Search

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Thermoacoustic devices are converters of thermal energy into acoustic energy and vice versa. Although these machines contain simple components, the design of these machines is very challenging. In order to predict the behavior and optimize the efficiency of a standing-wave thermoacoustic engine designed to drive a thermally driven thermoacoustic refrigerator, considering changes in geometrical parameters and operating conditions, two analogies have been presented in this paper. The first analogy is based on a CFD simulation carried out to investigate the influence of stack parameters, working gas and boundary conditions on the thermoacoustic process. The second analogy is performed by the use of an optimization algorithm based on the simplified linear thermoacoustic theory to design and optimize the parameters investigated by the CFD study. Stack of parallel plates of normalized stack center positions of 0.007 to 0.26, normalized stack lengths of 0.018 to 0.11, and several gaps and thicknesses of plates and working gases are used. The results from the algorithm give the ability to design any thermoacoustic engine with high efficiency by picking the appropriate parameters. Simulation results show that decreasing thickness and position of the plates gives a significant efficiency. However, there are optimum values for length of the stack and the gap between two plates. The material chosen for the construction of plates should have a low thermal conductivity and gases with higher ratios of specific heats and lower Prandtl numbers are well suitable for thermoacoustic systems. [ABSTRACT FROM AUTHOR]

The decision making of a suitable heating, ventilating and air conditioning system's set-point temperature is an energy and environmental challenge in our society. In the present paper, a general framework to define such temperature based on a dynamic adaptive comfort algorithm is proposed. Due to the fact that the thermal comfort of the occupants of a building has different ranges of acceptability, this method is applied to learn such comfort temperature with respect to the running mean temperature and therefore to decide the suitable range of indoor temperature. It is demonstrated that this solution allows to dynamically build an adaptive comfort algorithm, an algorithm based on the human being's thermal adaptability, without applying the traditional theory. The proposed methodology based on the K-Nearest-Neighbour algorithm was tested and compared with data from an experimental thermal comfort field study carried out in a mixed mode building in the south-western area of Spain and with the Support Vector Machine method. The results show that K-Nearest-Neighbour algorithm represents the pattern of thermal comfort data better than the traditional solution and that it is a suitable method to learn the thermal comfort area of a building and to define the set-point temperature for a heating, ventilating and air-conditioning system. [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]

A set-valued information system (SVIS) is the generalization of a single-valued information system. A SVIS with missing information values is called an incomplete set-valued information system (ISVIS). This paper focuses on studying uncertainty measurement for an ISVIS with application to attribute reduction. First, the similarity degree between information values on each attribute is presented in an ISVIS. Then, the tolerance relation induced by each subsystem is given and rough approximations based on this relation is considered. Next, some tools to measure the uncertainty of an ISVIS are put forwarded. Moreover, the validity of the proposed measures is analyzed from the statistical point of view. Finally, information granulation and information entropy are applied to attribute reduction, the incomplete rate is adopted, and the effectiveness under different incomplete rates is analyzed and verified by k-means clustering algorithm and Mean Shift clustering algorithm. [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]

E-functions are entire functions with algebraic Taylor coefficients satisfying certain arithmetic conditions, and which are also solutions of linear differential equations with coefficients in Q ¯ (z) . They were introduced by Siegel (Über einige Anwendungen diophantischer Approximationen, vol. 1. S. Abhandlungen Akad, Berlin, 1929) to generalize the Diophantine properties of the exponential and the Bessel functions. The Siegel–Shidlovskii theorem (1956) deals with the algebraic (in)dependence of values at algebraic points of E-functions solutions of a differential system. In this paper, we present an algorithm to perform the following three tasks. Given as inputs some E-functions F 1 (z) ,..., F p (z) , (1) it computes a system of generators of the ideal of polynomial relations between F 1 (z) ,..., F p (z) with coefficients in Q ¯ (z) ; (2) given any α ∈ Q ¯ , it computes a system of generators of the ideal of polynomial relations between the values F 1 (α) ,..., F p (α) with coefficients in Q ¯ ; (3) if F 1 (z) ,..., F p (z) are algebraically independent over Q ¯ (z) , it determines the finite set of all α ∈ Q ¯ such that the values F 1 (α) ,..., F p (α) are algebraically dependent over Q ¯ . The existence of this algorithm relies on a variant of the Hrushovski–Feng algorithm (to compute polynomial relations between solutions of differential systems) and on Beukers’ lifting theorem (an optimal refinement of the Nesterenko–Shidlovskii theorem) in order to reduce these problems to an effective elimination procedure in multivariate polynomial rings. The latter is then performed using Gröbnerbasis. [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]

