Multiple Processes Research Articles

PARALLEL COMPUTING

Parallel computing is a computational technique in which two or more tasks or instructions can be processed concurrently by means of several processors resulting into enhanced computational efficiency as well as decreased time for solving complex problems. As an approach to solve a particular problem, parallel computing is an attempt that decomposes a problem into constituent subproblems to be solved concurrently; it uses multiple cores, hardware systems to gain advantage in speed The use of parallel computing is prominent in sciences that call for massive computations such as simulations, data mining, artificial intelligence and HPC. Key Words- Parallel computing, Concurrent processing, Computational efficiency, Problem decomposition, Subproblems, Simulations, Data mining, Artificial intelligence, High-performance computing (HPC), Multiple processors, Multiple cores, Hardware systems

Schwarz decomposition for parallel minimum lap-time problems: evaluating against ADMM

The Minimum Lap Time Problem (MLTP) remains a significant area of research, particularly in the motorsport context. This form of Optimal Control Problem (OCP) aims to minimise lap times on a specific track with a given vehicle. Various complexities in both vehicle and track models are employed across the literature to address optimal trajectory planning. While previous works have tackled MLTP as a singular task using a serial approach, the increasing model complexity and horizon length demands the utilisation of parallelisation techniques. This paper introduces a novel application of the Overlapping Schwarz Decomposition algorithm to address the MLTP. The algorithm divides the problem into smaller sub-problems based on different sectors of a track, distributing them among multiple processors. We validate and compare the Schwarz approach against a serial approach and the Alternating Direction Method of Multipliers (ADMM) in solving MLTP with over 2.5 million variables. Despite the general efficiency improvement of parallelisation compared to the serial approach, the Schwarz algorithm demonstrates superior speed, accuracy and robustness compared to ADMM. As a result of our findings, it emerges as the preferred choice when large-scale MLTPs need to be solved.

The Ithildin library for efficient numerical solution of anisotropic reaction-diffusion problems in excitable media

Ithildin is an open-source library and framework for efficient parallelized simulations of excitable media, written in the C++ programming language. It uses parallelization on multiple CPU processors via the message passing interface (MPI). We demonstrate the library’s versatility through a series of simulations in the context of the monodomain description of cardiac electrophysiology, including the S1S2 protocol, spiral break-up, and spiral waves in ventricular geometry. Our work demonstrates the power of Ithildin as a tool for studying complex wave patterns in cardiac tissue and its potential to inform future experimental and theoretical studies. We publish our full code with this paper in the name of open science.

Review of Vedic Mathematics Yavadunam Tavadunikritya Varganicha Yojayet Sutra for Cubing a Number

This paper discloses secrets and logics of the short formula of ancient Vedic Mathematics Yavadunam Tavadunikritya Varganicha Yojayet Sutra in finding the cube of a positive number. Through the formalization process, we have aimed to bridge the gap between ancient mathematical wisdom and modern theoretical foundations, showcasing the timeless utility of Vedic techniques in mathematical calculations. The sutra has been reviewed using the algebraic explanation in terms of decimal number system of the multiplication process by applying Binomial theorem for the positive integral exponent 3. Some more short-cut techniques have been discussed to explain the sutra and it has been found that they are producing the same result. There are shortcomings that have been found in applying the sutra, which has been solved using some other techniques as a special rule. Later the sutra has been discussed for negative integers also with a note. Before coming to the conclusion, we have discussed some limitations of the sutra, although that has been solved in the paper but with a different process. We have discussed the implications of the sutra for modern computational methods and educational practices, suggesting potential areas for further research.

A general framework for metaverse based on parallel computing and HPC

As virtual and actual universes merge inside the creating metaverse, requests have pointedly ascended for continuous, intuitive, and intense encounters. The ability of the metaverse to effectively analyze and render complicated links and information supplied by clients is critical for realizing that goal. These demanding computational demands are starting to be supported by parallel processing, and high-performance computing (HPC) is beyond uncertainty key to this domain. The integrative framework presented in this paper addresses the core challenges of inertness, flexibility, and ease of use while integrating equal registration into the metaverse. The system enables prompt handling of client actions and quick response times by distributing calculations over multiple processors, which is essential for the seamless client experience. It also manages the vast amount of metaverse material and interactions as well as the various data processing needs. The paper looks at intrinsic equal processing difficulties in this unique climate, including creating versatile and energy-effective equal calculations that consider load adjusting and asset designation. It features the need to democratize equal figuring assets to produce metaverse extension while accentuating the significance of information protection and security conventions in multi-client settings. The cooperative energy between metaverse development and equal registering progressions vows to push limits, empowering remarkable degrees of virtual submersion and collaboration.

