Search

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]

Metaheuristic is one of the techniques to improve the security of image encryption. However, existing metaheuristic image cryptosystems based on metaheuristic may have convergence difficulties during the optimization process, which cause insecurity and slow convergence. Besides, the time cost of the parallel execution model applied to metaheuristic image cryptosystems is not low enough. Therefore, a parallel many‐objective optimized key generation framework is proposed. Firstly, if four or more security indicators of cryptosystem, which are the results of security test, need to be optimized, the many‐objective optimization algorithm is employed to the proposed framework. With method adjusts the chaotic system parameters as the optimization key, which effectively avoid the convergence difficulty of the encryption key. Secondly, a master‐slave parallel model is improved to metaheuristic encryption. The model allocates the most time‐consuming fitness calculation work to slave nodes, which makes the modified model more reasonable and thus reduces the encryption time. To evaluate the performance of the proposed framework, a specific encryption scheme is constructed, that utilizes a 2D quadric map and many‐objective optimization algorithm based on dominance and decomposition (MOEA/DD) to optimize five security indicators. Experimental results reveal that this scheme has good security performances and less parallel encryption time. [ABSTRACT FROM AUTHOR]

With the ever-increasing intertwining of social networks and daily existence, the accumulation of personal privacy information is steadily mounting. However, the exposure of such data could lead to disastrous consequences. Current graph data protection algorithms lack sufficient research on the characteristics of social users, while simultaneously incurring significant time and space overhead. Additionally, the strategies lack adaptability in incorporating noise, often resulting in a subsequent decrease in data availability. To address these issues, a novel approach called the Information Entropy-driven Adaptive Differential Privacy Protection Scheme (IEA-DP) is presented for the release of social data in this study. The proposed solution initially designs the InfomapMerge algorithm to divide the data into communities based on the characteristics of social networks, thereby enabling parallel processing, and mitigating the time and space overhead. Subsequently, the Adaptive Edge Modification Algorithm (AEMA) is proposed to optimize the noise addition process by adaptively adding noise based on the score of node importance. This effectively reduces the amount of added noise, increasing data availability. Finally, the experimental results conducted on six public datasets demonstrate that the IEA-DP scheme strikes a desirable balance between data availability and privacy. [ABSTRACT FROM AUTHOR]

All‐optical logic gates (AOLGs) have attracted considerable attention for their high computing speed, low power consumption, and parallel processing; however, chemical design of new luminescent materials is a challenge to explore a facile platform for undertaking AOLGs. Here, an AOLG system is developed based on the organic–inorganic hybrid halides [NH3(CH2)2NH3]2ZnCl6:Mn2+ with tricolor luminescence under dual‐wavelength excitation, and the intrinsic emission mechanisms are further addressed. When employing 275 and 365 nm UV excitations as input signals, the AOLGs conduct Boolean operations “OR, YES, AND” via identifying the green (G) and red (R) emissions and their intensity ratio (R/G) as output signals. This work further demonstrates the AOLG operation imaging and image processing via hybrid halide emitters, indicating promise in optical computing applications. [ABSTRACT FROM AUTHOR]

Trends over the past two decades indicate that much of the performance gains of commercial optimization solvers is due to improvements in x86 hardware. To continue making progress, it is critical to consider alternative/specialized massively parallel computing architectures. In this work, we detail the development of an open-source source code transformation approach built using Symbolics.jl to construct McCormick-based relaxations of functions that enables their effective parallelized evaluation. We then apply this approach in a novel parallelized branch-and-bound routine that offloads lower- and upper-bounding problems to a GPU. The effectiveness of this new approach is demonstrated on three nonconvex problems of interest, where it yields convergence time improvements of 22–118x compared to an equivalent serial CPU implementation and in two cases outperforms vanilla branch-and-bound versions of existing state-of-the-art solvers that use tighter bounding techniques. This work exemplifies how deterministic global optimizers using alternative hardware architectures can compete with—or eventually outclass—even the most powerful serial CPU implementations, and to the best of the authors' knowledge, represents the first successful demonstration of deterministic global optimization using a GPU. [ABSTRACT FROM AUTHOR]

