Search Results (1,878)

In modern high-performance computing (HPC) and large-scale data processing environments, the efficient utilization and scalability of memory resources are critical determinants of overall system performance. Architectures such as non-uniform memory access (NUMA) and tiered memory systems frequently suffer performance degradation due to remote accesses stemming from shared data among multiple tasks. This paper proposes LACX, a shared data migration technique leveraging Compute Express Link (CXL), to address these challenges. LACX preserves the migration cycle of automatic NUMA balancing (AutoNUMA) while identifying shared data characteristics and migrating such data to CXL memory instead of DRAM, thereby maximizing DRAM locality. The proposed method utilizes existing kernel structures and data to efficiently identify and manage shared data without incurring additional overhead, and it effectively avoids conflicts with AutoNUMA policies. Evaluation results demonstrate that, although remote accesses to shared data can degrade performance in low-tier memory scenarios, LACX significantly improves overall memory bandwidth utilization and system performance in high-tier memory and memory-intensive workload environments by distributing DRAM bandwidth. This work presents a practical, lightweight approach to shared data management in tiered memory environments and highlights new directions for next-generation memory management policies. Full article

To address the problems of local optima and insufficient convergence accuracy in parameter identification of primary frequency regulation (PFR) for steam turbines, this paper proposed a hybrid identification method that integrated an Improved Bayesian Optimization (IBO) algorithm and an Improved Whale Optimization Algorithm (IWOA). By initializing the Bayesian parameter population using Tent chaotic mapping and the reverse learning strategy, employing a radial basis kernel function hyperparameter training mechanism based on the Adam optimizer and optimizing the Expected Improvement (EI) function using the Limited-memory Broyden–Fletcher– Goldfarb–Shanno with Bounds (L-BFGS-B) method, IBO was proposed to obtain the optimal candidate set with the smallest objective function value. By introducing a nonlinear convergence factor and the adaptive Levy flight perturbation strategy, IWOA was proposed to obtain locally optimized optimal solutions. By using the reverse-guided optimization mechanism and employing a fitness-oriented selection strategy, the optimal solution was chosen to complete the closed-loop process of reverse learning feedback. Nine standard test functions and the Proportional Integral Derivative (PID) parameter identification of the electro-hydraulic servo system in a 330 MW steam turbine were presented as examples. Compared with Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Bayesian Optimization (BO) and Particle Swarm Optimization-Grey Wolf Optimizer (PSO-GWO), the Improved Bayesian Optimization-Whale Optimization Algorithm (IBO-WOA) proposed in this paper has been validated to effectively avoid the problem of getting stuck in local optima during complex optimization and has high parameter recognition accuracy. Meanwhile, an Out-Of-Distribution (OOD) Test based on noise injection had demonstrated that IBO-WOA had good robustness. The time constant identification of the steam turbine were carried out using IBO-WOA under two experimental conditions, and the identification results were input into the PFR model. The simulated power curve can track the experimental measured curve well, proving that the parameter identification results obtained by IBO-WOA have high accuracy and can be used for the modeling and response characteristic analysis of the steam turbine PFR. Full article

