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Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper introduces Large Conference Matching (LCM) , a novel reviewer–paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022-2024, and IJCAI 2022-2024. LCM has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer–paper scores; (2) formulating and solving an optimization problem to find good reviewer–paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data—including a comparison with the matching algorithm used in AAAI's previous (2020) iteration—and supplements this with additional numerical experimentation.2 [ABSTRACT FROM AUTHOR]

Action imagery describes a mental representation of an action and its consequences. Although it is widely recognized that people differ in their ability to imagine actions, objective validated tests to measure such differences are scarce. In search of an objective testing method for action imagery ability, the present study investigated whether solving mental paper-folding tasks involves action imagery. The stimuli were two-dimensional grids of six squares. A total of 99 participants mentally folded each grid into a three-dimensional cube to judge whether two highlighted lines in the grid overlapped in the imagined cube. This was done in two sessions of 214 judgements each, where the grids differed in overlaps, the least number of imagined folds, and the least number of imagined directional changes. Error rates and reaction times increased with the number of imagined folds and with the number of directional changes. Furthermore, more errors were committed with overlapping lines than with no overlaps. This was not reflected in the reaction times. Hence, the reaction times increased when the stepwise folding process was enlarged, but not when the final selection was more difficult. We concluded that the participants predominantly used action imagery as a task-solving strategy rather than for abstract problem-solving. [ABSTRACT FROM AUTHOR]

The increasing pressure on natural resources and the climate has been noted by businesses and governments worldwide, who now face the difficult task of integrating paths of environmental sustainability and economic growth. One promising approach to sustainable development, reducing the pressure on natural resources and solving waste problems is circular economy. From different ways of implementing circular economy, this study focuses on the productization of industrial side streams, which carry a great deal of underutilized potential. In this article, the productization of pulp and paper industry side stream, fiber sludge, for commercial use as a dust-binding agent is studied via a descriptive, in-depth case study. Apart from inconsistent quality, fluctuating supply, cost of storage and logistics, what makes industrial side stream utilization challenging is the lack of knowledge that stakeholders have about side streams and their utilization and the lack of new value chain development for their commercial use. Therefore, it is essential for all parties involved to have a clearer vision of what is being sold for what purposes, through productization. This study found that the productization of side streams follows the same steps as the productization of any other traditional product, and the productized side streams can be added to the company's product portfolio as by-products. [ABSTRACT FROM AUTHOR]

Creativity is an important skill for engineers but many students face a lack of experience in idea generation, often compounded by low-self efficacy towards creativity. Providing students with online training modules has been suggested as one solution. However, the literature demonstrates using a computer can influence performance and self-efficacy in a different manner to using pen-and-paper. There is a current lack of research on whether computers may influence self-efficacy during idea generation tasks. Two experiments involving computer-based and pen-and-paper groups were designed. Groups were provided with templates that guided them through the process of applying an idea generation technique and were given sixteen minutes to generate ideas to solve a presented problem. Results revealed that students' performance was similar and using a computer does not negatively influence self-efficacy. This shows that students can engage in idea generation learning tasks using computer-based modes without negatively influencing performance or self-efficacy, suggesting an avenue for educators to engage students with building creativity skills. [ABSTRACT FROM AUTHOR]

Distributed multitarget tracking (MTT) is suitable for sensors with limited field of view (FoV). Generalized covariance intersection (GCI) fusion is used to solve the MTT problem based on label probability hypothesis density (PHD) filtering in this paper. Because the traditional GCI fusion only has good fusion performance for the targets in the intersection of each sensor's FoV, and the targets outside the intersection range would be lost, this paper redivides the Gaussian components according to the FoV and distinguishes the Gaussian components of the targets inside and outside the intersection. GCI fusion is sensitive to label inconsistency between different sensors. For label fusion in the intersection region, the best match of labels is found by minimizing label inconsistency index, and then GCI fusion is performed. Finally, the feasibility and effectiveness of the proposed fusion method are verified by simulation, and its robustness is proved. The proposed method is obviously superior to local sensor and traditional GCI algorithm. [ABSTRACT FROM AUTHOR]

Throughout the paper in the title by Jian Tang, Yanfeng Luo and Xiangyun Xie in Turk J Math 42 (2018) the following lemma has been used. Lemma: Let (S, *) be a semihypergroup and ρ an equivalence relation on S. Then (i) If ρ is a congruence, then (S/ρ, ⊗) is a semihypergroup with respect to the hyperoperation (a)ρ ⊗ (b)ρ = ... (c)ρ . (ii) If ρ is a strong congruence, then (S/ρ, ⊗) is a semigroup with respect to the operation (a)ρ ⊗ (b)ρ = (c)ρ for all c ∈ a * b. The property (i) of the paper is certainly wrong as ... (c)ρ is a subset of S and not a nonempty subset of S/ρ as it should be. Property (ii) has no sense in the way is written. In addition, according to the authors, as an application of the results of this paper they solved the open problem on ordered semihypergroups given by Davvaz, Corsini and Changphas in European J Combin 44 (2015). The problem is that the above mentioned problem has not been solved in the above mentioned article; we point out the reason, and we solve it in the present paper. Some further related results; also results necessarily for the completeness of the paper are given. Examples illustrate the results. [ABSTRACT FROM AUTHOR]

Purpose: This study aims at implementing lean six sigma to evaluate the productivity and manufacturing wastes in the production line of a paper company Methodology/Approach: The study is a case study in nature. The method illustrates how lean six sigma (LSS) is used to evaluate the existing production process in a paper production company with focus on productivity and manufacturing wastes. The study considered a real-time problem of customer's dissatisfaction. Gathered data were based on machine functionality (up time, down time and cycle time); materials and labour flow at every process stage of the production line. The optimization of the production process was based on lean tools like value stream mapping, process cycle efficiency, Kaizen, 5S and pareto chart Findings: Based on lean six sigma application, it was discovered that the present production performance was below standard and more manufacturing wastes were generated. The present productivity and manufacturing wastes are reported as low process cycle efficiency (23.4 %), low takt time (4.11 sec), high lead time (43200sec), high number of products not conforming to six sigma values, high down time (32.64 %) and excess labour flow (33). After the implementation of the lean six sigma tools for certain periods, there were lots of improvements in the production lines in terms of all the parameters considered. Research Limitations/ Implications: The study has demonstrated application of lean six sigma in the case of solving real-time problems of productivity and manufacturing wastes which have a direct implication on customer's satisfaction. The lesson learnt and implications presented can still be further modeled using some lean based software for validity Originality/Value: The study has contributed to the body of knowledge in the field of LSS with focus to process-based manufacturing, unlike most literature in the field which concentrated more on discrete based manufacturing. [ABSTRACT FROM AUTHOR]