Summary: Power system monitoring relies on the reliability of state estimation (SE) results. SE plays a dominant role in data debugging if sufficient data is available. Criticality analysis (CA) integrates SE as a module in which measurements—taken one‐by‐one or in groups (tuples) of minimal cardinality—are designated crucial. The combinatorial nature of extensive CA (not restricted to identifying low‐cardinality critical tuples) characterizes its computational complexity and imposes challenging limits to go beyond. In simple terms, these limits are established by the number of measurements to be combined, the cardinality of tuples, and the computing time required to check the criticality condition. This paper proposes an innovative computational solution to expand CA limits found to date in the literature. A framework with multi‐threads designed cleverly on a graphics processing unit (GPU) parallel processing environment is built. The conceived architecture favors evaluating massive measurement combinations of diverse cardinality in extensive CA. Numerical results reveal significant speed‐ups with the proposed approach, contrasting with those reported in research efforts published so far. [ABSTRACT FROM AUTHOR]

Benefiting from low power consumption and high processing speed, there is a growing interest in diffraction neural networks (DNNs), which are typically showcased with 3D printing devices, leading to large volumes, high costs, and low levels of integration. Metasurfaces can desirably manipulate wavefronts of electromagnetic waves, providing a compact platform for mimicking DNNs with novel functions. Although multi‐wavelength and multi‐target recognition provides a richer and more detailed understanding of complex environments, existing architectures are primarily trained to classify a single target at a specific wavelength. A metasurface approach is proposed to design multiplexed DNNs that can classify multiple targets and spatial sequences across various wavelengths in multiple channels. To realize multi‐task processing, the dielectric metasurface is designed based on phase and wavelength multiplexing, which can integrate multi‐target DNNs with different tasks such as operating at distinct wavelengths and classifying diverse targets. The efficacy of this method is exemplified through the numerical simulation and experimental demonstration of recognizing a single target with two wavelengths, two targets at a single wavelength, and two targets at dual wavelengths. This compact metasurface approach enables the design of multi‐target and multi‐wavelength DNNs, opening a new window to develop massively parallel processing and versatile artificial intelligence systems. [ABSTRACT FROM AUTHOR]

Liver cancer is one of the malignancies with high mortality rates worldwide, and its timely detection and accurate diagnosis are crucial for improving patient prognosis. To address the limitations of traditional image segmentation techniques and the U-Net network in capturing fine image features, this study proposes an improved model based on the U-Net architecture, named RHEU-Net. By replacing traditional convolution modules in the encoder and decoder with improved residual modules, the network's feature extraction capabilities and gradient stability are enhanced. A Hybrid Gated Attention (HGA) module is integrated before the skip connections, enabling the parallel processing of channel and spatial attentions, optimizing the feature fusion strategy, and effectively replenishing image details. A Multi-Scale Feature Enhancement (MSFE) layer is introduced at the bottleneck, utilizing multi-scale feature extraction technology to further enhance the expression of receptive fields and contextual information, improving the overall feature representation effect. Testing on the LiTS2017 dataset demonstrated that RHEU-Net achieved Dice scores of 95.72% for liver segmentation and 70.19% for tumor segmentation. These results validate the effectiveness of RHEU-Net and underscore its potential for clinical application. [ABSTRACT FROM AUTHOR]

Task‐related studies have consistently reported that listening to speech sounds activate the temporal and prefrontal regions of the brain. However, it is not well understood how functional organization of auditory and language networks differ when processing speech sounds from its resting state form. The knowledge of language network organization in typically developing infants could serve as an important biomarker to understand network‐level disruptions expected in infants with hearing impairment. We hypothesized that topological differences of language networks can be characterized using functional connectivity measures in two experimental conditions (1) complete silence (resting) and (2) in response to repetitive continuous speech sounds (steady). Thirty normal‐hearing infants (14 males and 16 females, age: 7.8 ± 4.8 months) were recruited in this study. Brain activity was recorded from bilateral temporal and prefrontal regions associated with speech and language processing for two experimental conditions: resting and steady states. Topological differences of functional language networks were characterized using graph theoretical analysis. The normalized global efficiency and clustering coefficient were used as measures of functional integration and segregation, respectively. We found that overall, language networks of infants demonstrate the economic small‐world organization in both resting and steady states. Moreover, language networks exhibited significantly higher functional integration and significantly lower functional segregation in resting state compared to steady state. A secondary analysis that investigated developmental effects of infants aged 6‐months or below and above 6‐months revealed that such topological differences in functional integration and segregation across resting and steady states can be reliably detected after the first 6‐months of life. The higher functional integration observed in resting state suggests that language networks of infants facilitate more efficient parallel information processing across distributed language regions in the absence of speech stimuli. Moreover, higher functional segregation in steady state indicates that the speech information processing occurs within densely interconnected specialized regions in the language network. [ABSTRACT FROM AUTHOR]

