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Most of the information security management research involving fear appeals is guided by either protection motivation theory or the extended parallel processing model. Over time, extant research has extended these theories, as well as their derivative theories, in a variety of ways, leading to several theoretical and empirical inconsistencies. The large body of fragmented, and sometimes conflicting, research has muddied the broader understanding of what drives protection- and defensive motivation. We provide guidance to the security discourse by offering the first study in the literature to employ two-stage meta-analytic structural equation modeling (TSSEM), which combines covariance-based structural equation modeling and meta-analysis. Information systems (IS) researchers have traditionally used meta-analysis for structural equation modeling for such purposes—an approach that has several serious statistical flaws. Using 341 systematically selected empirical security articles (representing 383 unique studies) and TSSEM, we pool a large series of five datasets to test six models, from which we examine the effects of constructs and paths in the security fear-appeals literature. We compare and test six versions of models inspired by issues in the broader fear-appeals literature. We confirm the importance of both the threat- and coping-appraisal processes; establish the central role of fear and that it has greater importance than threat; show that efficacy is a stronger predictor of protection motivation than is threat; demonstrate that response costs as currently measured are ineffective but that maladaptive rewards have a strong negative effect on protection motivation and a positive effect on defensive motivation; and provide evidence that dual models of danger control and fear control should be used. [ABSTRACT FROM AUTHOR]

Quantum mechanical calculations for material modeling using Kohn–Sham density functional theory (DFT) involve the solution of a nonlinear eigenvalue problem for N smallest eigenvector-eigenvalue pairs, with N proportional to the number of electrons in the material system. These calculations are computationally demanding and have asymptotic cubic scaling complexity with the number of electrons. Large-scale matrix eigenvalue problems arising from the discretization of the Kohn–Sham DFT equations employing a systematically convergent basis traditionally rely on iterative orthogonal projection methods, which are shown to be computationally efficient and scalable on massively parallel computing architectures. However, as the size of the material system increases, these methods are known to incur dominant computational costs through the Rayleigh–Ritz projection step of the discretized Kohn–Sham Hamiltonian matrix and the subsequent subspace diagonalization of the projected matrix. This work explores the potential of polynomial expansion approaches based on recursive Fermi-operator expansion as an alternative to the subspace diagonalization of the projected Hamiltonian matrix to reduce the computational cost. Subsequently, we perform a detailed comparison of various recursive polynomial expansion approaches to the traditional approach of explicit diagonalization on both multi-node central processing unit and graphics processing unit architectures and assess their relative performance in terms of accuracy, computational efficiency, scaling behavior, and energy efficiency. [ABSTRACT FROM AUTHOR]

Several computational models make predictions about the activation states of individual elements of an action sequence during planning and execution; however, the neural mechanisms of action planning are still poorly understood. Simple chaining models predict that only the first response in an action sequence should be active during planning. Conversely, some parallel activation models suggest that during planning, a serial inhibition process places the individual elements of the action into a serial order across a winner-takes-all competitive choice gradient in which earlier responses are more active, and hence likely to be selected for execution compared with later responses. We triggered transcranial magnetic stimulation pulses at 200 or 400 ms after the onset of a five-letter word, in which all but one response was planned and typed with the left hand, except for a single letter which required a right index finger response exclusively at one of five serial positions. We measured the resulting motor-evoked potentials at the right index finger as a marker for the activation state of that planned response. We observed no difference in motor-evoked potential amplitude across any serial position when a right index finger response was planned at 200 ms after the onset of the word; however, we observed a graded pattern of activation at 400 ms, with earlier positions that required a right index finger response showing greater motor-evoked potentials amplitude compared with later positions. These findings provide empirical support for competitive queuing computational models of action planning. [ABSTRACT FROM AUTHOR]

A positive correlation was observed between the enantiomeric excess (ee) of L-isovaline (L-iVal) and the degree of aqueous alteration (AqA) of carbonaceous meteorites. The origin of this remarkable phenomenon has remained enigmatic from two points of view: First, the correlation is between seemingly unrelated observables-nothing about AqA is of chiral characteristics; and second, following the accepted assumption that circularly polarized light (CPL) was the origin of the observed meteoritic ee of L-amino acids (AAs), it remined unclear why some of the observed levels of the ee of L-iVal in that correlation are significantly higher than those observed in laboratory simulations or those obtained from circular dichroism (CD) g-factor calculations. The current proposition accounting for this picture attributes late AqA conditions of the meteoritic parent bodies as providing the grounds for amplification of early initially CPL-generated low levels of L-ee. For reasons summarized below, this interpretation, which treats the CPL event and the AqA process as occurring in wide-time separated eras, is re-visited. An alternative interpretation of the observed correlation and of the high ee-values, is provided. It focuses on hydrophilic dust-aggregates clouds in wet star-forming regions in early presolar times, where both the CPL event and the grounds leading to the later AqA processes of the parent bodies, occurred. This mechanism removes the time separation between the initial ee formation and the AqA of the parent body, and replaces it with parallel processes, providing a scenario to the observation of high ee's without total destruction, and to the apparent AqA/L-ee correlation. Although iVal is at the focus of this report, the steps of the development of the alternative mechanism and the conclusions that arise from it, are relevant and applicable to the general observations of L-ee's of meteoritic AA's. [ABSTRACT FROM AUTHOR]

Synaptic devices have gained prominence as viable alternatives to conventional complementary metal‐oxide‐semiconductor‐based (CMOS) electronics in the information processing field owing to their inherent advantages in analog and parallel operations. The potential of synaptic devices has not yet been fully utilized for logic operations because only the conventional binary logic structure has been applied to analog synaptic devices, leading to the loss of advantages unique to analog signals. To resolve this issue, an innovative concept is proposed: an analog logic gate that can perform parallel operations at the device level and is coupled with logic reconfigurations, enabling comprehensive analog computation. This logic gate comprises two synaptic devices with different retention characteristics, adjusted by the side‐chain engineering of an organic polymer. The long‐term and short‐term synaptic devices serve as reconfigurable synapse for logic mode selection and parallel processable logic synapse, respectively. In this study, the reconfigurable and parallel processable synaptic logic circuit is effectively implemented in a personalized disease risk diagnostic system. This innovative approach not only allows for the simultaneous computation of analog‐formed diagnostic data, but also enhances both computing efficiency and system complexity in general synaptic systems. [ABSTRACT FROM AUTHOR]

This comprehensive review paper examines the challenges faced by blockchain technology in terms of scalability and proposes potential solutions and future research directions. Scalability poses a significant hurdle for Bitcoin and Ethereum, manifesting as low throughput, extended transaction delays, and excessive energy consumption, thereby compromising efficiency. The current state of blockchain scalability is analyzed, encompassing the limitations of existing solutions such as Sharding and off-chain scaling. Various proposed remedies, including layer 2 scaling solutions, consensus mechanisms, and alternative approaches, are investigated. The paper also explores the impact of scalability on diverse blockchain applications and identifies potential future research directions by integrating data science techniques with blockchain technology. Notably, nearly 110 primary research papers from reputable scientific databases like Scopus, IEEE Explore, Science Direct, and Web of Science were reviewed, demonstrating scalability in blockchain comprising several elements. Transaction throughput and network latency emerge as the most prominent concerns. Consequently, this review offers future research avenues to address scalability challenges by leveraging data science techniques like distributed computing and parallel processing to divide and process vast datasets across multiple machines. The synergy between data science and blockchain holds promise as an optimal solution. Overall, this up-to-date understanding of blockchain scalability is invaluable to researchers, practitioners, and policy makers engaged in this domain. [ABSTRACT FROM AUTHOR]

