Search Results (8,291)

Moist chilling is widely used to overcome seed dormancy in zoysiagrass (Zoysia japonica Steud.), but the coordinated physiological and molecular basis remains unclear. Here, freshly matured seeds were subjected to moist chilling at 4 °C in darkness for 0 (Control), 1 (CS1), 2 (CS2), 3 (CS3), or 4 weeks (CS4) and then transferred to germination conditions (30/20 °C, day/night). Prolonged moist chilling progressively improved dormancy release: final germination percentage increased from 40.5% (Control) to 73.5% (CS4), accompanied by a higher germination index and earlier, faster cumulative germination dynamics. Moist chilling also enhanced early seedling vigor, with stronger treatment differentiation in root elongation than in shoot growth. Physiologically, abscisic acid (ABA) content declined while gibberellic acid (GA) content increased, resulting in an elevated GA/ABA ratio with prolonged chilling. Metabolic activation was evidenced by increased α-amylase activity, greater soluble sugar and soluble protein accumulation, and stimulated oxygen uptake. In addition, CAT, SOD, and POD activities were enhanced under prolonged moist chilling, whereas H₂O₂ levels remained relatively stable, suggesting that redox adjustment during dormancy release was characterized by strengthened antioxidant buffering rather than pronounced oxidative accumulation. qRT-PCR supported a mechanistic transition from dormancy maintenance to germination execution, showing moist chilling-associated regulation of ABA/GA metabolism and signaling genes (e.g., NCED, CYP707A, ABI3/ABI5, and GA20ox) and downstream metabolic modules (e.g., GAMYB, AMY, ISA, INV, and HXK1), together with concurrent modulation of respiration- and ROS-related markers (e.g., AOX1a, RBOH, and CAT). Correlation analysis linked germination performance most strongly with α-amylase activity, oxygen uptake, and the GA/ABA ratio. Collectively, our data support a working model in which moist chilling rebalances the ABA–GA gate and activates downstream metabolic and redox adjustment modules to promote dormancy release and improve germination performance in zoysiagrass, providing practical markers for optimizing seed establishment through moist chilling treatment. Full article

Uncertainty in travel and service times poses significant challenges for vehicle routing in logistics systems. This paper proposes a stochastic model predictive control (SMPC) strategy to manage a Vehicle Routing Problem with time windows (VRPTW) under stochastic service times with correlation across customers. The approach combines a dynamic optimization model with single and joint chance constraints and a forecasting tool for updating travel plans as new information becomes available. A deterministic reformulation of the stochastic constraints is developed so that the problem can be solved via mixed-integer programming. The aim of this paper is to demonstrate that the SMPC strategy can maintain a high level of time-window reliability (meeting customer time windows with high probability) at a reasonable cost by re-optimizing routes over a moving horizon. In numerical case studies, the SMPC approach achieves the desired reliability levels while incurring only modest increases in total cost, and it flexibly adjusts the cost–risk tradeoff by switching between single and joint chance constraints. These results illustrate the potential of the proposed method for real-time distribution routing under uncertainty and highlight the novel contribution of integrating chance-constrained optimization with Model Predictive Control in a VRPTW context. Full article

This work presents a method for implementing dynamic difficulty adjustment in the arcade game of Air Hockey using reinforcement learning. The resulting AI-controlled opponent is capable of adapting its skill level to the player’s performance to maintain engagement and provide a balanced gameplay experience. The approach relies on generating several AI agents through progressively longer training durations, resulting in distinct and smoothly transitioning difficulty levels that can be switched dynamically. We discuss how this scheme can be extended with manually selected parameters that influence physical aspects of the agent’s behavior—such as movement speed, reaction latency, and control precision—to complement the variations in decision-making quality. The proposed method is applicable to a wide range of video games, and experimental results demonstrate its effectiveness in producing adaptive and varied opponent behavior. Full article

