Multiple Processes Research Articles

Load cascades in spatial networks: A sandpile model approach

In the real world, the construction of infrastructure networks is influenced by spatial factors. For such complex networks, We refer to them as spatial networks. This study investigates, based on the Bak–Tang–Wiesenfeld sandpile model, the impact of spatial factors on sandpile cascading avalanches. Through simulations, we discover that spatial factors not only facilitate the occurrence of moderate-scale avalanches in the system but also have a suppressing effect on the occurrence of the largest cascading avalanches. Using a multiplicative branching process, we analyze the influence of triangular structures composed of nodes on sandpile avalanches and find that nodes within triangular structures are relatively resistant to topples. Because of the relatively stable nature within triangular structures, more load tends to stay within this kind of structures. Once a cascading avalanche occurs in the system, the avalanche scale is often larger than that in the non-spatial counterpart. This increase in avalanche scale results in more load being released by the system, making the spatial networks more relaxed. And their probability of very-large-scale cascading avalanches is lower than that of non-spatial networks.

Task Scheduling by Hybrid Metaheuristic Approach in Cloud Environment

Abstract: Recent advancements in hardware, virtualization technology, distributed computing, and service delivery over the Internet have led to the formation of cloud computing. In a distributed computing platform, the scheduling of task workflow is a well-known NP-hard problem. This becomes more complex and challenging if the virtualized clusters execute a large number of tasks in a cloud computing platform. From low-level execution tasks in multiple processors to high-level execution tasks, many heuristics have been proposed. This chapter deals with the proposed min-min algorithm, max-min algorithm, PSO, and TS. The max-min and min-min algorithms estimate the execution time and overall completion time of all the tasks Then assign the tasks on better suitable resource. PSO is simple and effective in a wide range of applications with low computational cost and fast speed. TS approach is one of the popular local search techniques based on neighborhood search algorithm. TS can be directly applied to any kind of virtual optimization problem.

Investigating the impact of X-ray irradiation on the SF6 gas discharge in GIS equipment

ABSTRACT Gas Insulated Switchgear (GIS) is extensively utilized in power grid systems. However, the existence of numerous photoelectrons in the SF6 gas during X-ray non-destructive testing in ultra-high-voltage (UHV) GIS equipment may induce gas streamer discharge in a strong electric field. To address this issue, in this work, the impact of X-ray irradiation on the SF6 gas discharge in GIS equipment was systematically analyzed. Firstly, one GIS engineering mode with a three-dimensional electric field was established, and a physical GIS model was established by the Geant4 and Garfield++ codes. Secondly, the electrons’ ionization, and multiplication processes in SF6 gas during the X-ray irradiation were studied. Finally, a test platform with an equivalent electric field tube, which had the same intensity as in the GIS equipment, was established to verify the physical GIS model. X-ray beams with 0.1 MeV, 0.16 MeV, 0.3 MeV, and 1.25 MeV energy were used during the test process. From our analysis, it was concluded that X-ray irradiation can cause an electron avalanche in SF6 gas and the intensity of the avalanche is low, which is far away from the appearance of the streamer discharge.

Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings

– Factors models are commonly used to analyze high-dimensional data in both single-study and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte Carlo (MCMC) methods, which scale poorly as the number of studies, observations, or measured variables increase. To address this issue, we propose new variational inference algorithms to approximate the posterior distribution of Bayesian latent factor models using the multiplicative gamma process shrinkage prior. The proposed algorithms provide fast approximate inference at a fraction of the time and memory of MCMC-based implementations while maintaining comparable accuracy in characterizing the data covariance matrix. We conduct extensive simulations to evaluate our proposed algorithms and show their utility in estimating the model for high-dimensional multi-study gene expression data in ovarian cancers. Overall, our proposed approaches enable more efficient and scalable inference for factor models, facilitating their use in high-dimensional settings. An R package VIMSFA implementing our methods is available on GitHub (github.com/blhansen/VI-MSFA).

