Search Results (116)

The growing adoption of supercomputers across various scientific disciplines, particularly by researchers without a background in computer science, has intensified the demand for parallel applications. These applications are typically developed using a combination of programming models within languages such as C, C++, and Fortran. However, modern multi-core processors and accelerators necessitate fine-grained control to achieve effective parallelism, complicating the development process. To address this, developers commonly utilize high-level programming models such as Open Multi-Processing (OpenMP), Open Accelerators (OpenACCs), Message Passing Interface (MPI), and Compute Unified Device Architecture (CUDA). These models may be used independently or combined into dual- or tri-model applications to leverage their complementary strengths. However, integrating multiple models introduces subtle and difficult-to-detect runtime errors such as data races, deadlocks, and livelocks that often elude conventional compilers. This complexity is exacerbated in applications that simultaneously incorporate MPI, OpenMP, and CUDA, where the origin of runtime errors, whether from individual models, user logic, or their interactions, becomes ambiguous. Moreover, existing tools are inadequate for detecting such errors in tri-model applications, leaving a critical gap in development support. To address this gap, the present study introduces a static analysis tool designed specifically for tri-model applications combining MPI, OpenMP, and CUDA in C++-based environments. The tool analyzes source code to identify both actual and potential runtime errors prior to execution. Central to this approach is the introduction of error dependency graphs, a novel mechanism for systematically representing and analyzing error correlations in hybrid applications. By offering both error classification and comprehensive static detection, the proposed tool enhances error visibility and reduces manual testing effort. This contributes significantly to the development of more robust parallel applications for high-performance computing (HPC) and future exascale systems. Full article

The fir-tree blade tenon is an important connection part of the turbine blade; its machining quality directly affects the life and power of the aeroengine. At present, the machining of the blade tenon requires multiple profile grinding processes. This study highlights the whole profile of the grinding processes of K4002 nickel-based superalloy blade tenons in ultrasonic vibration-assisted grinding (UVG). A probability superposition method was utilized to calculate the undeformed chip thickness and contact rate considering the random distribution of the abrasive grains and the overlap of the grinding trajectories. Subsequently, the grinding force, grinding temperature and surface integrity of the blade tenons in conventional grinding (CG) and UVG were investigated. The results indicate that the ultrasonic vibration causes intermittent cutting behavior which can reduce the contact rate to 0.6 at most. The grinding force, grinding temperature and surface integrity are deeply affected by the fir-tree shape of the blade tenon. The maximum grinding force occurs at the start of the full contact stage; surface burnout easily occurs in the middle top area of the blade tenon. Compared to CG, the use of UVG leads to an average reduction in the grinding force and temperature by 20% and 23%, respectively, improving the surface burnout of the K4002 superalloy. Full article

Driven by policy incentives and economic pressures, energy-intensive industries are increasingly focusing on energy cost reductions amid the rapid adoption of renewable energy. However, the existing studies often isolate photovoltaic-energy storage system (PV-ESS) configurations from detailed load scheduling, limiting industrial park energy management. To address this, we propose a two-layer cooperative optimization approach (TLCOA). The upper layer employs a genetic algorithm (GA) to optimize the PV capacity and energy storage sizing through natural selection and crossover operations, while the lower layer utilizes mixed integer linear programming (MILP) to derive cost-minimized scheduling strategies under time-of-use tariffs. Multi-process parallel computing accelerates the fitness evaluations, resolving high-dimensional industrial data challenges. Multi-process parallel computing is introduced to accelerate fitness evaluations, effectively addressing the challenges posed by high-dimensional industrial data. Validated with real power market data, the TLCOA demonstrated rapid adaptation to load fluctuations while achieving a 23.68% improvement in computational efficiency, 1.73% reduction in investment costs, 7.55% decrease in power purchase costs, and 8.79% enhancement in renewable energy utilization compared to traditional methods. This integrated framework enables cost-effective PV-ESS deployment and adaptive energy management in industrial facilities, offering actionable insights for renewable integration and scalable energy optimization. Full article

Parallel computing techniques have been adopted in geospatial cellular automata (CA) models to improve computational efficiency, enabling large-scale complex simulations of land use and land cover (LULC) changes at fine scales. However, the spatial distribution of computational intensity often changes along with the spatiotemporal dynamics of LULC during the simulation, leading to an increase in load imbalance among computing units and degradation of the computational performance of a parallel CA. This paper presents a dynamic load balancing method based on hypergraph partitioning for multi-process parallel geospatial CA models. During the simulation, the sub-domains are dynamically reassigned to computing processes through hypergraph partitioning according to the spatial variation in computational workloads to restore load balance. In addition, a novel mechanism called Migrated-SubCellspaces-First (MSCF) is proposed to reduce the cost of workload migration by employing a non-blocking communication technique to further improve computational performance. To demonstrate and evaluate the effectiveness of our method, a parallel geospatial CA model with hypergraph-based dynamic load balancing is developed. Experiments using a dataset from California showed that the proposed dynamic load balancing method achieved a computational performance enhancement of 62.59% by using 16 processes compared with a parallel CA with static load balancing. Full article

