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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]

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]

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]

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]

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]

This paper is a review of the results on the real-time optimal control problem for linear systems obtained by the Minsk school by mathematical methods of optimal control. We consider optimal control problems for dynamical objects, their deterministic mathematical models and perfect measurements of states, objects with disturbances and imperfect measurements of the observed input and output signals, the problem of optimal decentralized control of groups of interconnected dynamical objects, and application of real-time optimal control problems and the control principle to solving stabilization problems. [ABSTRACT FROM AUTHOR]

The paper considers algorithms for solving inverse scattering problems based on the discretization of the Gelfand–Levitan–Marchenko integral equations, associated with the system of nonlinear Schrödinger equations of the Manakov model. The numerical algorithm of the first order approximation for solving the scattering problem is reduced to the inversion of a series of nested block Toeplitz matrices using the Levinson-type bordering method. Increasing the approximation accuracy violates the Toeplitz structure of block matrices. Two algorithms are described that solve this problem for second order accuracy. One algorithm uses a block version of the Levinson bordering algorithm, which recovers the Toeplitz structure of the matrix by moving some terms of the systems of equations to the right-hand side. Another algorithm is based on the Toeplitz decomposition of an almost block-Toeplitz matrix and the Tyrtyshnikov bordering algorithm. The speed and accuracy of calculations using the presented algorithms are compared on an exact solution (the Manakov vector soliton). [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]

In this paper, a hybrid algorithm called rough sine cosine algorithm (RSCA) is introduced for solving engineering optimization problems by merging the sine cosine algorithm (SCA) with the rough set theory concepts (RST). RSCA combines the benefits of SCA and RST to focus the search for a promising region where the global solution can be found. Due to imprecise information on the optimization problems, efficient algorithms roughly identify the optimal solution for this type of uncertain data. The fundamental motive for adding the RST is to deal with the imprecision and roughness of the available information regarding the global optimal, especially for large dimensional problems. The cut concept of RST targeted the more interesting search region so the optimal operation could be sped up, and the global optimum could be reached at a low computational cost. The proposed RSCA algorithm is tested on 23 benchmark functions and 3 design problems. RSCA's obtained results are mainly compared to the SCA, which is used as a first level of the proposed algorithm in this work and those of other algorithms in the literature. According to the comparisons, the RSCA can provide very competitive performance with different algorithms. [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]

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]

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 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]

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]

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]

This paper is inspired by traditional rural fishing methods and proposes a new metaheuristic optimization algorithm based on human behavior: Catch Fish Optimization Algorithm (CFOA). This algorithm simulates the process of rural fishermen fishing in ponds, which is mainly divided into two phases: the exploration phase and the exploitation phase. In the exploration phase, there are two stages to search: first, the individual capture stage based on personal experience and intuition, and second, the group capture stage based on human proficiency in using tools and collaboration. Transition from independent search to group capture during the exploration phase. Exploitation phase: All fishermen will surround the shoal of fish and work together to salvage the remaining fish, a collective capture strategy. CFOA model is based on these two phases. This paper tested the optimization performance of CFOA using IEEE CEC 2014 and IEEE CEC 2020 test functions, and compared it with 11 other optimization algorithms. We employed the IEEE CEC2017 function to evaluate the overall performance of CFOA. The experimental results indicate that CFOA exhibits excellent and stable optimization capabilities overall. Additionally, we applied CFOA to data clustering problems, and the final results demonstrate that CFOA’s overall error rate in processing clustering problems is less than 20%, resulting in a better clustering effect. The comprehensive experimental results show that CFOA exhibits excellent optimization effects when facing different optimization problems. CFOA code is open at https://github.com/Meky-1210/CFOA.git. [ABSTRACT FROM AUTHOR]