Background: Severe fever with thrombocytopenia syndrome (SFTS) has become an increasing public health threat in China, with Yantai City representing a major endemic focus. A fine-scale, long-term epidemiological analysis integrating human case data with vector surveillance is essential for understanding local transmission dynamics. Methods: We conducted a retrospective analysis using 12-year (2013–2024) county-level SFTS surveillance data from Yantai City. Temporal trends were analyzed by Joinpoint regression. Concurrent field surveillance of Haemaphysalis longicornis (2019–2024) was used to quantify local SFTSV infection rates in ticks. Associations between SFTS incidence and environmental/livestock factors were evaluated using Spearman’s correlation and multivariable negative binomial regression. Results: A total of 1964 SFTS cases were reported. The annual incidence rate increased from 0.65 to 5.12 per 100,000 population, with an average annual percentage change (AAPC) of 13.56% 2013–2024, showing the most substantial rise among the elderly. Marked spatial heterogeneity was observed, with county-level mean incidence ranging from 0.30 to 5.23 per 100,000. The SFTSV infection rate in ticks surged from 0.54% in 2019 to 3.24% in 2024, and showed a strong positive correlation with human incidence both seasonally (ρ = 0.998) and across counties (ρ = 0.79), a pattern likely driven by shared environmental factors. Multivariable analysis identified grassland coverage (adjusted IRR [aIRR] = 1.21), woodland coverage (aIRR = 2.31), goat density (aIRR = 1.49), and tick infection rate (aIRR = 1.65) as independent risk factors, while urban land was protective (aIRR = 0.83). The overall case fatality rate was 8.86%, showing a declining trend, but was significantly higher in males (10.90%) than in females (7.04%), particularly among the elderly. Conclusions: SFTS incidence in Yantai increased significantly over the past decade, characterized by a heightened burden on the elderly and strong spatiotemporal clustering. Risk is independently mediated by ecological interfaces, notably woodland/grassland habitats and goat rearing. These findings delineate high-risk areas and populations, offering crucial insights for developing targeted public health strategies. Full article

Accurate detection of small objects in video analytics is limited by low pixel resolution and insufficient visual cues. While software-based enhancements often fail to recover missing details, Pan–Tilt–Zoom (PTZ) cameras can physically increase spatial resolution through optical zoom. However, mechanical latency and configuration complexity hinder their real-time applicability. We propose ZoomPatch, a real-time video analytics framework tailored for small object detection. ZoomPatch actively schedules PTZ adjustments to capture optically enhanced subframes of regions of interest (ROIs) and fuses inference results back to the global reference frame. Specifically, it introduces a dynamic Cycle Length Proposer to adapt analysis cycles based on scene motion, and a Mixed Integer Linear Programming (MILP)-based Configuration Decider to determine the optimal sequence of pan, tilt, and zoom adjustments under time budget constraints. Simulation-based experimental evaluations across diverse workloads demonstrate that ZoomPatch significantly outperforms fixed-perspective, super-resolution (SR), and greedy baselines. Notably, in the detection task using YOLOv10, ZoomPatch improves the F1-score from 0.33 to 0.47 (a 42% increase) compared to the fixed-perspective baseline. Furthermore, ZoomPatch yields performance gains of 30% and 7% over the SR baseline (0.36) and the greedy baseline (0.44). Full article

This study presents the dynamic characterization of a gas turbine blade manufactured from two different nickel-based superalloys: on the first hand, a superalloy called René 80 and, on the second hand, a directionally solidified (DS) nickel-based anisotropic superalloy, investigated during the validation phase of the development process. Starting from the original CAD geometry, precise and very detailed finite-element models were developed, progressively refined and modified, and consequently validated to ensure mesh-independent modal predictions. The study examines multiple possible sources of discrepancy between experimentally measured and numerically predicted natural frequencies, including geometric deviations, grouping of different interesting points, broach-block test configuration, material anisotropy, and the influence of internal rib turbulators. Statistical analyses of dimensional variations revealed no significant correlation with the observed frequency scatter, redirecting the investigation toward material behavior and modeling fidelity. The inclusion of turbulators in the finite-element model proved essential, reducing prediction errors for the first two modes by approximately 2–3%. For the DS superalloy, the effect of grain orientation was evaluated over permissible angular deviations (extremes were considered); however, no systematic and clear improvement in frequency prediction was observed. Finally, several tuning strategies were assessed, leading to an optimization procedure that simultaneously adjusted the elastic moduli $E_x$ and $E_z$, reducing modal frequency deviations to below 1% for the first two modes. The proposed methodology provides a robust and solid framework for the validation of turbine blade dynamic behavior across different materials and manufacturing conditions. Full article