Abstract Successful problem solving relies on the availability of suitable mental representations of the task domain. Especially for more complex problems, there might be a wide variety of possible problem representations, and it might even be beneficial to change them during problem solving. In a first part, we argue that investigating the dynamics of understanding in terms of dynamically changing problem representations is an underexplored aspect of problem solving research, and that most classic tasks even preclude the opportunity of such dynamics to occur. Continuing this theoretical discussion, as an illustrative example of a task designed for the exploration of such representational dynamics, the second part of the paper discusses a novel, complex spatial transformation and problem solving task. In this task, one is asked to repeatedly mentally cross-fold a sheet of paper, and to predict the resulting sheet geometry without the use of external aids. Through its deliberate openness and difficulty, this task requires finding new and more efficient representations – ranging from kinaesthetic and visuospatial imagery to symbolic notions. We present an overview of the task domain and discuss various ways of representing the domain as well as potential dynamics between them. [ABSTRACT FROM AUTHOR]

Background. Until recently, information behaviour scholars have mainly focused on the workplace or educational settings, and defined information as either a tool for problem solving or a means to satisfy needs. This, however, only represents a partial picture of a larger horizon. In some arenas of life, such as serious leisure, people do not seek information to necessarily solve a problem or satisfy an urgent need. They look for information to enjoy a hobby or participate in an entirely voluntary activity. Objectives. The paper introduces transcendental information as a subjective and contextual concept to provide insights on lesser explored corners of the information behaviour scholarship. This concept is compared with existing theories and concepts in the information behaviour area. Method. This conceptual paper is based on a critical literature review of human information behaviour, and reflects on some key concepts in the field, including the nature of information, information needs, information seeking and sharing. The paper also provides a selective literature review of the serious leisure perspective to contextualise the analysis. Results. It is found that transcendental information usually has an aesthetic and intellectual essence. It may be expressed in various imaginative forms and can appear in different non-textual and embedded formats. Moreover, it can generate joyful and inspiring impacts. The paper refers to serious leisure as an exemplary setting to contextualise transcendental information within a relevant and well-established theoretical framework. [ABSTRACT FROM AUTHOR]

Introduction: Executive functions develop, particularly in the first 5 years of life. This study aimed to investigate the executive functions in Persian typically-developing (TD) children aged 4 to 6 years. Materials and Methods: In this cross-sectional study, 50 children (21 girls and 29 boys) participated. Executive functions (planning/problem solving, inhibition, shifting, and working memory) were measured by various performance-based tests (the Tower of London, Go/No-Go, Dimensional Change Card Sorting task (DCCS), forward Digit Span (FDS), Backward Digit Span (BDS) and Non-Word Repetition (NWR) and Behavior Rating Inventory Of Executive Function (BRIEF) questionnaire). After collecting the data, they were entered into SPSS version 16.0, and data analysis was done by Mann-Whitney U test and two-tailed Spearman test. The significant level was set at 0.05. Results: Regarding the age groups, 5-year-old children were significantly better than 4-year-old children in FDS and BDS, and regarding gender groups, performance-based tests were not significantly different between girls and boys but in the BRIEF questionnaire as parents reported, boys were significantly had better performance in daily life. BRIEF questionnaires in the boy’s group were correlated with FDS, BDS, the Tower of London, Go/No-Go, and DCCS. In the girl’s group, the BRIEF questionnaire was correlated with NWR and DCCS. In 4-year-old children, the BRIEF questionnaire was correlated with FDS, BDS, Go/No-Go, and DCCS. In 5-year-old children, the BRIEF questionnaire was correlated with BDS, the Tower of London, Go/No-Go, and DCCS. Conclusion: It seems that TD children between the ages of 4-6 years can have different working memories but does not differ in skills such as problem-solving, shifting, and inhibition. [ABSTRACT FROM AUTHOR]

This paper contains the solution to the problem of a semi-infinite moving crack situated in an orthotropic strip bonded between two identical strips. The crack moves with a constant velocity, and the surface is under shear wave disturbance. We first examine the equations of elasticity, which include equilibrium equations and stress and displacement constitutive relations with the model-specific continuity and boundary conditions. Using the Fourier integral transform, the standard form for the Wiener–Hopf (W-H) equation is obtained, which is solved using the W-H method. The analytical expressions for the considered crack problem have been obtained for stress intensity factor (SIF), normalized stress intensity factor (NSIF), and stress magnification factor (SMF). The behavior of NSIF has been graphically presented for particular cases of composite materials for different crack propagation velocities and various depth ratios of the strips. The novelty of this paper lies in the analytic solutions using the W-H method for the semi-infinite moving crack problem under the influence of anti-plane shear waves. The pictorial presentations of normalized SIF clearly show their dependency on strip depths, crack propagation velocities and elastic constants. [ABSTRACT FROM AUTHOR]

Critical chain buffer management (CCBM) has been extensively studied in recent years. This paper investigates a new formulation of CCBM, the multimode chance-constrained CCBM problem. A flow-based mixed-integer linear programming model is described and the chance constraints are tackled using a scenario approach. A reinforcement learning (RL)-based algorithm is proposed to solve the problem. A factorial experiment is conducted and the results of this study indicate that solving the chance-constrained problem produces shorter project durations than the traditional approach that inserts time buffers into a baseline schedule generated by solving the deterministic problem. This paper also demonstrates that our RL method produces competitive schedules compared to established benchmarks. The importance of solving the chance-constrained problem and obtaining a project buffer tailored to the desired probability of completing the project on schedule directly from the solution is highlighted. Because of its potential for generating shorter schedules with the same on-time probabilities as the traditional approach, this research can be a useful aid for decision makers. [ABSTRACT FROM AUTHOR]

