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The top eigenpairs at the title mean the maximal, the submaximal, or a few of the subsequent eigenpairs of an Hermitizable matrix. Restricting on top ones is to handle with the matrices having large scale, for which only little is known up to now. This is different from some mature algorithms, that are clearly limited only to medium-sized matrix for calculating full spectrum. It is hoped that a combination of this paper with the earlier works, to be seen soon, may provide some effective algorithms for computing the spectrum in practice, especially for matrix mechanics. [ABSTRACT FROM AUTHOR]

We address the numerical approximation of bilevel problems where a Nash equilibrium has to be determined both in the upper level and in the lower level. Widely applied in engineering and economic frameworks, such models are an extension of the well-known Stackelberg duopoly model and of the classical bilevel optimization problem. In this paper, the lower level involves a nonparametric ratio-bounded game (as introduced by Caruso, Ceparano and Morgan in CSEF Working Papers 593, 2020) and the upper level involves a potential game (as introduced by Monderer and Shapley in Games Econ. Behav. 14, 1996). After presenting existence and uniqueness results for the solutions of such bilevel Nash equilibrium problems, we define a numerical method relying on a derivative-free unconstrained optimization technique connected to direct-search methods. The associate algorithm is shown to globally converge toward a solution; error estimations, rates of convergence and illustrative examples are also provided. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new vectorized MATLAB-based algorithm for efficient serial computation of global matrix/force arising from finite element method (FEM) for meshes of any type and approximation order in linear elasticity. Because for-loops in MATLAB are very slow, we propose a modified process that takes advantage of vectorization and sparse assembly to achieve good performance while using the same memory as the standard algorithm. For this purpose, by using good programming practices, the implementation of this scheme is succinctly described and can be integrated into any MATLAB package dealing with FEM. Specifically, attention is paid to the calculation of the triplet (row index, column index, matrix components) as well as the assembly of the global stiffness matrix, mass matrix and force vector. Additionally, an extension of the proposed approach for Mindlin plate theory and functionally graded materials is outlined. Finally, the accuracy of this strategy is verified on selected numerical tests after comparing the obtained results with those of ABAQUS. In terms of performance, the study conducted on a set of meshes considering the standard algorithm and two other well-known MATLAB vectorized algorithms revealed that: (i) for a 2D beam problem meshed with P 1 -triangle elements, a speedup of about 8 and 15 is achieved with sparse and fsparse, respectively. (ii) for a 3D plate problem meshed with P 1 -tetrahedral elements, a speedup of about 4 and 8 is achieved with sparse and fsparse, respectively. When compared to ABAQUS performance, the proposed scheme results in a computational time that is about five times smaller. [ABSTRACT FROM AUTHOR]

Underground wastewater collection systems degrade with time, necessitating utility owners to engage in ongoing evaluations and enhancements of their asset management frameworks to preserve the performance of their assets. The inspection and condition assessment of sewer pipes are crucial for the effective operation and maintenance of sewer systems. The closed-circuit television (CCTV) is frequently employed to examine sewer pipes in the United States. This procedure is both costly and laborious because of the extensive number of pipes in a metropolis. Prioritisation of inspection for sanitary sewage pipe segments requiring repair or maintenance can be done in advance depending on their past performance. Hence, the aim of this study is to construct a predictive model for the state of sanitary sewer pipes, utilising data collected from a city located in the southcentral region of the United States. The main contribution is that this study used multiclass classification and predicted PACP scores of the pipes. Condition prediction models were developed using extensively utilised supervised machine learning algorithms including logistic regression (LR), k-nearest neighbors (k-NN), and random forest (RF). However, the bulk of the constructed models were assessed using a limited number of assessment measures, such as the receiver operator characteristic (ROC) curve and the area under the curve (AUC) value. This paper asserts that the assessment of the predictive capacity of these models cannot be determined only by relying on ROC and AUC values. Out of the three models evaluated in this study, the LR model had an AUC value of 0.76. However, this model had a higher number of misclassifications or inaccurate predictions compared to the other models. Consequently, these models were assessed using additional assessment measures, including precision, recall, and F-1 scores (which represent the harmonic mean of precision and recall). Curiously, the LR model achieved an F1-score of 0.28 on a scale ranging from 0 to 1. The RF model yielded an F1-score of 0.45 and an AUC value of 0.86. The existing model can be enhanced before it is employed by asset managers during the inspection phase to assess the state of their sanitary sewers and identify essential sewers that require immediate care. [ABSTRACT FROM AUTHOR]

In order to further improve the accurate detection signal, reduce interference between signals, this paper designs a new type of signal detection algorithm for satellite communication systems, using stochastic resonance technology improve the signal-to-noise ratio of the input signal, the signal by using energy detection, double threshold, accurate judgment. The first step in the conventional energy of double threshold detection, the second step into the energy detection method based on stochastic resonance detection process. The experimental results show that this algorithm under the condition of low SNR signals effectively detect, promoted the whole satellite communication system performance. [ABSTRACT FROM AUTHOR]