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]

We prove a quantitative version of the curve selection lemma. Denoting by s, d, k bounds the number, the maximum total degree and the number of variables of the polynomials describing a semi-algebraic set S and a point x in S ¯ , we find a semi-algebraic path starting at x and entering in S with a description of degree (O (d) 3 k + 3 , O (d) k) (using a precise definition of the description of a semi-algebraic path and its degree given in the paper). As a consequence, we prove that there exists a semi-algebraic path starting at x and entering in S, such that the degree of the Zariski closure of the image of this path is bounded by O (d) 4 k + 3 , improving a result in Jelonek and Kurdyka (Math Z 276:557–570, 2014). We also give an algorithm for describing the real isolated points of S whose complexity is bounded by s 2 k + 1 d O (k) improving a result in Le et al. (Computing the real isolated points of an algebraic hypersurface, 2020). [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]

We consider polynomials over a prime field of elements. With each polynomial under consideration, we associate the -valued function defined by the polynomial. A period of the -valued function is a tuple of elements in such that . In the paper, we propose an algorithm that, for an arbitrary prime and an arbitrary -valued function given by a polynomial over the field , finds a basis of the linear space of all periods of . Moreover, the complexity of the algorithm is , where is the degree of the polynomial defining . As a consequence, we show that for prime and each fixed number the problem of search for a basis of the linear space of all periods of a function given by a polynomial of degree at most can be solved by a polynomial-time algorithm with respect to the number of function variables. [ABSTRACT FROM AUTHOR]

It is generally acknowledged that driver pathway plays a decisive role in the occurrence and progress of tumors, and the identification of driver pathways has become imperative for precision medicine or personalized medicine. Due to the inevitable sequencing error, the noise contained in single omics cancer data usually plays a negative effect on identification. It is a feasible approach to take advantage of multi-omics cancer data rather than a single one now that large amounts of multi-omics cancer data have become available. The identification of driver pathways by integrating multi-omics cancer data has attracted attention of researchers in bioinformatics recently. In this paper, a weighted non-binary mutation matrix is constructed by integrating copy number variations, somatic mutations and gene expressions. Based on the weighted non-binary mutation matrix, a new identification model is proposed through defining new measurements of coverage and exclusivity. Then, a cooperative coevolutionary algorithm CGA-MWS is put forward for solving the presented model. Both real cancer data and simulated one were used to conduct comparisons among methods Dendrix, GA, iMCMC, MOGA, PGA-MWS and CGA-MWS. Compared with the pathways identified by the other five methods, more genes, belonging to the pathway identified by the CGA-MWS method, are enriched in a known signaling pathway in most cases. Simultaneously, the high efficiency of method CGA-MWS makes it practical in realistic applications. All of which have been verified through a number of experiments. [ABSTRACT FROM AUTHOR]

This paper describes an algorithm to compute the law of the Lie algebra $\mathfrak{g}_{n}$ associated with the Lie group G, formed of all the n× n upper-unitriangular matrices. The goal of this paper is to show the algorithm that computes the law of $\mathfrak{g}_{n}$ and its implementation using the symbolic computation package MAPLE. In addition, the complexity of the algorithm is described. [ABSTRACT FROM AUTHOR]

The paper focuses on concerns and legal challenges brought on by the use of algorithms. A particular class of algorithms that augment or replace analysis and decision-making by humans, i.e. data analytics and machine learning, is under scrutiny. Taking into account Balkin's work on "the laws of an algorithmic society", attention is drawn to obligations of transparency, matters of due process, and accountability. This US-centric analysis on drawbacks and loopholes of current legal systems is complemented with the analysis of norms and principles of the EU data protection law, or "GDPR". The aim is twofold. On the one hand, the intent is to shed light on some crucial differences between the US and EU law on the regulation of algorithmic operators, both public and private. Whereas, in the USA, scholars debate whether and to what extent new duties and responsibilities of algorithmic operators, e.g. information fiduciaries, have to amend the current framework of self-regulation and light government—as shown by the White House's Office of Science and Technology Policy report from November 2016—in EU law much of the new duties and responsibilities of algorithmic operators have been passed upon them as data controllers. Whether such approaches will successfully tackle the normative challenges of the algorithmic society is, on the other hand, an open issue that will likely represent the main topic of debate over the next years. Disagreement may concern: (i) the terms framing the legal question in e.g. statistical purposes of the data processing; (ii) how such terms are related to each other in legal reasoning (e.g. a right to explanation as valid law in the EU); and (iii) legal hard cases that will increasingly have to do with the principles that are at stake also but not only in data protection (e.g. informational self-determination). By entrusting such legal hard cases to algorithms, or some sort of smart artificial agent, humans still bear full responsibility for the judgment of what is socially, ethically, and legally "plain" and "hard" in social affairs. The balance between delegation of decisions to algorithms and non-delegation will be the leitmotiv of the algorithmic society. Since the devil is in the detail, the current paper is devoted to some of them. [ABSTRACT FROM AUTHOR]

