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To enhance the quality and efficiency of computer-enabled generation of papers for Test for English Majors Band 8 (TEM-8), a paper generation model supported by sparrow search algorithm-genetic algorithm was studied. First, a simplified test paper generation mathematical model was set up after analyzing and studying types and characteristics of TEM-8 tasks. In the model, quantity, type, difficulty, discrimination degree, scores, exposure, and answering time of test questions were taken into consideration. To enhance the optimizing effect of the genetic algorithm for searching test questions, the traditional genetic algorithm was improved by introducing the sparrow search algorithm into the model to achieve a better crossover rate, variance rate, optimization precision, and speed of the genetic algorithm. A new sparrow search-genetic algorithm (SSA-GA) was designed, and the optimizing effect of SSA-GA was verified to be ideal through optimizing six standard test functions. Then, SSA-GA was applied to conduct experimentation with test paper generation, and comparison with traditional genetic algorithms was also made. The values of best and average fitness of SSA-GA were better than those of the traditional genetic algorithm (GA) in the paper generation. Exposure rate and success rate in TEM-8 paper generation of SSA-GA were higher than those of traditional GA in TEM-8 paper generation. Results showed that the studied SSA-GA could implement test paper generation with higher speed and better quality. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study and analysis of the intelligent grouping of papers in adult higher education using a random matrix approach. Using the results of random matrix theory on the eigenvalues of the sample covariance matrix, the energy of each subspace is estimated, and the estimated energy is then used to construct a subspace weighting matrix. The statistical properties of the sample covariance matrix eigenvectors are analyzed using the first-order perturbation approximation, and then, asymptotic results from random matrix theory on the projection of the sample covariance matrix signal subspace to the real signal parametrization are used to obtain the weighting matrix based on the random matrix eigenvectors. Dynamic adjustment according to the fitness of individuals in the population is performed to ensure population diversity, while the combination of the small habitat technique can avoid the algorithm from falling into early convergence. The algorithm introduces chaos theory to optimize the population initialization process and uses the dynamic traversal randomness of chaos to select individuals in the population so that the initial population is close to the desired target solution. The design of the fitness function in the genetic algorithm generally maps the objective function of the problem to the fitness function. A good fitness function can directly reflect the quality of the individuals in the group. Based on the in-depth study of the basic attributes of the test questions and the principles of test paper evaluation, the mathematical model and objective function of intelligent paper grouping are determined by the difficulty, knowledge points, and cognitive level of the test questions as the main constraints, and NCAGA is applied to the intelligent paper grouping method, which better completes the intelligent paper grouping session for the computer system architecture course. In the process of designing the intelligent grouping algorithm, for the situations of premature convergence and convergence to locally optimal solutions that easily occur in the traditional genetic algorithm, this paper adopts the approach of adaptive adjustment of crossover probability and variation probability to improve the algorithm and achieves satisfactory results. Based on extensive business research, this paper completes the requirement analysis of the online practice system based on the intelligent grouping of papers and presents the functional design and database design of the key functional modules in the system in detail. Finally, this paper conducts functional tests on the system and analyses the test results. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of the Internet industry, the publicity effect of paperbark walnuts has been substantially increased, and the consumption and demand of paperbark walnuts have been rising, and the output of paperbark walnuts in China has also increased year by year. This paper takes the panel data of the top three prefecture-level cities in each GDP ranking of five provinces in northwest China from 2012 to 2021 as the sample and combines the DEA model with Malmquist to evaluate and analyze the logistics efficiency of paper walnuts in northwest China. It is found that the overall logistics efficiency of paperback walnuts in Northwest China has been improved in recent years, but there are still areas that need to be improved, such as the factor distribution is not reasonable enough; the logistics efficiency of paperback walnuts in Northwest China from 2012 to 2021 is in a fluctuating state; the logistics efficiency of paperback walnuts in Shaanxi Province is better than the other provinces and is in the leading position; the improvement of technological progress has a key role in the total factor productivity improvement. As a result, this paper puts forward five specific suggestions based on the research results to help accelerate the improvement of paper walnut logistics efficiency in Northwest China. [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]