Citation networks are the basis for main path analysis (MPA), which has become an important tool in bibliometric studies. MPA can be used to map the main body of work of a scientific field, highlighting its most important literature and chronological evolution. Its uses goes from surveying the state of the art of a given subject to selecting study material for new research. MPA is conducted on a citation network and there is a well established literature accounting for methods of finding the most relevant paths. However, the details of how the citation network is actually built are not richly described in the specialized literature. Manually relating the available references of a given field would prove to be a difficult task. Given this context, we propose an automatic method, providing a simple algorithm for building citation networks with computer implementations and preventing cyclic paths. The algorithm is built quantitatively and is applicable to studies on the mechanisms of any science field. As an example, we go through every proposed step to select the papers which constitute the main path of the literature on forecasting stock prices using machine learning techniques. [ABSTRACT FROM AUTHOR]

Context: The recent research trend has highlighted that multiple stakeholders are involved during requirement gathering in agile software development. Hence, leading to an increased number of duplicate user stories in agile product backlog during requirement gathering. Objective: The objective of this paper is to evaluate the existing techniques employed in identifying and eliminating the duplicate user stories from agile product backlog and to overcome the existing gaps with the help of a newly proposed clustering algorithm. Method: An agile user story is expressed as a function of input and output parameters. That said multiple user stories having similar set of input parameters are most likely to be duplicate causing a redundancy. The newly proposed algorithm is used for clustering user stories having similar set of input parameters through various iterations and then removing the identified duplicate user stories from agile product backlog. This paper also introduces the concept of mass clustering which means clustering a number of user stories in single run. Results: Experimental results prove the proposed model is capable of handling small and large releases ranging between 100 to 1000 user stories with similar efficiency. The proposed clustering algorithm outperformed the clustering algorithms and resulted in 37% decrease in agile product backlog by eliminating duplicate user stories causing redundancy. The experimental results are obtained from the logs of the MATLAB tool. However, the provided algorithm is generic in nature and can be implemented using R, Python or SAS programming tools. The provided algorithms employs proven matrix operations. Conclusion: The proposed clustering algorithm overcomes the limitation of existing user story management methods and clearly out performs when compared with other clustering algorithms. Finally, this paper gives recommendations about the usage of the provided clustering algorithm during agile release planning for eliminating duplicate user stories from agile product backlog. [ABSTRACT FROM AUTHOR]

By studying the edge-workpiece engagement (EWE), and taking two inclination (lead and tilt) angles into account, the time-varying cutting areas and boundaries are determined, the instantaneous numerical chip thickness (NCT) within different EWEs at each point of the edge curve during the milling process of the ball end milling tool is solved, and the milling force and stability are calculated. The milling force and SLD of the model in this paper (NCT-EWE model) and the model in the literature (Altintas and Otzurk model) are compared. It is verified that the algorithm adopted in this paper has higher accuracy, and the algorithm is simple and fast. In addition, it is found that increasing the lead and tilt angles can reduce the milling force and improve the stability, and the free vibration in the stable region decreases gradually, while in the non-stable region, it increases. The algorithm in this paper has certain theoretical and application value for five-axis milling. [ABSTRACT FROM AUTHOR]

Bilevel programming is widely used to model applications with two hierarchical decisions defining, respectively, an upper-level (leader) problem and a lower-level (follower) problem nested in the former. In this scenario, the stability of the solution is one of the main issues to address to have a well-posed optimization problem; indeed, the lower-level problem may admit more than one optimal solution generating uncertainty on what the real upper-level objective value will be (instability of the bilevel problem). Notwithstanding the importance of stability, the literature has concentrated the most on defining hypotheses and conditions warranting stability of bilevel problems rather than on algorithms able to find a stable solution to the latter. To give a contribution in this direction, in this paper, we define an algorithm capable of finding a stable solution in bilevel optimization problems. We restrict our analysis to the case in which both the leader and the follower problems are linear-continuous. The proposal starts with finding the optimistic solution of the single-level problem obtained by replacing the follower's problem with its Karush–Kuhn–Tucker conditions, and the corresponding pessimistic solution. In the presence of instability, the algorithm removes one of the follower constraints at a time producing additional constraints for the leader problem in the attempt to define a new stable bilevel problem. Experimental results show that the algorithm can produce stable bilevel solutions representing an effective trade-off between the optimistic and the pessimistic solution values. The proposed algorithm may represent an effective tool for decision-makers aiming at finding robust solutions to bilevel problems. [ABSTRACT FROM AUTHOR]

In the present paper, we have developed an innovative algorithm to improve the oscillometric waveform detection to increase the measurement of blood pressure (BP) accuracy. Most of the techniques for BP measurement are based on the extraction of the oscillometric waveform (OMW) from baseline pressure generated in the cuff. The physiological changes in the human body provide irregular shapes, magnitudes, and patterns in OMW. Besides human physiological activities, motion artifacts may affect the OMW during the BP measurement. The BP measurement using direct extraction followed by envelope construction shows significant errors beyond the permissible limits. To rectify this problem, a new algorithm is required. This work demonstrates a novel algorithm to improve OMW by creating a zero baseline. The proposed algorithm is performed on low to high BP values, giving better results than the conventional algorithm used on OMW. The systolic and diastolic values of blood pressure obtained from the developed algorithm are within the error limits of international protocol, i.e., American Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO). [ABSTRACT FROM AUTHOR]

