Search Results (531)

This work focuses on optimizing the scheduling of virtual power plants (VPPs)—as implemented in the Portuguese national project New Generation Storage (NGS)—to maximize social welfare and enhance energy trading efficiency within modern energy grids. By integrating distributed energy resources (DERs), including renewable energy sources and energy storage systems, VPPs represent a pivotal element of sustainable urban energy systems. The scheduling problem is formulated as a Mixed-Integer Linear Programming (MILP) task and addressed by using a parallelized simulated annealing (SA) algorithm implemented on high-performance computing (HPC) infrastructure. This parallelization accelerates solution space exploration, enabling the system to efficiently manage the complexity of larger DER networks and more sophisticated scheduling scenarios. The approach demonstrates its capability to align with the objectives of smart cities by ensuring adaptive and efficient energy distribution, integrating dynamic pricing mechanisms, and extending the operational lifespan of critical energy assets such as batteries. Rigorous simulations highlight the method’s ability to reduce optimization time, maintain solution quality, and scale efficiently, facilitating real-time decision making in energy markets. Moreover, the optimized coordination of DERs supports grid stability, enhances market responsiveness, and contributes to developing resilient, low-carbon urban environments. This study underscores the transformative role of computational infrastructure in addressing the challenges of modern energy systems, showcasing how advanced algorithms and HPC can enable scalable, adaptive, and sustainable energy optimization in smart cities. The findings demonstrate a pathway to achieving socially and environmentally responsible energy systems that align with the priorities of urban resilience and sustainable development. Full article

Steady-state visual evoked potential (SSVEP)-based brain—computer interfaces (BCIs) leverage high-speed neural synchronization to visual flicker stimuli for efficient device control. While SSVEP-BCIs minimize user training requirements, their dependence on physical EEG recordings introduces challenges, such as inter-subject variability, signal instability, and experimental complexity. To overcome these limitations, this study proposes a novel neural mass model for SSVEP simulation by integrating frequency response characteristics with dual-region coupling mechanisms. Specific parallel linear transformation functions were designed based on SSVEP frequency responses, and weight coefficient matrices were determined according to the frequency band energy distribution under different visual stimulation frequencies in the pre-recorded SSVEP signals. A coupled neural mass model was constructed by establishing connections between occipital and parietal regions, with parameters optimized through particle swarm optimization to accommodate individual differences and neuronal density variations. Experimental results demonstrate that the model achieved a high-precision simulation of real SSVEP signals across multiple stimulation frequencies (10 Hz, 11 Hz, and 12 Hz), with maximum errors decreasing from 2.2861 to 0.8430 as frequency increased. The effectiveness of the model was further validated through the real-time control of an Arduino car, where simulated SSVEP signals were successfully classified by the advanced FPF-net model and mapped to control commands. This research not only advances our understanding of SSVEP neural mechanisms but also releases the user from the brain-controlled coupling system, thus providing a practical framework for developing more efficient and reliable BCI-based systems. Full article

Topology optimization (TO) is a powerful generative design tool for innovative structural design, capable of optimizing material distribution to generate structures with superior performance. However, current topology optimization algorithms mostly target a single objective and are highly dependent on the problem definition parameters, causing two critical issues: limited human controllability and solution diversity. These issues often lead to burdensome design iterations and insufficient design exploration. This paper proposes a multi-solution TO framework to address them. Human designers express their stylistic preferences for structures through sketches which are decomposed into stroke and closed-shape elements to flexibly guide each TO process. Sketch-based constraints are integrated with Fourier mapping-based length-scale control to enhance human controllability. Solution diversity is achieved by perturbing Fourier mapping frequencies and load conditions in the neural implicit TO framework. Adaptive parallel scale adjustment is incorporated to reduce the computational cost for design exploration. Using the structural design of a wheel spoke as a case study, the mechanical performance and diversity of the generated TO solutions as well as the effectiveness of human control are analyzed both qualitatively and quantitatively. The results reveal that the sketch-based constraints and length-scale control have distinct control effects on structural features and have different impacts on the mechanical performance and diversity, thereby enabling fine-grained and flexible human controllability to better balance conflicting objectives. Full article

