based Particle Swarm Optimization with Multiple Strategies (GPSOM) was employed to search the primary product structure parameters and generate engineering-feasible primary product balance tables. In the inner layer, where the commercial coal blending problem is subject to multiple constraints, including ash content, moisture, calorific value, and supply demand, the Whale Optimization Algorithm (WOA) was adopted to optimize blending ratios within a restricted feasible region. A piecewise penalty function was introduced for quality-limit violations to support profit-oriented constrained optimization. Subject to commercial coal quality constraints on ash content, moisture, and calorific value, a case study of a coal preparation plant in Inner Mongolia was conducted to compare product structures and economic benefits under different process conditions. The results show that the proposed model can realize the joint optimization of primary product structure and commercial coal blending, and can provide a quantitative basis for product structure optimization and process selection in coal preparation plants. Full article

To mitigate local congestion and address the adaptability limitations of traditional static routing under dynamic traffic, this paper proposes an end-to-end routing method based on a Graph Attention Network (GAT), termed Congestion-Aware Graph Attention Routing (CA-GAR). To alleviate the issue of local optima in traditional heuristic iterative optimization, we design a dynamic link cost optimization algorithm with multi-start parallel exploration. This algorithm employs a ”penalty–reselection–reward” closed-loop feedback mechanism, performing global searches from multiple random initial states to generate a high-quality, empirically near-optimal cost matrix as supervised labels. Building on this, CA-GAR leverages a multi-head attention mechanism to adaptively aggregate high-order topological features of nodes and edges, and incorporates a staged hierarchical hyperparameter optimization strategy to map real-time network states to link costs. Simulation results demonstrate that CA-GAR outperforms traditional static routing under light, medium, and heavy loads. Under high-load burst conditions, the method exhibits effective congestion avoidance capability, reducing end-to-end delay by approximately 50% and lowering the packet loss rate to as low as 2%. Compared with QLRA, CA-GAR shows promising performance in multi-path traffic splitting and possesses robust fast rerouting capabilities during node failures, thereby achieving intelligent traffic distribution and global load balancing. Full article

Mine rescue operations are frequently conducted in hazardous underground environments characterized by damaged infrastructure, unstable communications, heat stress, and hypoxia risk, all of which threaten the safety of rescue personnel. To address these challenges, this study proposes a prototype-oriented mine-rescue monitoring framework that combines a Wi-Fi/optical-fiber communication architecture with flexible wearable sensing modules for physiological monitoring. The communication design employs Wi-Fi for local wireless data aggregation and optical fiber for reliable long-distance backhaul to the surface command side. For wearable monitoring, two flexible sensing modules were developed: a temperature sensor based on a polyaniline/graphene–polyvinyl butyral composite film and a PPG-oriented flexible optoelectronic module based on an ITO/Ag/ITO multilayer transparent electrode structure. Experimental results show that the temperature sensor exhibits a clear temperature-dependent resistance response within the tested range, while the optoelectronic module demonstrates low sheet resistance and acceptable electrical continuity under repeated bending. These results provide preliminary support for combining hybrid underground communication architecture with flexible wearable sensing components in mine-rescue scenarios. However, the present work remains at the stage of architecture design and component-level validation, and full end-to-end system verification under simulated or field rescue conditions will be the focus of future studies. Full article

Accurate sleep stage classification is essential for evaluating sleep quality, yet clinical polysomnography is impractical for continuous home-based monitoring. Ballistocardiography (BCG) enables unobtrusive sleep monitoring through sensors embedded in sleep furniture; however, existing BCG-based approaches either rely on complex physiological feature extraction or employ fixed-parameter signal-to-image transformations that cannot adapt to inter-subject variability. This study proposes a learnable recurrence plot (RP) framework for three-stage sleep classification (Wake, NREM, REM) from single-channel BCG signals. The Learnable RP introduces three innovations: multi-scale phase-space reconstruction at physiologically motivated time delays (τ = 5, 10, 20), differentiable per-scale thresholds optimized end-to-end, and attention-based spatial fusion of multi-scale recurrence maps. The framework was evaluated through 10-fold stratified cross-validation across six backbone architectures using 50 overnight recordings. The Learnable RP consistently outperformed four baseline transformation methods (GAF, MTF, Classical RP, Modified RP), achieving an aggregate mean accuracy of 73.60%, with EfficientNet-B5 reaching 78.91%. and 78.91%. Statistical validation across all 24 pairwise comparisons (4 baselines × 6 backbones) confirmed consistent superiority (all p < 0.001). The proposed framework achieves competitive performance without explicit physiological feature engineering, offering a viable path toward end-to-end unobtrusive sleep monitoring. Full article