Rewinding machines are used in the paper converting industrial sector to unroll paper veils from large source reels and wind them around smaller rolls, for the production of paper logs to be used in household activities. Many different types of rewinding machine exist, and they are mostly based on stapling devices which automatically unroll a large coil of paper, roll it up around a new log, stop the veil allowing it to be torn off, unload the completed paper log, and reload a new paper core to be rolled up. Winding units generally include a primary and a secondary roller, supplying the desired rotational speed for winding, and a pressure unit consisting of a pressure roller in contact with the paper log being rolled up, which assures adequate containment and avoids the paper log to escape. All known systems have some mechanical limitations, mainly due to the contact of the pressure roller with the paper log, causing instabilities and vibrations, particularly when working with soft paper veils. This limits the maximum possible winding speed. The aim of this paper is it to present a detailed kinematic analysis of a novel pressure unit which overcomes the aforementioned limitation. [ABSTRACT FROM AUTHOR]

In order to improve the effect of Chinese dance training, this paper combines digital feature recognition technology to correct and analyze Chinese dance training movements and constructs an intelligent auxiliary training system. In order to solve the travel time problem at the computationally complex interface and ensure its computational accuracy, a local adaptive triangulation technique is used in the fast-advance algorithm. Moreover, this paper designs the function of the system according to the user's needs, transforms the design concept into a figurative visual representation through the interactive prototype according to the function, carries out the visual design of the interface according to the interactive prototype, and uses the interactive technology to realize the development of the system. From the test analysis results, it can be seen that the Chinese dance training action correction system based on the digital feature recognition technology proposed in this paper has a good effect and can effectively promote the improvement of the Chinese dance training effect. [ABSTRACT FROM AUTHOR]

A security model of multimedia education resource fusion based on multistage integration of multimedia educational resources is constructed in this paper in the context of smart education. First, the teaching model of multimedia education resource fusion is analyzed and the IoT communication evolution model and functional architecture model are constructed accordingly, based on which the realization path of IoT intelligent sensing, intelligent management, emotional computing, device sharing, and vision simulation functions for smart education are discussed. The security model uses a decentralized cryptographic data security sharing method based on blockchain for protection and supports blockchain to record user attributes to ensure the confidentiality and integrity of data. The experimental and analytical results indicate that the scheme can effectively reduce the computational overhead of smart devices while ensuring the security of data sharing. Moreover, the proposed model in the paper achieves better performance than other methods in terms of security strength, encryption, and decryption time. [ABSTRACT FROM AUTHOR]

China, situated in the circum-Pacific seismic belt, experiences frequent seismic activity and faces diverse geological conditions, making structural stability of paramount importance, especially under seismic conditions. The majority of current earthquake generation methods do not consider the nonstationary nature of earthquakes. This paper introduces a spectral representation-random function model for generating nonstationary earthquakes, effectively simulating stochastic seismic ground motion. Furthermore, traditional slope stability analysis methods are deterministic and incapable of providing probabilistic assessments of slope instability. Therefore, this paper proposes a unified framework for static and dynamic structural reliability analysis based on the direct probability integration method, quantifying the impact of stochastic seismic ground motion on the dynamic reliability of slope stability. Finally, the proposed methods are applied to an excavation slope in Nanjing, using sliding displacement and safety factors as evaluation criteria to study the reliability of the slope under the influence of stochastic seismic events. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive speed control strategy for a surface-mounted permanent magnet synchronous motor by applying Laguerre functions. The model predictive controller (MPC) incorporates an integrator. A quadratic programming procedure is applied to solve the constrained optimization problem online. The paper also provides a solution for stability. The performance efficiency of the proposed scheme is validated by comparing the results with the performance of an optimal linear quadratic regulator, conventional state-space model predictive control, and a simple MPC algorithm with integral action. Extensive simulation results confirm the efficacy of the proposed scheme, showing that it achieves good steady-state performance while maintaining a fast dynamic response. [ABSTRACT FROM AUTHOR]