The hypercube, or n-cube, has been widely used as the interconnection network in parallel computers. However, the major drawback of the hypercube is the increase in the number of communication links for each node with the increase in the total number of nodes in the system. This paper introduces a new interconnection network, namely dual-cube, for large-scale parallel computers and describes the algorithms for efficient collective communications in dual-cube. The dual-cube network mitigates the problem of increasing number of links in the large-scale hypercube network while retains hypercube’s topological properties. Design of efficient routing algorithms for collective communications is the key issue for any interconnection network. In this paper, we show that the collective communications can be done in dual-cube with almost the same communication times as in hypercube. [ABSTRACT FROM AUTHOR]

We propose a dead-end control algorithm for the exact solution of NP-hard combinatorial optimization problems. The efficiency of the algorithm is demonstrated by examples of solving the set-partition and 0-1 knapsack problems. The paper also shows that the use of the idea of dead-end controls when implementing the dynamic programming method can considerably reduce the number of problem state variables at each optimization step. A comparative analysis of the proposed method with known algorithms for solving these problems is carried out. [ABSTRACT FROM AUTHOR]

Inverse optimization is one of the interesting areas in both fundamental and applied research. This paper introduces a new approach, named as Khalifa, Kumar, and Mirjalili (KKM) approach, for solving the inverse capacitated transportation problem (ICTP) in a neutrosophic environment. The problem is considered with unit transportation cost associated with the single-valued trapezoidal neutrosophic numbers. Using the proposed KKM approach, the objective of the research work is to make the transportation cost as low as possible, which can lead to an optimal feasible solution. Based on the score function, the neutrosophic problem is first converted into an equivalent deterministic problem and then into a linear programming (LP) problem. Afterwards, by applying the dual and optimality conditions the inverse problem is obtained. In the end, an illustrative example is given to support the proposed approach and to gain more insights. [ABSTRACT FROM AUTHOR]

This paper presents the single-axis angular motion equations of a large space structure assembled in orbit from separate elastic construction elements. We introduce a method to calculate the parameters of discretely varying dynamical properties of an assembled structure whose model has variable coefficients and inherent attributes of an elastic multi-frequency vibrating system. In addition, we suggest a parametrically tuneable algorithm for powered gyro control of such objects that guarantees desired dynamics at all steps of robotized assembly. Simulation results demonstrate the efficiency of the suggested algorithm. [ABSTRACT FROM AUTHOR]

Missing data estimation is an important strategy for improving learning performance in learning from incomplete data, especially, when there are non discardable records with missing values. However, most of the existing algorithms are focused on missing at random (MAR) or missing completely at random (MCAR), and less attention has been paid to data not missing at random (NMAR). In this paper, an information decomposition imputation (IDIM) algorithm using fuzzy membership function is proposed for addressing the missing value problem under NMAR. Firstly, the proposed IDIM algorithm is presented with detailed examples. Then, the proposed approach is evaluated with extensive experiments compared with some typical algorithms. The experimental results demonstrate that the proposed algorithm has higher accuracy than the exiting imputation approaches in terms of normal root mean square error (NRMSE) and TP+TN evaluation under different missing strategies. [ABSTRACT FROM AUTHOR]

In this paper we consider a coordinated in-house production scheduling and outsourcing model, where a manufacturer might outsource part of the production to a subcontractor so as to achieve a tight production due date. The manufacturer pays a specific outsourcing cost for each job that is outsourced. To encourage the manufacturer to outsource more jobs, the subcontractor provides a specific discount scheme on the total outsourcing cost. Previous studies focus on the balance between in-house production performance and the total outsourcing cost. Our model is the first to investigate the impact on the manufacturer's decision-making under different discount schemes on the total outsourcing cost. Four distinct discount schemes on the total outsourcing cost are studied. We either show the intractability of those problems, or provide efficient exact or approximation algorithms. [ABSTRACT FROM AUTHOR]

