Computational Aspects Research Articles

Computational aspects of the equivalent-layer technique: review

Equivalent-layer technique is a powerful tool for processing potential-field data in the space domain. However, the greatest hindrance for using the equivalent-layer technique is its high computational cost for processing massive data sets. The large amount of computer memory usage to store the full sensitivity matrix combined with the computational time required for matrix-vector multiplications and to solve the resulting linear system, are the main drawbacks that made unfeasible the use of the equivalent-layer technique for a long time. More recently, the advances in computational power propelled the development of methods to overcome the heavy computational cost associated with the equivalent-layer technique. We present a comprehensive review of the computation aspects concerning the equivalent-layer technique addressing how previous works have been dealt with the computational cost of this technique. Historically, the high computational cost of the equivalent-layer technique has been overcome by using a variety of strategies such as: moving data-window scheme, column- and row-action updates of the sensitivity matrix, reparametrization, sparsity induction of the sensitivity matrix, iterative methods using the full sensitivity matrix, iterative deconvolution by using the concept of block-Toeplitz Toeplitz-block (BTTB) matrices and direct deconvolution. We compute the number of floating-point operations of some of these strategies adopted in the equivalent-layer technique to show their effectiveness in reducing the computational demand. Numerically, we also address the stability of some of these strategies used in the equivalent-layer technique by comparing with the stability via the classic equivalent-layer technique with the zeroth-order Tikhonov regularization. We show that even for the most computationally efficient methods, which can save up to 109 flops, the stability of the linear system is maintained. The two most efficient strategies, iterative and direct deconvolutions, can process large datasets quickly and yield good results. However, direct deconvolution has some drawbacks. Real data from Carajás Mineral Province, Brazil, is also used to validate the results showing a potential field transformation.

An enhanced affective computing-based framework using machine learning & medical IoT for the efficient pre-emptive decision-making of mental health problems

Globally, the two main causes of young people dying are mental health issues and suicide. A mental health issue is a condition of physiological disorder that inhibits with the vital process of the brain. The amount of individuals with psychiatric illnesses has considerably increased during the past several years. The majority of individuals with mental disorders reside in India. The mental illness can have an impact on a person’s health, thoughts, behaviour, or feelings. The capacity of controlling one’s thoughts, emotions, and behaviour might help an individual to deal with challenging circumstances, build relationships with others, and navigate life’s problems. With a primary focus on the healthcare domain and human-computer interaction, the capacity to recognize human emotions via physiological and facial expressions opens up important research ideas as well as application-oriented potential. Affective computing has recently become one of the areas of study that has received the greatest interest from professionals and academics in a variety of sectors. Nevertheless, despite the rise in articles published, the reviews of a particular aspect of affective computing in mental health still are limited and have certain inadequacies. As a result, a literature survey on the use of affective computing in India to make decisions about mental health issues is discussed. As a result, the paper focuses on how traditional techniques used to monitor and assess physiological data from humans by utilizing deep learning and machine learning approaches for humans’ affect recognition (AR) using Affective computing (AfC) which is a combination of computer science, AI, and cognitive science subjects (such as psychology and psychosocial).

Efficient Data Gathering in Cloud Computing Using Clustering Schemes, Cloud Agent-Based Data Schemes and Efficient Path Planning Techniques

Cloud computing is a rapidly evolving field with a wide range of applications, including data storage, processing, and analysis. Clustering schemes, cloud agent-based data schemes, and efficient path planning techniques are all important aspects of cloud computing. Clustering schemes are used to group similar data items together, which can improve the efficiency of data processing and analysis. Cloud agent-based data schemes allow for the collection and management of data from a variety of sources, including cloud and edge devices. Efficient path planning techniques are used to optimize the routing of data packets in cloud networks. This research paper provides a comprehensive overview of clustering schemes, cloud agent-based data schemes, and efficient path planning techniques for cloud computing. The paper discusses the different types of clustering schemes and cloud agent-based data schemes, as well as their advantages and disadvantages. The paper also presents an overview of efficient path planning techniques for cloud computing. The paper concludes by discussing the challenges and future research directions in cloud computing. The authors believe that cloud computing is a rapidly evolving field with a bright future, and they encourage researchers to continue to develop new and innovative solutions for cloud computing.