Impact of jasmonates, salicylic acid, and fungal elicitor on the health of <i>Hippeastrum hybridum</i> bulbs achieved by <i>in vitro</i> procedure

Production of high-quality vigorous plant materials obtained by micropropagation requires not only a good regeneration procedure but also efficient ways to enhance their post-transplanting ability to cope with external environmental factors. There is increasing interest in <i>Hippeastrum hybridum</i> on the horticultural market. However, the traditional method of cultivation using seeds and bulbs does not meet the growing demand and requirements of available priming procedures, thus leading to a search for more effective methods of reproduction. The aim of the study was to assess the morphogenetic abilities of bulb and flower explants for regeneration and investigate whether the enrichment of the multiplication process by exposure to elicitors will improve the health of the obtained plants through faster synthesis of phytoalexin. The study revealed that high regeneration efficiency can be obtained by using twin-scales, peduncles, and receptacles. Over 76% of explants were capable of bulblet induction and the micropropagation rates were 2.3, 2.7, and 5.0, respectively. At the rooting stage, the use of regulator-free medium was required, yielding a high percentage of rooted shoots of over 90% (shoots obtained on twin-scales and peduncles). The materials obtained <i>in vitro</i> did not differ morphologically from the initial materials. However, it was noted that, after exposure to the <i>Phoma narcissi</i> fungal elicitor or to jasmonic acid (JA) and jasmonic acid methyl ester (MeJA), the <i>in vitro</i>-derived bulbs were able to synthesize phytoalexin with higher efficiency than the untreated ones. Therefore, the described methods can be useful for <i>H. hybridum</i> multiplication. Moreover, the enrichment of the multiplication process with the stage of exposure to elicitors ensures initial immunity of <i>H. hybridum</i> to the pathogen <i>Phoma narcissi</i>, which can be used as a priming procedure.

PLEIADES: A numerical framework dedicated to the multiphysics and multiscale nuclear fuel behavior simulation

The aim of this paper is to introduce the PLEIADES framework offering a set of services and numerical tools to model and simulate the behavior of nuclear fuels of different concepts of reactors. The framework provides in particular the following features: interfaces to manipulate meshes and fields, services to deal with different physical solvers, setup of coupling trees to realize multiphysics partitioned (accelerated) fixed point couplings, automated time-marching algorithm, checkpoint/restart strategies, capability to realize on-the-fly multiscale couplings. It is built with a permanent concern for sustainability, scalability and maintainability. PLEIADES framework supports multidimensional simulations, typically 1D, 2D and 3D, possibly multilayered. To date, this framework relies on the generic thermomechanical finite elements solver Cast3M to deal with partial derivative problems (mechanical, thermal or diffusion problems) at the scale of the structure or the (heterogeneous) microstructure. It also makes use of so-called point models (mainly based on ODE — ordinary differential equations) to describe the local (mesoscale) evolution of the material through the physics of irradiation. Several software applications are built on the PLEIADES framework, they can use its parallel features to use multiple processors. The multilayered calculation scheme provides a way to loosen the computations and access to a good parallel performance up to hundred cores. It is also possible to achieve efficient scalable concurrent multiscale simulations through finite element square (FE2) computational homogenization algorithms.

Interplay of intermolecular and intramolecular vibrations mediates ultrafast singlet fission

Singlet fission (SF) is an exciton multiplication process that splits a singlet exciton in organic semiconductors into two triplet excitons and thus, can overcome the Shockley–Queisser limit to improve the solar energy conversion efficiency in photovoltaics. In this paper, we construct a unified model for the ultrafast primary step of the SF process. To achieve this, we investigate the dynamics of vibrational modes and their interactions with the relevant electronic excited states in prototypical SF materials, pentacene (exothermic SF) and tetracene (endothermic SF) single crystals. Additionally, the functional role of the charge transfer (CT) state is also examined. Using the refined parameters obtained from the reported experimental results, we deduce that the intermolecular vibrations mediate the SF in pentacene with the assistance from strong vibronic couplings to intramolecular modes, which drives the SF process to occur within 100 fs. In this timescale, the CT state has a limiting role towards the SF process in pentacene. However, the CT state plays an important role in a relatively slower SF process in tetracene. Our results disentangle the role of underlying vibrational coherences and clarify the importance of the CT state in tetracene crystal. Hence, with our unified model, we can study the coherent dynamics of the SF process, which can principally be extended to other SF materials as well.