Vehicle antilock systems play a very important role in the stability and reliability during vehicle braking. Due to the complexity of the braking process, antilock braking system (ABS) usually face the problems such as nonlinearity, time-varying, and uncertain parameter modeling. Thus, aiming at the parameter model uncertainty problem of ABS, an adaptive neural network sliding mode controller (ADRBF-SMC) is designed in this paper. On this basis, establishing the quarter-vehicle model and the seven-degree-of-freedom vehicle model, and treating the difference between the two models as a kind of disturbance, carrying out vehicle braking performance simulation experiments to analyze the variation of braking performance parameters such as vehicle and wheel speeds, slip ratio, braking distance, braking torque, under the three cases of adaptive neural network sliding mode controller, traditional sliding mode controller, and no control. Simulation results show that the adaptive neural network sliding mode controller (ADRBF-SMC) proposed in this paper can play an effective control role in both vehicle dynamics models. In addition, the control method proposed in this paper has stronger anti-interference capability and higher robustness compared with the sliding mode controller (SMC). [ABSTRACT FROM AUTHOR]

In a democratic regime, voting is crucial to making collective decisions. Unfortunately, although this activity has great significance and value, little effort has been made to improve the way we vote. Paper ballots are still the most used method, although this method is relatively simple, brings many inconveniences, and represents a contradiction to the modern world and its advances. This paper mostly focuses on a review study of blockchain-based voting systems. It aims at identifying the strategies and the guidelines as well as provides a comprehensive end-to-end electronic voting system based on blockchain, with the help of cryptographic techniques such as zero-knowledge proofs to improve privacy. The novelty of this paper is that we tackle the limitations of electronic voting systems found in the literature, including cost, identity management, and scalability problems. Our purpose is to provide key elements for organizations on how to design their proper electronic voting system based on blockchain technology. [ABSTRACT FROM AUTHOR]

Localization is an important method for autonomous indoor robots to recognize their positions. Generally, the navigation of a mobile robot is conducted using a camera, Lidar, and global positioning system. However, for an indoor environment, GPS is unavailable. Therefore, a, state-trajectory tracking method is utilized based on a Lidar map. This paper presents the path following of an autonomous indoor mobile robot, that is, a shuttle robot, using a state-flow method via a robot operating system network. MATLAB and Linux high-level computers and an inertial measurement unit sensor are used to obtain the Cartesian coordinate information of a bicycle-type mobile robot. The path following problem can be solved in the state-flow block by setting appropriate time and linear and angular velocity variables. After the predetermined time, the linear and angular velocities are set based on the length of the path and radius of the quarter-circle of the left and right turns in the state-flow block, path planning, which can execute the work effectively, is established using the state-flow algorithm. The state-flow block produces time-series data that are sent to Linux system, which facilitates real-time mobile platform path following scenario. Several cases within the path-following problem of the mobile robot were considered, depending on the linear and angular velocity settings: the mobile robot moved forward and backward, turned in the right and left directions on the circular path. The effectiveness of the method was demonstrated using the desktop-based indoor mobile robot control results. Thus, the paper focuses on the application of the state-flow algorithm to the shuttle robot specifically in the narrow indoor environment. [ABSTRACT FROM AUTHOR]

In seismic data processing, data recovery including reconstruction of the missing trace and removal of noise from the recorded data are the key steps in improving the signal-to-noise ratio (SNR). The reconstruction of seismic data and removal of noise becomes a sparse optimization problem that can be solved by using sparse regularization. Sparse regularization is a key tool in the solution of inverse problems. They are used to introduce prior knowledge and make the approximation of ill-posed inverses feasible. It deals with ill-posedness by replacing an ill-posed inverse problem with a well-posed problem that has a solution close to the true solution. In the last 2 decades, interest has shifted from linear toward nonlinear regularization methods even for linear inverse problems. In inverse problems, regularizations serve as stabilizing the solution of ill-posed inverse problems and give a solution that adequately fits measurements without producing unjustifiably complex artifacts. In this paper, we present a novel sparse regularization based on a tensor-based dictionary method for inverse problems (seismic data interpolation and denoising). This regularization avoids the vectorization step for sparse representation of seismic data during the reconstruction process. The key step in sparsifying signals is the choice of sparsity-promoting dictionary learning. The learning-based approach can adaptively sparsify datasets but has high computational complexity and involves no prior-constraint pattern information for the dataset. Many existing dictionary learning methods would be computationally infeasible for the high dimensional seismic data processing. These methods also destroy the essential information as well as it reduces the discriminability and expressibility of the signal, since they deal with vectorization. In this paper, the orthogonal tensor dictionary learning that learns a dictionary from the input data by employing orthogonality and separability is proposed as sparse regularization for the inverse problems. The performance of the proposed method was validated in seismic data interpolation and denoising individually as well as simultaneously. Numerical examples of synthetic and real seismic datasets demonstrate the validity of the proposed method. The SNR of the recovered data confirms that the proposed method is the most effective method than K-singular value decomposition and orthogonal dictionary learning methods. [ABSTRACT FROM AUTHOR]