In order to solve the huge impact of the digital information age on many technical and industrial fields, a periodic fast search genetic algorithm is proposed. Based on the reconnaissance mission, this paper introduces the common allocation strategy into mission planning, and constructs the mathematical model of multi unmanned aerial vehicle (UAV) cooperative reconnaissance mission planning decision‐making. The proposed periodic fast search genetic algorithm is used to solve the problem of multi UAV cooperative reconnaissance mission planning. In 2020, the industry growth rate of global UAV technology expenditure was as high as 30.6%, and the compound growth rate of UAV in China reached 63.5%, which is enough to see the great prospect of the integrated development of UAV technology and different industries. The experiment evaluates the log verification module of UAV by comparing the two data structures of Merkle tree and linear, and the time and memory overhead of storing and verifying logs, which shows the effectiveness of the log verification scheme in this paper. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose a novel transfer learning regularization method based on knowledge distillation. Recently, transfer learning methods have been used in various fields. However, problems such as knowledge loss still occur during the process of transfer learning to a new target dataset. To solve these problems, there are various regularization methods based on knowledge distillation techniques. In this paper, we propose a transfer learning regularization method based on feature map alignment used in the field of knowledge distillation. The proposed method is composed of two attention-based submodules: self-pixel attention (SPA) and global channel attention (GCA). The self-pixel attention submodule utilizes both the feature maps of the source and target models, so that it provides an opportunity to jointly consider the features of the target and the knowledge of the source. The global channel attention submodule determines the importance of channels through all layers, unlike the existing methods that calculate these only within a single layer. Accordingly, transfer learning regularization is performed by considering both the interior of each single layer and the depth of the entire layer. Consequently, the proposed method using both of these submodules showed overall improved classification accuracy than the existing methods in classification experiments on commonly used datasets. [ABSTRACT FROM AUTHOR]

In this paper, we describe a sheet-shaped throwable transforming robot. Sheet-type robots can change their shape to perform tasks according to the situation. Therefore, they are expected to be useful in places with many restrictions, such as disaster sites. However, most of them can only move slowly on the ground. Therefore, in order to actually deliver the robot to the disaster site, it must be carried manually. To solve this problem, we are developing a sheet-shaped robot that can be thrown from the sky. Previously developed prototypes could only move in the forward direction, and the transition from falling to walking was complicated and uncertain. In this paper, we report on a new prototype that improves on these shortcomings. [ABSTRACT FROM AUTHOR]

We study the mechanical response, and tearing features of crêpe paper, a two-dimensional, very anisotropic material, with one direction much less stiff than the other one. Depending on how the soft direction has been pre-stretched or not, the apparent Young modulus of the material can be varied over a broad range, while its fracture energy remains unaltered. The classical tearing concertina problem shows that a macroscopic measurement (the shape of the teared region) provides a direct access to the fracture properties of the material (effective Young's modulus, and fracture energy). The overall discussion is conducted in the frame of Griffith's theory of fracture. [ABSTRACT FROM AUTHOR]

We consider a one-dimensional cutting stock problem (CSP) in which the stock widths are not used to fulfill the order but kept for use in the future for the industrial-use paper production. We present a new model based on the flexible stock allocation and trim loss control to determine the production quantity. We evaluate our approach using a real data and show that we are able to solve industrial-size problems, while also addressing common cutting considerations such as aggregation of orders, multiple stock widths, and cutting different patterns on the same machine. In addition, we compare our model with others, including trim loss minimization problem (TLMP) and cutting stock problem (CSP). The results show that the proposedmodel outperforms the other two models regarding total flexibility and trim loss ratio. [ABSTRACT FROM AUTHOR]

Thomas L. Saaty, a world-renowned scholar and our beloved Advisory Editor, passed away on August 14, 2017 at the age of 91. He has made many fundamental contributions to operations research, analytics, business and mathematics. Despite his heavy illness for 14 months, as passionate about science, he has also enthusiastically published his research. We review his final works published in 2017 and 2018. We are also honored to reveal and publish his final statements on neural firing and synthesis in making comparisons & life satisfaction, respectively, in his final two papers; the previous one submitted by him just before his passing and the other one submitted by his co-authors after his death to European Journal of Pure and Applied Mathematics (EJPAM). We just would like to remember and honor Saaty's memory by publishing his final papers at EJPAM and this humble remarks dedicated to the memory of Thomas L. Saaty on the occasion of the first anniversary of his passing, August 14, 2018. [ABSTRACT FROM AUTHOR]

This research addresses the issue of how to support students' representational fluency—the ability to create, move within, translate across, and derive meaning from external representations of mathematical ideas. The context of solving linear equations in a combined computer algebra system (CAS) and paper‐and‐pencil classroom environment is targeted as a rich and pressing context to study this issue. We report results of a collaborative teaching experiment in which we designed for and tested a functions approach to solving equations with ninth‐grade algebra students, and link to results of semi‐structured interviews with students before and after the experiment. Results of analyzing the five‐week experiment include instructional supports for students' representational fluency in solving linear equations: (a) sequencing the use of graphs, tables, and CAS feedback prior to formal symbolic transpositions, (b) connecting solutions to equations across representations, and (c) encouraging understanding of equations as equivalence relations that are sometimes, always, or never true. While some students' change in sophistication of representational fluency helps substantiate the productive nature of these supports, other students' persistent struggles raise questions of how to address the diverse needs of learners in complex learning environments involving multiple tool‐based representations. [ABSTRACT FROM AUTHOR]