Rang-adjusted measure: modelling and computational aspects from internal and external perspectives for network DEA

Rang-adjusted measure: modelling and computational aspects from internal and external perspectives for network DEA

A review of mathematical and computational aspects of CSIDH algorithms

CSIDH is a post-quantum key-exchange scheme based on the action of ideal class groups on supersingular elliptic curves over prime fields. Its short keys and ciphertexts, together with its flexibility as a building block to construct complex cryptographic primitives, has motivated significant research on the efficiency of CSIDH and its resistance against side-channel attacks. In this work, some cutting-edge results from recent contributions are reviewed in a unified treatment, focusing on the mathematical ideas lying behind them rather than on cryptographic and low-level implementation techniques. In particular, we first describe ways to speed up the class-group-action evaluation, which range from the use of different models of elliptic curves to working with different ideal class groups. We then survey some constant-time variants of CSIDH, that make the time and memory consumption during the computation of a public/shared key independent of the secret key. Finally, we examine the computation of the ideal class action when the structure of the ideal class group is known, which is the case for a specific set of CSIDH parameters.

Using Blockchain Technology to Curb Voter Fraud in Nigeria

Blockchain is gradually changing the way in which data is stored, accessed and processed. Could this help Nigeria to achieve free, fair and credible elections? This study discusses how blockchain technology could be used in that country’s electoral processes – specifically, how it could prevent electoral fraud by providing a secure and transparent way of storing and accessing voting data. It reviews existing literature on the subject, and analyses current and potential applications of blockchain technology in ensuring electoral integrity. The study does not focus on the computational aspects of blockchain, but on its potential to curb electoral fraud in Nigeria. It also discusses the potential benefits and challenges of implementing blockchain technology in electoral systems in Nigeria, examines the various policy considerations and potential solutions, provides actionable recommendations, and suggests areas where blockchain could be effectively integrated into Nigeria’s current electoral system.

Online User and Power Allocation in Dynamic NOMA-Based Mobile Edge Computing

This study tackles the online user allocation problem in mobile edge computing (MEC) systems powered by non-orthogonal multiple access. App vendors need to determine a proper wireless channel in a base station/edge server and sufficient transmit power for every user. We consider a stochastic MEC system where users arrive and depart over time. When an edge server runs out of computing resources, some users will have to wait until the resources become available again, which incurs an allocation delay cost. This cost is often not investigated in many studies, which also do not consider a multi-cell, multi-channel system as we do in this work, due to its complexity. We aim to minimize the allocation delay and transmit power costs, increasing the system’s energy efficiency. To achieve this objective while guaranteeing users’ data rate requirements over time, we adopt the Lyapunov framework to convert this long-term optimization problem into a series of subproblems to be solved in every time slot. To solve the aforementioned subproblems efficiently, we present a distributed game theory-based approach. The proposed algorithm is theoretically evaluated and experimentally demonstrated to outperform several baseline and state-of-the-art methods, highlighting the significance of systematic consideration for both computation and communication aspects of this problem.

The Proposed Framework for Cloud Computing System Security and Privacy Services

Public cloud computing has emerged as a popular and efficient solution for organizations to store, process, and access their data and applications. However, with the increasing adoption of public cloud services, concerns regarding security have become a critical factor in decision-making processes. This study aims to investigate the security aspects of public cloud computing and identify potential vulnerabilities and threats that organizations may face. The aim of this study is to examine the challenges associated with cloud computing and its security concerns. Cloud computing is a technology that utilizes the internet to share data and resources. It is characterized by five key features: On-Demand self-service, Resource Pooling, Broad network access, Rapid elasticity, and Measured Service. However, despite these benefits, cloud computing has several open issues, including Security, Availability, Scalability, and Interoperability. Traditional security techniques are insufficient in fully securing cloud computing. Security and privacy issues often arise during cloud deployment and delivery models. As a result, researchers are interested in learning more about cloud computing security and its methods. Industries are creating architectural models with a high level of protection for their data center to solve current security challenges. This article examines the security of cloud deployment strategies and analyzes cloud security and privacy challenges. The cloud computing service models are also discussed, along with any security issues. The study emphasizes the significance of Audit as a key factor in offering a security solution that covers all relevant security characteristics as determined by the literature. The study's conclusions present a fresh challenge to cloud service providers and suggest a new security paradigm that explains customer security needs. Overall, this study offers a parameter-by-parameter assessment of available security solutions and makes recommendations for enhancements to address cloud security issues.