Intelligent jet systems are widely used in various fields, including firefighting, marine operations, and underwater exploration. Accurate extraction and prediction of jet trajectories are essential for optimizing their performance, but challenges arise due to environmental factors such as climate, wind direction, and suction efficiency. To address these issues, we introduce two novel jet segmentation datasets, Libary and SegQinhu, which cover both indoor and outdoor environments under varying weather conditions and temporal intervals. These datasets present significant challenges, including occlusions and strong light reflections, making them ideal for evaluating jet trajectory segmentation methods. Through empirical evaluation of several state-of-the-art (SOTA) techniques on these datasets, we observe that general methods struggle with highly imbalanced pixel distributions in jet trajectory images. To overcome this, we propose a data-driven pipeline for jet trajectory extraction and segmentation. At its core is MobileHDC, a new baseline model that leverages the MobileNetV2 architecture and integrates dilated convolutions to enhance the receptive field without increasing computational cost. Additionally, we introduce a parallel convolutional block and a decoder to fuse multi-level features, enabling a better capture of contextual information and improving the continuity and accuracy of jet segmentation. The experimental results show that our method outperforms existing SOTA techniques on both jet-specific datasets, highlighting the effectiveness of our approach. 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

The Internet of Things is used in many application areas in our daily lives. Ensuring the security of valuable data transmitted over the Internet is a crucial challenge. Hash functions are used in cryptographic applications such as integrity, authentication and digital signatures. Existing lightweight hash functions leverage task parallelism but provide limited scalability. There is a need for lightweight algorithms that can efficiently utilize multi-core platforms or distributed computing environments with high degrees of parallelization. For this purpose, a data-parallel approach is applied to a lightweight hash function to achieve massively parallel software. A novel structure suitable for data-parallel architectures, inspired by basic tree construction, is designed. Furthermore, the proposed hash function is based on a lightweight block cipher and seamlessly integrated into the designed framework. The proposed hash function satisfies security requirements, exhibits high efficiency and achieves significant parallelism. Experimental results indicate that the proposed hash function performs comparably to the BLAKE implementation, with slightly slower execution for large message sizes but marginally better performance for smaller ones. Notably, it surpasses all other evaluated algorithms by at least 20%, maintaining a consistent 20% advantage over Grostl across all data sizes. Regarding parallelism, the proposed PLWHF achieves a speedup of approximately 40% when scaling from one to two threads and 55% when increasing to three threads. Raspberry Pi 4-based tests for IoT applications have also been conducted, demonstrating the hash function’s effectiveness in memory-constrained IoT environments. Statistical tests demonstrate a precision of ±0.004, validate the hypothesis in distribution tests and indicate a deviation of ±0.05 in collision tests, confirming the robustness of the proposed design. Full article

III-nitrides are crucial materials for solar flow batteries due to their versatile properties. In contrast to the well-studied MOVPE reaction mechanism for AlN and GaN, few works report gas-phase mechanistic studies on the growth of InN. To better understand the reaction thermodynamics, this work revisited the gas-phase reactions involved in metal–organic vapor-phase epitaxy (abbreviated as MOVPE) growth of InN. Utilizing the M06-2X function in conjunction with Pople’s triple-ζ split-valence basis set with polarization functions, this work recharacterized all stationary points reported in previous literature and compared the differences between the structures and reaction energies. For the reaction pathways which do not include a transition state, rigorous constrained geometry optimizations were utilized to scan the PES connecting the reactants and products in adduct formation and XMIn (M, D, T) pyrolysis, confirming that there are no TSs in these pathways, which is in agreement with the previous findings. A comprehensive bonding analysis indicates that in TMIn:NH₃, the In-N demonstrates strong coordinate bond characteristics, whereas in DMIn:NH₃ and MMIn:NH₃, the interactions between the Lewis acid and base fragments lean toward electrostatic attraction. Additionally, the NBO computations show that the H radical can facilitate the migration of electrons that are originally distributed between the In-C bonds in XMIn. Based on this finding, novel reaction pathways were also investigated. When the H radical approaches MMInNH₂, MMIn:NH₃ rather than MMInHNH₂ will generate and this is followed by the elimination of CH₄ via two parallel paths. Considering the abundance of H₂ in the environment, this work also examines the reactions between H₂ and XMIn. The Mulliken charge distributions indicated that intermolecular electron transfer mainly occurs between the In atom and N atom whiling forming (DMInNH₂)₂, whereas it predominately occurs between the In atom and the N atom intramolecularly when generating (DMInNH₂)₃. Full article

Driven by rapid advancements in interconnection, packaging, integration, and computing technologies, parallel and distributed systems have significantly evolved in recent years. These systems have become essential for addressing modern computational demands, offering enhanced processing power, scalability, and resource efficiency. This paper provides a comprehensive overview of parallel and distributed systems, exploring their interrelationships, their key distinctions, and the emerging trends shaping their evolution. We analyse four parallel computing paradigms—heterogeneous computing, quantum computing, neuromorphic computing, and optical computing—and examine emerging distributed systems such as blockchain, serverless computing, and cloud-native architectures. The associated challenges are highlighted, and potential future directions are outlined. This work serves as a valuable resource for researchers and practitioners aiming to stay informed about trends in parallel and distributed computing while understanding the challenges and future developments in the field. Full article

