Search Results (324)

This research proposes the design and implementation of an adaptive optical system (AOS) for monitoring low-orbit satellites (LOSs) to ensure that they do not deviate from their pre-planned path. This is achieved by designing a telescope with an optical system that contains six mirrors in a regular hexagonal shape; the side length of one mirror is 30 cm, and there is also a spectral analyzer system in the middle to separate the spectra emitted by stars from those reflected from low-orbit satellites. A SwinTrack-Tiny (STT) is used, with modifications using temporal information via insertion. The model incorporates a new purpose-built image update template as a third input to the model and combines the attributes of the new image with the attributes of the primary template via an attention block. To maintain the dimensions of the original model and take advantage of its weights, an attention block with four vertices is used. Full article

The working conditions of bearings, as a key component in electromechanical systems, are becoming increasingly complex with the rapid development of current intelligent manufacturing technology. Therefore, it is difficult to accurately identify the abnormal operating state of the bearing through a single signal. In addition, data-based bearing fault diagnosis methods insufficiently utilize bearing prior knowledge under complex working conditions. To address the above issues, this paper proposes a bearing fault diagnosis method based on multi-source information fusion with physical prior knowledge (MSIF-PPK). An information fusion module and a physical embedding module are designed: the former module fuses frequency-domain, time–frequency-domain, and working condition information through an attention mechanism, while the latter one embeds physical working condition data and features. The feasibility and the effectiveness of the modules are verified through comparative experiments and ablation experiments using the Southeast University (SEU) Bearing Dataset, the Mehran University of Engineering and Technology (MUET) Induction Motor Bearing Vibration Dataset, and the Harbin Institute of Technology (HIT) Aeroengine Bearing Dataset. Experimental results show that this method is feasible, reliable, and interpretable for bearing fault diagnosis under complex working conditions. Full article

Unmanned Aerial Vehicles (UAVs) have become indispensable tools, playing a pivotal role in diverse applications such as rescue missions, agricultural surveying, and air defense. They significantly reduce operational costs while enhancing operator safety, enabling new strategies across multiple domains. The growing demand for UAVs calls for structural components that are not only robust and lightweight, but also cost-efficient. This research introduces a novel approach that employs a pressure distribution map on the external surface of a UAV wing to optimize its internal structure through a variable-thickness TPMS (Triply Periodic Minimal Surface) design. Beyond structural optimization, the study explores a second novel approach with the use of filaments containing chemical blowing agents printed at different temperatures for both the infill and shell, producing varying porosities. As a result, the tailoring of density and weight is achieved through two different methods, and case studies were developed by combining them. Compared to the conventionally manufactured wing, a weight reduction of up to 7% was achieved while the wing could handle the aerodynamic loads under extreme conditions. Beyond enabling lightweight structures, the process has the potential to be substantially faster and more cost-effective, eliminating the need for molds and advanced composite materials such as carbon fiber sheets. Full article

Background: Submarines represent extremely confined environments where breathing air is continuously recirculated for extended periods with minimal renewal, generating complex multipollutant atmospheres. Objectives: This critical narrative review aims to (i) summarize sources and composition of submarine indoor air, (ii) evaluate respiratory and cardiovascular risks for crews, and (iii) assess current purification technologies. Methods: A narrative review was conducted following PRISMA recommendations applicable to non-systematic reviews. The PubMed search covered all years from inception to September 2025, complemented by backward citation tracking and technical reports. Results: Eligible studies consistently report elevated levels of CO₂, VOCs, NO_X, CO, PM_2.5, and bioaerosols aboard submarines. Evidence from submariner cohorts and toxicological studies indicates risks of airway irritation, impaired mucociliary defenses, endothelial dysfunction, cardiovascular stress, and neurobehavioral alterations. Conclusions: Submarine indoor air quality is a credible determinant of crew health. Existing filtration systems mitigate some risks but do not address multipollutant mixtures adequately. Improved real-time monitoring, advanced filtration, CFD-guided airflow optimization, and longitudinal medical surveillance are necessary. Full article