A comparative analysis of big data processing paradigms: Mapreduce vs. apache spark

The paper addresses a highly relevant and contemporary topic in the field of data processing. Big data is a crucial aspect of modern computing, and the choice of processing framework can significantly impact performance and efficiency. The technical revolution of big data has changed how organizations handle and value large databases. As data quantities expand quickly, effective and scalable data processing systems are essential. MapReduce and Apache Spark are two of the most popular large data processing techniques. This study compares these two frameworks to determine their merits, shortcomings, and applicability for big data applications. Nearly quintillion bytes of data are created daily. Approximately 90% of data was produced in the previous two years. At this stage, data comes from temperature sensors, social media, movies, photographs, transaction records (like banking records), mobile phone conversations, GPS signals, etc. In this article, all key big data technologies are introduced. This document compares all big data technologies and discusses their merits and downsides. Run trials using multiple data sets of varying sizes to validate and explain the study. Graphical depiction shows how one tool outperforms others for given data. Big Data is data generated by the rapid usage of the internet, sensors, and heavy machinery, with great volume, velocity, variety, and veracity. Numbers, photos, videos, and text are omnipresent in every sector. Due to the pace and amount of data generation, the computing system struggles to manage large data. Data is stored in a distributed architectural file system due to its size and complexity. Big distributed file systems, which must be fault-tolerant, adaptable, and scalable, make complicated data analysis dangerous and time-consuming. Big data collection is called ‘datafication’. Big data is ‘datafied’ for productivity. Organisation alone does not make Big Data valuable; we must choose what we can do with it.

Total 2-Rainbow Domination in Graphs: Complexity and Algorithms

For a simple, undirected graph [Formula: see text] without isolated vertices, a function [Formula: see text] which satisfies the following two conditions is called a total 2-rainbow dominating function (T2RDF) of [Formula: see text]. (i) For all [Formula: see text], if [Formula: see text] then [Formula: see text] and (ii) Every [Formula: see text] with [Formula: see text] is adjacent to a vertex [Formula: see text] with [Formula: see text]. The weight of a T2RDF [Formula: see text] of [Formula: see text] is the value [Formula: see text]. The total 2-rainbow domination number is the minimum weight of a T2RDF on [Formula: see text] and is denoted by [Formula: see text]. The minimum total 2-rainbow domination problem (MT2RDP) is to find a T2RDF of minimum weight in the input graph. In this article, we show that the problem of deciding if [Formula: see text] has a T2RDF of weight at most [Formula: see text] for star convex bipartite graphs, comb convex bipartite graphs, split graphs and planar graphs is NP-complete. On the positive side, we show that MT2RDP is linear time solvable for threshold graphs, chain graphs and bounded tree-width graphs. On the approximation point of view, we show that MT2RDP cannot be approximated within [Formula: see text] for any [Formula: see text] unless [Formula: see text] and also propose [Formula: see text]-approximation algorithm for it. Further, we show that MT2RDP is APX-complete for graphs with maximum degree 4. Next, it is shown that domination problem and the total 2-rainbow domination problems are not equivalent in computational complexity aspects. Finally, an integer linear programming formulation for MT2RDP is presented.