Transformers have shown remarkable success in modeling sequential data and capturing intricate patterns over long distances. Their self-attention mechanism allows for efficient parallel processing and scalability, making them well-suited for the high-dimensional data in hyperspectral and LiDAR imagery. However, further research is needed on how to more deeply integrate the features of two modalities in attention mechanisms. In this paper, we propose a novel Multi-Feature Cross Attention-Induced Transformer Network (MCAITN) designed to enhance the classification accuracy of hyperspectral and LiDAR data. The MCAITN integrates the strengths of both data modalities by leveraging a cross-attention mechanism that effectively captures the complementary information between hyperspectral and LiDAR features. By utilizing a transformer-based architecture, the network is capable of learning complex spatial-spectral relationships and long-range dependencies. The cross-attention module facilitates the fusion of multi-source data, improving the network's ability to discriminate between different land cover types. Extensive experiments conducted on benchmark datasets demonstrate that the MCAITN outperforms state-of-the-art methods in terms of classification accuracy and robustness. [ABSTRACT FROM AUTHOR]

6D object pose estimation is an important application of computer vision and a basic module in robotic manipulation, but dealing with occlusion in a cluttered environment, handling symmetries, and textureless surfaces, are real issues. Other issues with such systems are accuracy and efficiency. The recent two-stage methods perform well in terms of accuracy; however, a linear increase in their runtimes occurs due to the increase in the number of objects in a scene. This paper proposes a fully convolutional and parallel architecture that obtains the 3D translation and orientation for object poses from the same pixel-wise dense estimation. It exploits the same voting block for inliers for multiple instances, final 3D translation estimation, and quaternions aggregation. Only the center point estimation of the objects decreases the model's running time, while still useful for occlusions and texturelessness. Symmetries and varieties are handled with a loss function based on shape matching and the pose of the object. Our proposed approach has fewer parameters and takes less time to train and evaluate, achieving great accuracy. Experiments on LINEMOD and occlusion LINEMOD datasets using ADD (-S) and 2D projection evaluation metrics show that the proposed method outperforms state-of-the-art approaches for 6D pose estimation. [ABSTRACT FROM AUTHOR]

The final years of Franco's dictatorship and the subsequent transition to a democracy saw the unfolding of parallel processes of economic and scientific modernisation in Spanish society. An unexpected interference between both processes occurred in the fields of ecology and the environmental sciences, when environmentally minded scientists defended threatened ecosystems, rural landscapes and traditional land-use systems. At the time, prevailing discourses and policies considered these ecosystems and landscapes backward remnants of an undeveloped Spain, ready to be transformed by irrigation plans, afforestation schemes and urban development. From the early 1970s onwards, ecologist Fernando González Bernáldez consciously chose to take on a dual role, combining and mutually reinforcing two distinct yet associated personae: a highly respected scientist and an outspoken defender of the environment. This paper examines González Bernáldez's various strategies to strike the right balance between both identities, and how his aims were challenged by the changing political context at the time. [ABSTRACT FROM AUTHOR]

The growing demand for high-performance and energy-efficient processing in edge-oriented Systems-on-Chip is driving the adoption of dedicated integrated circuits that accelerate computationally intensive workloads. To minimize area and performance overhead, low-power, general-purpose CPUs are often tightly coupled with domain-specific coprocessors implementing custom instructions, thereby delivering higher throughput and reduced memory traffic. However, commonly used in-order CPUs are not optimized for instruction-level parallelism, leading to stalls in the instruction stream while waiting for long-latency coprocessor operations and under-utilization of the coprocessor while executing other instructions. This work investigates the benefits of replacing simple in-order cores with a more complex out-of-order architecture to dynamically schedule instructions for the main core and coprocessor, optimizing resource utilization and reducing execution time. To ensure generality, an in-depth analysis was carried out by offloading instructions to a custom dummy coprocessor capable of emulating iterative and pipelined operations with arbitrary latency. Various workloads simulating real-world applications were executed on two variants of an open-source microcontroller equipped with a recent out-of-order core and the state-of-the-art CV32E40X in-order core, respectively. Results from Register Transfer Level simulations show that the former configuration executes up to 60% more instructions per cycle, with a modest 12% system area overhead on a 65 nm CMOS technology node. [ABSTRACT FROM AUTHOR]

Deep neural network (DNN) comprises multiple stages of data processing sub-systems with one of the primary sub-systems is a fully connected neural network (FCNN) model. This fully connected neural network model has multiple layers of neurons that need to be implemented using arithmetic units with suitable number representation to optimize area, power, and speed. In this work, the network parameters are analyzed, and redundancy in weights is eliminated. A pipelined and parallel structure is designed for the fully connected network information. The proposed FCNN structure has 16 inputs, 3 hidden layers, and an output layer. Each hidden layer consists of 4 neurons and describes how the inputs are connected to hidden layer neurons to process the raw data. A hardware description language (HDL) model is developed for the proposed structure and the verified model is implemented on Xilinx field-programmable gate array (FPGA). The modified structure comprises registers, demultiplexers, weight registers, multipliers, adders, and read-only memory lookup table (ROM/LUT). The modified architecture implemented on FPGA is estimated to reduce area by 87.5% and improve timing by 3x compared with direct implementation methods. [ABSTRACT FROM AUTHOR]

Target detection in satellite images is an essential topic in the field of remote sensing and computer vision. Despite extensive research efforts, accurate and efficient target detection in remote sensing images remains unsolved due to the large target scale span, dense distribution, and overhead imaging and complex backgrounds, which result in high target feature similarity and serious occlusion. In order to address the above issues in a comprehensive manner, within this paper, we first propose a Centralised Visual Processing Center (CVPC), this structure is a parallel visual processing center for Transformer encoder and CNN, employing a lightweight encoder to capture broad, long-range interdependencies. Pixel-level Learning Center (PLC) module is used to establish pixel-level correlations and improve the depiction of detailed features. CVPC effectively improves the detection efficiency of remote sensing targets with high feature similarity and severe occlusion. Secondly, we propose a centralised feature cross-layer fusion pyramid structure to fuse the results with the CVPC in a top-down manner to enhance the detailed feature representation capability at each layer. Ultimately, we present a Context Enhanced Adaptive Sparse Convolutional Network (CEASC), which improves the accuracy while ensuring the detection efficiency. Based on the above modules, we designed and conducted a series of experiments. These experiments are conducted on three challenging public datasets, DOTA-v1.0, DIOR, and RSDO, showing that our proposed 3CNet achieves a more advanced detection accuracy while balancing the detection speed (78.62% mAP for DOTA-v1.0, 79.12% mAP for DIOR, and 95.50% mAP for RSOD). [ABSTRACT FROM AUTHOR]