Background: Pancreatic tumors in pediatric and adolescent patients are rare, and guidance on prognostication and management is limited. Methods: Using the Surveillance, Epidemiology, and End Results (SEER) database (2004–2021), we analyzed clinicopathological characteristics, treatment patterns, and survival outcomes in patients younger than 20 years with pancreatic tumors. Analyses integrated conventional survival models with machine learning approaches to identify key predictors. Results: The cohort included 203 patients, of whom 108 (53.2%) had solid pseudopapillary neoplasms (SPNs), 59 (29.1%) neuroendocrine neoplasms, 16 (7.9%) pancreatoblastomas, 5 (2.5%) adenocarcinoma variants, 4 (2.0%) acinar cell carcinomas, and 11 (5.4%) other rare histologies. Most patients had localized disease (61.1%) and underwent surgical resection (85.2%). Estimated 5-year and 10-year overall survival rates were 87.8% and 84.0%, respectively. Survival differed significantly by histology, stage, and surgery status (all log-rank p < 0.001). In multivariable analysis, SPN histology was associated with lower mortality (hazard ratio (HR) 0.03, 95% confidence interval (CI) 0.01–0.13; p < 0.001), whereas distant disease was associated with markedly higher mortality (HR 21.49, 95% CI 7.52–133.41; p < 0.001). Surgical resection was independently associated with lower mortality (HR 0.13, 95% CI 0.02–0.29; p = 0.003). Among patients with known 5-year status, the Random Forest and Gradient Boosting models achieved cross-validated area under the curve values of 0.935 ± 0.060 and 0.886 ± 0.093, respectively; stage and surgery were the dominant predictors in both models. Conclusions: Surgery remains the cornerstone of management for pediatric pancreatic tumors, and advanced analytic approaches may enhance risk stratification in this rare population. Full article

Network function virtualization (NFV) enables flexible service deployment by implementing network functions as software, with service function chains (SFCs) linking virtual network functions (VNFs) in a specific order to deliver end-to-end services. However, ensuring SFC resilience against large-scale disasters that can disrupt entire disaster zones (DZs) remains a significant challenge. In this paper, we study the multipath disaster-resilient SFC deployment problem, aiming to minimize the total bandwidth and computing resource overhead by jointly optimizing VNF placement, multipath routing, and protection mechanisms, subject to DZ-disjoint constraints. We formulate this problem as a Mixed-Integer Nonlinear Programming (MINLP) model and prove it to be NP-hard. To solve it efficiently, we propose a two-stage adaptive deployment approach; the first stage employs heuristic rules to generate a set of candidate paths satisfying DZ-disjoint constraints, and the second stage leverages deep reinforcement learning to intelligently place VNFs along these candidate paths, approximating the global optimum. Simulation results on real network topologies demonstrate that, compared to traditional dedicated protection strategies and a state-of-the-art exact algorithm, the proposed approach reduces resource overhead by up to 20% while effectively guaranteeing SFC disaster resilience, exhibiting good scalability and online deployment potential. Full article