Long-strip imaging is an important way of improving the coverage and acquisition efficiency of remote sensing satellite data. During the agile maneuver imaging process of the satellite, the LuoJia3-01 satellite can obtain a sequence of array long-strip images with a certain degree of overlap. Limited by the relative accuracy of satellite attitude, there will be relative misalignment between the sequence frame images, requiring high-precision geometric processing to meet the requirements of large-area remote sensing applications. Therefore, this study proposes a new method for the geometric correction of long-strip images without ground control points (GCPs) through GPU acceleration. Firstly, through the relative orientation of sequence images, the relative geometric errors between the images are corrected frame-by-frame. Then, block perspective transformation and image point densified filling (IPDF) direct mapping processing are carried out, mapping the sequence images frame-by-frame onto the stitched image. In this way, the geometric correction and image stitching of the sequence frame images are completed simultaneously. Finally, computationally intensive steps, such as point matching, coordinate transformation, and grayscale interpolation, are processed in parallel using GPU to further enhance the program's execution efficiency. The experimental results show that the method proposed in this study achieves a stitching accuracy of less than 0.3 pixels for the geometrically corrected long-strip images, an internal geometric accuracy of less than 1.5 pixels, and an average processing time of less than 1.5 s per frame, meeting the requirements for high-precision near-real-time processing applications. [ABSTRACT FROM AUTHOR]

Applying blockchain technology to the cloud manufacturing platform (CMfg) endowed it with features such as decentralized resource management, distributed data storage, and platform robustness. However, during the implementation of service optimization composition, challenges are encountered, including the need for centralized resource scheduling, data entry dependence on the moment of block generation, and a lack of dynamic adaptability in the execution scheme. To address this, this paper proposed a solution in the form of cloud dual-chain oriented to service optimization composition. The cloud dualchain comprised a manufacturing service chain (MSC) and a process data chain (PDC). It segregated the management of service resources and composition scheme into distinct chains, allowing the CMfg to reconcile blockchain characteristics with real-world service collaboration needs. The manufacturing service within the cloud dual-chain was self-managed by the enterprise, and the MSC chain was formed by smart contracts based on task orders, balancing distributed and centralized management. The PDC, generated based on service composition, recorded process data in parallel. In case of service failure, a new service was invoked in the corresponding MSC node, updating the information in the PDC node to ensure dynamic adaptability of the execution plan. Experimental results demonstrate that cloud dual-chain facilitates flexible service management, distributed parallel entry of process data, and provides dynamic adaptability to execution scheme. Cloud dual-chain can effectively support the development of CMfg. [ABSTRACT FROM AUTHOR]

In this paper, a design model for resource allocation is formulated beyond 5G networks for effective data allocations in each network nodes. In all networks, data is transmitted only after allocating all resources, and an unrestrained approach is established because the examination of resources is not carried out in the usual manner. However, if data transmission needs to occur, some essential resources can be added to the network. Moreover, these resources can be shared using a parallel optimization approach, as outlined in the projected model. Further the designed model is tested and verified with four case studies by using resource allocator toolbox with parallax where the resources for power and end users are limited within the ranges of 1.4% and 6%. Furthermore, in the other two case studies, which involve coefficient determination and blockage factors, the outcomes of the proposed approach fall within the marginal error constraint of approximately 31% and 87%, respectively. [ABSTRACT FROM AUTHOR]

Pansharpening is an important technology for obtaining high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images and high-resolution panchromatic (PAN) images. Although many pansharpening models have emerged by taking advantage of deep learning (DL) technology, there remains a pressing need to further assess pansharpening accuracy and stability when LRMS images with complex land-cover types. What's more, these models often overlook the exploitation of PAN images' inherent high-frequency information. To address these issues, we propose a pansharpening model combining multi-level and multi-scale network architectures. The multi-level network architecture is used to build spatial-spectral dependence on LRMS-PAN pairs, and strengthen the network's feature capture capability by keeping the multi-level texture details. The multi-scale architecture is subsequently used to extract the spatial structure and deep texture of the PAN images at different scales. Downsampled experiments and real experiments in four standard datasets show that the proposed model achieves a state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Purpose: Previous studies examining the inter-relations between serial and discrete naming with reading have found that the ability to efficiently process multiple items presented in a sequence (indexed by serial naming) is a unique predictor of word- and text-reading fluency. However, conclusions have been tempered by the concurrent nature of the available data and the uniformly low demands of the materials (words and texts). Here we go beyond previous studies by using more varied materials to examine the relations of serial and discrete naming with the discrete reading of words and the serial reading of word lists and connected text over time. Method: Two hundred and eight English-speaking Canadian children (51% female, Mage = 7.2 years) were followed from Grade 2 to Grade 5 and were assessed on serial and discrete digit naming and serial and discrete word reading at both measurement points. Results: Strong associations between discrete naming and discrete reading already from Grade 2 indicated that short and high-frequency words were processed in parallel early in development. By Grade 5, when word recognition was presumably automatized, serial naming accounted for unique variance in serial reading of word lists and connected texts after controlling for discrete word reading. More importantly, Latent Change Score modeling indicated that serial naming was the main predictor of growth in serial reading from Grade 2 to Grade 5. Conclusion: These findings suggest that, beyond individual word recognition, reading fluency development also requires efficient processing of multiple items presented in serial format (termed "cascaded processing"). [ABSTRACT FROM AUTHOR]