The Bouc–Wen nonlinear hysteretic model is widely used in nonlinear systems, and it is difficult to identify the parameters of Bouc–Wen model because of its high nonlinearity. In the paper, a hybrid genetic algorithm combined with sequential quadratic programming is proposed to solve the problem about the parameter identification of Bouc–Wen model. Sequential quadratic programming method has good local convergence and superlinear convergence rate. Therefore, compared with the genetic algorithm, the proposed identification method can accelerate the convergence speed in parameter recognition of Bouc–Wen model and achieve excellent recognition accuracy. Through the function test and the numerical simulation of the shear structure, it is proved that the proposed method has high convergence speed and accurate identification. In addition, the theory and method proposed in this paper are combined with Bouc–Wen model to identify parameters of overall restoring force model of the energy-dissipation structures, the identification results are in good agreement with the experimental results, verifying reliability and accuracy of the model and robustness of the proposed identification method. [ABSTRACT FROM AUTHOR]

Extracting balanced geostrophic motions (BM) from sea surface height (SSH) observations obtained by wide‐swath altimetry holds great significance in enhancing our understanding of oceanic dynamic processes at submesoscale wavelength. However, SSH observations derived from wide‐swath altimetry are characterized by high spatial resolution while relatively low temporal resolution, thereby posing challenges to extract the BM from a single SSH snapshot. To address this issue, this paper proposes a deep learning model called the BM‐UBM Network, which takes an instantaneous SSH snapshot as input and outputs the projection corresponding to the BM. Training experiments are conducted both in the Gulf Stream and South China Sea, and three metrics are considered to diagnose model's outputs. The favorable results highlight the potential capability of the BM‐UBM Network to process SSH measurements obtained by wide‐swath altimetry. Plain Language Summary: Oceanic dynamic processes can be classified into two categories: balanced geostrophic motions (BM), including large‐scale circulation, mesoscale and submesoscale eddy turbulence, and unbalanced wave motions (UBM), including barotropic tides, and inertia–gravity waves (IGWs). Both types of motions coexist and have respective contributions to the sea surface height (SSH). How to extract the BM from the total SSH observations obtained by satellite altimetry is the crucial problem to be solved in this paper. To tackle this issue, we propose a deep learning model named the BM‐UBM Network to establish the relationship between the total SSH and the BM component. The BM‐UBM Network can generate SSH estimations for the BM when provided with a well‐resolved SSH snapshot. Key Points: A Deep learning model is developed to extract balanced motions from sea surface height snapshot based on a realistic simulationDiagnostics of three metrics reveal the effectiveness of the model in extracting balanced motionsThe model exhibits remarkable advantages over the Gaussian filter (baseline) in capturing the gradient and Laplacian information [ABSTRACT FROM AUTHOR]

Cryo-electron micrograph images have various characteristics such as varying sizes, shapes, and distribution densities of individual particles, severe background noise, high levels of impurities, irregular shapes, blurred edges, and similar color to the background. How to demonstrate good adaptability in the field of image vision by picking up single particles from multiple types of cryo-electron micrographs is currently a challenge in the field of cryo-electron micrographs. This paper combines the characteristics of the MixUp hybrid enhancement algorithm, enhances the image feature information in the pre-processing stage, builds a feature perception network based on the channel self-attention mechanism in the forward network of the Swin Transformer model network, achieving adaptive adjustment of self-attention mechanism between different single particles, increasing the network's tolerance to noise, Incorporating PReLU activation function to enhance information exchange between pixel blocks of different single particles, and combining the Cross-Entropy function with the softmax function to construct a classification network based on Swin Transformer suitable for cryo-electron micrograph single particle detection model (Swin-cryoEM), achieving mixed detection of multiple types of single particles. Swin-cryoEM algorithm can better solve the problem of good adaptability in picking single particles of many types of cryo-electron micrographs, improve the accuracy and generalization ability of the single particle picking method, and provide high-quality data support for the three-dimensional reconstruction of a single particle. In this paper, ablation experiments and comparison experiments were designed to evaluate and compare Swin-cryoEM algorithms in detail and comprehensively on multiple datasets. The Average Precision is an important evaluation index of the evaluation model, and the optimal Average Precision reached 95.5% in the training stage Swin-cryoEM, and the single particle picking performance was also superior in the prediction stage. This model inherits the advantages of the Swin Transformer detection model and is superior to mainstream models such as Faster R-CNN and YOLOv5 in terms of the single particle detection capability of cryo-electron micrographs. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of determining the exact value of the maximum allowable upper bound (MAUB) of delay of an LTI system in the presence of the delay parameters. The delay parameter is supposed to be time-invariant, and this paper proposes a systematic method to exactly solve the mentioned problem. For this purpose, a modified system matrix is defined based on the delay system's model that involves an extra complex variable on the boundary of the unit circle. It will be shown that the characteristic polynomial of the modified system can equivalently assess the system's stability. The characteristic polynomial of the modified system is a quadratic function of the imaginary part of the extra complex variable which enables us to rewrite the polynomial based on only the real part of the extra variable. This property is exploited to develop an explicit formula to find the MAUB based on the critical numbers (complex numbers that force the modified characteristic polynomial to have pure imaginary roots) and their related pure imaginary roots. The method is tested for some sample delay systems and the results demonstrate a tighter bound for the MAUB of the systems. [ABSTRACT FROM AUTHOR]

Ethical morality is one of the significant issues in self-driving cars. The paper provides a newer approach to solve the ethical decision problems in self-driving cars until there is no concrete ethical decision to all problems. This paper gives a two-way approach to solve a problem, with first being the mapping of problem to the solution already known or which has a fixed set of solutions and action priorities defined to a problem previously. Now, if no solution is found or mapping is unsuccessful, then the second stage activates, where the solution from Deep Q-learning model is calculated. It estimates the best Q value and returns that solution or action which maximizes the reward at that instance. The reward function is designed with decreasing priorities and acts accordingly, where the users can change or define their priorities if needed. The case study and results show that the solution that is present in the paper will lead to solving ethical morality problems in self-driving cars up to a great extent. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of parameter identification for discrete-time stochastic systems with unknown exogenous inputs. These systems form an important class of dynamic stochastic system models used to describe objects and processes under a high level of a priori uncertainty, when it is not possible to make any assumptions about the evolution of the unknown input signal or its statistical properties. The main purpose of this paper is to construct a new SVD-based modification of the existing Gillijns and De Moor filtering algorithm for linear discrete-time stochastic systems with unknown exogenous inputs. Using the theoretical results obtained, we demonstrate how this modified algorithm can be applied to solve the problem of parameter identification. The results of our numerical experiments conducted in MATLAB confirm the effectiveness of the SVD-based parameter identification method that was developed, under conditions of unknown exogenous inputs, compared to maximum likelihood parameter identification when exogenous inputs are known. [ABSTRACT FROM AUTHOR]