This paper presents a new method for calculating the insulation health index (HI) of oil-paper transformers rated under 110 kV to provide a snapshot of health condition using binary logistic regression. Oil breakdown voltage (BDV), total acidity of oil, 2-Furfuraldehyde content, and dissolved gas analysis (DGA) are singled out in this method as the input data for determining HI. A sample of transformers is used to test the proposed method. The results are compared with the results calculated for the same set of transformers using fuzzy logic. The comparison results show that the proposed method is reliable and effective in evaluating transformer health condition. [ABSTRACT FROM AUTHOR]

The spectral radius of a square matrix is the maximum among absolute values of its eigenvalues. Suppose a square matrix is nonnegative; then, by Perron–Frobenius theory, it will be one among its eigenvalues. In this paper, Perron–Frobenius theory for adjacency matrix of graph with self-loops A G S will be explored. Specifically, it discusses the nontrivial existence of Perron–Frobenius eigenvalue and eigenvector pair in the matrix A G S − σ n I , where σ denotes the number of self-loops. Also, Koolen–Moulton type bound for the energy of graph G S is explored. In addition, the existence of a graph with self-loops for every odd energy is proved. [ABSTRACT FROM AUTHOR]

The gearbox seal system is a critical component of the electrical multiple units drive system, with a direct impact on the safety and reliability of train operations. This study intends to analyze how structural parameters of the gearbox seal system influence the seal performance from the viewpoint of "oil–air separation and scavenge oil" by using a discrete phase model and volume of fluid. The results show that the oil–air separation performance is mainly affected by the oil droplets' inertia force and the airflow traction force, and the scavenge oil performance is decided by the oil–air separation performance of each seal chamber. The relationships between seal chamber size, axial seal clearance width, height difference, scavenge oil hole diameter, and seal performance are analyzed, and the study also found that optimizing the stator chamber depth–width ratio of the seal system studied in this paper at 2.5, reducing the rotor chamber depth–width ratio and axial seal clearance width, and increasing the relative height difference can improve the seal performance. And increasing the scavenge oil hole diameter also enhances the seal system's performance. The simulation results can be used as design references for gearbox seal systems. [ABSTRACT FROM AUTHOR]

This research paper presents a new iterative method (NIM) for obtaining the solution to the potential Kadomtsev–Petviashvili (PKP) equation. NIM is a promising approach to solving complex mathematical problems, and its effectiveness and efficiency are highlighted through its application to the PKP equation. The results obtained through the use of NIM are compared to the exact solutions of the PKP equation, and it is found that the NIM approach provides results that are in close agreement with the exact solutions. This demonstrates the utility and accuracy of NIM and makes it a valuable tool for solving similar mathematical problems in the future. Furthermore, the lack of discretization in the NIM approach makes it a more convenient method for solving the PKP equation compared to traditional approaches that require discretization. Overall, the findings of this research paper suggest that NIM is a highly effective and convenient method for obtaining approximate analytical solutions to complex mathematical problems, such as the 2 + 1 -dimensional PKP equation. [ABSTRACT FROM AUTHOR]

This paper presents a control strategy for a photovoltaic (PV)-based distributed generator (DG) to enhance the frequency regulation of a microgrid. A microgrid is a small-scale power system that integrates distributed energy sources and interconnected loads, which can operate independently or in parallel with the main grid. However, the high integration of DGs poses stability problems, particularly the frequency regulation under contingencies in the microgrid. For example, the shutdown of one generating unit or the reduction of system inertia owing to the high integration of converter-based DGs will increase the risk of instability of the microgrid and the main grid. Therefore, maintaining and increasing the frequency regulation capability is crucial in microgrid operation. This paper focuses on developing a controller for PV DG based on virtual inertia control combined with the conventional control method of a converter in order to regulate the microgrid frequency under contingency. The proposed control strategy for a PV-based DG is then verified through simulation of the 14-bus microgrid model using MATLAB/Simulink, showing regulation in frequency under island mode operation of the microgrid. In addition, comparisons between the original and proposed control strategy microgrid model are then carried out. [ABSTRACT FROM AUTHOR]