We explore the possibility of assuming largely flat syntactic structures in Simpler Syntax, suggesting that these are plausible alternatives to conventional hierarchical structures. We consider the implications of flat structure for analyses of various linguistic phenomena in English, including heavy NP shift, extraposition, topicalization and constituent order variation in the VP. We also sketch a general strategy to circumvent some of the problems flat structure is said to cause for semantic interpretation. Our proposals eliminate the need for movement, unpronounced copies and feature-bearing nodes postulated to trigger syntactic operations. We assume the Parallel Architecture and use declarative schemas to establish direct correspondences between phonology on the one hand and syntactic and semantic structures on the other. The resulting picture is one in which narrow syntax can be relatively stable across languages and constructions, largely reflecting the structure of human thought, and the main source of linguistic variation is the linearization of conceptual and syntactic structures. Unlike other minimalist theories that reach a similar conclusion, the theory we propose takes mappings to phonology to be central to the architecture of grammar. [ABSTRACT FROM AUTHOR]

Deep cascaded networks have been extensively studied and applied to accelerate Magnetic Resonance Imaging (MRI) and have shown promising results. Most existing works employ a large cascading number for the sake of superior performances. However, due to the lack of proper guidance, the reconstruction performance can easily reach a plateau and even face degradation if simply increasing the cascading number. In this paper, we aim to boost the reconstruction performance from a novel perspective by proposing a parallel architecture called DIRECTION that fully exploits the guiding value of the reconstruction residual of each subnetwork. Specifically, we introduce a novel Reconstruction Residual-Based Feature Modulation Mechanism (RRFMM) which utilizes the reconstruction residual of the previous subnetwork to guide the next subnetwork at the feature level. To achieve this, a Residual Attention Modulation Block (RAMB) is proposed to generate attention maps using multi-scale residual features to modulate the image features of the corresponding scales. Equipped with this strategy, each subnetwork within the cascaded network possesses its unique optimization objective and emphasis rather than blindly updating its parameters. To further boost the performance, we introduce the Cross-Stage Feature Reuse Connection (CSFRC) and the Reconstruction Dense Connection (RDC), which can reduce information loss and enhance representative ability. We conduct sufficient experiments and evaluate our method on the fastMRI knee dataset using multiple subsampling masks. Comprehensive experimental results show that our method can markedly boost the performance of cascaded networks and significantly outperforms other compared state-of-the-art methods quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

Suggesting a new reading of A74 in an exclusively physiological context and in combination with a new interpretation of B100, this article argues for a new reconstruction of Empedocles' respiration theory, involving two complementary and parallel respiratory processes, where the pressure of the innate heat acts together with the movement of the blood to drive the air in and out and reach all body parts and organs. [ABSTRACT FROM AUTHOR]

Longitudinal studies on mainstream children and training studies in the dyslexic population suggest that visual attention span (VAS) abilities contribute to reading acquisition. We evaluated to what extent VAS training in beginning readers might enhance later literacy skills. A large cohort of 453 children was followed from the beginning to the end of Grade 1. A first group of students was trained on a custom‐designed digital application—called EVASION—that targeted VAS abilities. Another group used the GraphoGame application, while the third was a “business‐as‐usual” group. A total training time of 10 h was recommended; training was performed during the regular school day, under the sole supervision of teachers. Pre–post intervention assessment revealed higher VAS, higher reading fluency improvement, and higher postintervention spelling skills in the EVASION group. Children who spent more time playing with EVASION improved more in both VAS and literacy skills. In the whole population, VAS enhancement predicted reading fluency improvement and posttraining spelling skills, independently of other reading related skills and of the class effect. The overall findings suggest that training VAS in the classroom might prevent difficulties in learning to read and spell. Evidence for longitudinal effects of VAS training on literacy skills support a causal relationship. In improving multiletter parallel processing, training would translate into better orthographic learning, yielding higher reading fluency and spelling skills. [ABSTRACT FROM AUTHOR]