The purpose of this paper is twofold. Firstly, to emphasise that the class of Lie algebras with chain lattices of ideals are elementary blocks in the embedding or decomposition of Lie algebras with finite lattice of ideals. Secondly, to show that the number of Lie algebras of this class is large and they support other types of Lie structures. Beginning with general examples and algebraic decompositions, we focus on computational algorithms to build Lie algebras in which the lattice of ideals is a chain. The chain condition forces gradings on the nilradicals of this class of algebras. Our algorithms yield to several positive naturally graded parametric families of Lie algebras. Further generalizations and other kind of structures will also be discussed. [ABSTRACT FROM AUTHOR]

A good stacking method can increase the packaging utility rate and reduce production costs. Much research has focused on 2D arrangements for rectangular, circular, or irregular shapes and regularly shaped 3D objects such as rectangular boxes. Genetic algorithms, simulated annealing, and other heuristic algorithms have been proposed. Recent research on the stacking of irregular-shaped 3D stone pieces has focused on balancing one stone piece on top of others to form one or more vertical towers, given the geometry of the stone pieces and the number of stone pieces available for the task. Stacking irregular-shaped 3D objects in a package is common in industry. However, there has been relatively little emphasis on the development of algorithms for stacking irregular-shaped 3D objects in a fixed-size container without prior knowledge of the stone geometries and the number of pieces available, with the goal of packing as many stone pieces as possible while maintaining stability. In this paper, three heuristic algorithms are proposed to solve the problem of nesting irregularly shaped stone pieces in layers within a container. All three algorithms use the following approach: (1) approximate the alignment of irregular shapes to a cluster of straight lines; (2) arrange stones one by one at the approximated angles using a step-by-step process; (3) for stability, consider the weight of the stone pieces based on pixel calculations. An automated real-time stacking system—including sensors, pneumatic suction cups, webcams, conveyor, robot, and programmable logic controller—was developed to evaluate the proposed algorithms using space utilization, stability, and cycle time as measures of performance. The developed algorithms and an existing stacking algorithm (bottom left most, or BLM) were tested using 25 sequences of 30 randomly ordered stone pieces. Results suggest that the developed algorithms effectively solve the stone piece packing problem. All three were significantly better than the BLM algorithm in terms of space utilization and stability, and there was no difference in cycle time. Algorithm 3 was better than Algorithms 1 and 2. [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]

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]

Theories related to cognitive sciences, Human-in-the-loop Cyber-physical systems, data analysis for decision-making, and computational ethics make clear the need to create transdisciplinary learning, research, and application strategies to bring coherence to the paradigm of a truly human-oriented technology. Autonomous objects assume more responsibilities for individual and collective phenomena, they have gradually filtered into routines and require the incorporation of ethical practice into the professions related to the development, modeling, and design of algorithms. To make this possible, it is pertinent and urgent to bring them closer to the problems and approaches of the humanities. Increasingly transdisciplinary research must be part of the construction of systems that provide developers and scientists with the necessary bases to understand the ethical debate and therefore their commitment to society. This article considers two theories as articulating axes: Blumenberg's, coming from the field of philosophy, for whom the process of technification and especially the implementation of mathematical models in their algorithmic form leads to an emptying of meaning and therefore makes programmers who implement their functions to be alien to the concerns that gave them origin; Daston's, belonging to the field of the history of science and according to which the division of labor in the processes of technification of the calculation implies a kind of subordination in which those who implement the inventions of a small group of privileged mathematicians ignore the procedures that put them into operation. Given these two theories, the black box models prevalent in AI development, and the urgency of establishing explanatory frameworks for the development of computational ethics, this article exposes the need to give a voice to the cultural history of consciousness for promoting the discussions around the implementation of mathematical algorithms. The paper takes as a reference the different points of view that have emerged around the study of technological ethics, its applicability, management, and design. It criticizes the current state of studies from a humanistic perspective and explains how the historical perspective allows promoting the training of software engineers, developers and creators so that they assume intuitions and moral values in the development of their work. Specifically, it aims to expose how cultural history, applied to the study of consciousness and its phenomena, makes those involved in this technological revolution aware of the effect that they, through their algorithms, have on society in general and on human beings in particular. [ABSTRACT FROM AUTHOR]