In this paper, 316 L stainless steel deposited samples were fabricated by direct layer deposition (DED) using both continuous-wave (CW) and pulsed-wave (PW) laser modes. Effects of laser modes on residual stress of deposited samples were investigated. On this basis, a mathematical model of thermal stress evolution during DED was established for the first time based on the energy equation. The variation law of thermal stress on the top of the substrate under multi-material and multi-process conditions was qualitatively predicted and the corresponding residual stress reduction mechanism has been studied using this model. Meanwhile, in situ thermal strain evolution is used to prove the correctness of the mathematical model. This model lays the foundation for predicting the thermal stress evolution and the magnitude of the residual stress of deposited samples under multi-material and process conditions during DED. Full article

Flood models are essential for simulating and analysing urban flooding; however, accurately capturing the complex physical processes and their interactions remains challenging. This research introduces a multi-process flood modelling framework designed to generate realistic urban flood simulations. It integrates various hydrological and hydrodynamic processes through data-exchange synchronisation. A new surface flood control model (SFCM) was developed and applied in Huai’an District, China, using the storm water management model as its foundation. The SFCM was used to assess storm events, detect drainage outlets hindered by high river network water levels during extreme rainfall, and evaluate how river backflow affects drainage overflow and surface flooding. Results indicated that higher return periods of rainstorms reduced the number of drainage outlets obstructed by backwater, though backwater worsened surface flooding and drainage overflow. Compared to the current capacity of drainage outlets, using the maximum drainage capacity reduced the overflow rate of rainwater wells by 10.62% on average but increased river cross-section overflow by 1.72%. The average surface inundation area and maximum depth decreased by 0.78 km² and 0.05 m, respectively. This research introduces an innovative approach for simulating and analysing large-scale urban flooding, offering essential perspectives for urban planning and strategies to prevent flooding. Full article

One of the important problems of sustainable logistics is routing vehicles in a sustainable manner, the green vehicle routing problem, or vehicle routing problems which aim to reduce CO₂ emissions. In the literature research, it was seen that these problems were solved with heuristic, metaheuristic, or hyper-heuristic methods and hybrid approaches since they are in the NP-hard class. This work presents a parallel multi-process genetic algorithm that incorporates problem-specific genetic operators to minimize CO₂ emissions in the capacity-constrained vehicle routing problem. Unlike previous research, the algorithm combines parallel computing with tailored genetic operators in order to enhance the diversity of solutions and speed up convergence. Genetic algorithm models were developed to minimize total distance, CO₂ emissions, and both objectives simultaneously. Two genetic algorithm models were developed to minimize total distance and CO₂ emissions. Experimental results using the reference CVRP examples such as A-n32-k5 and B-n44-k7 show that the proposed approach reduces CO₂ emissions by 1.2% more than hybrid artificial bee colony optimization, 1.3% more than ant colony optimization, and 4% more than the traditional genetic algorithm. Experimental results using benchmark CVRP instances demonstrate that the proposed approach outperforms hybrid artificial bee colony optimization, ant colony optimization, and traditional genetic algorithms for most of the test cases. This is done by exploiting multi-core processors, and the parallel architecture has improved computational efficiency; the modules compare and update solutions against the global optimum. Results obtained show that prioritizing CO₂ emissions as the only objective yields better results compared to multi-objective models. This study makes two significant contributions to the literature: (1) it introduces a novel parallel genetic algorithm framework optimized for CO₂ emission reduction, and (2) it provides empirical evidence underscoring the advantages of emission-focused optimization in CVRP. Full article