A network is an indispensable tool for studying complex systems and large data. Any systems or data could be partitioned into components based on traits. This could be interpreted as the community in a network, and detection is essential for comprehending real-world systems and revealing profound insights from diverse realms. Over the past decade, much attention has been given to studying overlapping community structures without considering the weights of connections. However, the current frontier in network science involves expanding this understanding to encompass overlapping community structures with weighted connections. This introduces a new layer of complexity due to the nuanced nature of edge weights, making it an intriguing challenge for modern network science. In this paper, we proposed a refined version of the existing neighbourhood Proximity based Community Detection algorithm and call it "neighbourhood Proximity based Community Detection using Weighted Centrality (NPCD-WC)" algorithm for extracting overlapping community structures in undirected weighted networks. This proposed algorithm is based on the weighted node centrality concept which makes it more stable and includes improved neighbourhood proximity measures. Subsequently, the proposed algorithm is based on expansion where a chosen node into a community is facilitated through an innovative adaptation of neighbourhood proximity. Two input threshold parameters ρ 0 , θ 0 are needed to output cover of membership communities. Rigorous experimentation on real-world and artificial networks, employing well-established quality-based metrics compelling evidence that NPCD-WC outperforms other baseline algorithms. The algorithm evidenced time complexity better than most baseline algorithms and was highly used for applicable world. [ABSTRACT FROM AUTHOR]

The reduction of electrical energy needs in buildings is accompanied by the use of hybrid renewable energy systems (HRES) for the production of energy on-site to be used for appliances and lighting and also for mobility through electric vehicle (EV) charging. With reference to this framework, the paper compared the tradition Home to Vehicle (H2V) technology with the Vehicle to Home (V2H) technology in HRES for residential users. The HRES consists of a photovoltaic (PV) generator, a wind generator, an electrical storage system and a charging station for an EV, in a stand-alone context. V2H technology provides for the use of the EV as an emergency energy source if the generation and storage systems are unable to guarantee the satisfaction of the residential load. Considering an off-grid single-family user located in the Sila National Park (Calabria, Italy), two enhanced EV charging strategies were investigated that both foresee the possibility of EV charging at night and an additional extra charge during the day to reduce the load demand and the produced HRES excess energy, which must be dissipated in an off-grid context. The two charging strategies differ in the choice of whether or not to carry out nocturnal charging depending on the EV state of charge. For this purpose, an algorithm is proposed that can optimally manage both enhanced EV charging strategies for both H2V and V2H technologies. The dynamic simulations of the HRES operation were carried out through TRNSYS, for the dynamic simulation of the PV and wind systems, which is coupled with MATLAB to dynamically simulate the residential battery and the H2V and V2H technologies. Starting from the hourly results obtained, weekly and yearly energy analyses were carried out for two different residential storage system sizes to identify in which conditions V2H and charging strategies provide beneficial effects in terms of energy missing to satisfy the load and overall energy required by the load. [ABSTRACT FROM AUTHOR]

With the transformation of transmission and distribution grids into smart grids that are more dominated by renewable energy, power electronics-based inverters that can improve power quality are becoming more visible. In order to maximize the output voltage quality and reduce the total harmonic distortion (THD), efficient operation of inverters is required. Therefore, in this paper, the problem of harmonic elimination in multilevel inverters is solved by using an adaptive grey wolf optimizer with local search. We have performed a grid search-based landscape analysis of the seven-level inverter to understand the behaviour of the proposed algorithm. For verification, the numerical results of the proposed adaptive grey wolf optimizer are compared with those of the original grey wolf optimization algorithm, a modified version of the grey wolf optimization algorithm, the particle swarm optimization algorithm, multi-verse optimization algorithm, and salp swarm algorithm. In the simulations, we solved the optimization model for three different structures of multilevel inverters (7, 11, and 15 levels) by changing the modulation indexes. It is found that the adaptive grey wolf optimization provides lower total harmonic distortion for different modulation indexes. [ABSTRACT FROM AUTHOR]

The stock index plays an increasingly important role in investors' decision-making. With the continuous development of the stock markets and the advancement of financial technology, the methods of compiling stock indices have consistently improved. Index tracking attempts to match the performance of a target market index by setting up a portfolio of assets to obtain similar returns to the target index. Therefore, the methods of selecting which stocks constitute a portfolio are very important. In daily investing, investors select quality assets from the target index to include in their tracking portfolio. In this paper, a nonnegative group bridge method is proposed for variable selection and estimation of grouping variables without overlapping to aid stock selection. The estimation consistency, variable-selection consistency, and asymptotic property of this method are provided. To obtain the solution of this model, we use an idea based on the local group coordinate descent method. Using tracking error as the criterion, the nonnegative group bridge estimation method is found superior to other nonnegative methods in terms of goodness-of-fit. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and machine learning (ML) systems increasingly purport to deliver knowledge about people and the world. Unfortunately, they also seem to frequently present results that repeat or magnify biased treatment of racial and other vulnerable minorities. This paper proposes that at least some of the problems with AI’s treatment of minorities can be captured by the concept of epistemic injustice. To substantiate this claim, I argue that (1) pretrial detention and physiognomic AI systems commit testimonial injustice because their target variables reflect inaccurate and unjust proxies for what they claim to measure; (2) classification systems, such as facial recognition, commit hermeneutic injustice because their classification taxonomies, almost no matter how they are derived, reflect and perpetuate racial and other stereotypes; and (3) epistemic injustice better explains what is going wrong in these types of situations than does the more common focus on procedural (un)fairness. [ABSTRACT FROM AUTHOR]