Single cell RNA sequencing – a valuable tool for cancer immunotherapy: a mini review

Abstract Single-cell RNA sequencing (scRNA-seq) technology has made great strides in research over the last decade. Data analysis has been aided by developments in bioinformatics tools and artificial intelligence, allowing biological and clinical researchers to get a deeper understanding of the different cell clusters and their dynamics within tumours. Combining conventional treatment modalities like chemotherapy and radiation with immunotherapy is a growing trend in cancer treatment. Hence, knowledge of the tumour microenvironment and the effect of each treatment modality on the TME, at a single cell level can provide treating clinicians with better clues for patient stratification and prognostication. With this knowledge, immunotherapy could become successful in treating a wide range of cancers, opening the path for the creation of even more effective treatment strategies. Despite the widespread availability of scRNA-seq technology, computational analysis and data interpretation are still challenges. Worldwide, such challenges are being addressed by various researchers, strengthening the contribution of this technology towards cancer elimination. In this mini-review, we primarily focus on the technique, its workflow, and the computational aspects of scRNA technology, along with an overview of the current challenges in the analysis and interpretation of the data generated.

Nonlinear Soil Behavior Model in Localized Lagrange Multipliers Mixed Formulation (u,p) for Dynamical Analysis of Wind Turbine Coupled Systems

Coupled mechanical systems can be complex, especially if there are many systems connected together and nonlinearities are present. Soil-structure interaction refers to the interaction between a structure and the soil or foundation upon which it is built. This interaction is important because it can affect the behavior of the structure, particularly during earthquakes or other dynamic events. For the wind turbine foundation design, the soil should support the weight of a wind turbine and anchor it to the ground. This paper presents a nonlinear material behavior soil coupled to elastic structure in offshore conditions. The goal of this work is to develop a coupled structural finite element procedure using Localized Lagrange Multipliers (LLM) at idealized offshore wind turbines with nonlinear poroelastic model for soil foundation. In this work, an anisotropic sand constitutive model is used to describe the soil foundation behavior. This model is based in a critical state soil mechanics and bounding surface plasticity. Classical elasto-plasticity theory is used to obtain the soil stiffness. The numerical model is validated through a fully coupled model at classical problems results. The mixed formulation [Formula: see text] is used to model the interface frames between the domains. The momentum equilibrium and mass continuity equations are solved by algebraic equation system imposed by Lagrange multipliers methodology. In order to excite the system, aerodynamic forces are imposed in random wind velocity conditions. In another numerical case, the response of coupled system during shaking is simulated by a horizontal input motion at bottom of soil foundation. Some computational aspects are discussed and the numerical model is clarified. The classical Newton Method to solve nonlinear problems is used in a finite element approach. In addition, two foundation models are tested and time dynamic responses are evaluated for a range of physical parameters including the wind nature and soil properties.

DFT-assisted low-dimensional carbon-based electrocatalysts design and mechanism study: a review.

Low-dimensional carbon-based (LDC) materials have attracted extensive research attention in electrocatalysis because of their unique advantages such as structural diversity, low cost, and chemical tolerance. They have been widely used in a broad range of electrochemical reactions to relieve environmental pollution and energy crisis. Typical examples include hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO2RR), and nitrogen reduction reaction (NRR). Traditional "trial and error" strategies greatly slowed down the rational design of electrocatalysts for these important applications. Recent studies show that the combination of density functional theory (DFT) calculations and experimental research is capable of accurately predicting the structures of electrocatalysts, thus revealing the catalytic mechanisms. Herein, current well-recognized collaboration methods of theory and practice are reviewed. The commonly used calculation methods and the basic functionals are briefly summarized. Special attention is paid to descriptors that are widely accepted as a bridge linking the structure and activity and the breakthroughs for high-volume accurate prediction of electrocatalysts. Importantly, correlated multiple descriptors are used to systematically describe the complicated interfacial electrocatalytic processes of LDC catalysts. Furthermore, machine learning and high-throughput simulations are crucial in assisting the discovery of new multiple descriptors and reaction mechanisms. This review will guide the further development of LDC electrocatalysts for extended applications from the aspect of DFT computations.