Recent advancements in neuromorphic computing have led to the development of hardware architectures inspired by Spiking Neural Networks (SNNs) to emulate the efficiency and parallel processing capabilities of the human brain. This work focuses on testing the HEENS architecture, specifically designed for high parallel processing and biological realism in SNN emulation, implemented on a ZYNQ family FPGA. The study applies this architecture to the classification of digits using the well-known MNIST database. The image resolutions were adjusted to match HEENS' processing capacity. Results were compared with existing work, demonstrating HEENS' performance comparable to other solutions. This study highlights the importance of balancing accuracy and efficiency in the execution of applications. HEENS offers a flexible solution for SNN emulation, allowing for the implementation of programmable neural and synapticmodels. It encourages the exploration of novel algorithms and network architectures, providing an alternative for real-time processing with efficient energy consumption. [ABSTRACT FROM AUTHOR]

Radar signal intra-pulse modulation recognition can be addressed with convolutional neural networks (CNNs) and time–frequency images (TFIs). However, current CNNs have high computational complexity and do not perform well in low-signal-to-noise ratio (SNR) scenarios. In this paper, we propose a lightweight CNN known as the cross-scale aware network (CSANet) to recognize intra-pulse modulation based on three types of TFIs. The cross-scale aware (CSA) module, designed as a residual and parallel architecture, comprises a depthwise dilated convolution group (DDConv Group), a cross-channel interaction (CCI) mechanism, and spatial information focus (SIF). DDConv Group produces multiple-scale features with a dynamic receptive field, CCI fuses the features and mitigates noise in multiple channels, and SIF is aware of the cross-scale details of TFI structures. Furthermore, we develop a novel time–frequency fusion (TFF) feature based on three types of TFIs by employing image preprocessing techniques, i.e., adaptive binarization, morphological processing, and feature fusion. Experiments demonstrate that CSANet achieves higher accuracy with our TFF compared to other TFIs. Meanwhile, CSANet outperforms cutting-edge networks across twelve radar signal datasets, providing an efficient solution for high-precision recognition in low-SNR scenarios. [ABSTRACT FROM AUTHOR]

Music genre classification (MGC) is the basis for the efficient organization, retrieval, and recommendation of music resources, so it has important research value. Convolutional neural networks (CNNs) have been widely used in MGC and achieved excellent results. However, CNNs cannot model global features well due to the influence of the local receptive field; these global features are crucial for classifying music signals with temporal properties. Transformers can capture long-range dependencies within an image thanks to adopting the self-attention mechanism. Nevertheless, there are still performance and computational cost gaps between Transformers and existing CNNs. In this paper, we propose a hybrid architecture (CNN-TE) based on CNN and Transformer encoder for MGC. Specifically, we convert the audio signals into mel spectrograms and feed them into a hybrid model for training. Our model employs a CNN to initially capture low-level and localized features from the spectrogram. Subsequently, these features are processed by a Transformer encoder, which models them globally to extract high-level and abstract semantic information. This refined information is then classified using a multi-layer perceptron. Our experiments demonstrate that this approach surpasses many existing CNN architectures when tested on the GTZAN and FMA datasets. Notably, it achieves these results with fewer parameters and a faster inference speed. [ABSTRACT FROM AUTHOR]

When performing high-resolution imaging with ground-based synthetic aperture radar (GB-SAR) systems, the data collected and processed are vast and complex, imposing higher demands on the real-time performance and processing efficiency of the imaging system. Yet a very limited number of studies have been conducted on the real-time processing method of GB-SAR monitoring data. This paper proposes a real-time imaging scheme based on parallel processing models, optimizing each step of the traditional  ω K imaging algorithm in parallel. Several parallel optimization schemes are proposed for the computationally intensive and complex interpolation part, including dynamic parallelism, the  G r o u p - N s t r e a m  processing model, and the  F t h r e a d - G r o u p - N s t r e a m  processing model. The  F t h r e a d - G r o u p - N s t r e a m  processing model utilizes  F t h r e a d ,  G r o u p , and  N s t r e a m  for the finer-grained processing of monitoring data, reducing the impact of the nested depth on the algorithm's performance in dynamic parallelism and alleviating the issue of serial execution within the  G r o u p - N s t r e a m  processing model. This scheme has been successfully applied in a synthetic aperture radar imaging system, achieving excellent imaging results and accuracy. The speedup ratio can reach 52.14, and the relative errors in amplitude and phase are close to 0, validating the effectiveness and practicality of the proposed schemes. This paper addresses the lack of research on the real-time processing of GB-SAR monitoring data, providing a reliable monitoring method for GB-SAR deformation monitoring. [ABSTRACT FROM AUTHOR]