That discussion recognizes that the above paper deals with one of the main problems for reserves and reservoir evaluation, i.e. the determination of capillary pressure curves using the centrifuge technique. However, it is explained that the subject was not properly addressed and that sections of the paper are out of date, confusing or eventually wrong. For a more relevant overview on the subject, it is recommended to refer to the corresponding survey by the Society of Core Analysts. [ABSTRACT FROM AUTHOR]

The paper proposes the technique based on solutions of the Reynolds-averaged Navier–Stokes (RANS) equations for selecting the optimal aerodynamic configuration of the wing at the early design stages. The general optimization algorithm is presented taking into account a great number of design variables and constraints. A parameterization approach is presented that allows the efficient manipulation of the wing geometry. The results of aerodynamic optimization of the isolated wing of the second-generation supersonic passenger aircraft are presented. [ABSTRACT FROM AUTHOR]

In this paper, we present a new nonsmooth Newton-type algorithm for solving the generalized complementarity problem based on its reformulation as a system of nonlinear equations using a one-parametric family of complementarity functions. We demonstrate, under suitable hypotheses, that this algorithm converges locally and q-quadratically. In addition, we show numerical experiments that allow us to see the good performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

If a set of spanning trees of a graph do not share any edge with each other, they are called edge-disjoint spanning trees (for short EDSTs), which have widespread practical applications, such as fault-tolerant broadcasting, the distributed algorithms against Man-in-the-Middle attacks, the efficient collective communication algorithms, and so on. Crossed cubes, folded cubes, and folded crossed cubes, as three important variations of hypercubes, are optimized in terms of communication efficiency and fault tolerance of networks. In this paper, we propose a recursive algorithm to construct the maximum number of EDSTs in the three kinds of cube-based networks. Additionally, relying on the resulting EDSTs, the performance of one-to-one communication and one-to-all communication with edge failures are evaluated by simulation results in folded crossed cubes. [ABSTRACT FROM AUTHOR]

Since frequent traffic accidents bring great losses to people's safety and social property, this paper takes the results of traffic accident risk prediction as the basis so that it can help city managers to reasonably deploy police force, relieve traffic pressure, avoid traffic accidents and provide safe guidance to pedestrians. Based on this paper, a deep learning framework including spatiotemporal attention mechanism is proposed to solve the problem of traffic accident prediction in urban areas, and the experimental simulation shows that the accuracy of traffic accident risk prediction proposed in this paper reaches 94%. [ABSTRACT FROM AUTHOR]

Designing quantum arithmetic logic unit, quantum full adder and quantum full subtractor are the most vital digital circuits. The paper first presents the fundamentals of reversible quantum computing and quantum implementation design methodology with matrix modeling approach of realization of controlled gates. For any designer, it is challenging to reduce the controlled-gate count in circuit implementation. Based on the available 3 × 3 reversible quantum gates, the optimized quantum implementations of five novel and efficient controlled adders/subtractors are presented. Significant improvements in all the performance parameters have been achieved. Moreover, optimized quantum cost improvement of 20%, 16%, 9%, 10% and 31.25% has been obtained. The paper makes comparison of adder/subtractor circuits for efficient quantum realization. It has been deducted that our proposed designs are combined in nature and are more efficient than existing separate adder/subtractor and combined designs. [ABSTRACT FROM AUTHOR]

Nowadays, people pay more and more emotional to the emotional analysis of specific goals. Due to the long training time of many networks, this paper proposes a neural network with specific Objective sentiment analysis. Compared with the current neural network, the algorithm proposed in this paper has a shorter training time, which can effectively make up for the lack of emotional mechanism. Finally, we use the emotional data set to carry out simulation experiments. The experimental results show that the proposed algorithm is better than the ordinary neural network algorithm. [ABSTRACT FROM AUTHOR]

This is a conceptual paper based on existing literature aiming to provide practical information on designing and implementing activities to promote children's computational thinking. Computational thinking is a relatively new term in early childhood education that refers to a specific problem-solving thinking process involving various logical and analytical thinking skills. Four foundational skills have been identified as core thinking skills of computational thinking: decomposition, abstraction, pattern recognition, and algorithm. We explain these four skills in this paper and their practical applications to teaching and learning in early childhood education. Early computational thinking skills are found in common early childhood activities. This paper identifies activities teachers can use in the classroom to explicitly promote children's computational thinking and provides a new perspective on how to adapt classroom activities to integrate computational thinking. In particular, we emphasize the need to vary the demands of the content in the activity and incorporate computational thinking based on children's needs and development to ensure that children progress through the thinking process. [ABSTRACT FROM AUTHOR]