Investigating the performance of nickel molybdate nanostructure in dye sensitized solar cells: Computational and experimental analysis

This study explores both computational and experimental aspects of third-generation solar cells, specifically dye-sensitized solar cells (DSSCs) using Nickel Molybdate (NiMoO4) as both a new alternative electron transport layer (working electrode, WE) and counter electrode (CE). The synthesized NiMoO4 nanoparticles have an average size of 800 nm with crystalline size of 24.12 nm using Scanning electron microscopy (SEM) and X-ray diffraction (XRD) spectroscopy. The results indicate that the DSSCs based on NiMoO4 exhibit low photovoltaic performance due to poor photocurrent production. However, when NiMoO4 is employed as the WE (with platinum serving as the CE); it yields a better short circuit current (Jsc = 20.04 μA/cm2) and overall photovoltaic performance compared to TiO2 WE (with NiMoO4 serving as the CE) (Jsc = 0.23 μA/cm2). Interestingly, the DSSCs with NiMoO4 as WE featured the lowest performance degradation (90 % efficiencies has been maintained after 250 h). Additionally, this study investigates the distribution of electrolyte between NiMoO4/Pt and TiO2/NiMoO4 slabs using molecular dynamics (MD) simulations, highlighting its effective contribution to ionic conductivity (Λ), ultimately leading to an increase in photocurrent for the NiMoO4/Pt system (Λ of 27.51 10−4 S.m2/mol) compared to the TiO2/NiMoO4 (Λ of 13.45 10−4 S.m2/mol). Interestingly, the iodide ions interact more tightly with the TiO2 semiconductor (at a distance of 0.32 nm) than with NiMoO4.

Memory effect in time fractional Schrödinger equation

A significant obstacle impeding the advancement of the time fractional Schrödinger equation lies in the challenge of determining its precise mathematical formulation. In order to address this, we undertake an exploration of the time fractional Schrödinger equation within the context of a non-Markovian environment. By leveraging a two-level atom as an illustrative case, we find that the choice to raise i to the order of the time derivative is inappropriate. In contrast to the conventional approach used to depict the dynamic evolution of quantum states in a non-Markovian environment, the time fractional Schrödinger equation, when devoid of fractional-order operations on the imaginary unit i, emerges as a more intuitively comprehensible framework in physics and offers greater simplicity in computational aspects. Meanwhile, we also prove that it is meaningless to study the memory of time fractional Schrödinger equation with time derivative 1 < α ≤ 2. It should be noted that we have not yet constructed an open system that can be fully described by the time fractional Schrödinger equation. This will be the focus of future research. Our study might provide a new perspective on the role of time fractional Schrödinger equation.

Synthesis and spectroscopic investigation of N-allyl-N-ethylformamide: computational aspects of DFT, molecular docking and drug-likeness analyses

The present work deals with the understanding of the integrated experimental and theoretical study of the molecular structure and vibrational spectra of N-allyl-N-ethylformamide (NAEF). Density functional theory (DFT) calculations were used to study the subject molecule, N-allyl-N-ethylformamide (NAEF), utilising the B3LYP method and the basis set 6-311++G (d,p). Geometrical parameters and the structure of NAEF were computed such that global descriptors and reactive sites were obtained. Frontier’s analysis of molecular orbital energy demonstrates that the molecule’s charge exchange is considerably high. Non-linear optical properties of N-allyl-N-ethylformamide (NAEF) were computed and compared with the standard material of urea. The vibrational assignments were analysed for the existence of the probable functional groups and tabulated. The thermodynamic functions were computed at different temperatures and listed accordingly. NMR spectroscopy and quantum computational chemistry methods have been employed for the understanding of the structures of NAEF. Moreover, different techniques such as topological analysis and drug-likeness were utilised to determine the structure and other properties of NAEF. The molecular docking study could determine the residue with the highest docking score (lowest binding energy) and the most non-covalent interactions.