Striving for better simulation results, transport planners want to simulate larger domains with increased levels of detail. Achieving fast execution times for these complex traffic simulations requires the parallel computing power of modern hardware. This paper presents an architectural update to the MATSim traffic simulation framework, introducing a prototype that adapts the existing traffic flow model to a distributed parallel algorithm. The prototype is capable of scaling across multiple compute nodes, utilizing the parallel computing power of modern hardware. Benchmarking reveals a 119-fold improvement in execution speed over the current implementation, and a 43 times speedup when compared to single-core performance. The prototype can simulate 24 h of large-scale traffic in just 3.5 s. Based on these results, we advocate for integrating a distributed simulation approach into MATSim and outline steps for further optimizing the prototype for large-scale applications. Full article

Zigzag plastic greenhouses are a type of greenhouse with a high natural ventilation capacity, and the number and quantities of their roof vents affect their ventilation and cooling effect. In this study, a CFD model of a greenhouse was constructed based on computational fluid dynamics (CFD) theory to simulate the temperature and airflow distribution of a zigzag plastic greenhouse and to investigate the effects that the number of zigzags and the construction orientation have on the cooling effect of this type of greenhouse. The results show that the average air temperature in a double zigzag plastic greenhouse (DZPG) was 0.58 °C lower than that in a single zigzag plastic greenhouse (SZPG) of the same size during the experiment. When the outdoor temperature is higher than 35 °C, the maximum temperature of the DZPG is significantly lower than that of the SZPG in a 1.5 m horizontal section; when the top vent is on the windward side, there is an obvious advantage of DZPG ventilation and the utilization efficiency of its top vent is higher, and when the top vent is on the leeward side, the distribution of the airflow in the DZPG is more intensive and more uniform. The maximum difference in the average temperature between the eight orientations of the DZPG was 0.17 °C. Therefore, the cooling effect in summer is not influenced by the construction orientation, but the airflow in the greenhouse is slightly worse when the direction of the roof vents is parallel to the prevailing wind direction. Full article

Probabilistic models in physics often require the evaluation of normalized Boltzmann factors, which in turn implies the computation of the partition function Z. Obtaining the exact value of Z, though, becomes a forbiddingly expensive task as the system size increases. A possible way to tackle this problem is to use the Annealed Importance Sampling (AIS) algorithm, which provides a tool to stochastically estimate the partition function of the system. The nature of AIS allows for an efficient and parallel implementation in Restricted Boltzmann Machines (RBMs). In this work, we evaluate the partition function of magnetic spin and spin-like systems mapped into RBMs using AIS. So far, the standard application of the AIS algorithm starts from the uniform probability distribution and uses a large number of Monte Carlo steps to obtain reliable estimations of Z following an annealing process. We show that both the quality of the estimation and the cost of the computation can be significantly improved by using a properly selected mean-field starting probability distribution. We perform a systematic analysis of AIS in both small- and large-sized problems, and compare the results to exact values in problems where these are known. As a result, we propose two successful strategies that work well in all the problems analyzed. We conclude that these are good starting points to estimate the partition function with AIS with a relatively low computational cost. The procedures presented are not linked to any learning process, and therefore do not require a priori knowledge of a training dataset. Full article

The study of the process of laser action on powder materials requires the construction of mathematical models of the interaction of laser radiation with powder particles that take into account the features of energy supply and are applicable in a wide range of beam parameters and properties of the particle material. A model of the interaction of pulsed or pulse-periodic laser radiation with a spherical metal particle is developed. To find the temperature distribution in the particle volume, the non-stationary three-dimensional heat conductivity equation with a source term that takes into account the action of laser radiation is solved. In the plane normal to the direction of propagation of laser radiation, the change in the radiation intensity obeys the Gaussian law. It is possible to take into account changes in the intensity of laser radiation in space due to its absorption by the environment. To accelerate numerical calculations, a computational algorithm is used based on the use of vectorized data structures and parallel implementation of operations on general-purpose graphics accelerators. The features of the software implementation of the method for solving a system of difference equations that arises as a result of finite-volume discretization of the heat conductivity equation with implicit scheme by the iterative method are presented. The model developed describes the heating and melting of a spherical metal particle exposed by multi-pulsed laser radiation. The implementation of the computational algorithm developed is based on the use of vectorized data structures and GPU resources. The model and calculation results are of interest for constructing a two-phase flow model describing the interaction of test particles with laser radiation on the scale of the entire calculation domain. Such a model is implemented using a discrete-trajectory approach to modeling the motion and heat exchange of a dispersed admixture. Full article