This paper introduces a novel and innovative approach to simulating random variates from two distinct probability distributions, namely the neutrosophic uniform distribution and the neutrosophic Weibull distribution. The primary objective of this research is to present a cutting-edge methodology for generating random variates by leveraging the accept-reject simulation method, particularly in the context of managing and addressing uncertainty. In addition to introducing the simulation methodology, this work will also provide comprehensive algorithms tailored to these proposed methods. These algorithms are essential for implementing the simulation techniques and will be instrumental in their practical applications. Furthermore, this study aims to explore the relationship between the level of indeterminacy and the resulting random variates. By investigating how varying degrees of indeterminacy impact random variates, we gain valuable insights into the dynamics of these distributions under different uncertainty conditions. Preliminary results suggest that random variates exhibit a trend of decreasing as indeterminacy levels increase, shedding light on the intriguing interplay between indeterminacy and random variate generation. [ABSTRACT FROM AUTHOR]

Nature-Based Solutions can be considered one of the best answers to the various consequences and problems caused by climate change, poor urbanisation and population growth. They are used not only as measures for the protection, sustainable management and restoration of natural and modified ecosystems but also as measures to mitigate certain natural disasters such as erosion, flooding, drought, storm surge and landslide. The benefit is for both biodiversity and human well-being. This paper reviews articles about optimising the selection and placement of Nature-Based Solutions. It presents several Operations Research approaches used in the context of climate adaptation. The analysis provided in this paper focuses on various case studies, state-of-the-art on Nature-Based Solutions, Operations Research algorithms, dissertations, and other papers dealing with infrastructure placement approaches in the context of climate adaptation. [ABSTRACT FROM AUTHOR]

The research papers assessing the content of soil organic carbon with the help of Vis-NIR spectroscopy approaches are systematically analyzed and subject to meta-analysis. This meta-analysis included 134 studies published in 1986–2022 with a total sample of 709 values of quantitative metrics. The papers have been searched for in databases of scientific periodicals (RSCI, Science Direct, Scopus, and Google Scholar) by the key word combination "Vis-NIR spectroscopy AND soil organic carbon". The meta-analysis using the nonparametric one-sided Kruskal–Wallis variance analysis in conjunction with nonparametric pairwise method shows the presence of a statistically significant difference between the median values of the accepted quantitative metrics of the predictive power of the models, namely, coefficient of determination (R2cv/val), root mean square error (RMSE), and the ratio of performance to deviation (RPD). The best performance of the preprocessing method for spectral curves is demonstrated and the estimates of soil organic carbon content obtained by laboratory and field spectroscopies are compared. [ABSTRACT FROM AUTHOR]

A quasi-static geometrically non-linear initial-boundary value problem is considered in a rate form for investigating deformation of a solid. Within the framework of the finite element method, an approach is proposed to computationally implement unilateral contact conditions for this problem with friction defined by the Coulomb–Siebel's law. Such formulations are often encountered in simulating technological processing operations based on severe inelastic deformations. The approach consider nodes and facets as contacting discrete fragments, where the nodes correspond to a body representing a deformable billet, and the facets refer to a rigid tool performing a processing program. Modeling contact in this way reduces to imposing special kinematic and quasi-static constraints in the nodes at each time slice. Procedurally, these constraints can be realized by the well-known standard modifications of a resolving system of linear algebraic equations which establish relationships between nodal velocities and force rates. In this paper, we provide a detailed description of main techniques required for this task and formulate a variant of an algorithm executing them in an appropriate sequence. [ABSTRACT FROM AUTHOR]

This paper tackles the problem of coefficient field choice in persistent homology. When we compute a persistence diagram, we need to select a coefficient field before computation. We should understand the dependence of the diagram on the coefficient field to facilitate computation and interpretation of the diagram. We clarify that the dependence is strongly related to the torsion part of Z relative homology in the filtration. We show the sufficient and necessary conditions of the independence of coefficient field choice. An efficient algorithm is proposed to verify the independence. A slight modification of the standard persistence algorithm gives the verification algorithm. In a numerical experiment with the algorithm, a persistence diagram rarely changes even when the coefficient field changes if we consider a filtration in R 3 . The experiment suggests that, in practical terms, changes in the field coefficient will not change persistence diagrams when the data are in R 3 . [ABSTRACT FROM AUTHOR]

In this paper, a new alternating nonmonotone projected Barzilai–Borwein (BB) algorithm is developed for solving large scale problems of nonnegative matrix factorization. Unlike the existing algorithms available in the literature, a nonmonotone line search strategy is proposed to find suitable step lengths, and an adaptive BB spectral parameter is employed to generate search directions such that the constructed subproblems are efficiently solved. Apart from establishment of global convergence for this algorithm, numerical tests on three synthetic datasets, four public face image datasets and a real-world transcriptomic dataset are conducted to show advantages of the developed algorithm in this paper. It is concluded that in terms of numerical efficiency, noise robustness and quality of matrix factorization, our algorithm is promising and applicable to face image reconstruction, and deep mining of transcriptomic profiles of the sub-genomes in hybrid fish lineage, compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective version of the artificial vultures optimization algorithm (AVOA) for a multi-objective optimization problem called a multi-objective AVOA (MOAVOA). The inspirational concept of the AVOA is based on African vultures' lifestyles. Archive, grid, and leader selection mechanisms are used for developing the MOAVOA. The proposed MOAVOA algorithm is tested oneight real-world engineering design problems and seventeen unconstrained and constrained mathematical optimization problems to investigates its appropriateness in estimating Pareto optimal solutions. Multi-objective particle swarm optimization, multi-objective ant lion optimization, multi-objective multi-verse optimization, multi-objective genetic algorithms, multi-objective salp swarm algorithm, and multi-objective grey wolf optimizer are compared with MOAVOA using generational distance, inverted generational distance, maximum spread, and spacing performance indicators. This paper demonstrates that MOAVOA is capable of outranking the other approaches. It is concluded that the proposed MOAVOA has merits in solving challenging multi-objective problems. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm capable of modelling shadows from nearby obstructions onto photovoltaic arrays is proposed. The algorithm developed is based on the calculation of the solar position in the sky for any given instant in order to obtain the shadow projection for any object point. The computation is based on considering the shadows as convex regions and on a rasterization process to evaluate the shadowed area of the array. The idea is extended to provide the shading patterns for a desired range of time and to calculate the efficiency rate of the irradiation power incident on the array in comparison with the non-shadowed case. The algorithm has interesting applications, such as optimizing array positioning and orientation, evaluating the impact of new obstructions on pre-existing array installations, allowing precise and practical data for control strategies and MPPT techniques for partially shaded systems, calculating more realistically constrained payback scenarios and finding the optimal PV array interconnection. The results are illustrated by three numerical examples, in which the effects of a nearby building in the irradiation received by a photovoltaic array throughout the year, panel relocation and different interconnections are analysed. [ABSTRACT FROM AUTHOR]