In low-Earth-orbit (LEO) satellite networks, the need for intelligent parameter-adjustment strategies has become increasingly critical due to the presence of highly dynamic channel conditions, limited spectrum resources, and complex interference environments. In this paper, a method for optimizing LEO satellite communication links based on deep reinforcement learning (DRL) is proposed. Through the optimization of the transmit power, the modulation and coding scheme (MCS), the beamforming parameters, and the retransmission mechanisms, adaptive link control is achieved in dynamic operational scenarios. A multidimensional state space is constructed, within which the channel state information, the interference environment, and the historical performance metrics are integrated. The spatio-temporal characteristics of the channel are extracted by means of a hybrid neural architecture that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM) network. To effectively accommodate both continuous and discrete action spaces, a hybrid DRL framework that combines proximal policy optimization (PPO) with a deep Q-network (DQN) is employed, thereby enabling cross-layer optimization of the physical-layer and link-layer parameters. The results demonstrate that substantial improvements in throughput, bit error rate (BER), and transmit-power efficiency are achieved under severely time-varying channel conditions, which provides a new idea for resource management and dynamic-environment adaptation in satellite communication systems. Full article

This study presents a novel cooperative control strategy designed to mitigate torque ripple and enhance the disturbance rejection capability of switched reluctance motors (SRMs). The proposed approach integrates model predictive control (MPC) with direct instantaneous torque control (DITC), leveraging the torque sharing function (TSF) to generate phase-specific reference torque profiles. MPC employs rolling optimization to compute the optimal duty cycle in real time, achieving low torque ripple and consistent switching frequency during steady-state operation. To overcome the inherent delay in MPC’s dynamic response, DITC is incorporated as a fast-acting compensation loop that activates immediately upon detecting abrupt variations in speed or load, thereby delivering rapid torque adjustment and reinforcing system resilience. For validation, an 8/6-pole SRM control model was developed using Ansys/Maxwell and MATLAB/Simulink, and subjected to multi-scenario simulations. The results reveal that, compared to conventional MPC, the proposed method reduces steady-state torque ripple by 19.4% and shortens dynamic recovery time by 40%, demonstrating superior torque smoothness and improved robustness against external disturbances. Full article

This research proposes a novel hardware-optimized design to accelerate Convolutional Neural Networks (CNNs) using Verilog HDL. The design is specifically developed for the DARKNET-19 system model, which serves as the backbone of the YOLOv3-tiny algorithm, a widely used framework for real-time object detection in dynamic environments. The CNN architecture was implemented in Verilog HDL and synthesized using Synopsys Design Compiler, with a focus on improving both object detection accuracy and hardware resource efficiency. The proposed design efficiently performs key CNN operations, including convolution, pooling, and activation, enabling faster real-time object detection compared to many existing methods. To improve performance, the hardware design incorporates parallel processing techniques, allowing multiple computations to be executed simultaneously. This significantly reduces the system latency and power consumption. The convolutional layers of the DARKNET-19 architecture are efficiently mapped onto the hardware platform, ensuring optimized data storage and fast memory access, which further enhances processing speed and detection accuracy. An innovative feature of the design is a 2-dimensional image preprocessing module that prepares input images before they are fed into the CNN. This preprocessing stage includes image resizing, brightness normalization, and color adjustment, which helps the CNN process visual data more effectively. After preprocessing, the images pass through several CNN layers. The convolutional layers extract key features from the images, while the pooling and activation layers refine these features to improve detection performance. Finally, the processed data is analyzed by the YOLOv3-tiny algorithm, which identifies and locates objects in the images with high precision. Experimental results demonstrate that the proposed high-speed and resource-efficient hardware architecture is well-suited for real-time object detection applications, particularly in highly dynamic and unpredictable environments. Full article