Exploration of an unknown network by one or multiple mobile entities is a well studied problem which has various applications like treasure hunt, collecting data from some node in the network or samples from contaminated mines. In this paper, we study the problem of edge exploration of an n node graph by a mobile agent. The nodes of the graph are anonymous, and the edges at a node of degree d are arbitrarily assigned unique port numbers in the range 0 , 1 , ⋯ , d - 1 . A mobile agent, starting from a node, has to visit all the edges of the graph and stop. The time of the exploration is the number of edges the agent traverses before it stops. The task of exploration can not be performed even for a class of cycles if no additional help is provided. We consider two different ways of providing additional help to the agent by an Oracle. In the first scenario, the nodes of the graph are provided some short labels by the Oracle. In the second scenario, some additional information, called advice, is provided to the agent in the form of a binary string. For the first scenario, we show that exploration can be done by providing constant size labels to the nodes of the graph. For the second scenario, we show that exploration can not be completed within time o (n 8 3) regardless of the advice provided to the agent. We propose an upper bound result by designing an O (n 3) algorithm with O (n log n) advice. We also show a lower bound Ω (n 8 3) on the size of advice to perform exploration in O (n 3) time. In addition, we have done experimental studies on randomly created anonymous graph to analyze time complexity of exploration with O (n log n) size advice. [ABSTRACT FROM AUTHOR]

Purpose: This PRISMA-compliant systematic review aims to analyze the existing applications of artificial intelligence (AI), machine learning, and deep learning for rhinological purposes and compare works in terms of data pool size, AI systems, input and outputs, and model reliability. Methods: MEDLINE, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov databases. Search criteria were designed to include all studies published until December 2021 presenting or employing AI for rhinological applications. We selected all original studies specifying AI models reliability. After duplicate removal, abstract and full-text selection, and quality assessment, we reviewed eligible articles for data pool size, AI tools used, input and outputs, and model reliability. Results: Among 1378 unique citations, 39 studies were deemed eligible. Most studies (n = 29) were technical papers. Input included compiled data, verbal data, and 2D images, while outputs were in most cases dichotomous or selected among nominal classes. The most frequently employed AI tools were support vector machine for compiled data and convolutional neural network for 2D images. Model reliability was variable, but in most cases was reported to be between 80% and 100%. Conclusions: AI has vast potential in rhinology, but an inherent lack of accessible code sources does not allow for sharing results and advancing research without reconstructing models from scratch. While data pools do not necessarily represent a problem for model construction, presently available tools appear limited in allowing employment of raw clinical data, thus demanding immense interpretive work prior to the analytic process. [ABSTRACT FROM AUTHOR]

Along with the sustainable and rapid accumulation of user-generated contents in social networking websites, how to push a certain content to the corresponding interested users, named recommendation for short, has successfully received wide attention. Considering the continuous updated contents and the constant changing of users' interests, recommendation is expected to be completed immediately to send the most fresh content to appropriate users after the corresponding new contents have become available. In other words, recommendation needs to meet real-time requirements to fit the content-consumption behavior of users. In the traditional recommendation system field, the corresponding attributes of users and contents could be characterized by feature vectors in a certain high-dimensional space, subsequently the recommendation problem could also be converted into how to obtain the K appropriate contents for each user, which could be called kNN join. Due to the massive, high-dimensional, and continuously updated contents, the corresponding recommendation based on traditional kNN join (continuously updating the kNN join results) will be undoubtedly faced with unacceptable computational costs. In this paper, we propose a locality-sensitive hashing (LSH)–based index called LSHI, which is built on user set to find the specific users who might be affected by the updated contents efficiently. With the help of LSHI, the recommendation lists of the affected users could be adjusted accordingly and the holistic effectiveness of the recommendation (for all users) could be guaranteed simultaneously. Finally, extensive experiments have been conducted to demonstrate the superiorities of our proposed method in this paper. [ABSTRACT FROM AUTHOR]