Participatory research processes for the production of plant material in food security context

The production of quality seed constitutes one of the main challenges for food security and sovereignty of both urban and rural populations, requiring processes that recognize not only the potential of the territories but also the diversity associated withpeasant communities and their forms of production. , so as to reduce the gaps resulting from the use of seeds of low or no physical, genetic, physiological and sanitary quality that reduce the impact on the yields of banana, pineapple and cassava crops. To this end, a participatory research process was carried out to meet the demand for projects that improve the food and nutritional security of the population of the department of Casanare -Colombia. As a result, the methodology was implemented based on meetings with producers of productive systems for the construction of knowledge from social cartography, the preparation of economic calendars and mappings of actors and needs. This reading of the environments allowed the development of activities for the participatory selection of planting materials, processes of intensive multiplication of materials, as well as the transformation and production of banana, pineapple and cassava derivatives. The conclusions have to do with the importance of considering the producer as an active participant in the research processes in the construction of knowledge that contributes to the generation and appropriation of solutions based on science, technology and innovation in favor of food security and with this the reduction of poverty. In the near future, it is planned to carry out a training process for trainers with allied universities to leave installed capacities and to promote access and availability of quality seed.

The Unveiling of Therapeutic Targets for Alzheimer's Disease: An Integrative Review.

Alzheimer's disease (AD) is characterized by a complex pathological landscape, necessitating a comprehensive treatment approach. This concise review paper delves into the idea of addressing multiple mechanisms in AD, summarizing the latest research findings on pathogenesis, risk factors, diagnostics, and therapeutic strategies. The etiology of AD is multifaceted, involving genetic, environmental, and lifestyle factors. The primary feature is the accumulation of amyloid-- beta and tau proteins, leading to neuroinflammation, synaptic dysfunction, oxidative stress, and neuronal loss. Conventional single-target therapies have shown limited effectiveness, prompting a shift toward simultaneously addressing multiple disease-related processes. Recent advancements in AD research underscore the potential of multifaceted therapies. This review explores strategies targeting both tau aggregation and amyloid-beta, along with interventions to alleviate neuroinflammation, enhance synaptic function, and reduce oxidative stress. In conclusion, the review emphasizes the growing importance of addressing various pathways in AD treatment. A holistic approach that targets different aspects of the disease holds promise for developing effective treatments and improving the quality of life for Alzheimer's patients and their caregivers.

Implementations of efficient non-blocking crossbar network with novel multiplexer design in quantum-dot cellular automata

As an emerging nanotechnology, Quantum-dot Cellular Automata (QCA) has attracted extensive attentions due to its characteristics of high density, high speed, and low energy consumption. Modern high-performance System-on-Chips (SoCs) with multiple processors require interconnection networks to connect each core for improving data throughput and reducing latency, while the crossbar network is broadly used as a non-blocking interconnection architecture with high efficiency. In this paper, a method to design an efficient QCA-based N × N crossbar network utilizing optimally designed N:1 multiplexers (MUXs) is proposed, followed by a multi-layer and a single-layer implementation of its 8 × 8 design. A simplified matrix model is then introduced to provide a concise and intuitive switch control strategy, and expressions for cell count, area, latency, QCA cost, and QCA complexity of the proposed crossbar networks are derived according to the size N. Experimental results manifest that the proposed 8 × 8 crossbar networks have significant advantages on most performance parameters compared with other existing QCA-based networks.

Feature selection for sentiment analysis using hybrid multiobjective evolutionary algorithm

As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model’s efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it’s crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach.

Simulation study of secondary electron multiplication on microwave dielectric window

The phenomenon of dielectric breakdown in microwave windows restricts the enhancement of power capacity in high-power microwave systems. This process starts with the secondary electron multiplication, which significantly influences the breakdown threshold. In this study, we developed 3D simulation models for rectangular, circular, and annular windows to examine the secondary electron multiplication on their surfaces using the particle-in-cell method. Through a comparative study, we assessed the effects of various input powers, initial emission current densities from particle sources, and magnetic fields. Our findings reveal that the initial field emission current density, which ranges from 10−9 to 109 μA/cm2, marginally affects electron multiplication. However, increased microwave power (with power density ranging from 0.1 to 18.75 MW/cm2) accelerates this multiplication. Strong magnetic fields and non-uniform electric fields further enhance the electron multiplication process.

An integrated design concept evaluation model based on interval valued picture fuzzy set and improved GRP method.