People in midlife interact with several different environments during their daily life including employment, leisure, commuting, and various family responsibilities, a concept defined as activity space. However, little is known about how these activity spaces contribute to individuals’ daily health behavior choices. The Everyday Environments and Experiences (E3) study was conducted to explore these relationships. In this paper, we provide a reproducible GPS processing workflow to generate time-weighted exposure measures (activity spaces) inferred from 21 days of continuous GPS monitoring among 340 midlife adults in Cook County, Illinois (n = 340) from the E3 study. Data from waist-mounted GPS devices that recorded one-minute location epochs were aggregated after excluding time spent within an 800 m buffer around the home. For each epoch, we derived proximity and kernel density measures for eleven food and physical-activity-related location types (e.g., supermarkets, fitness facilities), along with twenty-six environmental context variables (e.g., land use, crime, population density). Time-weighted averages characterized each participant’s typical non-home environmental exposure. After adjustment for environmental context, age and gender were generally unrelated to activity-space measures. However, Black and Hispanic participants (as compared to White participants) spent less time near both food and physical-activity resources, suggesting systemic inequities in access beyond neighborhood composition. These findings highlight the need to move beyond static residential measures toward time-weighted, dynamic assessments of environmental exposure. They also indicate that racial and ethnic disparities in routine activity space may reflect structural inequities shaping daily physical activity and access to healthy food. Future research is needed to explore how these observed disparities translate into differences into disease risk, using longer exposure periods and different geographic settings to identify causal pathways and inform multi-level interventions. Full article

A novel shallow oil chamber hybrid journal bearing with adjustable oil chamber depth was designed based on piezoelectric ceramics, inspired by conventional shallow oil chamber bearing structures. The computational fluid dynamics method is used to analyze the bearing characteristics of shallow oil chamber bearings, including the volume flow, the seal oil pressure, load capacity and stiffness. An experimental platform equipped with signal acquisition device and piezoelectric ceramic control device was developed. The eddy current sensors collected the displacement signal at the shaft end. The required voltage was calculated by the displacement signal. The piezoelectric ceramics elongated or shortened, causing a displacement of the same magnitude in the depth of the oil chamber, thereby controlling the radial displacement of the shaft. The adjustment effect of this bearing was verified by experiment for no-load and 500 N load at 200–1000 rpm, with a baseline initial oil chamber depth of 20 and an oil supply pressure of 2 MPa. The results showed that compared with the case without adjustment, the accuracy in Y direction has increased from 8.9 μm to 1.9 μm (max. 78.4%) after adjustment. Under the above load conditions, the displacement can be controlled below 2 μm, indicating a significant improvement in shaft vibration resistance. Full article

In recent years, video recognition models have witnessed the rapid development of Deep Neural Networks (DNNs). However, these models remain not robust to adversarial examples that are created by adding imperceptible perturbations to clean samples. Recent studies indicate that generating adversarial examples in the hard-label black-box setting is particularly challenging yet highly practical. Compared to image recognition models, there are few hard-label black-box adversarial example generation algorithms for video recognition models. To this end, we propose a hard-label black-box video adversarial example generation algorithm, referred to as Dynamic Black-box Algorithm (DBA). First, DBA uses the binary search algorithm to find the boundary video between two original videos; then, the sampling-based algorithm is used to estimate the gradient on the boundary video; finally, with a dynamic step size adjustment strategy, DBA moves the boundary video towards the direction of the estimated gradient to generate the adversarial video. Additionally, we designed another strategy to skip invalid samples generated during the adversarial example generation process. Experiments demonstrate that DBA attains a superior trade-off between the magnitude of perturbations and query efficiency. Specifically, DBA outperforms state-of-the-art algorithms, achieving an average reduction in Mean Squared Error (MSE) of over 50%. Full article