Some efficient top-k algorithms, i.e., Fagin's Algorithm, threshold algorithm (TA), and best position algorithm (BPA), can be used to answer k nearest neighbor (kNN) queries. However, extending the existing algorithms without further changes to the algorithms themselves would not be efficient since there are the different characteristics between the kNN queries and top-k queries. For example, the kNN queries are more distance-sensitive rather than the position of data points. Second, it is necessary to add some novel parallel heuristics and pruning policies for the kNN queries. Third, there are still many redundant random accesses among FA, TA, and BPA. In this paper, we address aforementioned these problems and take these algorithms to answer parallel kNN (PkNN) queries in spatial databases. We integrate the advantages of the B + -tree and Open MP programming and propose three efficient parallel kNN query algorithms, namely distance priority-based PkNN, optimized PkNN, and partition-based PkNN. Our performance evaluation shows that our proposed algorithms achieve significant improvement in comparison with existing algorithms, i.e., BPA and BPA2. In addition, our approaches are also capable of returning kNN results incrementally which greatly shorten the query response time and enhance user experience. [ABSTRACT FROM AUTHOR]

In the real world, there may exist manifold data (e.g., Boolean, categorical, real-valued, set-valued, interval-valued, image, decision and missing data or attributes) in an information system which is referred to as a hybrid information system with images (HISI). Handling an HISI is conducive to generalize applications of rough set theory. This paper studies entropy measurement for a hybrid information system with images and considers an application for attribute reduction. We first give the distance between information values on each attribute in an HISI. Then, we present tolerance relations on the object set of an HISI based on this distance. Next, we define the rough approximations in an HISI by means of the presented tolerance relations. Furthermore, we study entropy measurement for an HISI by using θ -information entropy, θ -conditional information entropy and θ -joint information entropy. Based on Kryszkiewicz's ideal, we introduce the concepts of θ -generalized decision and θ -consistent in an HISI. Finally, we apply entropy measurement to perform attribute reduction in a θ -consistent HISI. It is worth mentioning that attribute reduction based on generalized decision and common attribute reduction in a θ -consistent HISI are the same. [ABSTRACT FROM AUTHOR]

Three-way concept analysis provides a novel model for three-way decisions and also extends the classical formal concept analysis, which has attracted many attentions in recent years. By forgetting the point set, many mathematical structures related to sets can be extended to certain structures of powersets and consequently be studied as ordered structures; research in this area has yielded some interesting results. In order to study three-way concept analysis in the ordered structures, in this paper, a kind of lattice-theoretic formal contexts is proposed unprecedentedly. Firstly, we introduce the lattice-theoretic three-way formal concept lattices. Secondly, the relationship between lattice-theoretic three-way formal concepts and the lattice-theoretic formal concepts is investigated. Thirdly, we illustrate that the lattice-theoretic three-way formal concept lattice is a direct generalization of L-fuzzy formal concepts based on three-way decisions. Fourthly, the fundamental theorem for the special cases of lattice-theoretic three-way formal contexts is studied. Finally, we show that the lattice-theoretic three-way concepts are pointfree forms of some kinds of classical concepts; moreover, by the fundamental theorem, we get the characterization theorems for the classical three-way concept lattices, three-way rough concepts lattices and mixed concepts lattices; these results are compared with the available ones. [ABSTRACT FROM AUTHOR]

Recently, the issue of malicious circuit alteration and attack draws more attention than ever before due to the globalization of IC design and manufacturing. Malicious circuits, also known as hardware Trojans, are found able to degrade the circuit performance or even leak confidential information, and accordingly it is definitely an issue of immediate concern to develop detection techniques against hardware Trojans. This paper presents a ring oscillator-based detection technique to improve the hardware Trojan detection performance. A circuit under test is divided into a great number of blocks, path assignment is optimized using a path tracking algorithm, and a high coverage is reached accordingly. [ABSTRACT FROM AUTHOR]