Edible Grass (EG) is a hybrid vegetable variety valued for its high biomass and protein content, garnering significant interest in recent years for its potential in food, feed, and health product applications. However, in subtropical climates, intense light and high temperatures severely affect the growth and development of Edible Grass (EG), leading to substantial reductions in yield and quality. This study was conducted in the subtropical humid monsoon climate zone of Changsha, Hunan, China, comparing two growth conditions: natural light (CK) and shading treatment (ST). High light-aggravated heat damage under CK significantly reduced EG yield and quality (p < 0.05), with severe cases leading to plant death. and could even lead to plant death in severe cases. Specifically, maximum air and leaf temperatures under CK reached 38.85 °C and 38.14 °C, respectively, well exceeding the plant’s optimal growth range. Shading treatment (ST) effectively alleviated this damage, significantly increasing the net photosynthetic rate, stomatal conductance, and intercellular CO₂ concentration, while decreasing leaf temperature and transpiration rate (p < 0.001). The analysis of physiological and biochemical indicators indicates that after ST, the activities of SOD, CAT, and POD in the leaves decreased, while the contents of MDA and H₂O₂ were significantly lower compared to the CK group (p < 0.001). The transcriptome sequencing results indicate that a total of 8004 DEGs were identified under shading treatment (ST) relative to natural light (CK), with 3197 genes upregulated and 4807 genes downregulated. Significantly enriched Gene Ontology (GO) terms include ‘cell membrane’, ‘extracellular region’, and ‘protein kinase activity’, while significantly enriched KEGG metabolic pathways include ‘plant hormone signal transduction’, ‘photosynthesis–antenna proteins’, and ‘glutathione metabolism’. Compared to CK, the expression of genes associated with oxidative stress (e.g., CAT1, OXR1, APX, GPX) was significantly downregulated in ST, indicating a relief from light-aggravated heat stress. This transcriptional reprogramming was corroborated by metabolomic data, which showed reduced accumulation of key flavonoid compounds, aligning with the downregulation of their biosynthetic genes as well as genes encoding heat shock proteins (e.g., Hsp40, Hsp70, Hsp90). It indicated that plants switch from a ‘ROS stress–high energy defense’ mode to a ‘low oxidative pressure–resource-saving’ mode. Collectively, ST significantly alleviated the physiological damage of forage grasses under heat stress by modulating the processing of endoplasmic reticulum heat stress proteins, plant hormones, and related genes and metabolic pathways, thereby improving photosynthetic efficiency and yield. The findings provide a theoretical basis for optimizing the cultivation management of EG, particularly in subtropical regions, where shade treatment serves as an effective agronomic strategy to significantly enhance the stress resistance and yield of EG. Full article

Rice (Oryza sativa L.) production in the Philippines remains highly vulnerable to recurrent outbreaks of the Rice Black Bug (RBB; Scotinophara coarctata F.) and White Stemborer (WSB; Scirpophaga innotata W.), two of the most destructive pests in Southeast Asian rice ecosystems. Classical economic threshold levels (ETLs) are difficult to estimate in smallholder settings due to the lack of cost–loss data, often leading to either delayed or excessive pesticide application. To address this, the present study developed an adaptive outbreak-forecasting framework that integrates the Number–Size (N–S) fractal model with machine learning (ML) classifiers to define and predict pest regime transitions. Seven years (2018–2024) of light-trap surveillance data from the Philippine Rice Research Institute–Midsayap Experimental Station were combined with daily climate variables from the NASA POWER database, including air temperature, humidity, precipitation, wind, soil moisture, and lunar phase. The N–S fractal model identified natural breakpoints in the log–log cumulative frequency of pest counts, yielding early-warning and severe-outbreak thresholds of 134 and 250 individuals for WSB and 575 and 11,383 individuals for RBB, respectively. Eight ML algorithms such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Balanced Bagging, LightGBM, XGBoost, and CatBoost were trained on variance-inflation-filtered climatic and temporal predictors. Among these, CatBoost achieved the highest predictive performance for WSB at the 94.3rd percentile (accuracy = 0.932, F1 = 0.545, ROC–AUC = 0.957), while Logistic Regression performed best for RBB at the 75.1st percentile (F1 = 0.520, ROC–AUC = 0.716). SHAP (SHapley Additive exPlanations) analysis revealed that outbreak probability increases under warm nighttime temperatures, high surface soil moisture, moderate humidity, and calm wind conditions, with lunar phase exerting additional modulation of nocturnal pest activity. The integrated fractal–ML approach thus provides a statistically defensible and ecologically interpretable basis for adaptive pest surveillance. It offers an early-warning system that supports data-driven integrated pest management (IPM), reduces unnecessary pesticide use, and strengthens climate resilience in Philippine rice ecosystems. Full article