Occlusion or posture change of the face in natural scenes has typical asymmetry; however, an asymmetric face plays a key part in the lack of information available for facial expression recognition. To solve the problem of low accuracy of asymmetric facial expression recognition, this paper proposes a fusion of channel global features and a spatial local information expression recognition network called the "Channel–Spatial Integration Network" (CSINet). First, to extract the underlying detail information and deepen the network, the attention residual module with a redundant information filtering function is designed, and the backbone feature-extraction network is constituted by module stacking. Second, considering the loss of information in the local key area of face occlusion, the channel–spatial fusion structure is constructed, and the channel features and spatial features are combined to enhance the accuracy of occluded facial recognition. Finally, before the full connection layer, more local spatial information is embedded into the global channel information to capture the relationship between different channel–spatial targets, which improves the accuracy of feature expression. Experimental results on the natural scene facial expression data sets RAF-DB and FERPlus show that the recognition accuracies of the modeling approach proposed in this paper are 89.67% and 90.83%, which are 13.24% and 11.52% higher than that of the baseline network ResNet50, respectively. Compared with the latest facial expression recognition methods such as CVT, PACVT, etc., the method in this paper obtains better evaluation results of masked facial expression recognition, which provides certain theoretical and technical references for daily facial emotion analysis and human–computer interaction applications. [ABSTRACT FROM AUTHOR]

This paper designs a new facial Expression recognition network called a multi‐scale feature Fusion Convolutional neural Network (EFCN). This network is proposed to solve two problems in the facial expression recognition task. First, there are many commonalities between faces of different expression categories, and the recognition task cannot be precisely performed when the commonality is greater than the individuality. Secondly, facial detail features have a significant impact on the final results of expression recognition, while the image detail features extracted by traditional convolutional neural networks are not sufficient. In order to address the above issues, the feature enhancement network (FEN) and the detail information enhancement module (DEM) are designed. The FEN fuses deep and shallow features. Accordingly, the feature map contains richer information, making it easy to identify the samples. The DEM extracts and fuses the features passed by the backbone network with multi‐scale features to enhance the network's ability to extract features from small regions of the face. We validated the proposed method on three datasets, RAF‐DB, CK+, and JAFFE, and achieved 84.50%, 97.86%, and 91.05% accuracy, respectively, and the experimental results showed the effectiveness of the proposed method in this paper. For example, on the JAFFE dataset, the recognition accuracy of this method surpasses the MLT method by 1.87%. [ABSTRACT FROM AUTHOR]

For the actual circuit with fractional order characteristics, the fractional order circuit model is more concise and precise than the classical circuit model. So studying the synthesis of fractional order immittance is of great significance. This paper investigates the passive synthesis method of the three-element-kind fractional order immittance function. It means designing a network with three kinds of passive fractional-order elements that exhibit a specific impedance function. This paper proposes a variable substitution method to solve this problem. The method consists of three steps: The first step of the process is to establish the correspondence between the fractional order and lossy reactance. This way, the fractional order immittance function is transformed into an integer order immittance function. Then the Brune procedure realizes the immittance function as a network with lossy reactance. Finally, a fractional order network is obtained by replacing the lossy reactance with the corresponding fractional reactance. A simulation example and an experiment are also given for verification. This method applies to all three-element-kind network impedance functions that meet the passive condition. [ABSTRACT FROM AUTHOR]

In this paper, we study a method of polynomial interpolation that lies in-between Lagrange and Hermite methods. The novelty is that we use very general nodal systems on the unit circle as well as on the bounded interval only characterized by a separation property. The way in which we interpolate consists in considering all the nodes for the prescribed values and only half for the derivatives. Firstly, we develop the theory on the unit circle, obtaining the main properties of the nodal polynomials and studying the convergence of the interpolation polynomials corresponding to continuous functions with some kind of modulus of continuity and with general conditions on the prescribed values for half of the derivatives. We complete this first part of the paper with the study of the convergence for smooth functions obtaining the rate of convergence, which is slightly slower than that when equidistributed nodal points are considered. The second part of the paper is devoted to solving a similar problem on the bounded interval by using nodal systems having good properties of separation, generalizing the Chebyshev–Lobatto system, and well related to the nodal systems on the unit circle studied before. We obtain an expression of the interpolation polynomials as well as results about their convergence in the case of continuous functions with a convenient modulus of continuity and, particularly, for differentiable functions. Finally, we present some numerical experiments related to the application of the method with the nodal systems dealt with. [ABSTRACT FROM AUTHOR]

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]

The problem of a stabilisation system synthesis for the motion of a control object along a given spatial trajectory is considered. The complexity of the problem is that the preset trajectory is defined in the state subspace and not in time. This paper describes a stabilisation system synthesis for motion along a trajectory specified in time and along a trajectory specified in the form of a manifold in a state space. In order to construct a stabilisation system, it is necessary to determine a distance between an object and the given trajectory at each moment in time. For trajectories that are not given in time, the determination of this distance can be ambiguous. An object may be exactly on a trajectory but at a different time. This paper proposes some approaches to solve the problem. One of the approaches is to transform a given trajectory in a state subspace into a trajectory given in time. A description of a universal method to perform this transformation is presented. In order to solve the synthesis problem automatically, without having to analyse the mathematical model of the control object, it is suggested that machine learning control by symbolic regression is used. In computational experiments, examples of stabilisation system syntheses for quadcopter motion along a given spatial trajectory are presented. [ABSTRACT FROM AUTHOR]