For a graph G = (V , E) with maximum degree Δ , let f : V ⟶ { 0 , 1 , ... , ⌈ Δ 2 ⌉ + 1 } be a function and let B i = { v ∈ V : f (v) = i } for each i ∈ { 0 , 1 } and B 2 = { v ∈ V : f (v) ≥ 2 } = V - (B 0 ∪ B 1) . The function f is a strong Roman dominating function (StRDF) on G if every vertex v ∈ V with f (v) = 0 is adjacent to a vertex u with f (u) ≥ ⌈ 1 2 | N G (u) ∩ B 0 | ⌉ + 1 . The weight of f is the sum f (V) = ∑ v ∈ V f (v) . The minimum weight of a StRDF on G is the strong Roman domination number of G. In this paper, we give a linear algorithm that computes the strong Roman domination number of trees, answering a problem which was posed in 2017. Then, we give a linear algorithm to compute the strong Roman domination number of unicyclic graphs. [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]

A graph is said to be two connected if between every pair of nodes there are at least two node-disjoint paths. Given weights on the edges of the graph, the two connected subgraph problem is to find a two connected spanning subgraph of G whose weight is minimum. This problem has many applications in telecommunications. In this paper we consider a new variant of this problem with additional disjunctive constraints (called also conflict constraints) related to the survivability of telecommunication networks. This can be called the Disjunctive Two-Connected Subgraph Problem (DTCSP). First, we give an extended formulation for the problem whose variables are the cycles of the graph. Then, we use a column generation algorithm to solve its linear relaxation, and further show that the pricing reduces to finding a specific cycle in the graph which can be formulated as an integer programming problem. We also describe several valid inequalities for the polytope. Moreover, we study the related separation problems and devise separation routines for these inequalities. Using this, we devise a Branch-and-Cut-and-Price algorithm for the problem along with an extensive experimental study. [ABSTRACT FROM AUTHOR]

This paper studies a mixed batch scheduling problem with non-identical job sizes to minimize the makespan. Multiple jobs can be processed simultaneously as a batch on a mixed batch machine as long as the total size of the jobs in the batch does not exceed the machine capacity. The processing time of a batch is the weighted sum of the maximum processing time and total processing time of the jobs in the batch. We show that the problem is strongly NP-hard even with a single machine, and analyze the worst-case performance ratio of the longest processing time first fit (LPTFF) algorithm. Furthermore, we present the longest processing time first fit greedy (LPTFFG) algorithm, and show that the worst-case performance ratio of algorithm LPTFFG is better than that of algorithm LPTFF. Computational experiments show that algorithm LPTFFG fits the case with a large number of machines, small job sizes, and small weight of the maximum processing time. [ABSTRACT FROM AUTHOR]

Shadows, in aerial and satellite high-resolution images of earth, are a common phenomenon. Shadow causes false-color image, loss of information in the image, and false image segmentation. This leads to incorrect outputs of many image processing applications. In this paper, we address the problem of shadow detection in aerial high-resolution images of earth. The paper presents a proposed method that can be valuable in comparing it with other existing methods. The proposed work exploits the impact of ratio image pixel values on the process of shadow detection. The ratio image is the mathematical division of hue over the intensity component in the invariant color model. We propose a method, design, and develop an algorithm. In the designed algorithm, the input RGB aerial image of the earth is transformed into the invariant color model hue, saturation, and value (HSV). It acquires the average intensity value of pixels of the input RGB image components. Then, the ratio image of Hue (H) over Value (V) is calculated. Afterward, a power function is applied to this ratio to modify it by increasing the difference between pixel values very effectively. Finally, a threshold is applied to the modified ratio image to classify pixels into shadow and nonshadow. The proposed power function helps the threshold to better classify pixels into shadow and nonshadow. It has been implemented and experimented extensively. A comparative study has also been made with existing methods in the literature. In comparing the proposed algorithm and some existing algorithms, the experimental results show that the proposed has the ability to detect shadows with satisfying accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm for computing the Nash equilibrium based on an iterative approach of both the proximal and the gradient method for homogeneous, finite, ergodic and controllable Markov chains. We conceptualize the problem as a poly-linear programming problem. Then, we regularize the poly-linear functional employing a regularization approach over the Lagrange functional for ensuring the method to converge to some of the Nash equilibria of the game. This paper presents two main contributions: The first theoretical result is the proposed iterative approach, which employs both the proximal and the gradient method for computing the Nash equilibria in Markov games. The method transforms the game theory problem in a system of equations, in which each equation itself is an independent optimization problem for which the necessary condition of a minimum is computed employing a nonlinear programming solver. The iterated approach provides a quick rate of convergence to the Nash equilibrium point. The second computational contribution focuses on the analysis of the convergence of the proposed method and computes the rate of convergence of the step-size parameter. These results are interesting within the context of computational and algorithmic game theory. A numerical example illustrates the proposed approach. [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]