Waste management of multiple food products through IoT enabled preservation policies and secondary supply chains

Recycling food waste (FW) into secondary products can transform the waste-to-value. This study proposes reducing FW through IoT controlled preservation and consuming it through a secondary supply chain (SSC). It explores the best policies for SSC management that recycles FW. The food chain comprises of multiple food processors for preparing multiple food products, and a common retailer where IoT based preservation curtails food deterioration. The SSC consumes the FW by recycling into multiple secondary products at multiple recycling plants and retailing at a secondary retailer. A dual channel waste collection viz. from retailers and consumers is maintained. Each product within primary and secondary supply chains (PSSCs) has a different lifetime, rate of deterioration (RoD), and preservation effectiveness. A nonlinear mathematical model is presented that optimizes decision-making for maximizing profitability. Outcomes of computational experiments demonstrate that the SSC removes 100% of the FW, conserves 89% of material resources, reduces 16% of the total cost, minimizes preservation efforts by 50%, improves replenishment cycle, and increases profitability by 39%. Some managerial insights are provided for making vital supply chain decisions.

The Role of Resveratrol in Alzheimer's Disease: A Comprehensive Review of Current Research

: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progres-sive cognitive decline, memory loss, and impaired daily functioning. The etiology of AD is complex and multifactorial, involving various pathological mechanisms such as the accumula-tion of amyloid-beta plaques, neurofibrillary tangles, neuroinflammation, and oxidative stress. As the global prevalence of AD continues to rise, there is a growing interest in identifying po-tential therapeutic interventions to prevent or slow down the progression of the disease. Resvera-trol, a natural polyphenolic compound found in various plant sources such as grapes, berries, and peanuts, has gained considerable attention due to its potential neuroprotective effects. Numerous preclinical studies utilizing in vitro and animal models have investigated the impact of resvera-trol on AD pathology and associated cognitive impairments. This review aims to provide a com-prehensive summary of the current research on the role of resveratrol in AD. In conclusion, resveratrol holds promise as a potential therapeutic agent for AD due to its ability to target mul-tiple pathological processes involved in the disease. Further research, including well-designed clinical trials with larger sample sizes, is needed to fully elucidate the efficacy, optimal dosage, and long-term effects of resveratrol in AD patients. Nevertheless, resveratrol remains an intri-guing compound with neuroprotective properties and may contribute to the development of nov-el therapeutic approaches for AD in the future.

An application of simultaneous stochastic optimisation on an open-pit copper mining complex with supply, recovery, and market uncertainties

ABSTRACT A mining complex is an integrated value chain that consists of multiple mines, stockpiles, processors, waste dumps, and tailing facilities, and where the in-situ materials mined are transformed into products sold to customers. Simultaneous stochastic optimisation provides a strategic mine planning framework that incorporates all components of a mining complex into a single mathematical representation, capitalising on the synergies between components while considering uncertainty. An application of this optimisation framework at a coper mining complex demonstrates that the incorporation of material supply, market demand, and processing recovery uncertainties into the modelling leads to production schedules that yield a higher NPV and metal recovery.

Visualization at exascale: Making it all work with VTK-m

The VTK-m software library enables scientific visualization on exascale-class supercomputers. Exascale machines are particularly challenging for software development in part because they use GPU accelerators to provide the vast majority of their computational throughput. Algorithmic designs for GPUs and GPU-centric computing often deviate from those that worked well on previous generations of high-performance computers that relied on traditional CPUs. Fortunately, VTK-m provides scientific visualization algorithms for GPUs and other accelerators. VTK-m also provides a framework that simplifies the implementation of new algorithms and adds a porting layer to work across multiple processor types. This paper describes the main challenges encountered when making scientific visualization available at exascale. We document the surprises and obstacles faced when moving from pre-exascale platforms to the final exascale designs and the performance on those systems including scaling studies on Frontier, an exascale machine with over 37,000 AMD GPUs. We also report on the integration of VTK-m with other exascale software technologies. Finally, we show how VTK-m helps scientific discovery for applications such as fusion and particle acceleration that leverage an exascale supercomputer.

Study of Electronic and Optical Properties of Low-Dimensional Nanosystems by First Principles Calculations

The development of new techniques for material manipulation at the nanoscale, as well as advanced numerical tools for material design, has opened up new prospects in the engineering of novel low-dimensional systems with properties specifically designed for applications in different fields, from photonics to energy conversion. Zero-, one-, and two-dimensional nanosystems show unique electronic, optical, and transport properties that can be tuned, for instance, by size reduction, passivation, and doping. In this talk, we discuss ab-initio results obtained in the study of structural, electronic, and optical properties of low-dimensional systems, in particular focusing on silicon, germanium, and silicon/germanium nanostructures. The analysis of mechanisms that can be exploited to tune Auger recombination and carrier multiplication processes, as well as of those that affect the formation of the band offset (Type I and Type II) at the nanoscale, will be discussed. At the end, effects induced by anion ordering on electronic and optical properties of metal oxynitrides will be analyzed.