The objective of this research is to enhance the precision and efficiency of design concept assessments during the initial stages of new product creation. Design concept evaluation, which occurs at the end of the conceptual design phase, is a critical step in product development. The outcome of this evaluation significantly impacts the product's eventual success, as flawed design concepts are difficult to remedy in later stages. However, the evaluation of new product concepts is a procedure that encompasses elements of subjectivity and ambiguity. In order to deal with the problem, a novel decision-making method for choosing more logical new product concepts is introduced. Basically, the evaluation process is outlined in three main phases: the construction of evaluation index system for design concept alternatives, the calculation of weights for evaluation criteria and decision-makers, the selection of the best design concept alternatives. These stages are composed of a hybrid method based on kano model, multiplicative analytic hierarchy process (AHP) method, the entropy of IVPFS and improved grey relational projection (GRP) under interval-valued picture fuzzy set (IVPFS). The novel approach integrates the strength of interval-valued picture fuzzy number in handling vagueness, the advantage of multiplicative AHP and the merit of improved GRP method in modelling multi-criteria decision-making. In final, the effectiveness of the proposed model is validated through comparisons with other models. The potential applications of this study include but are not limited to product development, industrial design, and innovation management, providing decision-makers with a more accurate and comprehensive design concept evaluation tool.

ARM-CO-UP: ARM CO operative U tilization of P rocessors

HMPSoCs combine different processors on a single chip. They enable powerful embedded devices, which increasingly perform ML inference tasks at the edge. State-of-the-art HMPSoCs can perform on-chip embedded inference using different processors, such as CPUs, GPUs, and NPUs. HMPSoCs can potentially overcome the limitation of low single-processor CNN inference performance and efficiency by cooperative use of multiple processors. However, standard inference frameworks for edge devices typically utilize only a single processor. We present the ARM-CO-UP framework built on the ARM-CL library. The ARM-CO-UP framework supports two modes of operation – Pipeline and Switch. It optimizes inference throughput using pipelined execution of network partitions for consecutive input frames in the Pipeline mode. It improves inference latency through layer-switched inference for a single input frame in the Switch mode. Furthermore, it supports layer-wise CPU/GPU DVFS in both modes for improving power efficiency and energy consumption. ARM-CO-UP is a comprehensive framework for multi-processor CNN inference that automates CNN partitioning and mapping, pipeline synchronization, processor type switching, layer-wise DVFS , and closed-source NPU integration.

Enhanced mobility of dislocation network nodes and its effect on dislocation multiplication and strain hardening

Understanding plastic deformation of crystals in terms of the fundamental physics of dislocations has remained a grand challenge in materials science for decades. To overcome this, the Discrete Dislocation Dynamics (DDD) method has been developed, but its lack of atomistic resolution leaves open the possibility that certain key mechanisms may be overlooked. By comparing large-scale Molecular Dynamics (MD) with DDD simulations performed under identical conditions we uncover significant discrepancies in the predicted strength and microstructure evolution in BCC crystals under high-strain rate conditions. These are traced to unexpected behaviors of dislocation network nodes forming at dislocation intersections, that can move in ways not previously anticipated as revealed by MD. Once these newfound freedoms of nodal motion are incorporated, DDD simulations begin to closely match plastic evolution observed in MD. This additional mechanism of motion whereby non-screw dislocations can change their glide plane profoundly affects fundamental processes of dislocation multiplication, recovery and storage that define strength of metals.

Impact of Parallel Processing on LightGBM Implementation a Comparative Analysis CPU on Iris Plants Dataset

Parallel processing has emerged as base for machine learning to address the computational requirements of complex models and expanded datasets. In additions, Parallel functions give the ability to algorithms exploiting the full potential of available accounting resources. This mechanism enhances parallel processing capabilities, as calculations are distributed through a multiple processor. This research explores the impact of the parallel processing of the central processing unit on the performance of LightGBM. The gradient-based learning in LightGBM enables efficient feature split decisions during tree construction. framework for scaling up, using the IRIS plant dataset. The study aims at comparing accuracy measures and training time for trained models with or without parallel processing units. The methodology includes advance data processing steps and the formation of environmentally sound management models with or without parallel processing units. The results reveal marked differences in accuracy and training time between the model and parallel processing of the central processing unit and its counterpart without it. Research contributes to understanding the role of parallel processing in the optimal functioning of the automated learning model.

Improving Pareto Local Search Using Cooperative Parallelism Strategies for Multiobjective Combinatorial Optimization.