Video classification has achieved remarkable success in recent years, driven by advanced deep learning models that automatically categorize video content. This paper provides a comprehensive review of video classification techniques and the datasets used in this field. We summarize key findings from recent research, focusing on network architectures, model evaluation metrics, and parallel processing methods that enhance training speed. Our review includes an in-depth analysis of state-of-the-art deep learning models and hybrid architectures, comparing models to traditional approaches and highlighting their advantages and limitations. Critical challenges such as handling large-scale datasets, improving model robustness, and addressing computational constraints are explored. By evaluating performance metrics, we identify areas where current models excel and where improvements are needed. Additionally, we discuss data augmentation techniques designed to enhance dataset accuracy and address specific challenges in video classification tasks. This survey also examines the evolution of convolutional neural networks (CNNs) in image processing and their adaptation to video classification tasks. We propose future research directions and provide a detailed comparison of existing approaches using the UCF-101 dataset, highlighting progress and ongoing challenges in achieving robust video classification. [ABSTRACT FROM AUTHOR]

Diversion from the syntactic norm, as manifested in the absence of otherwise expected lexical and syntactic material, has been extensively studied in theoretical syntax. Such modifications are observed in headlines, telegrams, labels, and other specialized contexts, collectively referred to as “reduced” registers. Focusing on search queries, a type of reduced register, I propose that they are generated by a simpler grammar that lacks a full‐fledged syntactic component. The analysis is couched in the Parallel Architecture framework, whose assumption of relative independence of linguistic components—their parallelism—and the rejection of syntactocentrism are essential to explain properties of queries. [ABSTRACT FROM AUTHOR]

Sorting is one of the basic algorithms in computer science, and has been extensively used in a variety of applications. In the big data era, as the volumes of data increase rapidly, parallel sorting has attracted much attention. Existing parallel sorting algorithms suffered from excessive communication overhead and load imbalance, making it difficult to scale massively. To solve above problems, a scalable parallel algorithm sorting by regular sampling (ScaPSRS) was proposed, which sampled the p-1 pivot elements to divide the entire data set into p disjoint subsets by all parallel processes, rather than by only one given process as PSRS did. Furthermore, ScaPSRS adopted a novel iterative update strategy of selecting pivots to guarantee that the workloads and data were evenly scheduled among the parallel processes, thus ensuring superior overall performance. A variety of experiments conducted on the Tianhe-Ⅱ supercomputer demonstrated that ScaPSRS succeeded in scaling to 32 000 cores and outperformed state-of-the-art works significantly. [ABSTRACT FROM AUTHOR]

Production and inventory routing problems consider a single-product supply chain operating under a vendor-managed inventory system. A plant creates a production plan and vehicle routes over a planning horizon to replenish its customers at minimum cost. In this paper, we present two- and three-index formulations, implement a branch-and-cut algorithm based on each formulation, and introduce a local search matheuristic-based algorithm to solve the problem. In order to combine all benefits of each algorithm, we design a parallel framework to integrate all three fronts, called the three-front parallel branch-and-cut algorithm (3FP-B&C). We assess the performance of our method on well-known benchmark instances of the inventory routing problem (IRP) and the production routing problem (PRP). The results show that our 3FP-B&C outperforms by far other approaches from the literature. For the 956 feasible small-size IRP instances, our method proves optimality for 746, being the first exact algorithm to solve all instances with up to two vehicles. 3FP-B&C finds 949 best known solutions (BKS) with 153 new BKS (NBKS). For the large-size set, our method provides two new optimal solutions (OPT), and finds 82% of BKS, being 70% of NBKS for instances with up to five vehicles. This result is more than twice the number of BKS considering all heuristic methods from the literature combined. Finally, our 3FP-B&C finds the best lower bounds (BLB) for 1,169/1,316 instances, outperforming all previous exact algorithms. On the PRP, our method obtained 278 OPT out of the 336 instances of benchmark set of small- and medium-size instances being 19 new ones in addition to 335 BKS (74 NBKS) and 313 BLB (52 new ones). On another set of PRP with medium- and large-size instances, our algorithm finds 1,105 BKS out of 1,440 instances with 584 NBKS. Besides that, our 3FP-B&C is the first exact algorithm to solve the instances with an unlimited fleet, providing the first lower bounds for this subset with an average optimality gap of 0.61%. We also address a very large-size instance set, the second exact algorithm to address this set, outperforming the previous approach by far. Finally, a comparative analysis of each front shows the gains of the integrated approach. History: This paper has been accepted for the Transportation Science Special Issue: DIMACS Implementation Challenge: Vehicle Routing. Funding: C. M. Schenekemberg was supported by the São Paulo Research Foundation (FAPESP) [Grant 2020/07145-8]. A. A. Chaves was supported by FAPESP [Grants 2018/15417-8 and 2016/01860-1] and Conselho Nacional de Desenvolvimento Científico e Tecnológico [Grants 312747/2021-7 and 405702/2021-3]. L. C. Coelho was supported by the Canadian Natural Sciences and Engineering Research Council [Grant 2019-00094]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0261. [ABSTRACT FROM AUTHOR]

Electricity consumption pattern recognition is the foundation of intelligent electricity distribution data analysis. However, as the scale of electricity consumption data increases, traditional clustering analysis methods encounter bottlenecks such as low computation speed and processing efficiency. To meet the efficient mining needs of massive electricity consumption data, in this paper a parallel processing method of the density-based k-means clustering is presented. First, an initial cluster center selection method based on data sample density is proposed to avoid inaccurate initial cluster center point selection, leading to clustering falling into local optima. The dispersion degree of the data samples within the cluster is also used as an important reference for determining the number of clusters. Subsequently, parallelization of density calculation and clustering for data samples were achieved based on the MapReduce model. Through experiments conducted on Hadoop clusters, it has been shown that the proposed parallel processing method is efficient and feasible, and can provide favorable support for intelligent power allocation decisions. [ABSTRACT FROM AUTHOR]

Multi-objective optimization is challenging for computationally expensive objectives because most optimization methods rely on a large number of function evaluations to approach the Pareto-optimal front. To this end, this article develops a novel multi-objective optimization algorithm, the Multi-Objective Parallel Local Surrogate-assisted search (MOPLS) algorithm, which combines surrogate approximation and parallel computing. In each iteration, MOPLS incorporates a tabu mechanism to determine new points for expensive evaluations via a series of independent surrogate-assisted local searches. A master–worker architecture in MOPLS allows the algorithm to conduct either synchronous or asynchronous parallel processing. On a number of benchmark problems, MOPLS outperforms recent surrogate-assisted algorithms within a limited computational budget. Empirical results from parallel experiments indicate that MOPLS can significantly improve its efficiency through parallelism, and the asynchronous MOPLS shows advantages in handling objectives with non-constant evaluation times. Finally, the better performance of MOPLS over alternatives in calibrating a complex watershed simulation model demonstrates its competitiveness in solving real-world engineering applications. [ABSTRACT FROM AUTHOR]