Previous research has demonstrated that episodic future thinking (EFT) can enhance prospective memory (PM); however, its effects on older adults have been less explored. This study examines the impact of EFT training on PM in both older and younger adults under varying delay intervals. Experiment 1 employed a 2 (EFT training: present vs. absent) × 2 (age: younger adults vs. older adults) × 2 (delay interval: 5 min vs. 20 min) between-subjects design. The results revealed a significant main effect of EFT training (p < 0.001), indicating that such training improves PM performance. Among younger adults, a significant difference in PM performance was found between the trained and untrained groups (p = 0.03), while among older adults, this difference was only marginally significant. This suggests that the facilitative effect of EFT training is more pronounced in younger adults. Additionally, there was a significant main effect of delay interval (p = 0.01), with shorter intervals yielding better PM performance than longer intervals. Experiment 2 focused on the impact of specificity in EFT training on PM in both age groups. A 2 (training: specific vs. non-specific) × 2 (age: younger vs. older adults) × 2 (delay interval: 5 min vs. 20 min) between-subjects design was used. Results indicated that older adults in the specific training group outperformed those in the non-specific training group (p = 0.03), whereas no difference was observed among younger adults. This finding suggests that specific training is more effective for enhancing prospective memory in older adults. Moreover, older adults exhibited differences based on the delay interval, with a 20 min interval impairing performance (p = 0.04), while younger adults showed no difference between the two intervals. These findings will be discussed in relation to the Multiprocess Model and the Preparatory Attention and Memory Processes Theory. Full article

Background/Objectives: Magnetic resonance spectroscopy (MRS) is a valuable tool for studying metabolic processes in vivo. While numerous quantification methods exist, the advanced method for accurate, robust, and efficient spectral fitting (AMARES) is among the most used. This study introduces pyAMARES, an open-source Python implementation of AMARES, addressing the need for a flexible, user-friendly, and versatile MRS quantification tool within the Python ecosystem. Methods: PyAMARES was developed as a Python library, implementing the AMARES algorithm with additional features such as multiprocessing capabilities and customizable objective functions. The software was validated against established AMARES implementations (OXSA and jMRUI) using both simulated and in vivo MRS data. Monte Carlo simulations were conducted to assess robustness and accuracy across various signal-to-noise ratios and parameter perturbations. Results: PyAMARES utilizes spreadsheet-based prior knowledge and fitting parameter settings, enhancing flexibility and ease of use. It demonstrated comparable performance to existing software in terms of accuracy, precision, and computational efficiency. In addition to conventional AMARES fitting, pyAMARES supports fitting without prior knowledge, frequency-selective AMARES, and metabolite residual removal from mobile macromolecule (MM) spectra. Utilizing multiple CPU cores significantly enhances the performance of pyAMARES. Conclusions: PyAMARES offers a robust, flexible, and user-friendly solution for MRS quantification within the Python ecosystem. Its open-source nature, comprehensive documentation, and integration with popular data science tools enhance reproducibility and collaboration in MRS research. PyAMARES bridges the gap between traditional MRS fitting methods and modern machine learning frameworks, potentially accelerating advancements in metabolic studies and clinical applications. Full article

Addressing the resource constraints, such as manpower and equipment, faced by mine backfilling operations, this study proposed an optimization model for backfill scheduling based on the Resource-Constrained Project Scheduling Problem (RCPSP). The model considered backfilling’s multi-process, multi-task, and multi-resource characteristics, aiming to minimize total delay time. Constraints included operational limits, resource requirements, and availability. The goal was to determine optimal resource configurations for each stope’s backfilling steps. A heuristic genetic algorithm (GA) was employed for solution. To handle equipment unavailability, a new encoding/decoding algorithm ensured resource availability and continuous operations. Case verification using real mine data highlights the advantages of the model, showing a 20.6% decrease in completion time, an 8 percentage point improvement in resource utilization, and a 47.4% reduction in overall backfilling delay time compared to traditional methods. This work provides a reference for backfilling scheduling in similar mines and promotes intelligent mining practices. Full article

Investment theory and related theoretical approaches suggest a dynamic interplay between crystallized intelligence, fluid intelligence, and investment traits like need for cognition. Although cross-sectional studies have found positive correlations between these constructs, longitudinal research testing all of their relations over time is scarce. In our pre-registered longitudinal study, we examined whether initial levels of crystallized intelligence, fluid intelligence, and need for cognition predicted changes in each other. We analyzed data from 341 German students in grades 7–9 who were assessed twice, one year apart. Using multi-process latent change score models, we found that changes in fluid intelligence were positively predicted by prior need for cognition, and changes in need for cognition were positively predicted by prior fluid intelligence. Changes in crystallized intelligence were not significantly predicted by prior Gf, prior NFC, or their interaction, contrary to theoretical assumptions. This pattern of results was largely replicated in a model including all constructs simultaneously. Our findings support the notion that intelligence and investment traits, particularly need for cognition, positively interact during cognitive development, but this interplay was unexpectedly limited to Gf. Full article