To investigate the influence of asymmetric support clearances (caused by manufacturing and assembly tolerances in practical engineering) on the fretting wear behavior of steam generator heat exchange tubes, this study focuses on 2.25Cr-1Mo alloy heat exchange tubes and 405 stainless steel anti-vibration bars. A high-precision impact wear test platform with adjustable bilateral clearances was designed, and its dynamic reliability was verified by theoretical calculations, finite element simulations and modal tests. An experimental model with asymmetric clearances (0.15 mm and 0.20 mm) was established to study the nonlinear contact force response and wear evolution under excitation frequencies of 60 Hz, 65 Hz and 70 Hz. The results show that asymmetric clearances induce two contact modes: high-frequency “quasi-static friction” on the small-clearance side and intermittent “collision-rebound-flight” impacts on the large-clearance side. The system exhibits a clear excitation instability threshold that shifts backward with increasing excitation frequency. The 0.20 mm side triggers dynamic instability, with wear volume and rate increasing explosively (106.2% and 41.36% at 65 Hz) beyond the threshold. Microscopic analysis reveals that the wear mechanism on the large-clearance side transitions from mild abrasive wear to severe fatigue delamination when crossing the threshold, with surface morphology deteriorating sharply from faint contact spots to extensive spalling craters. This study clarifies the energy distribution mechanism and identifies the large-clearance side as the core “trigger” for system instability and catastrophic failure, providing a theoretical basis for nuclear heat exchange tube monitoring and anti-vibration design. Full article

In aerospace applications, the vibration of aircraft structures results in a reduction in their fatigue life. Vibration-suppression technology utilizing macro fiber composite (MFC) materials constitutes a significant research direction. Aiming at the specific requirements of the MFC actuator operating in the asymmetric high-voltage range of −500 V to 1500 V and the miniaturization of the drive system for aircraft, this study designs a compact self-tuning digital high-voltage controller which adopts a discontinuous conduction mode (DCM) flyback topology as the fundamental model for the switching power supply high-voltage controller, uses the STM32G431 chip as the main controller, and incorporates a Type-II digital compensator designed to enhance the system stability under constant parameters. A Backpropagation (BP) neural network is proposed to enable dynamic adjustment of the digital compensator control parameters, thereby achieving self-tuning, while also supporting program download and real-time data transmission. The high-voltage controller effectively addresses the size and weight constraints in vibration active control systems. Laboratory tests demonstrated its excellent transient response and robust load-driving capability. Vibration-suppression experiments on a high-aspect-ratio UAV wing achieved a 74% vibration attenuation rate, validating the effectiveness of the proposed high-voltage controller. Full article

The monitoring of harmful algal blooms (HABs) constitutes a vital component of marine environmental protection and the sustainable development of the marine economy. However, the highly dynamic nature of these small targets, compounded by the complex water color interference prevalent in the coastal waters where HABs frequently occur, has resulted in traditional remote sensing monitoring methods, particularly those relying on fixed spectral index thresholds and pixel-wise binarization, suffering from imprecise identification in turbid coastal waters where suspended sediments, cloud cover, and sun glint create spectral confusion. These methods also exhibit low automation due to manual threshold adjustment requirements and poor transferability across different spatiotemporal conditions. Consequently, these methods struggle to meet practical application requirements. This study establishes a U-net model-based remote sensing identification framework for red tides using HY-1D CZI imagery (50 m resolution, 1–3 day revisit), targeted negative sample strategies, and event-level accuracy validation methods to achieve efficient marine red tide detection. Targeted negative sample selection involves purposefully selecting spectrally ambiguous regions as negative samples, aiming to enhance recognition accuracy and sample selection efficiency. The combination of targeted sampling with deep learning enables portability to new spatiotemporal contexts by learning invariant spectral–spatial features rather than relying on scene-specific thresholds. Experimental results demonstrate that the targeted negative sample strategy reduces event-level model false negatives by 27%, false positives by 36%, and increases the F1 score by 0.3217. Using an identical sample size, the targeted sample selection strategy yields an F1 score 0.0479 higher than random sampling. To achieve equivalent recognition accuracy, an increased number of random samples would be required. Comparative experiments reveal that the proposed method enhances sample selection efficiency by 87.5%. Transferability is demonstrated through successful identification of red tide patches in Wenzhou waters on 13 April 2022, without model retraining. This demonstrates that red tide remote sensing recognition based on targeted sample selection enables efficient, precise, and automated identification without human intervention, providing a reliable technical solution for operational marine red tide monitoring. Full article