The high-latitude cold regions of northeastern China present scarce thermal resources, exhibit a short frost-free period, and lack high-yielding maize (Zea mays L.) varieties suitable for dense planting. These factors have long constrained the realization of maize yield potential under dense planting conditions. This study investigated the effects of intercropping maize varieties with different growth periods on the photosynthetic performance, yield formation, and interspecific competition. The long-season varieties Zhengdan958 (ZD958) and Xianyu335 (XY335), which are representative of the region, were intercropped with the shorter-season variety Yinongyu10 (YNY10), six intercropping row ratios (6:6, 4:4, 2:2, 1:1, 0:1, and 1:0) were set, and monoculture plots (0:1 and 1:0) were used as the controls. The results indicated that as the row ratio decreased in the intercropped plots, the leaf area index, relative leaf chlorophyll content, photosynthetic rate, stomatal conductance, and transpiration rate increased while the intercellular CO₂ concentration gradually decreased compared with those in the monoculture plots. Simultaneously, dry matter accumulation, allocation, transport efficiency, 100-kernel weight, number of kernels per ear, and grain yield progressively increased, reaching maximum values at a 1:1 intercropping row ratio. Conversely, YNY10 in the intercropped plots exhibited opposite trends in these parameters. The land equivalent ratios for all intercropped row ratios exceeded 1. During the 2023–2024 growing season, the composite population grain yield was significantly higher (p < 0.05) at an intercropping row ratio of 1:1 for ZD958 (4.11–4.26%) and XY335 (3.54–3.65%) compared with the monoculture treatments, demonstrating the strong yield advantage of intercropping. Furthermore, in the intercropping systems, ZD958 and XY335 exhibited positive aggressivity and a competitive ratio greater than 1, thus showing stronger competitive ability than YNY10. Moreover, the increased grain yield of ZD958 and XY335 effectively compensated for the ecological disadvantages of YNY10, thereby leveraging the synergistic effects of close planting and intercropping patterns to promote improvements in maize composite population productivity. Full article

Accurate angular alignment of wafers is essential in ion implantation to prevent channeling effects that degrade device performance. This study proposes a real-time notch-angle-detection system based on you only look once version 8 with oriented bounding boxes (YOLOv8-OBB). The proposed method compares YOLOv8 and YOLOv8-OBB, demonstrating the superiority of the latter in accurately capturing rotational features. To enhance detection performance, hyperparameters—including initial learning rate (Lr0), weight decay, and optimizer—are optimized using an one factor at a time (OFAT) approach followed by grid search. Architectural improvements, including spatial pyramid pooling fast with large selective kernel attention (SPPF_LSKA), a bidirectional feature pyramid network (BiFPN), and a high-resolution detection head (P2 head), are incorporated to improve small-object detection. Furthermore, a gradual unfreezing strategy is employed to support more effective and stable transfer learning. The final system is evaluated over 100 training epochs and tracked up to 5000 epochs to verify long-term stability. Compared to baseline models, it achieves higher accuracy and robustness in angle-sensitive scenarios, offering a reliable and scalable solution for high-precision wafer-notch detection in semiconductor manufacturing. Full article

Mineral prospectivity mapping (MPM) is a powerful approach for identifying mineralization zones with high potential for economically viable mineral deposits. This study proposes a hybrid framework combining a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), a Convolutional Neural Network (CNN) and a Fuzzy-Kernel Extreme Learning Machine (FKELM) to address the challenges of imbalanced and uncertain datasets in mineral exploration. The approach was applied to the G.R. Halli gold prospect, in the Chitradurga Schist Belt, Western Dharwar Craton, India, using nine geochemical pathfinder elements. WGAN-GP generated high-quality negative samples, balancing the dataset and reducing overfitting. Compared with Support Vector Machines, Gradient Boosting, and a baseline CNN, FKELM (AUC = 0.976, accuracy = 92%) and WGAN-GP + CNN (AUC = 0.973, accuracy = 91%) showed superior performance and produced geologically coherent prospectivity maps. Promising gold targets were delineated, closely aligned with known mineralized zones and geochemical anomalies. This hybrid framework provides a robust, cost-effective, and scalable MPM solution for structurally controlled geological tracts, insufficient data terrains, and integration with additional geoscience datasets for other complex mineral systems. Full article

Driven by the dual-carbon goals, photovoltaic (PV) battery systems at renewable energy stations are increasingly clustered on the distribution side. The rapid expansion of these clusters, together with the pronounced uncertainty and spatio-temporal heterogeneity of PV generation, degrades battery utilization and forces conservative dispatch. To address this, we propose a “spatio-temporal clustering–deep estimation” framework for short-term interval forecasting of PV clusters. First, a graph is built from meteorological–geographical similarity and partitioned into sub-clusters by a self-supervised DAEGC. Second, an attention-based spatio-temporal graph convolutional network (ASTGCN) is trained independently for each sub-cluster to capture local dynamics; the individual forecasts are then aggregated to yield the cluster-wide point prediction. Finally, kernel density estimation (KDE) non-parametrically models the residuals, producing probabilistic power intervals for the entire cluster. At the 90% confidence level, the proposed framework improves PICP by 4.01% and reduces PINAW by 7.20% compared with the ASTGCN-KDE baseline without spatio-temporal clustering, demonstrating enhanced interval forecasting performance. Full article