The problems of operating range and costs are the two most critical bottlenecks restricting the extensive application of electric vehicles (EVs) in China and some other countries. There are also some prominent problems in China's EVs, which lead to poor competitiveness of EVs compared with traditional internal combustion engine vehicles. This paper analyzes the key mathematical factors restricting the development and popularization of EVs in China from the aspects of strategic policy, sales situation, and self-problems. For the best-selling family cars in China, select the representative models with the same body structure, and make statistics on various parameters most concerned by consumers, establish a database through data statistics and analysis software. Through summarizing France's standardization development strategy and relevant issues reflected by the development data of its EVs, this paper puts forward the hypothesis leading the development of EVs through standardization to enhance their competitiveness, gives the specific suggestions, and briefly analyzes the feasibility from the aspects of product situation. The research content of this paper provides a certain basis and ideas for the future research work. [ABSTRACT FROM AUTHOR]

In the consumer goods supply chain, there are three different modes of demand: real, false, and semireal. It is an interesting topic to discuss the optimal profit of enterprises according to these three demand patterns. This paper develops three mathematical models that can be used to investigate the factors that affect retailers with respect to the design of product supply strategies for individual customers under a semireal demand pattern and thereby addresses the problem of retailers' maximum profitability. The results of the present study show that these models may effectively help retailers develop appropriate supply strategies for individual customers under a semireal demand pattern; in turn, this may help retailers improve operational performance. The main contribution of the current study lies in the construction of mathematical models of product supply strategies for the individual customer in the off-invoice mode, the scan-back mode, and the unsold-item processing mode under the semireal demand pattern. The effectiveness of the models has been verified through numerical calculations. In concrete management practice, the mathematical model given in this paper can be used to effectively adjust the quantity of goods purchased, correct retail prices, and optimize sales discounts to maximize profits. [ABSTRACT FROM AUTHOR]

This paper offers a system for an electric vehicle. It consists of digitally controlling an induction motor without using a speed sensor. The machine is powered by a five-level cascading H-bridge inverter. The SVM control principle is used to manage the status of the five-level inverter; this removes harmonics. The H-bridge inverter converter is powered by photovoltaic sources via a serial converter, using the maximum power point tracker control principle. This structure can also reduce shading losses. In the absence of a mechanical sensor, a dynamic model of the asynchronous machine is utilized with the state variables defined in the stator reference frame. The state vector consists of the components of the rotor flux and stator current. The article provides a comparison of two methods widely used on an induction motor drive. The adaptive model-reference system method and Luenberger observer are evaluated using an active control strategy to reject disturbances to minimize the impact of disturbances. The operating principles of each method are described, and the mathematical models of training systems are developed. Both methods provide a promise for high-speed estimate applications in simulation environments. The simulation results obtained show the correct operation of both observers. Perfect decoupling between the velocity and flow control loops is observed, taking into account any disturbances that may affect the system. [ABSTRACT FROM AUTHOR]

It is difficult to give a fractional global threshold (FGT) that works well on all images as the image contents are totally different. This paper describes an interesting use of fractional calculus in the field of digital image processing. In the proposed method, the fractional global threshold-based edge detector (FGTED) is established using the Riemann–Liouville fractional integral operator. FGTED is used to find the microedges in minimum time for any input digital images. The results demonstrate that the FGTED outperforms conventional techniques for detecting microtype edges. The image with a higher entropy was produced by the FGT value-based approach. Tables and images are used to summarize the output performance analysis of various images using structural similarity index measure, F-score (F-measure), precision and recall, signal-to-noise ratio, peak signal-to-noise ratio, and computational time. The FGTED can be used to detect very thin or microtype edges more accurately in minimum time without training or prior knowledge. [ABSTRACT FROM AUTHOR]