This paper introduces a novel formulation of the clustering problem, namely, the minimum sum-of-squares clustering of infinitely tall data (MSSC-ITD), and presents HPClust, an innovative set of hybrid parallel approaches for its effective solution. By utilizing modern high-performance computing techniques, HPClust enhances key clustering metrics: effectiveness, computational efficiency, and scalability. In contrast to vanilla data parallelism, which only accelerates processing time through the MapReduce framework, our approach unlocks superior performance by leveraging the multi-strategy competitive–cooperative parallelism and intricate properties of the objective function landscape. Unlike other available algorithms that struggle to scale, our algorithm is inherently parallel in nature, improving solution quality through increased scalability and parallelism and outperforming even advanced algorithms designed for small- and medium-sized datasets. Our evaluation of HPClust, featuring four parallel strategies, demonstrates its superiority over traditional and cutting-edge methods by offering better performance in the key metrics. These results also show that parallel processing not only enhances the clustering efficiency, but the accuracy as well. Additionally, we explore the balance between computational efficiency and clustering quality, providing insights into optimal parallel strategies based on dataset specifics and resource availability. This research advances our understanding of parallelism in clustering algorithms, demonstrating that a judicious hybridization of advanced parallel approaches yields optimal results for MSSC-ITD. Experiments on the synthetic data further confirm HPClust's exceptional scalability and robustness to noise. [ABSTRACT FROM AUTHOR]

Segmenting clouds and their shadows is a critical challenge in remote sensing image processing. The shape, texture, lighting conditions, and background of clouds and their shadows impact the effectiveness of cloud detection. Currently, architectures that maintain high resolution throughout the entire information-extraction process are rapidly emerging. This parallel architecture, combining high and low resolutions, produces detailed high-resolution representations, enhancing segmentation prediction accuracy. This paper continues the parallel architecture of high and low resolution. When handling high- and low-resolution images, this paper employs a hybrid approach combining the Transformer and CNN models. This method facilitates interaction between the two models, enabling the extraction of both semantic and spatial details from the images. To address the challenge of inadequate fusion and significant information loss between high- and low-resolution images, this paper introduces a method based on ASMA (Axial Sharing Mixed Attention). This approach establishes pixel-level dependencies between high-resolution and low-resolution images, aiming to enhance the efficiency of image fusion. In addition, to enhance the effective focus on critical information in remote sensing images, the AGM (Attention Guide Module) is introduced, to integrate attention elements from original features into ASMA, to alleviate the problem of insufficient channel modeling of the self-attention mechanism. Our experimental results on the Cloud and Cloud Shadow dataset, the SPARCS dataset, and the CSWV dataset demonstrate the effectiveness of our method, surpassing the state-of-the-art techniques for cloud and cloud shadow segmentation. [ABSTRACT FROM AUTHOR]

Infrared (IR) small target detection in sky scenes is crucial for aerospace, border security, and atmospheric monitoring. Most current works are typically designed for generalized IR scenes, which may not be optimal for the specific scenario of sky backgrounds, particularly for detecting small and dim targets at long ranges. In these scenarios, the presence of heavy clouds usually causes significant false alarms due to factors such as strong edges, streaks, large undulations, and isolated floating clouds. To address these challenges, we propose an infrared dim and small target detection algorithm based on morphological filtering with dual-structure elements. First, we design directional dual-structure element morphological filters, which enhance the grayscale difference between the target and the background in various directions, thus highlighting the region of interest. The grayscale difference is then normalized in each direction to mitigate the interference of false alarms in complex cloud backgrounds. Second, we employ a dynamic scale awareness strategy, effectively preventing the loss of small targets near cloud edges. We enhance the target features by multiplying and fusing the local response values in all directions, which is followed by threshold segmentation to achieve target detection results. Experimental results demonstrate that our method achieves strong detection performance across various complex cloud backgrounds. Notably, it outperforms other state-of-the-art methods in detecting targets with a low signal-to-clutter ratio (MSCR ≤ 2). Furthermore, the algorithm does not rely on specific parameter settings and is suitable for parallel processing in real-time systems. [ABSTRACT FROM AUTHOR]

Bearings are foundational supporting components in diverse mechanical systems, essential for the reliable operation of these systems through real‐time monitoring and precise health state assessment. However, vibration signals from bearings in practical equipment often contain excessive noise and redundant information, complicating health state assessment. To address this challenge, this paper proposes a neural network‐based method named parallel encoder‐decoder (PED). This method features a parallel architecture that combines the long short‐term memory network and the temporal convolutional network for the encoder, along with a self‐attention module for the decoder. PED is adept at learning the temporal representations hidden in original signals and filtering vibration signals to remove noise and redundant information. Additionally, a multi‐objective loss function is developed to enhance the prediction results. A normalized Mahalanobis distance‐based metric is then employed to compare residual signals during bearing operation with those under normal conditions. The case study evaluates the PED observer's proficiency in accurately predicting vibration signals and assessing the performance of health indicator curves, demonstrating the proposed PED observer's superiority over conventional networks. [ABSTRACT FROM AUTHOR]

Summary: With the rapid development of the global renewable energy source field, the importance of dynamic index processing technology in distributed energy systems has become more and more obvious. To better improve the real‐time dynamic interaction means of microgrids in the energy Internet and optimize the relevant methods for microgrid energy consumption detection, this article proposes to introduce the distributed Hadoop platform into the electrical thermal coupling multivariate data in the form of cluster configuration, and then use the Spark framework to detect and capture real‐time data, to complete the tracking and analysis of energy consumption data. At the same time, the Internet of Things and the cloud intelligent monitoring system are combined to further clean and explore the data, to achieve the in‐depth detection of the energy consumption problem of the microgrid under the premise of reducing the initial investment, and achieve the purpose of reducing the operating cost. In this case, the outliers are detected according to the photovoltaic indicators of photovoltaic power stations, the filtration and purification functions of photovoltaic indicators are used by the nuclear density curve, and the sustainable solar energy is optimized by combining multiple indicators such as wind direction and temperature. Based on reducing energy consumption, the overfitting phenomenon of the controller is controlled, and an optimized controller‐led cloud platform is established. By establishing the objective function model, the robustness of the controller is guaranteed and the detection expectation is satisfied by the experiment of energy consumption data. In addition, when the cloud platform is created, this study uses a genetic algorithm to optimize the controller index and then builds a cloud console detection mechanism that collaborates with the Internet. Through the research, it is found that outliers may lead to the redundancy of energy consumption indicators in the non‐processing state. This study adopts the optimization of energy consumption parameters and the help of a distributed data framework to deal with and effectively solve this problem. In terms of interpolation simulation verification combined with experimental data, this paper proposes to use the Internet of Things, wearable devices, sensors, and other means to monitor the cost of energy consumption, to realize the distributed dynamic storage of massive real‐time data in the process of parallel processing, as well as the evaluation and detection of real‐time data replacement. [ABSTRACT FROM AUTHOR]