Small unmanned aerial vehicles are now commonly equipped with integrated navigation systems to obtain high-precision navigation parameters. However, affected by the dual impacts of multipath effects and dynamic environmental changes, their state estimation process is vulnerable to interference from measurement outliers, which in turn leads to the degradation of navigation accuracy and poses a threat to flight safety. To address this issue, this research presents an adaptive minimum error entropy cubature Kalman filter. Firstly, the cubature Kalman filter is introduced to solve the problem of model nonlinear errors; secondly, the cubature Kalman filter based on minimum error entropy is derived to effectively curb the interference that measurement outliers impose on filtering results; finally, a kernel bandwidth adjustment factor is designed, and the kernel bandwidth is estimated adaptively to further improve navigation accuracy. Through numerical simulation experiments, the robustness of the proposed method with respect to measurement outliers is validated; further flight experiment results show that compared with existing related filters, this proposed filter can achieve more accurate navigation and positioning. Full article

This paper introduces a new approach to obtain robust tracking performance, disturbance resistance, and input variation resistance, and eliminate chattering phenomena in the control signal and output responses of an unmanned aerial vehicle (UAV) quadcopter with parametric uncertainty. This method involves a modified exponential reaching law (ERL) of the sliding mode control (SMC) based on a Gaussian kernel function with a continuous nonlinear Smoother Signum Function (SSF). The smooth continuous signum function is proposed as a substitute for the signum function to prevent the chattering effect caused by the switching sliding surface. The closed-loop system’s stability is ensured according to Lyapunov’s stability theory. Optimal trajectory tracking is attained based on particle swarm optimization (PSO) to select the controller parameters. A comparative analysis with a classical hierarchical SMC based on different ERLs (sign function, saturation function, and SSF) is presented to further substantiate the superior performance of the proposed controller. The outcomes of the simulation prove that the suggested controller has much better effectiveness, unknown disturbance resistance, input variation resistance, and parametric uncertainty than the other controllers, which produce chattering and make the control signal range fall within unrealistic values. Furthermore, the suggested controller outperforms the classical SMC by reducing the tracking integral mean squared errors by 96.154% for roll, 98.535% for pitch, 44.81% for yaw, and 22.8% for altitude under normal flight conditions. It also reduces the tracking mean squared errors by 99.05% for roll, 99.26% for pitch, 40.18% for yaw, and 99.998% for altitude under trajectory tracking flight conditions in the presence of external disturbances. Therefore, the proposed controller can efficiently follow paths in the presence of parameter uncertainties, input variation, and external disturbances. Full article

This study focuses on human awareness, a critical component in human–robot interaction, particularly within agricultural environments where interactions are enriched by complex contextual information. The main objective is identifying human activities occurring during collaborative harvesting tasks involving humans and robots. To achieve this, we propose a novel and lightweight deep learning model, named 1D-ResNeXt, designed explicitly for recognizing activities in agriculture-related human–robot collaboration. The model is built as an end-to-end architecture incorporating feature fusion and a multi-kernel convolutional block strategy. It utilizes residual connections and a split–transform–merge mechanism to mitigate performance degradation and reduce model complexity by limiting the number of trainable parameters. Sensor data were collected from twenty individuals with five wearable devices placed on different body parts. Each sensor was embedded with tri-axial accelerometers, gyroscopes, and magnetometers. Under real field conditions, the participants performed several sub-tasks commonly associated with agricultural labor, such as lifting and carrying loads. Before classification, the raw sensor signals were pre-processed to eliminate noise. The cleaned time-series data were then input into the proposed deep learning network for sequential pattern recognition. Experimental results showed that the chest-mounted sensor achieved the highest F1-score of 99.86%, outperforming other sensor placements and combinations. An analysis of temporal window sizes (0.5, 1.0, 1.5, and 2.0 s) demonstrated that the 0.5 s window provided the best recognition performance, indicating that key activity features in agriculture can be captured over short intervals. Moreover, a comprehensive evaluation of sensor modalities revealed that multimodal fusion of accelerometer, gyroscope, and magnetometer data yielded the best accuracy at 99.92%. The combination of accelerometer and gyroscope data offered an optimal compromise, achieving 99.49% accuracy while maintaining lower system complexity. These findings highlight the importance of strategic sensor placement and data fusion in enhancing activity recognition performance while reducing the need for extensive data and computational resources. This work contributes to developing intelligent, efficient, and adaptive collaborative systems, offering promising applications in agriculture and beyond, with improved safety, cost-efficiency, and real-time operational capability. Full article