This paper analyzes Christopher Alexander's combined use of mathematical graphs and hand-made diagrams, and argues that such affinities marked the insertion of roughness into architectural computational thinking. Within the techno-scientific context of American postwar architecture, the techniques of transcription and calculation used by Alexander at the Center for Environmental Structure reveal the progressive erasure of determinacy that took place within an architecture practice with empiricist, mathematic and computational preferences. Rather than establishing an optimized and quantified standard to which architecture had to conform, Alexander's rough diagrams and mathematical graphs serialized variation and provided room for indeterminacy and contingency within a clearly defined set of rules. [ABSTRACT FROM AUTHOR]

Franke Philosophy & Technology, 35(4), 1-7, (2022) offers an interesting claim that algorithmic transparency as manipulation does not necessarily follow that it is good or bad. Different people can have good reasons to adopt different evaluative attitudes towards this manipulation. Despite agreeing with some of his observations, this short reply will examine three crucial misconceptions in his arguments. In doing so, it defends why we are morally obliged to care about the manipulative potential of algorithmic transparency. It suggests that we as society have a moral duty to incorporate the value of transparency into algorithmic systems while keeping algorithmic transparency itself sensitive to power relations. [ABSTRACT FROM AUTHOR]

Springback is the most common defect in sheet metal forming, especially for the advanced high-strength steel (AHSS) parts. The compensation method is an effective way to overcome this defect by modifying the die-face. A new geometrical springback compensation algorithm based on the advanced displacement adjustment (DA) method is proposed in this paper for automobile AHSS parts. The die-face is reconstructed by the non-uniform rational B-spline surface with the advantage of UV curves; furthermore, the continuity and topology of the die-face are also studied. The high-quality geometry compensation die-face obtained from this algorithm can be directly applied to actual machining. To reduce the time and cost of the die manufacturing, this algorithm is seamlessly integrated into the CATIA platform. A new compensation user-defined feature is created, which can meet the need for iterative compensation and parametric association. At the end of this paper, two engineering examples of AHSS parts verify the feasibility and validity of this algorithm and system. [ABSTRACT FROM AUTHOR]

The important aspect of computer-aided process planning (CAPP) is to recognize surfaces and features of parts to aid downstream manufacturing of prismatic parts. Ample work is done on recognition of surface and its non-complex shape features by various methods, but there is shortfall in auto-recognition of interacting and compound features. The non-recognition of interacting and compound features limits the user from knowing individual feature type and material removal volume (MRV) of feature leading to lack of feature information provision to subsequent generative process planning. Therefore, this paper presents (i) an enriched classification of regular form features and (ii) a novel algorithm to automatically recognize interacting and compound volumetric features of prismatic part and to auto-generate material removal volume for the recognized volumetric features. All the faces of a feature are designated based on their geometrical shape, and combination of these designations expresses the type of feature present in a part. [ABSTRACT FROM AUTHOR]

In this paper, the facts that every equivalence relation may be considered as a special case of soft equivalence relations and there exists a one-to-one correspondence between the family of all knowledge bases and the family of all soft equivalence relations are proved. Parameter reductions of soft equivalence relations are investigated from the angle of rough sets and their algorithms are given. Parameters in a soft equivalence relation are divided into four categories (i.e., necessary parameters, relatively necessary parameters, absolutely dispensable parameters, dispensable parameters) according to the importance. [ABSTRACT FROM AUTHOR]

This paper considers the problem of scheduling n jobs on a single machine to minimize the total weighted completion time in the presence of sequence-dependent setup times and release times. To the best of our knowledge, little research has been devoted to this scheduling problem. Therefore, we developed two exact algorithms, including a constraint programming model and a branch-and-bound method for small problems. The obtained optimal solutions can be used as a benchmark for evaluating the performance of heuristics. With the complexity in mind, two heuristics, including a best index dispatch (BID) and a modified weighted shortest processing time (MWSPT) based on non-delay concepts are also proposed for large problems. The time complexities of the two proposed heuristics are O( n4) and O( n3), respectively. The computational results showed that the branch-and-bound method could solve most instances with 40 jobs under the time limit of 7,200 s. The BID heuristic is superior to the MWSPT in solution quality, although both can efficiently and effectively obtain near-optimal solutions for large instances. [ABSTRACT FROM AUTHOR]