The elliptic Fourier transform is a common method of describing the shape of objects by an unique sequence of coefficients that allow comparing the shapes by mathematical methods. However, the raw coefficients contain unnecessary data unrelated to the shape, which does not provide a correct comparison. For this reason, the coefficients are normalized. This removes some of the superfluous data, but leaves information about mirror symmetry and the order in which the contour vertices are declared, that are encoded in the signs of the coefficients. This also interferes with shape comparison. The paper describes an algorithm for harmonizing the coefficients, leveling the influence of the information mentioned. Based on the example of attachment organs of monogeneans, the advantages of using harmonized coefficients for comparing the shapes of biological structures are shown. [ABSTRACT FROM AUTHOR]

The solar photovoltaic system suffers from varied environmental condition and causes power losses. This paper presents a Modified Artificial Rabbit Optimization based algorithm for MPPT. The performance of the Artificial Rabbit Optimization algorithm is improved by replacing the hidden parameter in the random hiding stage with levy flight strategy. The performance of the proposed algorithm is compared with particle swarm optimization, slap swarm optimization and artificial rabbit optimization under varying environmental conditions like change in uniform irradiance to partial shaded condition to uneven shaded condition and load variation. The simulation and experimental result gives a promising result by outperforming the particle swarm optimization, slap swarm optimization and artificial rabbit optimization algorithm in terms of quick convergence and decreasing oscillation towards maximum power points. [ABSTRACT FROM AUTHOR]

Based on the criterion of minimum error entropy, this paper proposes a novel conjugate gradient algorithm, called MEE-CG. This algorithm has robust performance under non-Gaussian interference. Theoretical analysis and experimental results demonstrate that the proposed algorithm displays more robust performance than the conventional conjugate gradient methods on the basis of the mean square error and the maximum correntropy criterion. Compared with the stochastic gradient minimum error entropy algorithm and the recursive minimum error entropy algorithm, the proposed algorithm provides a trade-off between computational complexity and convergence speed. [ABSTRACT FROM AUTHOR]

Robot localization is one of the most fundamental problems in motion planing, with many important applications in tracking vehicles and router systems. One way for localizing the robot is trilateration, which is used in the environments without GPS antenna by using a number of signals that can be measure their distance to robot at any time. It has already been shown that ⌊ 8 n 9 ⌋ landmarks are sufficient for trilaterating a simple n-side. Later, as a better result, it has been proved that ⌊ 2 n 3 ⌋ landmarks are sufficient for this purpose. In this paper, we discuss the problem for orthogonal polygons and show that n 2 landmarks suffice for trilaterating an orthogonal n-gon. This theoretical achievement is important for most evolutionary algorithms and neuro-computing techniques which need more speedup and high efficiency. [ABSTRACT FROM AUTHOR]

Automated algorithms are silently making crucial decisions about our lives, but most of the time we have little understanding of how they work. To counter this hidden influence, there have been increasing calls for algorithmic transparency. Much ink has been spilled over the informational account of algorithmic transparency—about how much information should be revealed about the inner workings of an algorithm. But few studies question the power structure beneath the informational disclosure of the algorithm. As a result, the information disclosure itself can be a means of manipulation used by a group of people to advance their own interests. Instead of concentrating on information disclosure, this paper examines algorithmic transparency from the perspective of power, explaining how algorithmic transparency under a disciplinary power structure can be a technique of normalizing people’s behavior. The informational disclosure of an algorithm can not only set up some de facto norms, but also build a scientific narrative of its algorithm to justify those norms. In doing so, people would be internally motivated to follow those norms with less critical analysis. This article suggests that we should not simply open the black box of an algorithm without challenging the existing power relations. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth literature review of the Black Hole Algorithm (BHA) which is considered as a recent metaheuristic. BHA has been proven to be very efficient in different applications. There has been several modifications and variants of this algorithm in the literature, so this work reviews various variants of the BHA. The applications of BHA in engineering problems, clustering, task scheduling, image processing, etc. have been thoroughly reviewed as well. This review article sheds lights on the pros and cons of this algorithm and enables finding a right variant of this algorithm for a certain application area. The paper concludes with an in-depth future direction. [ABSTRACT FROM AUTHOR]