Pragmatics is key to communicating effectively, and its assessment in vulnerable populations is of paramount importance. Although tools exist for this purpose, they are often effortful and time-consuming, with complex scoring procedures, which hampers their inclusion in clinical practice. To address these issues, we present the Brief Assessment of Pragmatic Abilities and Cognitive Substrates (APACS Brief), a rapid (10 min), easy-to-use and freely distributed tool for evaluating pragmatics in Italian, inspired by the existing APACS test and already validated in the remote version (APACS Brief Remote). The APACS Brief test measures–with a simplified scale–the domains of discourse production and figurative language understanding and is developed in two parallel forms, each including novel items differing from APACS. Psychometric properties, cut-off scores, and thresholds for change were computed on 287 adults. The analysis revealed satisfactory internal consistency, good test–retest reliability, and strong concurrent and construct validity. Moreover, APACS Brief showed excellent discriminant validity on a sample of 56 patients with schizophrenia, who were also cross-classified consistently by APACS Brief and APACS cut-off values. Overall, APACS Brief is a reliable tool for evaluating pragmatic skills and their breakdown, with brief administration time and simple scoring making it well-suited for screening in at-risk populations. Full article

Deep Neural Networks (DNNs) have been widely adopted in several advanced artificial intelligence applications due to their competitive accuracy to the human brain. Nevertheless, the superior accuracy of a DNN is achieved at the expense of intensive computations and storage complexity, requiring custom expandable hardware, i.e., graphics processing units (GPUs). Interestingly, leveraging the synergy of parallelism and edge computing can significantly improve CPU-based hardware platforms. Therefore, this manuscript explores levels of parallelism techniques along with edge computation offloading to develop an innovative hardware platform that improves the efficacy of deep learning computing architectures. Furthermore, the multitask learning (MTL) approach is employed to construct a parallel multi-task classification network. These tasks include face detection and recognition, age estimation, gender recognition, smile detection, and hair color and style classification. Additionally, both pipeline and parallel processing techniques are utilized to expedite complicated computations, boosting the overall performance of the presented deep face analysis architecture. A computation offloading approach, on the other hand, is leveraged to distribute computation-intensive tasks to the server edge, whereas lightweight computations are offloaded to edge devices, i.e., Raspberry Pi 4. To train the proposed deep face analysis network architecture, two custom datasets (HDDB and FRAED) were created for head detection and face-age recognition. Extensive experimental results demonstrate the efficacy of the proposed pipeline-parallel architecture in terms of execution time. It requires 8.2 s to provide detailed face detection and analysis for an individual and 23.59 s for an inference containing 10 individuals. Moreover, a speedup of 62.48% is achieved compared to the sequential-based edge computing architecture. Meanwhile, 25.96% speed performance acceleration is realized when implementing the proposed pipeline-parallel architecture only on the server edge compared to the sever sequential implementation. Considering classification efficiency, the proposed classification modules achieve an accuracy of 88.55% for hair color and style classification and a remarkable prediction outcome of 100% for face recognition and age estimation. To summarize, the proposed approach can assist in reducing the required execution time and memory capacity by processing all facial tasks simultaneously on a single deep neural network rather than building a CNN model for each task. Therefore, the presented pipeline-parallel architecture can be a cost-effective framework for real-time computer vision applications implemented on resource-limited devices. Full article

Simultaneous localization and mapping (SLAM) is crucial for autonomous driving, drone navigation, and robot localization, relying on efficient point cloud registration and loop closure detection. Traditional Normal Distributions Transform (NDT) odometry frameworks provide robust solutions but struggle with real-time performance due to the high computational complexity of processing large-scale point clouds. This paper introduces an improved NDT-based LiDAR odometry framework to address these challenges. The proposed method enhances computational efficiency and registration accuracy by introducing a unified feature point cloud framework that integrates planar and edge features, enabling more accurate and efficient inter-frame matching. To further improve loop closure detection, a parallel hybrid approach combining Radius Search and Scan Context is developed, which significantly enhances robustness and accuracy. Additionally, feature-based point cloud registration is seamlessly integrated with full cloud mapping in global optimization, ensuring high-precision pose estimation and detailed environmental reconstruction. Experiments on both public datasets and real-world environments validate the effectiveness of the proposed framework. Compared with traditional NDT, our method achieves trajectory estimation accuracy increases of 35.59% and over 35%, respectively, with and without loop detection. The average registration time is reduced by 66.7%, memory usage is decreased by 23.16%, and CPU usage drops by 19.25%. These results surpass those of existing SLAM systems, such as LOAM. The proposed method demonstrates superior robustness, enabling reliable pose estimation and map construction in dynamic, complex settings. Full article