Digital twin (DT) is a prolific buzzword in this era, where digitization plays a significant role. The perception of solar energy harvesting has been gaining popularity with the advent of solar panels. Solar asset maintenance is a need of the hour for investors because of the smart city scheme and green building certificate evaluation for all industries, educational institutions, etc. Among the list of factors that reduce PV system efficiency, the issue of partial shading is a vital distress that must be resolved. This paper focuses on the development of a digital twin framework that is proactively driven by shading patterns and a proposed optimization-based reconfiguration embedded controller that electronically relocates the panel in the physical world. The real-time system has been created for a three-by-three series parallel panel arrangement. The proposed switching matrix controller achieves the maximum power, i.e., 40% of increased power output, row current difference is made almost zero from 3 to 4 A, and fill factor increases by 20% by reconfiguring the solar array. It is done based on a decision taken by a nature-inspired (equal irradiation distribution algorithm) or puzzle-based (skyscraper) optimization algorithm. Switching matrix controllers overcome the disadvantages of physical relocation. The users can query the digital twin build to know the historical performance and current operating conditions of the system. It can trigger alarms as early warnings and make predictions about possible system anomalies, if and when they occur using a digital twin. [ABSTRACT FROM AUTHOR]

The ball and beam experimental platform is an unstable nonlinear system widely used as a benchmark control setup for testing different controller approaches, especially for beginners on automatic control to improve their control knowledge skills. In this paper, we innovate it by governing the angular position of the beam with a twin-rotor system. Our experiment consists of a beam that rotates through a pivot, in which two propellers are attached to the ends of this beam. Hence, we have a recent one-degree aerial device, and instead of using a ball, we employ a mass moving on the beam, presenting friction on position to its movements on the beam. Then, the control objective is to regulate the mass position at some predefined zone on the beam, ensuring stability and robustness in front of external perturbations and unmodeled uncertainties. To do so, we define a classical PI controller. To assess closed-loop robustness, a mass was introduced to one propeller to induce perturbation, thereby simulating modeling variations or disturbances. The experimental results prove the goodness of our experimental platform for drone applications. [ABSTRACT FROM AUTHOR]

The edge k -labeling ψ of G is defined by a mapping from E G to a set of integers 1 , 2 , ... , k , where the integer weight assigned to the vertex x ∈ V G is given as w ψ x = ∑ ψ x y , such that the sum is taken over every vertex of y ∈ V G that is adjacent to x and the integer weights of adjacent vertices must be distinct for all vertices with x ≠ y. An irregular assignment of G using atmost k labels which is considered to be a minimum k is defined as irregularity strength of a graph G and can be denoted as s G . There are also further works on familiar irregular assignments, such as edge irregular labelings, vertex irregular total labelings, edge irregular total labelings, and face irregular entire k -labelings of plane graphs. A plane graph can be defined as a graph that is embedded in the plane in which no two lines will be intersected. In a plane graph the number of regions present are called faces and we denote it as F. The concept of total face irregularity strength is defined by the motivation of irregular networks and entire irregular face k -labeling. In our paper, we have obtained a minimum bound for the total face irregularity strength of two-connected plane graphs like cycle-of-ladder, C -necklace graph, P -necklace graph, sibling tree, and triangular graph. [ABSTRACT FROM AUTHOR]

Analyzing monitoring data to recognize structural anomalies is a typical intelligent application of structural safety monitoring, which is of great significance to hydraulic engineering operational management. Many regression modeling methods have been developed to describe the complex statistical relationships between engineering safety monitoring points, which in turn can be used to recognize abnormal data. However, existing studies are devoted to introducing the correlation between adjacent response points to improve prediction accuracy, ignoring the detrimental effects on anomaly recognition, especially the pseudo-regression problem. In this paper, an anomaly recognition method is proposed from the perspective of causal inference to realize the best exploitation of various types of monitoring information in model construction, including four steps of constructing causal graph, regression modeling, model interpretation, and anomaly recognition. In regression modeling stage, two deconfounding machine learning models, two-stage boosted regression trees and copula debiased boosted regression trees, are proposed for recovering the causal effects of correlated response points. The validation was carried out with Shanmen River culvert monitoring data, and experiment results showed that the proposed method in this paper has better anomaly recognition compared to existing regression modeling methods, as shown by lower false alarm rates and lower averaged missing alarm rates under different structural anomaly scenarios. [ABSTRACT FROM AUTHOR]