Diabetic kidney disease (DKD) affects approximately 40% of patients with diabetes mellitus and remains a leading cause of end-stage renal disease worldwide. Early diagnosis and identification of therapeutic targets are critical for improving patient outcomes, yet reliable biomarkers are lacking. This study integrated transcriptomic data from the Gene Expression Omnibus (GEO) database (GSE96804, GSE30528, and GSE142025) with machine learning algorithms and Mendelian randomization (MR) to identify diagnostic biomarkers for DKD. Differentially expressed genes (DEGs) were identified and intersected with key modules from weighted gene co-expression network analysis (WGCNA). Four machine learning methods—least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and extreme gradient boosting (XGBoost)—were applied for feature selection. Five hub genes (SPP1, CD44, VCAM1, C3, and TIMP1) were identified at the intersection of these approaches. Two-sample MR analysis using eQTL data from the eQTLGen Consortium and kidney function GWAS from the CKDGen Consortium provided evidence supporting potential causal associations between SPP1, C3, and TIMP1 expression and estimated glomerular filtration rate decline. Immune infiltration analysis via CIBERSORT estimated elevated proportions of M1 macrophages and activated CD4${}^{+}$ memory T cells in DKD samples, with all five hub genes showing correlations with macrophage infiltration. A diagnostic model based on these five genes achieved a cross-validated area under the receiver operating characteristic curve (CV-AUC) of 0.938 in the discovery dataset and AUC values of 0.917 and 0.889 in two independent external validation cohorts. Drug–gene interaction analysis identified 10 candidate compounds targeting the hub genes. These findings provide a computational framework for identifying candidate diagnostic biomarkers and generating hypotheses regarding potential therapeutic targets for DKD; however, all results are derived from in silico analyses and require experimental validation—including qPCR, immunohistochemistry, and prospective clinical cohort studies—before clinical applicability can be established. Full article

The CAELESTIS project aims to promote the development and design of innovative aircraft and engine structures through an integrated ecosystem of simulations and digital tools, enabling synergy across all stages of the manufacturing process. The component selected was an Outlet Guide Vane (OGV), a static engine part composed of a central composite section and titanium inserts at both ends, joined in a single manufacturing step. A detailed investigation of the joints between these materials was carried out using surface treatments of different natures to evaluate properties that directly influence the final joint quality. Optical analysis techniques were employed to characterize the morphology, roughness and surface free energy (SFE), complemented by mechanical tests to determine the adhesion and shear strength. All specimens were manufactured using the Resin Transfer Molding (RTM) “one-shot” process. Full article

The rapid development of smart energy infrastructures and renewable energy systems requires advanced sensing solutions that provide high accuracy, expandability, and stability under real operating conditions. However, conventional gas monitoring systems are predominantly based on resistive or voltage-output sensors, which require complex analog front-end circuits and analog-to-digital conversion, leading to increased system complexity, cost, and susceptibility to electromagnetic interference. This paper tackles this limitation by proposing a frequency-domain sensing approach for multichannel monitoring of biogas plant parameters. The objective of this study is to develop and experimentally validate an extendable sensing architecture based on autogenerator microelectronic gas transducers with direct gas concentration–frequency conversion and FPGA-based digital acquisition. The proposed method is grounded in a physical–mathematical model of the space-charge capacitance of gas-sensitive semiconductor structures derived from Poisson’s equation, facilitating analytical formulation of conversion and sensitivity functions. A multichannel FPGA-based measurement system is implemented to process frequency signals without analog conditioning or ADC stages. Experimental validation was performed for CH₄ (0–85%), CO₂ (0–60%), H₂, NH₃, and H₂S (1–20,000 ppm). The results demonstrate measurement uncertainty within 0.25–0.5%, with sensitivity reaching 350–748 Hz/ppm for H₂, 455–750 Hz/ppm for NH₃, and 253–375 Hz/ppm for H₂S, while methane and carbon dioxide sensitivities reach up to 112 kHz/% and 98.7 kHz/%, respectively. Spectral analysis in the LTE-1800 band confirms improved noise immunity (up to 4.5×) and extended transmission capabilities. A 12-channel FPGA-based monitoring system (RDM-BP-1) with a 1 s sampling interval, IP67 protection, and wireless connectivity is developed and validated. The proposed architecture eliminates analog signal conditioning, reduces hardware complexity, and provides an easily expandable and reliable sensing solution for smart buildings, renewable energy systems, and cloud-integrated energy infrastructures. Full article