This study presents a molecular dynamics (MD) investigation of water evaporation in copper nanochannels, with a focus on accurately modeling copper–water interactions through forcefield calibration. The TIP4P/2005 water model was coupled with the Modified Embedded Atom Method (MEAM) for copper, and the oxygen–copper Lennard–Jones (LJ) parameters were systematically tuned to match experimentally reported water contact angles (WCAs) on Cu (111) surfaces. Contact angles were extracted from simulation trajectories using a robust five-step protocol involving 2D kernel density estimation, adaptive thresholding, circle fitting, and mean squared error (MSE) validation. The optimized forcefield demonstrated strong agreement with experimental WCA values (50.2°–82.3°), enabling predictive control of wetting behavior by varying ε in the range 0.20–0.28 kcal/mol. Using this validated parameterization, we explored nanoscale evaporation in copper channels under varying thermal loads (300–600 K). The results reveal a clear temperature-dependent transition from interfacial-layer evaporation to bulk-phase vaporization, with evaporation onset and rate governed by the interplay between copper–water adhesion and thermal disruption of hydrogen bonding. These findings provide atomistically resolved insights into wetting and evaporation in metallic nanochannels, offering a calibrated framework for simulating phase-change heat transfer in advanced thermal management systems. Full article

The railway architectural heritage in Jilin Province, as a significant component of Northeast China’s modern railway network, demonstrates how construction techniques, cultural integration, and social transformation have evolved throughout different historical periods. In this study, we conducted a systematic survey of 474 railway heritage buildings along the province’s main line. In order to quantitatively classify the spatiotemporal distribution characteristics of the heritage sites, we used five key Geographic Information System (GIS) methods—kernel density estimation, nearest neighbour index, spatial autocorrelation, standard deviational ellipses, and mean centre analysis—along with information entropy, relative richness, and the Bray–Curtis dissimilarity index. We continued our binary logistic regression using four prerequisite parameters—location, structure, architecture, and function—which contribute to the prerequisite, fundamental, and driving factors of architectural heritage. We concluded that local culture shapes geopolitics, population migration triggers economic conservation, and design trends carry ideology. These three factors intertwine to influence architecture and spatial patterns. Compared with previous studies, this research fills the gap concerning the architectural characteristics of towns at various lower-and mid-level stations, as well as the construction activities during the affiliated land period. This study provides a systematic framework for analysing railway heritage corridors and supports their sustainable conservation and reuse. Full article

System call telemetry is essential for understanding runtime behavior in cloud-native infrastructures, but existing eBPF-based monitors suffer from high per-event overhead, unreliable delivery under load, and limited context for correlating multi-step activities. These issues reduce scalability, create blind spots in telemetry streams, and complicate the analysis of complex workload behaviors. This work presents Aquila, a lightweight telemetry framework that emphasizes efficiency, reliability, and semantic fidelity. Aquila employs a dual-path kernel pipeline that separates fixed-size metadata from variable-length attributes, reducing serialization costs and enabling high-throughput event processing. It introduces priority-aware buffering and explicit drop detection to retain loss-sensitive events while providing visibility into overload conditions. In the user space, kernel traces are enriched with Kubernetes metadata, mapping low-level system calls to pods, containers, and namespaces. Evaluation under representative workloads shows that Aquila improves scalability, reduces event loss, and enhances the semantic completeness of system call telemetry compared with existing approaches. Full article