Geospatial cellular automata (Geo-CA) models have encountered challenges in computing efficiency and scalability when conducting large-scale land use change simulation applications. Parallel computing has proven to be effective to address these challenges. However, parallelization strategies for existing parallel Geo-CA models are always optimized for specific models and lack applicability to others. Besides, most parallel Geo-CA models focus on parallelizing land use change simulations, ignoring land use transition rule mining. Moreover, there is a lack of effective parallel strategies for demand-constrained land use change simulation on distributed heterogeneous architectures. This study proposes a parallel framework on hybrid parallel computing architectures applicable for raster-based Geo-CA models to enhance their computing efficiency and scalability while maintaining simulation accuracy. The framework provides parallelization strategies for both the land use transition rule mining for multiple land use types and the demand-constrained land use change simulation on distributed heterogeneous architectures. The framework was employed to parallelize two contemporary Geo-CA models, i.e., PLUS and MCCA. Experiments showed that the parallelized models achieved significant improvements in computing efficiency and scalability, confirming the effectiveness of the proposed framework for large-scale land use change simulation studies. [ABSTRACT FROM AUTHOR]

Transposing two words in a sentence (e.g. "cat" and "was" in "the white cat was big") creates a sequence that is harder to classify as ungrammatical than control sequences (e.g. "the white was cat slowly"), suggesting that word position coding is noisy and can be affected by syntactic expectations. In the present research, this transposed-word effect was examined more closely using Rapid Serial Visual Presentation (RSVP) formats which provided either clear temporal cues to word order but no spatial cues, or both types of cues. Compared to when all words were presented simultaneously, the two RSVP formats reduced the transposed-word effect to the same degree while having no parallel impact on another ungrammatical comparison condition involving no transposition. These results are discussed in the context of serial and parallel models of reading as well as models that propose a later processing stage for the locus of the transposed-word effect. [ABSTRACT FROM AUTHOR]

The rapidly advancing fields of machine learning and mathematical modeling, greatly enhanced by the recent growth in artificial intelligence, are the focus of this special issue. This issue compiles extensively revised and improved versions of the top papers from the workshop on Mathematical Modeling and Problem Solving at PDPTA'23, the 29th International Conference on Parallel and Distributed Processing Techniques and Applications. Covering fundamental research in matrix operations and heuristic searches to real-world applications in computer vision and drug discovery, the issue underscores the crucial role of supercomputing and parallel and distributed computing infrastructure in research. Featuring nine key studies, this issue pushes forward computational technologies in mathematical modeling, refines techniques for analyzing images and time-series data, and introduces new methods in pharmaceutical and materials science, making significant contributions to these areas. [ABSTRACT FROM AUTHOR]

Monitoring the data sources for possible changes is an important consumption requirement for applications running in interaction with the Web of Data. In this article, MonARCh which is an architecture for monitoring the result changes of registered SPARQL queries in the Linked Data environment, is proposed. MonARCh can be comprehended as a publish/subscribe system in the general sense. However, it differs in how communication with the data sources is realized. Data sources in the Linked Data environment do not publish the changes in the data. MonARCh provides the necessary communication infrastructure between the data sources and the consumers for the notification of changes. Users subscribe SPARQL queries to the system which are then converted to federated queries. MonARCh periodically checks for updates by re-executing SERVICE clauses and notifying users in case of any result change. In addition, to provide scalability, MonARCh takes the advantage of concurrent computation of the actor model. The parallel join algorithm utilized speeds up query execution and result generation processes. The design science methodology is used during the design, implementation and evaluation of the architecture. When compared to the literature MonARCh meets all the sufficient requirements from the linked data monitoring and state of the art perspectives while having many outstanding features from both points of view. The evaluation results show that even while working under the limited two-node cluster setting MonARCh could reach from 300 to 25,000 query monitoring capacity according to the diverse query selectivities executed within our test bench. [ABSTRACT FROM AUTHOR]

Research in video coding has gained significant importance in recent years, driven by the increasing demand for multimedia transmission. High Efficiency Video Coding (HEVC) has emerged as a prominent standard in this field. Interpolation is a crucial aspect of HEVC, particularly when using fixed half-pel interpolation filters derived from traditional signal processing techniques. In recent times, there has been an exploration of interpolation filters that are based on Convolutional Neural Networks (CNNs). Conventional signal processing techniques are used in traditional HEVC methods to employ fixed half-pel interpolation filters. Recent advancements have delved into the application of Convolutional Neural Networks (CNNs) to enhance interpolation performance. Our proposed method utilises a sophisticated CNN architecture specifically crafted to extract valuable features from low-resolution image patches and accurately predict high-resolution images. The network consists of multiple layers of CNN blocks, which utilise 1×1 and 3×3 convolutional kernels to enable efficient and thorough feature extraction through parallel processing. This architecture improves computational efficiency and greatly enhances prediction accuracy The suggested interpolation filter shows a 2.38% enhancement in bitrate savings, as evaluated by the BD-rate metric, specifically in the low delay P configuration. This highlights the potential of deep learning techniques in improving video coding efficiency. [ABSTRACT FROM AUTHOR]

The implementation of reservoir computing using resistive random-access memory as a physical reservoir has attracted attention due to its low training cost and high energy efficiency during parallel data processing. In this work, a NbOx/Al2O3-based memristor device was fabricated through a sputter and atomic layer deposition process to realize reservoir computing. The proposed device exhibits favorable resistive switching properties (>103 cycle endurance) and demonstrates short-term memory characteristics with current decay. Utilizing the controllability of the resistance state and its variability during cycle repetition, electrical pulses are applied to investigate the synapse-emulating properties of the device. The results showcase the functions of potentiation and depression, the coexistence of short-term and long-term plasticity, excitatory post-synaptic current, and spike-rate dependent plasticity. Building upon the functionalities of an artificial synapse, pulse spikes are categorized into three distinct neural firing patterns (normal, adapt, and boost) to implement 4-bit reservoir computing, enabling a significant distinction between "0" and "1." [ABSTRACT FROM AUTHOR]

In today's complex and linked technology environments, attaining business efficiency and innovation is crucial. In this paper, an innovative architecture combining parallel distributed algorithms and collaboratively co-signing intelligent verification frameworks is introduced. By promoting safe and effective interaction among heterogeneous systems, this synergistic method seeks to improve the integration process. Diverse systems are proliferating in both internal and external domains, requiring sophisticated integration solutions. Real-time data interchange, interoperability, and security are common issues with existing methods. By using parallel distributed algorithms and intelligent verification models, the suggested framework aims to overcome these difficulties. The framework presents a novel approach to confirming the integrity and validity of data transferred between systems by utilizing collaborative co-signing. Co-signing increases security and confidence in the integrated environment by having several parties jointly validate the information. Through the introduction of parallel distributed algorithms and collaborative co-signing smart verification models, this research advances a comprehensive strategy for smooth integration of systems. The results highlight how the framework can facilitate safe and effective data interchange between external and internal systems, opening the door for more developments in connected technology environments in the future. [ABSTRACT FROM AUTHOR]