This paper studies a new mixed batch scheduling problem that arises in vacuum heat treatment. A mixed batch machine can process at most a given number of jobs simultaneously. The processing time of a batch is the weighted sum of the maximum processing time and the total processing time of jobs in the batch. The objective is to minimize the makespan. We first prove that the problem on a single machine can be solved in polynomial time, while the problem on multiple identical machines is NP-hard. Then, we develop a pseudopolynomial time exact algorithm when the number of machines is fixed. Further, we analyze the worst-case performance ratio of a full batch longest processing time algorithm and design Algorithm LPT-Greedy with improved worst-case performance. [ABSTRACT FROM AUTHOR]

This paper presents two algorithms. The first decides the existence of a pointed homotopy between given simplicial maps f , g : X → Y , and the second computes the group [ Σ X , Y ] ∗ of pointed homotopy classes of maps from a suspension; in both cases, the target Y is assumed simply connected. More generally, these algorithms work relative to A ⊆ X . [ABSTRACT FROM AUTHOR]

Multigranulation rough set over two universes provides a new perspective to combine multiple granulation knowledge in a multigranulation space in practical reality. Note that there are always non-essential neighborhood granulations, which would affect the efficiency and quality of decision making. Therefore, selecting valuable granulations and reducing worthless ones are necessary for the application of multigranulation rough set in decision process. In this paper, we first define several measurements to compare the granularity of neighborhood granulations, using which the granulation selection with multigranulation rough set is characterized. Then, the selection algorithms in the multigranulation space are developed. Third, we generate "OR" and "AND" decision rules based on multigranulation fusion strategies. As an application, these decision rules are employed to make decisions in the presence of disease diagnosis problems. In the end, the effectiveness and efficiency of the proposed algorithms are examined with numerical experiments on selective data sets. [ABSTRACT FROM AUTHOR]

Traditional algorithms for the calculation of Kendall’s τ between two datasets of n samples have a calculation time of O(n2). This paper presents a suite of algorithms with expected calculation time of O(n log n) or better using a combination of sorting and balanced tree data structures. The literature, e.g. Dwork et al. (2001), has alluded to the existence of O(n log n) algorithms without any analysis: this paper gives an explicit descriptions of such algorithms for general use both for the case with and without duplicate values in the data. Execution times for sample data are reduced from 3.8 hours to around 1–2 seconds for one million data pairs. [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]

In this paper, multi-stage continuous belt (MSCB) dryer was used for carrot slices drying. Experiments were performed at three air speeds (1, 1.5, and 2 m/s) three belt linear velocities (2.5, 6.5, and 10.5 mm/s), and three air temperatures (40, 55, and 70 °C) in triplicate. Three intelligent systems including Ant-Lion-Optimizer (ALO), Grey-Wolf-Optimizer (GWO) and Whale-Optimization-Algorithm (WOA) models were developed to predict the thermodynamic properties of carrot slices including of effective moisture diffusivity (Deff) and specific energy consumption (SEC). The results revealed that Deff and SEC values were in the range of 1.77–2.90 × 10−9 m2/s and 169.77–551.19 MJ/kg, respectively. The models of ALO, GWO, and WOA were able to predict the value of Deff and SEC. The amounts of correlation coefficient (R ), root-mean-square error (RMSE ), and mean absolute error (MAE ) for ALO, GWO, and WOA models for predication Deff were obtained (0.9989, 7.81 × 10−12, and 1.50 × 10−12), (0.9993, 5.39 × 10−12, and 1.03 × 10−12) and (0.9994, 4.95 × 10−12, and 9.54 × 10−13), respectively. In addition, The amounts of R , RMSE , and MAE for ALO, GWO, and WOA model for predication SEC were obtained (0.9983, 0.6700, and 0.1289), (0.9988, 0.5274, and 0.0715) and (0.9996, 0.2566, and 0.0060), respectively. Therefore, model of WOA can be used to easily and accurately predict Deff and SEC values. [ABSTRACT FROM AUTHOR]

Similarity queries, including range queries and k nearest neighbor (kNN) queries, in metric spaces have applications in many areas such as multimedia retrieval, computational biology and location-based services. With the growing volumes of data, a distributed method is required. In this paper, we propose an Asynchronous Metric Distributed System (AMDS), to support efficient metric similarity queries in the distributed environment. AMDS uniformly partitions the data with the pivot-mapping technique to ensure the load balancing, and employs publish/subscribe communication model to asynchronous process large scale of queries. The employment of asynchronous processing model also improves robustness and efficiency of AMDS. In addition, we develop efficient similarity search algorithms using AMDS. Extensive experiments using real and synthetic data demonstrate the performance of metric similarity queries using AMDS. Moreover, the AMDS scales sublinearly with the growing data size. [ABSTRACT FROM AUTHOR]