Financial sustainability in higher education is increasingly fragile due to policy shifts, rising costs, and funding volatility. Legacy early-warning systems based on static thresholds or rules struggle to adapt to these dynamics and often overlook fairness and interpretability—two essentials in public-sector governance. We propose a university financial risk early-warning framework that couples a causal-attention Transformer with Multi-Objective Bayesian Optimization (MBO). The optimizer searches a constrained Pareto frontier to jointly improve predictive accuracy (AUC↑), fairness (demographic parity gap, DP_Gap↓), and computational efficiency (time↓). A sparse kernel surrogate (SKO) accelerates convergence in high-dimensional tuning; a dual-head output (risk probability and health score) and SHAP-based attribution enhance transparency and regulatory alignment. On multi-year, multi-institution data, the approach surpasses mainstream baselines in AUC, reduces DP_Gap, and yields expert-consistent explanations. Methodologically, the design aligns with LLM-style time-series forecasting by exploiting causal masking and long-range dependencies while providing governance-oriented explainability. The framework delivers earlier, data-driven signals of financial stress, supporting proactive resource allocation, funding restructuring, and long-term planning in higher education finance. Full article

Balancing the development of agricultural cold chain logistics with ecological conservation remains a critical challenge for green cold chain logistics in China’s three northeastern provinces. This study evaluates the efficiency of green cold chain logistics to promote synergy between logistics development and ecological sustainability. Using CiteSpace for keyword co-occurrence analysis and literature extraction, an evaluation index system comprising eight input and output indicators was constructed. The super-efficiency Slacks-Based Measure (SBM) model and the Malmquist–Luenberger (ML) productivity index were employed to assess efficiency from static and dynamic perspectives, respectively. Kernel density estimation was used to examine spatial distribution patterns, and the Dagum Gini coefficient was applied to decompose regional disparities. The results indicate that (1) overall efficiency remains relatively low, with ML index changes primarily driven by technological progress; (2) substantial regional differences exist among the three provinces in terms of distribution location, shape, and degree of polarization; and (3) inter-regional disparities are the main source of variation. A Tobit model further identified the key influencing factors, indicating that the level of economic development, growth of the tertiary industry, and informatization are the main drivers. These findings provide valuable insights for optimizing regional green cold chain logistics and promoting sustainable agricultural development. Full article

A precise understanding of the carbon dynamics of agricultural land use is essential for advancing China’s “dual carbon” goals and promoting sustainable rural development. Drawing on panel datasets for 31 Chinese provinces over the period 1997–2022, this study comprehensively analyzes the spatiotemporal evolution, regional disparities, and transition dynamics of agricultural carbon capture and emissions. Using a combination of the emission factor method, the Dagum Gini coefficient, kernel density estimation, and Markov chain models, the study finds that China’s total agricultural carbon capture has continued to increase, yet regional disparities are widening, with the central region leading and the northeastern region lagging. Meanwhile, agricultural carbon emissions exhibit a “strong west, weak east” spatial pattern and demonstrate a high degree of club convergence. Club convergence refers to the phenomenon where regions with similar initial levels converge to the same steady-state over the long run, while remaining persistently different from other regions. The net carbon effect exhibits a dual structure of carbon surplus zones and carbon deficit zones: 23 provinces act as carbon surplus zones, while 8 provinces are carbon deficit zones, primarily located in ecologically fragile or special-function regions. These findings highlight the spatial heterogeneity, path dependence, and policy sensitivity of carbon effects from agricultural land use. Accordingly, the study proposes differentiated policy recommendations, including region-specific carbon management strategies, the establishment of a unified agricultural carbon trading system, and the integration of technological and institutional innovations to achieve a balanced and low-carbon agricultural transformation. Full article