Network structure plays an important role in the natural and social sciences. Optimization of network structure in achieving specified goals has been a major research focus. In this paper, we focus on structural optimization in terms of minimizing the network's average path length (APL) by adding edges. We suggest a memetic algorithm to find the minimum-APL solution by adding edges. Experiments show that the proposed algorithm can solve this problem efficiently. Further, we find that APL will ultimately decrease linearly in the process of adding edges, which is affected by the network diameter. [ABSTRACT FROM AUTHOR]

The main result of the paper can be stated in the following way: a complete graph drawing in the sphere, where two edges have at most one common point, which is either a crossing or a common endpoint, and no three edges share a crossing, is determined by the circular ordering of edges at each vertex, or equivalently by the set of pairs of edges that cross, up to homeomorphism and a sequence of triangle mutations. A triangle mutation (or switch, or flip) consists in passing an edge across the intersection of two other edges, assuming the three edges cross each other and the region delimited by the three edges has an empty intersection with the drawing. Equivalently, the result holds for a drawing in the plane assuming the drawing is given with a pair of edges indicating where the unbounded region is. The proof is constructive, based on an inductive algorithm that adds vertices and their incident edges one by one (actually yielding an extra property for the sequence of triangle mutations). This result generalizes Ringel's theorem on uniform pseudoline arrangements (or rank 3 uniform oriented matroids) to complete graph drawings. We also apply this result to plane projections (or visualizations) of a geometric spatial complete graph, in terms of the rank 4 uniform oriented matroid defined by its vertices. Independently, we prove that, for a complete graph drawing, the set of pairs of edges that cross determine, by first order logic formulas, the circular ordering of edges at each vertex, as well as further information. [ABSTRACT FROM AUTHOR]

The paper describes the development of an automated digital system for monitoring and controlling the thermal regime of gas reverberatory furnaces for fire refining of nickel based on a mathematical model, which accounts for the control of medium-pressure burners. The specific features of the fuel combustion process and accompanying chemical reactions are considered; the chemical composition of the combustion products are analyzed for three different chemical compositions of natural gas; a mathematical modeling of the temperature field distribution is performed; the factors affecting the stability of the combustion process are considered; an algorithm has been developed to optimize the operation of the reverberatory furnace burners. New data were obtained regarding the chemical composition of the combustion products at various content ratios of natural gas and air in the temperature range from 94°C to 316°C. The thermal efficiency of the reverberatory furnace was calculated during the production of nickel alloys. The analytical results were used to derive the dependences of the temperature field distribution on the chemical composition of natural gas, which are necessary for creating an automatic control system for operating a reverberatory furnace based on the optimized control of the air-to-natural gas content ratio at a varying chemical composition of natural gas. [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]

In this paper, we consider an antagonistic differential game of two persons with dynamics described by a differential equation with simple motions and an integral terminal payment functional. In this game, there exists a price function, which is a generalized (minimax or viscous) solution of the corresponding Hamilton–Jacobi equation. For the case where the terminal function and the Hamiltonian are piecewise linear and the dimension of the phase space is equal to 2, we propose a finite algorithm for the exact construction of the price function. The algorithm consists of the sequential solution of elementary problems arising in a certain order. The piecewise linear price function of a differential game is constructed by gluing piecewise linear solutions of elementary problems. Structural matrices are a convenient tool of representing such functions. [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]