For wireless sensor network (WSN) coverage problems, since the sensing range of sensor nodes is a circular area with symmetry, taking symmetry into account when deploying nodes will help simplify problem solving. In addition, in view of two specific problems of high node deployment costs and insufficient effective coverage in WSNs, this paper proposes a WSN coverage optimization method based on the improved grey wolf optimizer with multi-strategies (IGWO-MS). As far as IGWO-MS is concerned, first of all, it uses Sobol sequences to initialize the population so that the initial values of the population are evenly distributed in the search space, ensuring high ergodicity and diversity. Secondly, it introduces a search space strategy to increase the search range of the population, avoid premature convergence, and improve search accuracy. And then, it combines reverse learning and mirror mapping to expand the population richness. Finally, it adds Levy flight to increase the disturbance and improve the probability of the algorithm jumping out of the local optimum. To verify the performance of IGWO-MS in WSN coverage optimization, this paper rasterizes the coverage area of the WSN into multiple grids of the same size and symmetry with each other, thereby transforming the node coverage problem into a single-objective optimization problem. In the simulation experiment, not only was IGWO-MS selected, but four other algorithms were also selected for comparison, namely particle swarm optimization (PSO), grey wolf optimizer (GWO), grey wolf optimization based on drunk walk (DGWO), and grey wolf optimization led by two-headed wolves (GWO-THW). The experimental results demonstrate that when the number of nodes for WSN coverage optimization is 20 and 30, the optimal coverage rate and average coverage rate using IGWO-MS are both improved compared to the other four comparison algorithms. To make this clear, in the case of 20 nodes, the optimal coverage rate of IGWO-MS is increased by 13.19%, 1.68%, 4.92%, and 3.62%, respectively, compared with PSO, GWO, DGWO, and GWO-THW; while IGWO-MS performs even better in terms of average coverage rate, which is 16.45%, 3.13%, 11.25%, and 6.19% higher than that of PSO, GWO, DGWO, and GWO-THW, respectively. Similarly, in the case of 30 nodes, compared with PSO, GWO, DGWO, and GWO-THW, the optimal coverage rate of the IGWO-MS is increased by 15.23%, 1.36%, 5.55%, and 3.66%; the average coverage rate is increased by 16.78%, 1.56%, 10.91%, and 8.55%. Therefore, it can be concluded that IGWO-MS has certain advantages in solving WSN coverage problems, which is reflected in that not only can it effectively improve the coverage quality of network nodes, but it also has good stability. [ABSTRACT FROM AUTHOR]

Aiming at the characteristics of complex structure, strong coupling and different multi-modal safety levels of more electric aircraft starter generator system, a safety analysis method based on the operating process and a multi-modal failure rate calculation method are proposed. This paper analyses the architecture, operating process and modals of more electric aircraft starter generator system and decomposes the system into eight operating modals. Based on the construction of SafetyLab, a domestic safety analysis platform, the structural models and failure rate calculation models of eight modals of starter generator system are established, and the top event failure rate of each modal, the highest transient failure rate and the steady state failure rate of the system are calculated for a complete safety analysis, taking a typical starter generator system as an example. The method proposed in this paper helps to solve the problem of multi-modal failure rate analysis of complex systems with different equipment involved in the operating process. The multi-modal failure rate calculation method proposed in this paper is also applicable to the safety analysis of other multimodal complex systems. [ABSTRACT FROM AUTHOR]

With the application of encoders in artificial intelligence and aerospace, the demand for the miniaturization and high measurement accuracy of encoders is increasing. To solve this problem, a new absolute matrix encoder is proposed in this paper, which can realize 19-bit matrix coding by engraving two circles of matrix code, and has the advantages of fewer circles of code disk engraving and higher measurement accuracy. This article mainly focuses on the design of a new matrix code disk, encoding and decoding methods, decoding circuit design, Matlab simulation analysis, and experimental error analysis. The experimental results show that the encoder designed in this paper achieves ultra-small volume Φ30 mm × 20 mm, and the angle measurement accuracy is 2.57". [ABSTRACT FROM AUTHOR]

We propose to study a solution approach for an inventory model regarding the use of the algebraic method to solve inventory problems. His process is more straightforward than other analytic methods based on calculus. However, there are questionable results contained in his approach. The purpose of this paper is threefold. First, we point out the problems included in his process. Second, we prepare our corrected solution approach by the algebraic method. Third, for completeness, we list the questionable results in his paper. [ABSTRACT FROM AUTHOR]

In this paper, a fault feature extraction method based on the combination of computational order tracking (COT) and variational mode decomposition (VMD) is proposed to solve the problem of fault feature extraction in a gearbox under variable speed conditions. First, a speed estimation method based on a forward-backward greedy algorithm and Fourier fitting is proposed to solve the speed estimation problem under variable speed conditions. Then, a feature extraction method based on COT, VMD, and fast kurtometry is proposed. COT is used to calculate the order. After the signal is decomposed by VMD, a fast kurtogram is used to filter the modal components with obvious fault features so as to extract fault features. Finally, combined with the analysis of actual equipment examples, the experimental results show that steady-state filtering can effectively filter the background noise and improve the signal-to-noise ratio. The rotation speed estimated by the forward–backward greedy algorithm and Fourier fitting is very close to the actual speed, which verifies that the method proposed in this paper can effectively solve the problem of fault feature extraction of a gearbox under variable speed conditions. [ABSTRACT FROM AUTHOR]

The application of blockchain has become a trend in the development of supply chain finance. Aiming to bridge the gap in the existing literature, this paper investigates a supply chain finance system based on blockchain technology which contains a manufacturer, a retailer and a financial institution and incorporates blockchain costs into the model. Firstly, this paper establishes a supply chain finance model based on blockchain technology and it presents a comparison with the process employed under the traditional model. Secondly, this paper establishes the revenue mathematical model of supply chain finance based on blockchain technology. Thirdly, the optimal decisions of each participant under centralized and decentralized decision-making are proved and obtained, respectively, and the influencing factors of the optimal decisions are analyzed. Finally, the conclusions are verified via simulations. This study finds that, when blockchain is used, the benefits of each participant in the chain are increased. In addition, centralized decision-making, which is more optimal in the traditional model, is also enhanced under blockchain. This paper demonstrates the superiority of blockchain-enabled supply chain finance in terms of model and revenue. This provides some suggestions for companies in the supply chain with regard to solving the problem of financing difficulties. [ABSTRACT FROM AUTHOR]