The large number of UAVs under supervision at low altitudes have brought serious security risks to the field of air defense. Accurately analyzing the characteristics of UAVs’ echo signals is of great research significance for the detection and recognition of UAVs. Based on the principle of radar detection, the echo spatial correlation in the distributed radar detection mode is studied. According to the influence of different movement speeds on the micro-motion characteristics of UAVs, the echo signal models of UAVs in two flight states are established. Combined with the instantaneous micro-Doppler frequency model of the ideal motion state of UAVs, micro-Doppler frequency calculation functions of UAVs at different attitude angles are constructed. Through simulation calculation, the variation curves between the observation angle and the echo spatial correlation using different detection distances are given. Based on time–frequency images of UAVs in their ideal motion state, changes in the time–frequency images at different motion speeds and attitude angles are analyzed. These research results will help radar detection systems to accurately recognize UAVs in an uncertain motion state and can also provide a basis for predicting the next motion action of UAVs in subsequent target tracking. Full article

Humans tend to rely on heuristic strategies for intuitive judgment during decision-making. Existing research proposes an availability heuristic, suggesting that individuals are inclined to use highly available information as a basis for judgment. To explore the behavioral and electrophysiological characteristics of the availability heuristic in information visualization, 24 right-handed participants were recruited for the experiment. Using behavioral and event-related potentials (ERPs) analysis techniques, within-subject behavioral and electroencephalogram (EEG) experiments were conducted under four conditions: polar coordinate system with higher number, polar coordinate system with lower number, Cartesian coordinate system with higher number, and Cartesian coordinate system with lower number. The behavioral results revealed that in the angle estimation task, the polar coordinate condition induced a more significant availability heuristic effect compared to the Cartesian coordinate condition, exhibiting smaller numerical estimation deviations. This indicates that the degree of semantic relevance between the available information and the target task is a critical factor determining the facilitative effect of such information on judgment. The ERPs results showed that the polar coordinate condition elicited smaller N2 and P2 amplitudes than the Cartesian coordinate condition during angle judgment, suggesting reduced semantic conflict and lower attentional demand in task processing under the polar coordinate condition. By providing behavioral and electrophysiological evidence of intuitive decision-making processes, this study lays a theoretical foundation for the rational application of intuitive effects in information visualization design. Furthermore, the findings imply that using available information semantically aligned with the target task can significantly enhance the effectiveness of the availability heuristic, thereby mitigating availability bias. Full article

The Space–Air–Ground Integrated Network (SAGIN), a core architecture for 6G, faces formidable routing challenges stemming from its high-dynamic topological evolution and strong heterogeneous resource characteristics. Traditional protocols like OSPF suffer from excessive convergence latency due to frequent topology updates, while existing intelligent methods such as DQN remain confined to a passive reactive decision-making paradigm, failing to leverage spatiotemporal predictability of network dynamics. To address these gaps, this study proposes an adaptive routing algorithm (GCN-T-PPO) integrating a GCN-Transformer hybrid encoder, Particle Swarm Optimization (PSO), and Proximal Policy Optimization (PPO) with spatiotemporal attention. Specifically, the GCN-Transformer encoder captures spatial topological dependencies and long-term temporal traffic evolution, with PSO optimizing hyperparameters to enhance prediction accuracy. The PPO agent makes proactive routing decisions based on predicted network states (next K time steps) to adapt to both topological and traffic dynamics. Extensive simulations on real dataset-parameterized environments (CelesTrak TLE data, CAIDA 100G traffic statistics, CRAWDAD UAV mobility models) demonstrate that under 80% high load and bursty Pareto traffic, GCN-T-PPO reduces end-to-end latency by 42.4% and packet loss rate by 75.6%, while improving QoS satisfaction rate by 36.9% compared to DQN. It also outperforms SOTA baselines including OSPF, DDPG, D2-RMRL, and Graph-Mamba. Ablation studies validate the statistical significance (p < 0.05) of key components, confirming the synergistic gains from spatiotemporal joint modeling and proactive decision-making. This work advances SAGIN routing from passive response to active prediction, significantly enhancing network stability, resource utilization efficiency, and QoS guarantees, providing an innovative solution for 6G global seamless coverage and intelligent connectivity. Full article