Motion-compensated image reconstruction enables new clinical applications of Magnetic Resonance Imaging (MRI), but it relies on computationally intensive algorithms. This study focuses on the Generalized Reconstruction by Inversion of Coupled Systems (GRICS) program, applied to the reconstruction of 3D images in cases of non-rigid or rigid motion. It uses hybrid parallelization with the MPI (Message Passing Interface) and OpenMP (Open Multi-Processing). For clinical integration, the GRICS needs to efficiently harness the computational resources of compute nodes. We aim to improve the GRICS’s performance without any code modification. This work presents a performance study of GRICS on two CPU architectures: Intel Xeon Gold and AMD EPYC. The roofline model is used to study the software–hardware interaction and quantify the code’s performance. For CPU–GPU comparison purposes, we propose a preliminary MATLAB–GPU implementation of the GRICS’s reconstruction kernel. We establish the roofline model of the kernel on two NVIDIA GPU architectures: Quadro RTX 5000 and A100. After the performance study, we propose some optimization patterns for the code’s execution on CPUs, first considering only the OpenMP implementation using thread binding and affinity and appropriate architecture-compilation flags and then looking for the optimal combination of MPI processes and OpenMP threads in the case of the hybrid MPI–OpenMP implementation. The results show that the GRICS performed well on the AMD EPYC CPUs, with an architectural efficiency of 52%. The kernel’s execution was fast on the NVIDIA A100 GPU, but the roofline model reported low architectural efficiency and utilization. Full article

Physical activity identity represents an important determinant of sustained physical activity behavior. The purpose of this investigation was to examine whether intention, behavioral regulation, and moderate-to-vigorous physical activity (MVPA) behavior explain significant variation in physical activity identity across time. Using a repeated measures observational design, lower-active adults new or returning to physical activity participation (N = 66) completed measures of study variables every three weeks over the course of a nine-week period (four assessments total). Based on the results of mixed-effects regression modelling, there was a small, non-significant increase in physical activity identity across time (b = 0.07, p = 0.13). Intention, MVPA, and behavioral regulation mostly had significant (ps < 0.05) bivariate correlations with physical activity identity at the same time point of assessment. Behavioral regulation explained significant variation in physical activity identity across time (b = 0.26, p < 0.0001), but intention and MVPA were non-significant (ps > 0.05) after including a random intercept and controlling for behavioral regulation. Identity was resistant to change among new physical activity initiates in this study and longer time frames of assessment are needed (e.g., six months). Behavioral regulation should be examined as a determinant of physical activity identity in future investigations. Full article

Understanding the correlates of physical activity behavior is imperative for informing the development of interventions to address the low rates of physical activity guideline adherence among adolescents living in the United States. This cross-sectional study examined the predictive utility of the Multi-Process Action Control (M-PAC) framework for explaining self-reported and device-measured physical activity behavior among a Hispanic-majority sample of adolescents. A total of 1849 high school students (mean age = 16.0 ± 1.22 SD years; 52.3% women; 87.8% Hispanic) enrolled in one school district in south-central Texas completed a survey including instruments to assess M-PAC framework constructs (instrumental and affective attitudes, perceived capability and opportunity, behavioral regulation, habit, identity) and moderate-to-vigorous physical activity (MVPA) behavior. A subsample (n = 435) wore accelerometers for seven days. The results from robust linear regression models revealed role identity and habit were significant predictors of self-reported MVPA. Role identity was a significant predictor of accelerometer-derived daily MVPA and raw acceleration in the most active hour but not daily raw acceleration. The findings indicated reflexive processes are robust predictors of adolescent physical activity and should be the focus of interventions designed to promote adoption and maintenance of physical activity during this developmental life stage. Full article

Machining quality prediction is the critical link of quality control in parts machining. With the advent of the Industry 4.0 era, intelligent manufacturing and data-driven technologies bring new ideas for quality control in complex machining processes. Quality control is complicated for multi-process, multi-condition, small-batch, and high-precision parts processing requirements. To solve this problem, this paper proposes a machining quality prediction method based on the machining error transfer network and the grey neural network. Initially, by constructing a processing error transfer network, the error transfer law in part processing is described, and the PageRank algorithm and the influence degree of the nodes are used to determine the critical quality features. Additionally, the problem of low prediction accuracy due to small sample data and multiple coupling relationships is solved using the grey neural network algorithm, and a high accuracy prediction of critical quality features is achieved. Finally, the effectiveness and reliability of the method are verified by the case of medium-speed marine diesel engine fuselage processing. The results indicate that this method not only effectively identifies critical quality features in the machining process of complex parts, but it also maintains a high predictive accuracy for these features, even with small samples and limited data. Full article