The Last decade saw significant growth in the digital terrestrial television (DTT) subscribers. The High peak-to-average power ratio (PAPR) of the transmitted signal is implied by the large fluctuations in its amplitude, which is a serious issue in the contemporary and next generation DTT broadcasting systems. These large fluctuations prevent feeding the high power amplifier (HPA) at an operating point near its non-linear (NL) saturation region, thereby lowering its power efficiency. For a better future, energy-efficient green communications is the need of hour. This paper focuses on digital predistortion (DPD), which is among the widely used techniques for HPA linearization in order to minimize the NL effects. DPD technique requires a feedback signal. This paper proposes a novel adding signal-based digital predistortion, where the output of the HPA is gradually linearized by adding a signal at the input in an iterative manner. By Taylor series approximation, the DPD problem can be posed as a convex one. We prove that a tighter upper bound exists for the convergence factor and instead of directly solving the convex problem, we pick a convergence factor value within its bounds that offers a good trade-off between convergence speed and residual error value. The simulation results show that the proposed technique can quickly linearize the HPA with good convergence. [ABSTRACT FROM AUTHOR]

The application of analytical methods to solve the problem of an anisotropic semi-infinite plane with a hole has not been observed thus far. This paper presents an analytical solution for an orthotropic semi-infinite plane with an arbitrary-shaped hole, considering the condition of a uniform stress applied at the hole boundary. First, the shapes of holes on the z 1 - and z 2 -planes are determined based on the shape of the hole on the z -plane. Next, the regions outside the holes on these physical planes are mapped to the rings on the ζ 1 - and ζ 2 -planes, respectively. Therefore, the boundary conditions with ζ 1 and ζ 2 as independent variables are established. Then, the boundary collocation method is used to solve the stress boundary conditions along the upper boundary and the hole boundary, thereby obtaining two analytical functions for calculating the stress and displacement of the structure. Finally, the correctness of the proposed method is verified by conducting boundary condition checks and comparing the stress and displacement results obtained by the proposed method with those obtained from ANSYS, and further investigates the influence of anisotropic parameters on normal stress and displacement. [ABSTRACT FROM AUTHOR]

Torricelli's law states that the speed of a fluid as it flows out of an orifice of a large reservoir open to the atmosphere is equal to 2 g h , where g is the acceleration of gravity and h is the distance between the free surface of the fluid in the reservoir and the orifice. Obtaining the flow rate from this speed is not at all trivial, despite how simple the situation seems, as the fluid jet issued at the orifice does not have a constant cross section and the motion of the fluid near the orifice is not really known. Here, we use Torricelli's experiment as a practical way to illustrate how to properly apply the momentum balance equation to solve hydrodynamic problems. We compare the horizontal component of the force exerted by the container on the fluid using both momentum conservation and the integral of the stress tensor; this results in a contradiction that we use to review, after considering simple experimental results, the assumptions made during the calculations, to finally resolve the discrepancy and rationalize the intricacies of this every-day situation. Editor's note: This paper shows how a familiar example from introductory fluid mechanics—the speed of a fluid as it exits a hole—contains subtleties that can be used to illustrate more advanced concepts in fluid mechanics. You'll want to adopt this as an example when you teach about control volumes. [ABSTRACT FROM AUTHOR]

The rapidly advancing fields of machine learning and mathematical modeling, greatly enhanced by the recent growth in artificial intelligence, are the focus of this special issue. This issue compiles extensively revised and improved versions of the top papers from the workshop on Mathematical Modeling and Problem Solving at PDPTA'23, the 29th International Conference on Parallel and Distributed Processing Techniques and Applications. Covering fundamental research in matrix operations and heuristic searches to real-world applications in computer vision and drug discovery, the issue underscores the crucial role of supercomputing and parallel and distributed computing infrastructure in research. Featuring nine key studies, this issue pushes forward computational technologies in mathematical modeling, refines techniques for analyzing images and time-series data, and introduces new methods in pharmaceutical and materials science, making significant contributions to these areas. [ABSTRACT FROM AUTHOR]

Using attribute graphs for node embedding to detect community structure has become a popular research topic. However, most of the existing algorithms mainly focus on the network structure and node features, which ignore the higher-order relationships between nodes. In addition, only adopting the original graph structure will suffer from sparsity problems, and will also result in suboptimal node clustering performance. In this paper, we propose a hypergraph network embedding (HGNE) for community detection to solve the above problems. Firstly, we construct potential connections based on the shared feature information of the nodes. By fusing the original topology with feature-based potential connections, both the explicit and implicit relationships are encoded into the node representations, thus alleviating the sparsity problem. Secondly, for integrating the higher-order relationship, we adopt hypergraph convolution to encode the higher-order correlations. To constrain the quality of the node embedding, the spectral hypergraph embedding loss is utilized. Furthermore, we design a dual-contrast mechanism, which draws similar nodes closer by comparing the representations of different views. This mechanism can efficiently prevent multi-node classes from distorting less-node classes. Finally, the dual-contrast mechanism is jointly optimized with self-training clustering to obtain more robust node representations, thus improving the clustering results. Extensive experiments on five datasets indicate the superiority and effectiveness of HGNE. [ABSTRACT FROM AUTHOR]

To solve the problem of oscillation instability in permanent magnetic synchronous generator (PMSG)-based wind power connected systems during low-voltage ride through (LVRT) process, a parameter adjustment strategy based on interaction energy path optimization is proposed in this paper. Firstly, a modular state-space model of PMSG under fault transient conditions is constructed, and the system is divided into five subsystems. Then, the dynamic energy function of subsystems reflecting the oscillation stability of the system is derived. Based on that, the dynamic energy flow path is described considering the introduction of LVRT control. On this basis, the interaction energy between LVRT control links and subsystems is analyzed, and the coupling mechanism of voltage support and damping characteristics in the LVRT process is explained. Further, aiming at the optimal change rate of the total interaction energy in the LVRT process, the adjustment strategy of LVRT control parameters is constructed to meet voltage and damping requirements. Finally, a PMSG-connected system model is built on the MATLAB/Simulink platform to verify the effectiveness of the adjustment strategy. The results show that the proposed method can effectively improve the damping level under the fault transient condition, as well as supporting system voltage. [ABSTRACT FROM AUTHOR]