To eliminate uncertainty, all data expressed by decision-makers must be processed correctly. The most useful mathematical models developed for this purpose are hybrid set types. Because they collect all the features of the set types they contain under a single model. In this paper, the bipolar soft rough sets, which are a combination of bipolar soft sets and rough sets, which have been actively preferred in studies aimed at eliminating uncertainty in recent years, have been taken into account. To use the bipolar soft rough sets discussed more actively in the decision-making process, the focus is on handling the data expressed by different decision-makers together. The aim of this paper is to develop the concept of "bipolar soft rough classes" to aim to highlight this contribution to bipolar soft rough set theory. Thus, the concept of bipolar soft rough classes is introduced and a more efficient decision-making algorithm for uncertainty problems is built. Moreover, many novel concepts such as bipolar soft class, bipolar soft partition and bipolar soft cover were proposed and some properties are examined in detail. [ABSTRACT FROM AUTHOR]

Network representation learning is a de facto tool for graph analytics. The mainstream of the previous approaches is to factorize the proximity matrix between nodes. However, if n is the number of nodes, since the size of the proximity matrix is n × n , it needs O (n 3) time and O (n 2) space to perform network representation learning; they are significantly high for large-scale graphs. This paper introduces the novel idea of using similarities between clusters instead of proximities between nodes; the proposed approach computes the representations of the clusters from similarities between clusters and computes the representations of nodes by referring to them. If l is the number of clusters, since l ≪ n , we can efficiently obtain the representations of clusters from a small l × l similarity matrix. Furthermore, since nodes in each cluster share similar structural properties, we can effectively compute the representation vectors of nodes. Experiments show that our approach can perform network representation learning more efficiently and effectively than existing approaches. [ABSTRACT FROM AUTHOR]

Continuous developments of machine learning algorithms have covered the various ways to analyze the atomistic structure and characteristics of quantum-confined nanostructures effectively. This work presents a machine learning model based on a regression fine tree algorithm to resolve the current–voltage characteristics model for GaAs nanotube during quantum confinement. The nanotube is 3.52 nm long and 3.61 nm wide. This paper presents predictive distributions of the current–voltage characteristic model with a sufficiently high level of confidence. This is a challenging task due to the backscattering effect of the quantum-confined nanostructures while the channel length is beyond the mean free path. Due to this quantum interference, it is difficult to predict the current–voltage characteristics correctly for quantum-confined nanostructures. Therefore, this machine learning approach helps to predict the model almost accurately with negligible erroneous values. This framework introduces a combined approach for both DFT and machine learning algorithms with lesser time cost and high predictivity response. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimation of hydrogen gas concentration using current output from a sensor chip, integrated with a thin film of ZnO as a sensing layer and inbuilt microheater to obtain desired operating temperature by proposing an appropriate algorithm. The proposed algorithm minimizes the estimation delay while maximizing the estimation accuracy. The proposed algorithm also accounts for the variation in sensor performance with relative humidity. The key features of the proposed algorithm are low computational complexity, low memory requirements, compatibility with any metal-oxides-based hydrogen sensors with independent calibration or turning points, and minimal requirements to the calibration time. As proof of concept, a demonstration of the algorithm efficiency is evaluated by estimating hydrogen concentration using ZnO thin film-based gas sensor device and comparing its performance with available state-of-the-art hydrogen sensor algorithm outputs. [ABSTRACT FROM AUTHOR]

Rapidly exploring random trees (RRT) are sampling-based approaches being widely applied for path planning of mobile robots. Since the output of these algorithms usually is a stream of discrete lines involving discontinuity at the linking points, kinematic constraints restrict the robot's movements. Consequently, robots may not pass discrete points in the path correctly. Hence, the using CAGD (Computer-Aided Geometry Design) curves can run simultaneously alongside those algorithms or may run after that to make a smooth path and that's the way in which non-holonomic constraints can be considered perfect and robots can be droved autonomously across them about the collision detection method which executed by the main sampling-based algorithm like RRT*. In this paper, an approach based on the combination of RRT* and B-spline is proposed for smoothing the path which is generated by RRT*-based algorithms, which are one of the most famous groups of algorithms in artificial intelligence. Some new functions are added to the outcome of the RRT* algorithm. To avoid collision in the generated path, some corrections are also provided. Finally, for illustrating the efficiency of the proposed method, the algorithm is implemented in the simulation environment of Webots® and for verification, the obtained results are compared and discussed. [ABSTRACT FROM AUTHOR]

Abstract—This paper presents the history of the method of approximating discretely specified values of gravitational and magnetic fields by sourcewise functions that, from the physical standpoint, correspond to the anomalous effects of equivalent sources. The method is widely used in applied geophysics for transformation, three-dimensional interpolation, and filtration at the stages of data processing and qualitative interpretation. It is an effective tool for building global, regional, and local digital models of geopotential fields. The method emerged from the task of determining the figure of the Earth, and there are three periods of its development: pre-computer era (1960–1980), early computer era (1980–2000) and computer technology era (from 2000 to the present). The authors have analyzed the main works of Russian and foreign researchers. In many ways, Russian and foreign technologies have been developed "in parallel," largely as a result of insufficient information exchange. The authors have identified the main trends in the development of the sourcewise approximation method. They have observed a steadily increasing number of publications regarding the practical application of the method. [ABSTRACT FROM AUTHOR]