Pareto local search (PLS) is a natural extension of local search for multiobjective combinatorial optimization problems (MCOPs). In our previous work, we improved the anytime performance of PLS using parallel computing techniques and proposed a parallel PLS based on decomposition (PPLS/D). In PPLS/D, the solution space is searched by multiple independent parallel processes simultaneously. This article further improves PPLS/D by introducing two new cooperative process techniques, namely, a cooperative search mechanism and a cooperative subregion-adjusting strategy. In the cooperative search mechanism, the parallel processes share high-quality solutions with each other during the search according to a distributed topology. In the proposed subregion-adjusting strategy, a master process collects useful information from all processes during the search to approximate the Pareto front (PF) and redivide the subregions evenly. In the experimental studies, three well-known NP-hard MCOPs with up to six objectives were selected as test problems. The experimental results on the Tianhe-2 supercomputer verified the effectiveness of the proposed techniques.

Temporal integration of target features across and within trials in the attentional blink.

Attentional blink research has typically investigated attentional limitations in multiple target processing. The current study investigated the temporal integration of target features in the attentional blink. Across two experiments, we demonstrated that the orientation estimations of individual target items in the attentional blink paradigm were systematically biased. Specifically, there was evidence for both within- and across-trial biases, revealing a general bias towards previously presented stimuli. Moreover, both biases were found to be more salient for targets suffering from the attentional blink. The current study is the first to demonstrate an across-trial bias in responses in the attentional blink paradigm. This set of findings is in line with the literature, suggesting that the human visual system can implicitly summarize information presented over time, which may lead to biases. By investigating temporal integration in the attentional blink, we have been able to address the modulatory role of attention on biases imposed by the implicit temporal effects in estimation tasks. Our findings may inform future research on attentional blink, serial dependence, and ensemble perception.

Distributed Aerial Image Stitching on Multiple Processors using Message Passing Interface

This study investigates the potential of using Message Passing Interface (MPI) parallelization to enhance the speed of the image stitching process. The image stitching process involves combining multiple images to create a seamless panoramic view. This research explores the potential benefits of segmenting photos into distributed tasks among several identical processor nodes to expedite the stitching process. However, it is crucial to consider that increasing the number of nodes may introduce a trade-off between the speed and quality of the stitching process. The initial experiments were conducted without MPI, resulting in a stitching time of 1506.63 seconds. Subsequently, the researchers employed MPI parallelization on two computer nodes, which reduced the stitching time to 624 seconds. Further improvement was observed when four computer nodes were used, resulting in a stitching time of 346.8 seconds. These findings highlight the potential benefits of MPI parallelization for image stitching tasks. The reduced stitching time achieved through parallelization demonstrates the ability to accelerate the overall stitching process. However, it is essential to carefully consider the trade-off between speed and quality when determining the optimal number of nodes to employ. By effectively distributing the workload across multiple nodes, researchers and practitioners can take advantage of the parallel processing capabilities offered by MPI to expedite image stitching tasks. Future studies could explore additional optimization techniques and evaluate the impact on speed and quality to achieve an optimal balance in real-world applications.

Computation of multiplicative topological aspects of hex-derived networks

A chemical network is numerically represented by a topological index in chemical graph theory. As opposed to its chemical representation, a topological descriptor correlates with specific physical properties of the underlying chemical molecules. In this article, third type of hex-derived networks HDN3(r), THDN3(r), are described. The goal of this study is to develop some updated and closed formulas based on multiplicative graph invariants. Such as ordinary geometric-arithmetic (OGA), general version of harmonic index (GHI), sum connectivity index (SI), general sum connectivity index (GSI), first and second Gourava and hyper-Gourava indices, Shegehalli and Kanabur indices, first generalized version of Zagreb index (GZI), and forgotten index (FI) for the hex-derived HDN3(r), THDN3(r), networks. Moreover, various types of edge for computing have been discovered and analyzed along with the order and size. The calculation of multiplicative topological features in networks that are generated from hexagonal structures is the main task of this work. Gaining more insight into the structural characteristics and possible uses of these networks requires examining the interaction between topological aspects and multiplication processes. To interpret the chemical compounds', physical and biological attributes, we can integrate the analysis of the networks stated above with the chemical compounds and their graphical structures. These results can be utilized to evaluate the biological and physio-chemical activities of compounds.