This study establishes a multi-scale validation framework for Computational Fluid Dynamics (CFD) simulations of building-induced pollutant dispersion, integrating wind tunnel experiments, the CEDVAL benchmark dataset, and field measurements from a thermal power plant that serves as a proxy for nuclear facilities. The RNG k-ε and Large Eddy Simulation (LES) models were evaluated across these validation tiers. Results demonstrate that both models effectively capture key flow characteristics, with LES showing superior performance in predicting roof-level velocity and turbulence intensities. A systematic overestimation of rooftop and leeward concentrations was observed, though predictive accuracy improved with downwind distance (e.g., FAC2 > 0.5). The RNG k-ε model provided the best balance between accuracy and computational efficiency for engineering applications, while LES is recommended for high-fidelity near-field analysis. This work provides validated methodologies for environmental risk assessment in nuclear power planning and emission control strategy development. Full article

The stability of unmanned aerial vehicles (UAVs) during propulsion failure remains a critical safety challenge. This study presents a center-of-mass (CoM) correction device, a compact, under-slung, and dual-axis prismatic stage, which can reposition a dedicated shifting mass within the UAV frame to generate stabilizing gravitational torques by the closed-loop feedback from the inertial measurement unit (IMU). Two major experiments were conducted to evaluate the feasibility of the system. In a controlled roll test with varying payloads, the device produced a corrective torque up to 1.2375 N·m, reducing maximum roll deviations from nearly 90° without the device to less than 5° with it. In a dynamic free-fall simulation, the baseline UAV exhibited rapid tumbling and inverted impacts, whereas with the CoM system activated, the UAV maintained a near-level attitude to achieve the upright recovery and greatly reduced structural stress prior to ground contact. The CoM device, as a fail-safe stabilizer, can also enhance maneuverability by increasing control authority, enable a faster speed response and more efficient in-air braking without reliance on the rotor thrust, and achieve comprehensive energy saving, at about 7% of the total power budget. In summary, the roll stabilization and free-fall results show that the CoM device can work as a practical pathway toward the safer, more agile, and energy-efficient UAV platforms for civil, industrial, and defense applications. Full article

This paper presents a portable cluster architecture based on a lightweight Kubernetes distribution designed to provide enhanced computing capabilities in isolated environments. The architecture is validated in two operational scenarios: (1) machine learning operations (MLOps) for on-site learning, fine-tuning and retraining of models and (2) web hosting for isolated or resource-constrained networks, providing resilient service delivery through failover and load balancing. The cluster leverages low-cost Raspberry Pi 4B units and virtualized nodes, integrated with Docker containerization, Kubernetes orchestration, and Kubeflow-based workflow optimization. System monitoring with Prometheus and Grafana offers continuous visibility into node health, workload distribution, and resource usage, supporting early detection of operational issues within the cluster. The results show that the proposed dual-mode cluster can function as a compact, field-deployable micro-datacenter, enabling both real-time Artificial Intelligence (AI) operations and resilient web service delivery in field environments where autonomy and reliability are critical. In addition to performance and availability measurements, power consumption, scalability bottlenecks, and basic security aspects were analyzed to assess the feasibility of such a platform under constrained conditions. Limitations are discussed, and future work includes scaling the cluster, evaluating GPU/TPU-enabled nodes, and conducting field tests in realistic tactical environments. Full article