Accelerating real-time deterministic discovery through single instruction multiple data graphical processor unit for executing distributed event logs

With the rapid expansion of process mining implementation in global enterprises distributed across numerous branches, there is a critical requirement to develop an application qualified for real-time operation with fast and precise data integration. To address this challenge, computational parallelism emerges as a feasible solution to accelerate data analytics, with graphical processor unit (GPU) computing currently trending for achieving parallelism acceleration. In this study, we developed a process mining application to optimize parallel and distributed process discovery through a combination of central processing unit (CPU) and GPU computing. The use of this computing combination is leveraged for executing multi-windowing threads within multi-instruction, multiple data (MIMD) in the CPU for streaming distributed event logs, using multi-instruction, single data (MISD) within the CPU to deploy a large footprint pipeline to the GPU, and then utilizing single instruction, multiple data (SIMD) to execute global thread discovery within the GPU. This method significantly accelerates performance in real-time distributed discovery. By reducing branch divergence in SIMD on the global thread GPU parallelism, it outperformed local-thread CPU execution in deterministic discovery, speeding up from 10 to 40 times under specific conditions using a novel min-max flag algorithm implemented within the main steps of the process discovery.

Review some engineering methods to facilitate basic mathematical operations

The research reviews the most important engineering methods in basic mathematical calculations. In these methods, we will present several calculations, for example, the multiplication process and the division process between different quantities, to facilitate the arithmetic process for the primary and intermediate stages of school students. These methods are considered applied research to facilitate students’ memorization of multiplication and division tables.

Spectroscopic performance of Low-Gain Avalanche Diodes for different types of radiation

LGADs (Low-Gain Avalanche Diodes or Detectors) are a type of silicon Avalanche Photo-Diodes originally developed for the fast detection of minimum ionizing particles in high-energy physics experiments. Thanks to their fast timing performance, the LGAD paradigm enables detectors to accurately measure minimum ionizing particles with a timing resolution of a few tens of picoseconds. Such a performance is due to a thin substrate and the presence of a moderate signal gain. This internal gain of a few tens is enough to compensate for the reduced charge deposition in the thinner substrate and the noise of fast read-out systems. While LGADs are optimized for the detection of minimum ionizing particles for high-energy particle detectors, it is critical to study their performance for the detection of different types of particle, such as X-rays, gamma-rays, or alphas. In this paper, we evaluate the gain of three types of LGADs: two devices with different geometries and doping profiles fabricated by Brookhaven National Laboratory, and one fabricated by Hamamatsu Photonics with a different process.Since the gain in LGADs depends on the bias voltage applied to the sensor, pulse-height spectra have been acquired for bias voltages spanning from the depletion voltage up to the breakdown voltage. The signal-to-noise ratio of the generated signals and the shape of their spectra allow us to probe the underlying physics of the multiplication process.

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). Supplementary materials for this article are available online.

Clonal micropropagation and ex-situ conservation of Rhycncholaelia digbyana (Lindley) Schltr

Objective: To obtain protocols for clonal micropropagation, crop planning, and in vitro conservation of Rhycholaelia digbyana (Lindley) Schltr. Methodology: The effects of the Kundson C basal medium and benzylaminopurine concentration were evaluated for clonal micropropagation. The treatment with the greatest number of shoots formed per apex was selected for crop planning. Experiments were conducted to determine the effect of basal medium Murashige and Skoog concentration at 2.2 gL-1 and 4.4 gL-1; sorbitol, mannitol, and sucrose at 1, 2, and 3% on slow growth. Results: The best treatment for clonal micropropagation and crop planning was identified as 21.60 gL-1 Knudson C with 8.80 µM benzylaminopurine. This treatment resulted in uniform-sized shoots produced. The multiplication process can yield 10,240 seedlings in 12 months. Slow growth was achieved using Murashige and Skoog basal media at 2.2 gL-1 with 1% mannitol. Implications: More experiments must be conducted to determine the best shoot induction conditions and improve resource efficiency. Conclusions: These findings represent the first report on micropropagation and ex-situ conservation to preserve germplasm for this species as an important resource for the floriculture industry.