According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. Consequently, the objectives, data collection techniques, development process and the validation metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using the methodology proposed by Arksey and O’Malley et al. and the PRISMA protocol was adopted to select the relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75% of the analyzed studies implement machine learning-based models using Python. [ABSTRACT FROM AUTHOR]

Retrieval of surrounding data objects has many potential applications in spatial databases including nearby point-of-interest retrieval surrounding a user and digital gaming. This paper presents a novel query, called direction-based spatial skyline (DSS), for retrieving surrounding objects in spatial databases. A DSS query retrieves all surrounding objects from a spatial database that are not directionally dominated by other data objects w.r.t. a given query object. The proposed DSS query is rotationally invariant, fair and stable. The paper also presents efficient algorithms for processing DSS queries in spatial databases by designing novel data pruning techniques using R-Tree data indexing scheme. We conduct extensive experiments using both real and synthetic datasets to demonstrate the effectiveness and efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Coding is defined as the "process of assigning a code (command/rule)" (http://Techopedia.com) that allows a machine or a person to act or move (McLennan, https://www.naeyc.org/resources/pubs/tyc/feb2017/creating-coding-stories-and-games, 2017). Though machines operated by a coding system are common in children's everyday lives, there has been a lack of resources on how to expose young children to coding in a developmentally appropriate manner. This paper aims to address this gap in the field of early education by providing developmentally appropriate guidelines for teachers of young children to utilize when implementing coding in their classrooms. Specifically, this paper focuses on unplugged coding activities that do not involve computers or computer programming and presents practical examples of tasks with algorithm designs, including a detailed step-by-step instruction set for solving a problem or completing a task (Tynker, https://www.tynker.com/blog/articles/ideas-and-tips/how-to-explain-algorithms-to-kids/, 2019). This paper presents coding practices that involve children's daily lives, incorporating sequential and directional commands and coding grids (coding sheet). [ABSTRACT FROM AUTHOR]

Real-world engineering design problems are widespread in various research disciplines in both industry and industry. Many optimization algorithms have been employed to address these kinds of problems. However, the algorithm's performance substantially reduces with the increase in the scale and difficulty of problems. Various versions of the optimization methods have been proposed to address the engineering design problems in the literature efficiently. In this paper, a comprehensive review of the meta-heuristic optimization methods that have been used to solve engineering design problems is proposed. We use six main keywords in collecting the data (meta-heuristic, optimization, algorithm, engineering, design, and problems). It is worth mentioning that there is no survey or comparative analysis paper on this topic available in the literature to the best of our knowledge. The state-of-the-art methods are presented in detail over several categories, including basic, modified, and hybrid methods. Moreover, we present the results of the state-of-the-art methods in this domain to figure out which version of optimization methods performs better in solving the problems studied. Finally, we provide remarkable future research directions for the potential methods. This work covers the main important topics in the engineering and artificial intelligence domain. It presents a large number of published works in the literature related to the meta-heuristic optimization methods in solving various engineering design problems. Future researches can depend on this review to explore the literature on meta-heuristic optimization methods and engineering design problems. [ABSTRACT FROM AUTHOR]

Models developed using machine learning are increasingly prevalent in scientific research. At the same time, these models are notoriously opaque. Explainable AI aims to mitigate the impact of opacity by rendering opaque models transparent. More than being just the solution to a problem, however, Explainable AI can also play an invaluable role in scientific exploration. This paper describes how post-hoc analytic techniques from Explainable AI can be used to refine target phenomena in medical science, to identify starting points for future investigations of (potentially) causal relationships, and to generate possible explanations of target phenomena in cognitive science. In this way, this paper describes how Explainable AI—over and above machine learning itself—contributes to the efficiency and scope of data-driven scientific research. [ABSTRACT FROM AUTHOR]

Bipolar soft set is an extended model of soft set. However, as in soft set theory, this theory can also leave mismatched objects in the universe set for a parameter set. In this paper, our aim is not to leave unmatched objects in the universe set for each parameter set. Thus, we have information about every object in the universe set and it becomes easier to get better approaches to uncertainty problems. For this, we have defined N-polar soft sets and some properties and several theoretical operations of N-polar soft sets are given. In addition to these, an algorithm is given on how to determine the value N for any uncertainty problem. Next, two decision-making algorithms based on N-polar soft sets are proposed. Finally, a detailed analysis was made by comparing some of the important algorithms previously studied with the algorithms we suggested in our paper. [ABSTRACT FROM AUTHOR]

The existing truncated variable method to generate random variate cannot be applied when indeterminacy is presented in either the parameters or observations. This paper addresses the truncated variable simulation under the indeterminate environment. The truncated variable simulation method will be introduced using the DUS-neutrosophic Weibull distribution. The algorithm to generate random variate will be presented and applied in random variate generation. Extensive simulation tables for various values of indeterminacy and truncated variables are presented. The proposed study for other neutrosophic statistical distribution can be extended as future research. [ABSTRACT FROM AUTHOR]