Developmental programming, shaped by environmental and lifestyle stressors during prenatal life, is increasingly recognized as a major contributor to non-communicable diseases (NCDs) later in life. Oxidative stress, one of key mechanisms linking these stressors to fetal metabolomic reprogramming and disease pathogenesis, leaves measurable metabolomic signatures that reflect disrupted redox balance. Alterations in glucose, lipid, and amino acid metabolism and antioxidant response could reveal the main pathways driving NCD development. This review summarizes epidemiological studies that have investigated biochemical responses of the prenatal exposure to metals, air pollution, and tobacco smoke and e-cigarette vapor in maternal–placental–fetal compartments using a metabolomic approach. Summarized studies indicate that maternal exposure to metals primarily disrupts amino acid pathways related to one-carbon metabolism, glutathione synthesis, and oxidative stress defense, while air pollution, particularly fine particulate matter, mainly affects lipid oxidation, fatty acid β-oxidation, and amino acid and carbohydrate metabolism. Tobacco smoke and e-cigarette vapor induce widespread disturbances involving reduced citric acid cycle intermediates, altered acylcarnitines and phospholipids, and impaired antioxidant capacity, collectively promoting oxidative damage and inflammatory signaling. The identification of these metabolome alterations might contribute to a deeper understanding of the toxicity and biological impact of environmental stressors on offspring health. These results may eventually lead to the identification of early biomarkers and to the development of therapeutic strategies aimed at reducing NCD risk. Full article

To address the challenge of decision making in close-range air combat for fixed-wing unmanned air vehicle (UAV) swarms, this paper proposes a distributed Hierarchical Cooperative Soft Actor-Critic with maximum entropy (HC-SAC) framework. A top-level target decision-making and bottom-level maneuvering framework is designed to resolve convergence issues in traditional multi-agent reinforcement learning, typically for long mission durations and complex state spaces. Friendly tactics are incorporated into top-level decisions to enhance coordination, with both offensive and defensive sub-policies balanced for swarm confrontations. These policies are trained using the Soft Actor-Critic (SAC) deep reinforcement learning algorithm with specific reward functions, and the effectiveness of this method is verified through 5v5 swarm game confrontation simulations. Full article

Objectives: Female fertility is increasingly threatened by environmental pollutants such as fine particulate matter (PM_2.5 and NO₂), endocrine-disrupting chemicals (BPA, phthalates, PFAS, and PCBs), and microplastics. These exposures are associated with impaired ovarian reserve, reduced implantation rates, and lower assisted reproductive technology (ART) success. Given the rising prevalence of obesity and weight-loss interventions, particularly bariatric surgery, understanding the combined influence of metabolic and environmental factors on reproductive outcomes is of critical importance. This review aimed to synthesize recent evidence on how these exposures interact to affect female fertility. Methods: A narrative review was conducted of studies published between 2019 and 2025 using PubMed, Google Scholar, Web of Science, and Wiley Online Library. The PubMed Boolean search string was “female fertility”, “ovarian function”, “IVF” and “pollution”, “endocrine disruptors”, “air pollutants”, and “microplastics”. Searches were limited to English language publications, with the last search performed on 30 March 2025. Human, animal, and in vitro data were screened separately. Human evidence was prioritized, and confounding factors (age, BMI, and smoking) were considered during interpretation. Results: Environmental pollutants were consistently associated with diminished ovarian reserve, poor oocyte quality, and reduced live birth rates in ART. PFAS exposure correlated with lower fecundability, while PM_2.5 and NO₂ were linked to decreased AMH and AFC levels. Mechanistic animal and in vitro studies support these findings through pathways involving oxidative stress, endocrine disruption, and epigenetic alterations. Rapid metabolic changes, particularly post-bariatric surgery, may transiently increase circulating lipophilic toxicants and reduce antioxidant defenses, amplifying reproductive vulnerability. Conclusions: Environmental exposures, especially PM_2.5, NO₂, PFAS, and microplastics, adversely influence ovarian and embryonic competence. Rapid metabolic transitions may further modulate this susceptibility through pollutant mobilization and micronutrient imbalances. Future interdisciplinary prospective studies integrating exposure monitoring, metabolic profiling, and reproductive endpoints are essential to guide clinical recommendations and precision fertility counseling. Full article