Real-Time Simulation System for Small Scale Regional Integrated Energy Systems

Regional Integrated Energy Systems (RIESs) integrate wide spectrum of energy sources and storage with optimized energy management and further pollution reduction. This paper presents a real-time simulation system for RIESs powered by multiple digital signal processors (DSPs) with different means of data exchange. The RIES encompasses the DC microgrid (DMG), the district heat network (DHN), and the natural gas network (NGN). To realize multi-energy flow simulation, averaged switch models are investigated for different types of device-level units in the DMG, and the unified energy path method is used to build circuit-dual models of the DHN and NGN. A hierarchical island strategy (HIS) and a multi-energy dispatch strategy (MEDS) are proposed to enhance the energy flow control and operating efficiency. The two-layer HIS can adjust the operating status of device-level units in real time to achieve bus voltage stability in the DMG; MEDS uses energy conversion devices to decouple multi-energy flows and adopts the decomposed flow method to calculate the flow results for each network. The real-time simulation hardware platform is built, and both electricity-led and thermal-led experiments are carried out to verify the accuracy of models and the effectiveness of the proposed strategy. The proposed system with an energy management strategy aims to provide substantial theoretical and practical contributions to the control and simulation of RIESs, thus supporting the advancement of integrated energy systems.

Arithmetic in two languages: Localizing simple multiplication processing in the adult bilingual brain.

Verbally memorized multiplication tables are thought to create language-specific memories. Supporting this idea, bilinguals are typically faster and more accurate in the language in which they learned math (LA+) than in their other language (LA- ) . No study has yet revealed the underlying neurocognitive mechanisms explaining this effect, or the role of problem size in explaining the recruitment of different brain regions in LA+ and LA- . To fill this gap in the literature, 29 Spanish-English early bilingual adults, proficient in both languages, verified simple multiplication problems in each language while functional magnetic resonance imaging (fMRI) was acquired. More specifically, this study aimed to answer two questions: 1) Does LA+ recruit left superior and middle temporal gyri (STG/MTG) to a greater extent than LA- , reflecting more robust verbal representations of multiplication facts in LA+? In contrast, does LA- recruit the inferior frontal gyrus (IFG), reflecting more effortful retrieval, or the intraparietal sulcus (IPS), reflecting reliance on quantity processes? 2) Is there an interaction between language and problem size, where language differences are more pronounced for less practiced, large multiplication problems (e.g., 8 × 9) in comparison to more familiar, small problems (e.g., 2 × 3). Functional localizer tasks were used to identify hypothesis-driven regions of interest in verbal areas associated with verbal representations of arithmetic facts (left STG/MTG) and with the effortful retrieval of these facts (left IFG) and quantity areas engaged when calculation-based strategies are used (bilateral IPS). In planned analyses, no cluster reached significance for the direct comparison of languages (question 1) or for the interaction between language and problem size (question 2). An exploratory analysis found a main effect of problem size, where small problems recruited left STG/MTG and left IFG to a greater extent than large problems, suggesting greater verbal involvement for these problems in both languages. Additionally, large problems recruited right IPS to a greater extent than small problems, suggesting reliance on quantity processes. Our results suggest that proficient early bilingual adults engage similar brain regions in both languages, even for more difficult, large problems.

Trainable parameterized quantum encoding and application based on enhanced robustness and parallel computing

One key advantage of quantum algorithms is based on the assumption that arbitrary quantum states can be efficiently prepared. However, limited by qubits’ number and decoherence time, existing preparation methods are not friendly to the quantum state preparation of high-dimensional data. In this paper, we propose a Trainable Parameterized Quantum Encoding (TPQE) method for realizing approximate encoding of arbitrary quantum states, and the representation ability is proposed as the verification criterion for TPQE for arbitrary quantum states. To enhance the robustness of the TPQE, we take into account noise as part of the TPQE and absorb the noise into the parameters through training. Moreover, we utilize parallel computing between multiple quantum processors to achieve a speedup of TPQE. Finally, the representation ability of TPQE is validated on a publicly available dataset of breast cancer using amplitude encoding as a benchmark. Experiments demonstrate that our method shows good robustness.

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.

Мультиплицирование форм капитала

Introduction. Revealing the content of the capital forms multiplication process is significant for modern economic science in theoretical and applied aspects. The political and economic peculiarities of the study are to reveal the essence of capital as a benefit that generates income in various forms. Theoretical analysis. The authors’ point of view is an alternative to the generally accepted point of view on capital as a self-increasing value, as a certain social attitude based on the exploitation of wage labor, and the allocation of forms corresponding to the stages of its circulation as priority functional forms of capital. The authors introduce a new category into scientific circulation – capital forms multiplication. According to the authors’ interpretation, it is an objective process of constant generation of various new forms of manifestation and movement of capital. Empirical analysis. The authors’ approach allows us to single out such a property of capital as autopoiesis, understood as self-building, self-reproduction, self-regulation. The authors’ capital forms classification is presented, where, along with traditional forms of capital (cash, trade, etc.), new types of capital are highlighted and valorized – intellectual, knowledge, information, network, symbolic (image, brand). In recent years, the autopoiesis of capital has manifested itself in the multiplication of such forms as emotional and aesthetic capital. Results. The capital multiplication process is revealed on the basis of its many forms’ analysis, which tend to grow.