Traditional big data filling algorithms often give inaccurate results due to vulnerabilities to different data types. To solve this problem, this study presents a new big data clustering algorithm powered by AI technology in a cloud environment. The study proposes an advanced Big Data clustering algorithm that leverages AI technology in a cloud environment. It optimizes clustering based on predicted strength using parallel processing. The research focuses on optimizing the clustering algorithm based on the predicted intensity through parallel processing. Experimental results demonstrate that image clustering stability is achieved when the number of clusters exceeds 4, indicating reduced sensitivity to random factors. Although it was not possible to precisely determine the optimal number of clusters, the use of an optimization algorithm showed that at four clusters the prediction intensity reached its peak, ensuring more accurate cluster identification. Through rigorous testing, the optimal number of clusters was determined to be 4. Clustering results show that visitors characterized by certain attributes show higher interest in most columns. This algorithm makes it easier to cluster incomplete large data, improves clustering speed, and improves the accuracy of filling in missing data. Compared to existing methods, this algorithm leverages AI technology in the cloud environment to optimize clustering based on prediction intensity, providing improved accuracy and efficiency during processing in big data management. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have shown great performance in computer vision tasks, from image classification to pattern recognition. However, CNNs′${\rm CNNs}^{\prime }$ superior performance arises at the expense of high computational costs, which restricts their employment in real‐time decision‐making applications. Computationally intensive convolutions can be offloaded to optical metasurfaces, enabling sub‐picosecond latency and nearly zero energy consumption, but the currently reported approaches require additional bulk optics and can only process polarized light, which limits their practical usages in integrated lightweight systems. To solve these challenges, a novel design of the metasurface‐based optical convolutional accelerator is experimentally demonstrated, offering an ultra‐compact volume of 0.016 mm3${\rm mm}^{3}$, a low cross‐talk of ‐20 dB, polarization insensitivity, and is capable of implementing multiple convolution operations and extracting simultaneously various features from light‐encoded images. The ultra‐compact metasurface‐based optical accelerator can be compactly integrated with a digital imaging system to constitute an optical‐electronic hybrid CNN, which experimentally achieves a consistent accuracy of 96 % in arbitrarily polarized MNIST digits classification. The proposed ultra‐compact metasurface‐based optical convolutional accelerator paves the way for power‐efficient edge‐computing platforms for a range of machine vision applications. [ABSTRACT FROM AUTHOR]

3D nanolithography based on two‐photon polymerization (TPP) allows for the high‐precision fabrication of nearly arbitrary 3D micro/nanostructures, finding extensive applications in areas such as micro‐optics, micro‐mechanics, and biomedicine. However, the large size, complexity of optical systems, and high costs have significantly constrained the widespread adoption of 3D nanolithography technology in both scientific research and industry. In this study, a metasurface is introduced, for the first time, into 3D nanolithography resulting in the construction of a miniaturized and simplified TPP system that achieved efficient multi‐focus parallel processing with high uniformity. A microlens array is fabricated, showcasing the system's application capacity to generate an array of devices with high consistency and quality. It is believed that the utilization of metasurface devices will provide a novel TPP operating platform, enabling richer and more flexible printing functionalities while maintaining system miniaturization and low cost. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System (GNSS) software-defined receivers offer greater flexibility, cost-effectiveness, customization, and integration capabilities compared to traditional hardware-based receivers, making them essential for a wide range of applications. The continuous evolution of GNSS research and the availability of new features require these software-defined receivers to upgrade continuously to facilitate the latest requirements. The Finnish Geospatial Research Institute (FGI) has been supporting the GNSS research community with its open-source implementations, such as a MATLAB-based GNSS software-defined receiver 'FGI-GSRx' and a Python-based implementation 'FGI-OSNMA' for utilizing Galileo's Open Service Navigation Message Authentication (OSNMA). In this context, longer datasets are crucial for GNSS software-defined receivers to support adaptation, optimization, and facilitate testing to investigate and develop future-proof receiver capabilities. In this paper, we present an updated version of FGI-GSRx, namely, FGI-GSRx-v2.0.0, which is also available as an open-source resource for the research community. FGI-GSRx-v2.0.0 offers improved performance as compared to its previous version, especially for the execution of long datasets. This is carried out by optimizing the receiver's functionality and offering a newly added parallel processing feature to ensure faster capabilities to process the raw GNSS data. This paper also presents an analysis of some key design aspects of previous and current versions of FGI-GSRx for a better insight into the receiver's functionalities. The results show that FGI-GSRx-v2.0.0 offers about a 40% run time execution improvement over FGI-GSRx-v1.0.0 in the case of the sequential processing mode and about a 59% improvement in the case of the parallel processing mode, with 17 GNSS satellites from GPS and Galileo. In addition, an attempt is made to execute v2.0.0 with MATLAB's own parallel computing toolbox. A detailed performance comparison reveals an improvement of about 43% in execution time over the v2.0.0 parallel processing mode for the same GNSS scenario. [ABSTRACT FROM AUTHOR]

Numerical radiation-hydrodynamics (RHD) for non-relativistic flows is a challenging problem because it encompasses processes acting over a very broad range of time-scales, and where the relative importance of these processes often varies by orders of magnitude across the computational domain. Here, we present a new implicit–explicit method for numerical RHD that has a number of desirable properties that have not previously been combined in a single method. Our scheme is based on moments and allows machine-precision conservation of energy and momentum, making it highly suitable for adaptive mesh refinement applications; it requires no more communication than hydrodynamics and includes no non-local iterative steps, making it highly suitable for massively parallel and Graphics Processing Unit (GPU)-based systems where communication is a bottleneck; and we show that it is asymptotically accurate in the streaming, static diffusion, and dynamic diffusion limits, including in the so-called asymptotic diffusion regime where the computational grid does not resolve the photon mean-free path. We implement our method in the GPU-accelerated RHD code quokka and show that it passes a wide range of numerical tests. [ABSTRACT FROM AUTHOR]

Despite their proficiency with typical environmental datasets, deep learning-based object detection algorithms struggle when faced with diverse adverse weather conditions. Moreover, existing methods often address single adverse weather scenarios, neglecting situations involving multiple concurrent adverse conditions. To tackle these challenges, we propose an enhanced approach to object detection in power construction sites under various adverse weather conditions, dubbed IDP-YOLOV9. This model leverages a parallel architecture comprising the Image Dehazing and Enhancement Processing (IDP) module and an improved YOLOV9 object detection module. Specifically, for images captured in adverse weather, our approach employs a parallel architecture that includes the Three-Weather Removal Algorithm (TRA) module and the Deep Learning-based Image Enhancement (DLIE) module, which, together, filter multiple weather factors to enhance image quality. Subsequently, we introduce an improved YOLOV9 detection network module that incorporates a three-layer routing attention mechanism for object detection. Experiments demonstrate that the IDP module significantly improves image quality by mitigating the impact of various adverse weather conditions. Compared to traditional single-processing models, our method improves recognition accuracy on complex weather datasets by 6.8% in terms of mean average precision (mAP50). [ABSTRACT FROM AUTHOR]