When scanning a document printed on both sides by using an electronic scanner, the printed material on the back (front) side may be transmitted to the front (back) side. This phenomenon is called show-through. The problem to remove the show-through from scanned images is called the show-through removal in the literature. In this paper, we propose a new method of show-through removal based on the following principle. The proposed method uses two scanned images with the front side and with the back side as input images. The proposed method is based on Ahmed's Blind Image Deconvolution method discovered in 2013, which succeeded in formulating Blind Image Deconvolution as a nuclear norm minimization. Since the structure of show-through removal resembles that of Blind Image Deconvolution, we discovered that the show-through removal can be reformulated into a nuclear norm minimization in the space of outer product matrix constructed from an image vector and a point spread function vector of blurring. Using this key idea, we constructed the proposed method as follows. First, our cost function consists of the following three terms. The first term is the data term and the second term is the nuclear norm derived from the above reformulation. The third term is a regularization term to overcome the underdetermined nature of show-through removal problem and the existence of noise in the measured images. The regularization term consists of Total Variation imposed on the images. The resulting nuclear norm minimization problem is solved by using Accelerated Proximal Gradient method and Singular Value Projection with some problem-specific modifications, which converges fast and requires a simple implementation. We show results of simulation studies as well as results of real image experiments to demonstrate the performances of the proposed method. [ABSTRACT FROM AUTHOR]

This paper discusses a case study of a zone-picking system in a distribution centre. In particular, we designed a basic simulation model for analysing the system using Simio and connected the model to the MySQL database using the existing and new steps in Simio. A ranking and selection problem was subsequently formulated to determine the capacity of the system that maximises the expected throughput while satisfying the constraints on: (1) the expected maximum utilisation, (2) expected time-averaged number of boxes in the main conveyor, and (3) expected flow time. To solve the problem under a limited simulation budget, we implemented an optimal computing budget allocation procedure, which may work in the presence of stochastic constraints. The experimental results demonstrate that our approach can more efficiently and effectively determine the capacity of the system than the equal allocation scheme. [ABSTRACT FROM AUTHOR]

With the continuous maturity of hyperspectral remote sensing imaging technology, it has been widely adopted by scholars to improve the performance of feature classification. However, due to the challenges in acquiring hyperspectral images and producing training samples, the limited training sample is a common problem that researchers often face. Furthermore, efficient algorithms are necessary to excavate the spatial and spectral information from these images, and then, make full use of this information with limited training samples. To solve this problem, a novel two-branch deep learning network model is proposed for extracting hyperspectral remote sensing features in this paper. In this model, one branch focuses on extracting spectral features using multi-scale convolution and a normalization-based attention module, while the other branch captures spatial features through small-scale dilation convolution and Euclidean Similarity Attention. Subsequently, pooling and layering techniques are employed to further extract abstract features after feature fusion. In the experiments conducted on two public datasets, namely, IP and UP, as well as our own labeled dataset, namely, YRE, the proposed DMAN achieves the best classification results, with overall accuracies of 96.74%, 97.4%, and 98.08%, respectively. Compared to the sub-optimal state-of-the-art methods, the overall accuracies are improved by 1.05, 0.42, and 0.51 percentage points, respectively. The advantage of this network structure is particularly evident in unbalanced sample environments. Additionally, we introduce a new strategy based on the RPNet, which utilizes a small number of principal components for feature classification after dimensionality reduction. The results demonstrate its effectiveness in uncovering compressed feature information, with an overall accuracy improvement of 0.68 percentage points. Consequently, our model helps mitigate the impact of data scarcity on model performance, thereby contributing positively to the advancement of hyperspectral remote sensing technology in practical applications. [ABSTRACT FROM AUTHOR]

The algorithm proposed in this paper aims at solving the problem of star map matching in high-limiting-magnitude astronomical images, which is inspired by geometric voting star identification techniques. It is a two-step star map matching algorithm relying only on angular features, and adopts a reasonable matching strategy to overcome the problem of poor real-time performance of the geometric voting algorithm when the number of stars is large. The algorithm focuses on application scenarios where there are a large number of dense stars (limiting magnitude greater than 13, average number of stars per square degree greater than 185) in the image, which is different from the sparse star identification problem of the star tracker, which is more challenging for the robustness and real-time performance of the algorithm. The proposed algorithm can be adapted to application scenarios such as unreliable brightness information, centroid positioning error, visual axis pointing deviation, and a large number of false stars, with high accuracy, robustness, and good real-time performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose an approach solving the holes problem in Wireless Sensor Networks (WSNs) based on Khalimsky k-Clustering and data routing protocol (MDKC). The aim of this solution is to establish optimized data routing paths between isolated nodes/clusters and the Sink in noisy environment with the presence of obstacles. This approach is an improvement of a previous work deploying stationary WSN not dealing with the problem of "holes". At first, the MDKC algorithm divides the WSN into k-hop (k ≥ 2) compact dynamic clusters. For each cluster, a node is elected cluster-head in its k-neighborhood according to some criteria such as the remaining energy, the k-degree and the communication probability average. Then, some nodes are selected as Khalimsky anchors to optimize the intra-cluster data routing process. The Khalimsky anchors at the border layers ensure the inter-cluster data routing between adjacent clusters. In the next phase of MDKC, Mobile Collectors (MCs) are used for data collecting and relaying from isolated nodes/clusters to the connected Khalimsky's nodes. The simulation results prove that MDKC minimizes the energy consumption and improves the connectivity rate between sensors and the delivery rate compared to some existing approaches. [ABSTRACT FROM AUTHOR]