Distance measures for geometric graphs

A geometric graph is a combinatorial graph, endowed with a geometry that is inherited from its embedding in a Euclidean space. Formulation of a meaningful measure of (dis-)similarity in both the combinatorial and geometric structures of two such geometric graphs is a challenging problem in pattern recognition. We study two notions of distance measures for geometric graphs, called the geometric edit distance (GED) and geometric graph distance (GGD). While the former is based on the idea of editing one graph to transform it into the other graph, the latter is inspired by inexact matching of the graphs. For decades, both notions have been lending themselves well as measures of similarity between attributed graphs. If used without any modification, however, they fail to provide a meaningful distance measure for geometric graphs—even cease to be a metric. We have curated their associated cost functions for the context of geometric graphs. Alongside studying the metric properties of GED and GGD, we investigate how the two notions compare. We further our understanding of the computational aspects of GGD by showing that the distance is NP-hard to compute, even if the graphs are planar and arbitrary cost coefficients are allowed.As a computationally tractable alternative, we propose in this paper the Graph Mover's Distance (GMD), which has been formulated as an instance of the earth mover's distance. The computation of the GMD between two geometric graphs with at most n vertices takes only O(n3)-time. The GMD demonstrates extremely promising empirical evidence at recognizing letter drawings.

Computational aspects of real-time auralizations of jazz venues

Historically, architectural acoustics has focused on concert halls for European Classical music, even though other genres, including rock, blues, and Jazz, have large groups of followers as well. Additionally, the acoustic requirements for European Classical music differ fundamentally from Jazz and other music. For example, Jazz venues are typically smaller and dryer than a traditional European Classical concert hall. A recent survey showed that reverberation in these venues plays a secondary role to parameters that describe the affordance of a space to allow communication between improvising musicians. This presentation focuses on the unique computational auralization aspects of Jazz venues, including wave-based approaches for smaller enclosures, low-latency requirements, intelligent accompanying systems that can adapt ad-hoc to jazz soloists, and the use of anechoic jazz recordings as source material. The adequacies of different reproduction systems, from higher-order ambisonics and wave-field synthesis to headphone-based systems, will also be discussed. [Work supported by James West Fellowship, Leo and Gabriella Beranek Scholarship, and NSF HCC-1909229.]

Cloud model-based best-worst method for group decision making under uncertainty

This study aims to enhance computational and analytical aspects of multi-criteria group decision-making (MCGDM) under uncertainty. For this, we use the best-worst method (BWM) and cloud models to develop a more reliable MCGDM algorithm including three stages: first, collecting data through the BWM reference pairwise comparison; second, extracting interval-weights using the BWM bi-level optimisation models and aggregating different opinions via cloud models; and third, using the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritise alternatives. We have also investigated the effectiveness of the proposed approach in a real-life problem of online learning platform selection within the context of the COVID-19 pandemic lockdown. The experiment results demonstrate the superiority of the proposed method over the Bayesian BWM in terms of computational time by 96%. Moreover, the proposed approach outperforms BWM and Bayesian BWM techniques by 33% and 25%, respectively, in terms of conformity to the decision-makers’ intuitive judgments. Our findings also bring important practical implications. Application of the proposed method led to robustness against the number of decision-makers and significantly increased time efficiency in group decision-making. Besides, the computations with the lower inconsistency enhanced the effectiveness of prioritisation in group decision-making.

Statistical Inference for Vaccine Efficacy: A Re-Randomization Procedure to Analyse Poisson Outcomes under Covariate-Adaptive Randomization

Re-randomization inference is used as an alternative approach to more traditional statistical methods. Free from parametric assumptions, its use is particularly suited for studies incorporating covariate-adaptive randomization, and can provide additional evaluation and confirmation of inferences drawn from the original analyses, for example, as a sensitivity analysis. We discuss methodological and computational aspects in the context of a Poisson regression and describe an approach to re-randomization inference. This is tested in a simulation study and then illustrated in a case study in which we evaluate vaccine efficacy data from a previously published influenza vaccine study. Our simulations indicate that re-randomization inference corrects for model misspecification. The case study, which accounted for the minimization factors, shows that the p-value and confidence limits from re-randomization inference agree with the original analysis. In conclusion re-randomization inference is a useful method that can be used to support vaccine clinical development.