Golani's concept of the "mobility gradient" describes the unfolding of motor behavior after immobility and in ontogeny. The two processes run parallel and are similar across vertebrates. In both time scales, the trend is for the behavior to progressively become enriched, cover more dimensions (motor expansion) and become less dependent on external stimuli (stimulus-bound). This paper addresses the question whether the gradient extends into social, interactive behavior. Observation of natural groups larger than dyads may help answering that question. As an example, the natural social behavior of a fish, Pethia nigrofasciata is described in some detail. It is concluded that their motor behavior expands in the course of their daily spawning period. [ABSTRACT FROM AUTHOR]

Low-dose computed tomography (LDCT) has gained increasing attention owing to its crucial role in reducing radiation exposure in patients. However, LDCT-reconstructed images often suffer from significant noise and artifacts, negatively impacting the radiologists' ability to accurately diagnose. To address this issue, many studies have focused on denoising LDCT images using deep learning (DL) methods. However, these DL-based denoising methods have been hindered by the highly variable feature distribution of LDCT data from different imaging sources, which adversely affects the performance of current denoising models. In this study, we propose a parallel processing model, the multi-encoder deep feature transformation network (MDFTN), which is designed to enhance the performance of LDCT imaging for multisource data. Unlike traditional network structures, which rely on continual learning to process multitask data, the approach can simultaneously handle LDCT images within a unified framework from various imaging sources. The proposed MDFTN consists of multiple encoders and decoders along with a deep feature transformation module (DFTM). During forward propagation in network training, each encoder extracts diverse features from its respective data source in parallel and the DFTM compresses these features into a shared feature space. Subsequently, each decoder performs an inverse operation for multisource loss estimation. Through collaborative training, the proposed MDFTN leverages the complementary advantages of multisource data distribution to enhance its adaptability and generalization. Numerous experiments were conducted on two public datasets and one local dataset, which demonstrated that the proposed network model can simultaneously process multisource data while effectively suppressing noise and preserving fine structures. The source code is available at https://github.com/123456789ey/MDFTN. [ABSTRACT FROM AUTHOR]

Using the properties of the Lambert function we review the analytical solutions of the Michaelis–Menten (MM) kinetics and other related models. We derive several quantities of interest such as the half-life and the area under the curve (AUC). The effect of varying the parameters in the Beal–Schnell–Mendoza solution and its asymptotic time behavior were analyzed. The Maclaurin expansion of the time evolution of substrate concentration up to sixth order is presented. These expressions were tested on the well-known problem of ethanol elimination from the human body and excellent agreement was found. In addition, a closed-form solution for the derived problem that combines simultaneously MM and zeroth-order kinetics is derived. This problem was solved by a suitable transformation of variables that casts the original differential equation into a functionally equivalent MM problem with termination time. To finish, analytical solutions for the MM process in parallel with zeroth- and first-order kinetics are presented here as well. We checked all equations against the numerically exact solution of the corresponding differential equation and perfect agreement was found in all cases. [ABSTRACT FROM AUTHOR]

A 1 millisecond (1‐ms) vision system that guarantees high efficiency and timely response for tomato defect detection is essential for factory automation. Because of various defect appearances, recently many existing researches focus on CNN based defect detection, but few of them attempt to reach high processing speed to adapt to the factorial assembly line. This paper proposes a global to multi‐scale local based parallel architecture with hardwired CNN for tomato defect detection. This architecture breaks down image‐wise detection into pixel‐wise localization and block‐wise classification. The pixel‐wise localization utilizes tomato‐aware information as constraints for localization performance. The block‐wise classification uses a fully pipelined network structure to obtain the classification result for each block as the pixel stream moves through the network. The classification network has a six‐layer lightweight network structure with quantization for hardwired type implementation on FPGA. The experiment results show that the proposed architecture processes 1000 FPS images with 0.9476 ms/frame delay. And for detection performance, this architecture keeps f1−score$f_{1}-score$ at 80.18%, only 1.31% lower than ResNet50 based detection system. [ABSTRACT FROM AUTHOR]

Contemporary industry and science expectations towards technological solutions set the bar high. Current approaches to increasing the computing power of standard systems are reaching the limits of physics known to humankind. Fast, programmable systems with relatively low power consumption are a different concept for performing complex calculations. Highly parallel processing opens up a number of possibilities in the context of accelerating calculations. Application of SoC (System On Chip) with FPGA (Field-Programmable Gate Array) enables the delegating of a part of computations to the gates matrix, thereby expediting processing by using parallelization of hardware operations. This paper presents the general concept of using SoC FPGA systems to support the CPU (Central Processing Unit) in many modern tasks. While some tasks might be really hard to implement on an FPGA in a reasonable time, the SoC FPGA platform allows for easy low-level interconnections, and with such virtualized access to the hardware computing resources, it is seen as making FPGAs, or hardware in general, more accessible to engineers accustomed to high-level solutions. The concept presented in the article takes into account the limited resources of cheaper educational platforms, which, however, still provide an interesting and alternative hybrid solution to the problem of parallelization and acceleration of data processing. This allows encountered limitations to be overcome and the flexibility known from high-level solutions and high performance achieved with low-level programming to be maintained without the need for a high financial background. [ABSTRACT FROM AUTHOR]

DNA methylation analysis has become an important topic in the study of human health. In previous work, we developed a suite of tools to perform this analysis. It includes HPG-Dhunter, a web-based tool for automatic detection of differentially methylated regions (DMRs) between different samples. The back-end of that tool receives an undefined number of simultaneous requests to detect DMRs on different datasets. Currently, simultaneous requests are queued and processed one at a time. This paper proposes a parallel architecture where multiple daemons serve requests simultaneously. Daemons can also share the same physical GPUs. A scheduler manages requests and forwards them to daemons. The number of daemons per GPU is configurable, thus adapting the architecture to the available hardware. Results show that the proposed parallel architecture hugely reduces the execution time. Furthermore, the speedup increases proportionally to the number of available GPUs (up to 7.47x in our experimental setup). [ABSTRACT FROM AUTHOR]

GrPPI library aims to simplify the burdening task of parallel programming. It provides a unified, abstract, and generic layer while promising minimal overhead on performance. Although it supports stream parallelism, GrPPI lacks an evaluation regarding representative performance metrics for this domain, such as throughput and latency. This work evaluates GrPPI focused on parallel stream processing. We compare the throughput and latency performance, memory usage, and programmability of GrPPI against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks and benchmarks with handwritten parallel code using the same backends supported by GrPPI. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is often competitive with handwritten parallel code, the infeasibility of fine-tuning GrPPI is a crucial drawback for emerging applications. Despite this, programmability experiments estimate that GrPPI can potentially reduce the development time of parallel applications by about three times. [ABSTRACT FROM AUTHOR]