Fully Homomorphic Encryption (FHE) allows computations on encrypted data without decryption, providing strong security for sensitive information. However, computational and memory demands for FHE are significant challenges, particularly in the Number Theoretic Transform (NTT) phase. This paper presents three efficient Twiddle Factor Generators (TFGs) to address these challenges: the Half-Memory TFG, the On-the-fly Serial TFG, and the On-the-fly Parallel TFG. The Half-Memory TFG reduces memory usage by storing only half of the twiddle factors and calculating the rest as needed. The On-the-fly Serial TFG eliminates memory requirements by computing twiddle factors, while the On-the-fly Parallel TFG enhances computational speed through parallel processing. Implemented on the FPGA KCU105 board, these TFGs demonstrated significant improvements in hardware resource utilization and computational efficiency. The Half-Memory TFG effectively reduces memory footprint, the On-the-fly Serial TFG eliminates memory usage with acceptable computational overhead, and the On-the-fly Parallel TFG offers superior performance for high-throughput applications. These innovations make FHE more practical for real-world applications, contributing to the broader goal of enabling secure, privacy-preserving computations on encrypted data. [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]

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]

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]

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]

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]

While the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing systems increasingly leverage parallel processing capabilities, multi-core CPUs, and specialized hardware accelerators, traditional indexing approaches may not fully capitalize on these advancements. This comprehensive experimental study investigates the effects of hardware-conscious indexing strategies tailored for contemporary and emerging hardware platforms. Through rigorous experimentation on a real-world database environment using the industry-standard TPC-H benchmark, this research evaluates the performance implications of indexing techniques specifically designed to exploit parallelism, vectorization, and hardware-accelerated operations. By examining approaches such as cache-conscious B-Tree variants, SIMD-optimized hash indexes, and GPU-accelerated spatial indexing, the study provides valuable insights into the potential performance gains and trade-offs associated with these hardware-aware indexing methods. The findings reveal that hardware-conscious indexing strategies can significantly outperform their traditional counterparts, particularly in data-intensive workloads and large-scale database deployments. Our experiments show improvements ranging from 32.4% to 48.6% in query execution time, depending on the specific technique and hardware configuration. However, the study also highlights the complexity of implementing and tuning these techniques, as they often require intricate code optimizations and a deep understanding of the underlying hardware architecture. Additionally, this research explores the potential of machine learning-based indexing approaches, including reinforcement learning for index selection and neural network-based index advisors. While these techniques show promise, with performance improvements of up to 48.6% in certain scenarios, their effectiveness varies across different query types and data distributions. By offering a comprehensive analysis and practical recommendations, this research contributes to the ongoing pursuit of database performance optimization in the era of heterogeneous computing. The findings inform database administrators, developers, and system architects on effective indexing practices tailored for modern hardware, while also paving the way for future research into adaptive indexing techniques that can dynamically leverage hardware capabilities based on workload characteristics and resource availability. [ABSTRACT FROM AUTHOR]

In this paper, a numerical model coupling the smoothed particle hydrodynamics (SPH) with peridynamics (PD) is developed to solve the problem of the hydroelastic water entry. By combining the geometric nonlinear peridynamics with the weakly compressible SPH, the proposed model can efficiently simulate the nonlinear deformation of the structure and the deformation of a fluid free surface. The transmission of information between the fluid and solid phases is implemented by a dummy particle boundary treatment method. By simulating benchmark tests, the ability of the proposed model to solve nonlinear fluid–structure coupling problems is verified. In order to improve the computational efficiency, the graphics processing unit parallel acceleration scheme is applied to the SPH-PD model. Compared with the serial scheme, the speedup effect of the parallel scheme in large-scale computation is verified. As an application of the numerical model, the water entry of the flexible panel at a constant entry velocity is studied. The mechanism of hydroelastic effect is explained by analyzing the variations in the fluid pressure field, slamming force, and panel deformation during water entry. In addition, the influences of plate rigidity, impact velocity, and deadrise angle on the hydroelastic effect are investigated. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]