Reliability evaluations of modern test systems under the Industry 4.0 technologies, play a vital role in the successful transformation to NDE 4.0. This is due to the fact that NDE 4.0 is mainly based on the interconnection between the cyber-physical systems. When the individual reliability of the various important technologies from the Industry 4.0 such as the digital twin, digital thread, Industrial Internet of Things (IIoT), artificial intelligence (AI), data fusion, digitization, etc. is high, then it is possible to obtain the reliability beyond the intrinsic capability of the test system. In this paper, the significance of the reliability evaluation is reviewed under the vision of NDE 4.0, including examples of data fusion concepts as well as the importance of algorithms (like explainable artificial intelligence), the practical use is discussed and elaborated accordingly. [ABSTRACT FROM AUTHOR]

The potential use, relevance, and application of AI and other technologies in the democratic process may be obvious to some. However, technological innovation and, even, its consideration may face an intuitive push-back in the form of algorithm aversion (Dietvorst et al. J Exp Psychol 144(1):114–126, 2015). In this paper, I confront this intuition and suggest that a more 'extreme' form of technological change in the democratic process does not necessarily result in a worse outcome in terms of the fundamental concepts of democracy and the Rule of Law. To provoke further consideration and illustrate that initial intuitions regarding democratic innovation may not always be accurate, I pose and explore four ways that AI and other forms of technology could be used to augment the representative democratic process. The augmentations range from voting online to the wholesale replacement of the legislature's human representatives with algorithms. After first noting the intuition that less invasive forms of augmented democracy may be less objectionable than more extreme forms, I go on to critically assess whether the augmentation of existing systems satisfies or enhances ideas associated with democracy and the Rule of Law (provided by Dahl and Fuller). By imagining a (not too far-fetched) future in a (not too far-removed) democratic society, my conclusion is that, when it comes to democracy and the Rule of Law, intuitions regarding technology may lead us astray. [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]

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]

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]

The networks of various problems have competing constituents, and there is a concern to compute the strength of competition among these entities. Competition hypergraphs capture all groups of predators that are competing in a community through their hyperedges. This paper reintroduces competition hypergraphs in the context of Pythagorean fuzzy set theory, thereby producing Pythagorean fuzzy competition hypergraphs. The data of real-world ecological systems posses uncertainty, and the proposed hypergraphs can efficiently deal with such information to model wide range of competing interactions. We suggest several extensions of Pythagorean fuzzy competition hypergraphs, including Pythagorean fuzzy economic competition hypergraphs, Pythagorean fuzzy row as well as column hypergraphs, Pythagorean fuzzy k-competition hypergraphs, m-step Pythagorean fuzzy competition hypergraphs and Pythagorean fuzzy neighborhood hypergraphs. The proposed graphical structures are good tools to measure the strength of direct and indirect competing and non-competing interactions. Their aptness is illustrated through examples, and results support their intrinsic interest. We propose algorithms that help to compose some of the presented graphical structures. We consider predator-prey interactions among organisms of the Bering Sea as an application: Pythagorean fuzzy competition hypergraphs encapsulate the competing relationships among its inhabitants. Specifically, the algorithm which constructs the Pythagorean fuzzy competition hypergraphs can also compute the strength of competing and non-competing relations of this scenario. [ABSTRACT FROM AUTHOR]

WE consider a modernization of the echo method of ultrasonic flaw detection that uses one or several receiving transducers on the object's lateral surface in addition to the array of transducers contacting with the working (front) surface of the object. This method of array arranging can substantially improve the resolution and, in the case of using an antenna array on the lateral surface of the object, obtain additional information for imaging the internal structure of the object. In order to obtain the potential resolution, an ultrawideband probing signal with a Gaussian envelope is used in combination with a novel signal processing algorithm that includes summation, one-sided clipping, and multiplication of recorded signals. Some practical recommendations are given in the paper. Potential resolution in various directions has been assessed. [ABSTRACT FROM AUTHOR]