Agent-based epidemiological simulators have been proven to be one of the most successful tools for the analysis of COVID-19 propagation. The ability of these tools to reproduce the behavior and interactions of each single individual leads to accurate and detailed results, which can be used to model fine-grained health-related policies like selective vaccination campaigns or immunity waning. One characteristic of these tools is the large amount of input data and computational resources that they require. This relies on the development of parallel algorithms and methodologies for generating, accessing, and processing large volumes of data from multiple data sources. This work presents a parallel workflow for extending the social modeling of EpiGraph, an agent-based simulator. We have included two novel parallel social generation stages that generate a detailed and realistic social model and one new visualization stage. This work also presents a description of the algorithms used in each stage, different optimization techniques that permit to reduce the application convergence time, and a practical evaluation of large workloads on HPC systems. Results show that this contribution can be efficiently executed in parallel architectures and the results allow to increase the simulation detail level, representing a significant advance in the simulator scenario modeling. As a summary of results, the first contribution of this paper is the development of two models (a spatial and a social one) that assign geographical and socioeconomic indicators to each simulated individual (i.e., agents), reproducing the real social distribution of the city of Madrid. The second contribution presents an improved parallel and distributed algorithm that executes the two aforementioned models using different parallelization strategies and preserving the load balance. [ABSTRACT FROM AUTHOR]

Durations in the several seconds' range are cognitively accessible during active timing. Functional neuroimaging studies suggest the engagement of the basal ganglia (BG) and supplementary motor area (SMA). However, their functional relevance and arrangement remain unclear because non‐timing cognitive processes temporally coincide with the active timing. To examine the potential contamination by parallel processes, we introduced a sensory control and a motor control to the duration‐reproduction task. By comparing their hemodynamic functions, we decomposed the neural activities in multiple brain loci linked to different cognitive processes. Our results show a dissociation of two cortical neural circuits: the SMA for both active timing and motor preparation, followed by a prefrontal–parietal circuit related to duration working memory. We argue that these cortical processes represent duration as the content but at different levels of abstraction, while the subcortical structures, including the BG and thalamus, provide the logistic basis of timing by coordinating the temporal framework across brain structures. [ABSTRACT FROM AUTHOR]

One of the challenges of renewable energy is its uncertain nature. Community shared energy storage (CSES) is a solution to alleviate the uncertainty of renewable resources by aggregating excess energy during appropriate periods and discharging it when renewable generation is low. CSES involves multiple consumers or producers sharing an energy storage system. This work presents an optimal strategy for CSES operators and community members to determine their optimal energy trading strategy based on social welfare maximization. Energy communities contain diverse resources, demand patterns, and constraints. The Alternating Direction Method of Multipliers (ADMM) technique is an iterative nature that decomposes the original problem into smaller sub-problems and enables parallel processing of these sub-problems. The final decomposed subproblems enable community agents to determine their optimal strategies independently decreasing the solving time, speeding up convergence to the optimal solution, and improving the scalability. The performance of the proposed model is evaluated by a case study. The presented model demonstrates that by leasing the capacity of CSES, community members could decrease their energy supply costs. Moreover, the CSES can supply the required charging and discharging power of community members from the local grid. [ABSTRACT FROM AUTHOR]

In order to improve the emotional expression effect of jewellery, this article combines artificial intelligence technology to analyse the emotional expression in jewellery design. Aiming at the characteristics of high‐gloss materials such as strong reflectivity and translucency, starting from the classic ray tracing algorithm, this article strengthens the reflected light and projected light on the surface of the material, and proposes a realistic rendering algorithm for high‐gloss materials. Moreover, this article introduces the CUDA parallel computing architecture to give full play to the computing power of multi‐core computers, accelerate the calculation speed of the ray tracing algorithm, save the consumption of storage space, and improve the speed of real‐time rendering under the premise of ensuring realism. The experimental research results show that the emotional expression system for smart jewellery design proposed in this article has a certain effect, which can effectively improve the effect of jewellery design. [ABSTRACT FROM AUTHOR]

Nowadays, it is difficult to imagine a powerful algorithm of cryptography that can continue cryptanalyzing and attacking without the use of unconventional techniques. Although some of the substitution algorithms are old, such as Vigenere, Alberti, and Trithemius ciphers, they are considered powerful and cannot be broken. In this paper we produce the novelty algorithm, by using of biological computation as an unconventional search tool combined with an uninhibited analysis method is the vertical probabilistic model, that makes attacking and analyzing these ciphers possible and very easy to transform the problem from a complex to a linear one, which is a novelty achievement. The letters of the encoded message are processed in the form of segments of equal length, to report the available hardware components. Each letter codon represents a region of the memory strand, and the letters calculated for it are symbolized within the probabilistic model so that each pair has a triple encoding: the first is given as a memory strand encoding and the others are its complement in the sticker encoding; These encodings differ from one region to another. The solution space is calculated and then the parallel search process begins. Some memory complexities are excluded even though they are within the solution paths formed, because the natural language does not contain its sequences. The precision of the solution and the time consuming of access to it depend on the length of the processed text, and the precision of the solution is often inversely proportional to the speed of access to it. As an average of the time spent to reach the solution, a text with a length of 200 cipher characters needs approximately 15 minutes to give 98% of the correct components of the specific hardware. The aim of the paper is to transform OTP substitution analysis from a NP problem to a O(nm) problem, which makes it easier to find solutions to it easily with the available capabilities and to develop methods that are harnessed to attack difficult and powerful ciphers that differ in class and type from the OTP polyalphabetic substitution ciphers. [ABSTRACT FROM AUTHOR]

In a previous research effort by this group, pseudo engine dynamometer data in multi-dimensional arrays were combined with dynamic equations to form a crank angle resolved engine model compatible with a real-time simulator. The combination of the real-time simulator and external targets enabled the development of a software-in-the-loop (SIL) environment that enabled near-real-time development of AI/ML and real-time deployment of the resulting AI/ML. Military applications, in particular, are unlikely to possess large quantities of non-sparse operational data that span the full operational range of the system, stove-piping the ability to develop and deploy AI/ML which is sufficient for near-real-time or real-time control. AI/ML has been shown to be well suited for predicting highly non-linear mathematical phenomena and thus military systems could potentially benefit from the development and deployment of AI/ML acting as soft sensors. Given the non-sparse nature of the data, it becomes exceedingly important that AI/ML be developed and deployed in near-real-time or real-time in parallel to a real-time system to overcome the inadequacy of applicable data. This research effort used parallel processing to reduce the training duration of the shallow artificial neural networks (SANN) and forest algorithms forming ensemble models. This research is novel in that the SIL environment enables pre-developed AI/ML to be adapted in near-real-time or develop AI/ML in response to changes within the operation of the applied system, different load torques, engine speeds, and atmospheric conditions to name a few. Over time it is expected that the continued adaptation of the algorithms will lead to the development of AI/ML that is suitable for real-time control and energy management. [ABSTRACT FROM AUTHOR]