With the development of virtual reality technology, people are increasingly aware that the combination of virtual reality technology and product design can enable companies to obtain stable profits and maintain long-term competitive advantages. And design evaluation plays a pivotal role in a large number of important decisions in product development. This article takes Lingnan culture and cultural products as an example, combining virtual reality technology with the design of Lingnan cultural and creative products, verifies the effectiveness and rationality of the virtual evaluation system of Lingnan cultural products through the evaluation of examples of Lingnan cultural products, and proposes amendments to the evaluation case. This paper constructs a product design evaluation system based on virtual reality technology and designs a theoretical model VR, which is the application of virtual reality technology in product design evaluation. The PDES system expounds the idea and method of constructing the system model. This paper studies the evaluation object, evaluation content, evaluation method, evaluation platform, and manifestation of evaluation results of the Lingnan cultural product virtual evaluation system and builds the framework of the Lingnan cultural product virtual evaluation system. This paper studies the product display method based on virtual reality technology and realizes the three-dimensional display of products on the Internet and the design of user interaction in virtual reality. In this paper, the two algorithms BRSK and SURF are used together, and the multiscale expression characteristics of BRISK in space and the rotation invariant characteristics of SURF are used. Experimental research shows that, compared with the pure BRISK algorithm, the rotation performance of the method in this paper can be seen through the experimental results in this paper to have better accuracy. The method in this paper ensures the accuracy and accuracy of matching as much as possible. [ABSTRACT FROM AUTHOR]

The availability of resources regarding budget and personnel for infrastructure development in the public sector is increasingly scarce, prioritizing personnel payments and basic expenses. Therefore, there is a need to prioritize the design of maintenance, refurbishment, and construction projects. Also, it is necessary to validate their impact and necessity before allocating public resources. The main contribution of this paper is a framework proposed to handle these issues frequently found in the public sector with a multicriteria approach, considering strategic and service aspects. To illustrate the proposed model, we considered the context of a federal institution of higher education that offers an infrastructure for about forty thousand students. This public institution is affected by issues of conjuncture and opportunities arising from political moments that hinder consistent programming for better allocation of these resources. Similarly to other institutions in the public sector, the chosen one has to cope with several demands for construction projects. However, limited resources and personnel to carry out those project designs are subject to the uncertainty of not having the financial resources to implement these construction projects. Or even worse, when unforeseen, funds to carry out specific projects arise without a feasible time to design those construction projects that the public institution did not prioritize. Thus, another contribution of the decision model proposed in this paper is to enable the department responsible for projects to establish the ranking for the project design, considering the multiple criteria that reflect the institution's needs. Among the advantages perceived from this real application, there is a priority ranking resulting from a mathematical model based on the TOPSIS method, which is easy to understand. Furthermore, the proposed approach gives the transparency and reproducibility required in such a public and democratic environment, enabling transparency on the criteria considered in the allocation process for all personnel involved considering the actors that manage the resources and those which demand the project design services. [ABSTRACT FROM AUTHOR]

This paper aims to study the law of pressure behavior and the characteristics of mutual influence in the process of steeply inclined coal seam mining. Based on the geological and engineering conditions of the B8 coal seam in Nanshan coal mine, this paper explores the law of mine pressure behavior in the process of steeply inclined coal seam mining by means of theoretical analysis and FLAC3D numerical simulation. Based on this, this paper also examines the influence of the interaction range between coal seams, the reasonable setting size of the section coal pillar, and the mining distance of the working face on the return air roadway. The results suggest as follows: (1) With an increase in the coal seam dip angle, the difference in the abutment pressure plastic zone between upper and lower coal pillars increases, and when mining in steeply inclined coal seams, the stress concentration in the lower and upper part of the working face deviates to the roof and floor respectively, which is obviously different from that of gently inclined coal seams. (2) The vertical stress above the section coal pillar changes in three stages with an increase in the width of the coal pillar, and the width of the coal pillar in the second stage can be taken as the reasonable size of the width of the coal pillar. The numerical simulation results are consistent with the theoretical calculation results, which indicate the high reliability of this method in determining the reasonable coal pillar size. (3) With an increase in the mining distance in steeply inclined coal seams, the maximum pressure around the working face and above the return air roadway has gone through three stages. When the distance between the working face and the adjacent return air roadway is close, the pressure above the return air roadway increases sharply, and disturbance is stronger. [ABSTRACT FROM AUTHOR]

With the development of smart classrooms, analyzing students' emotions for classroom learning is an effective means of accurately capturing their learning process. Although facial expression-based emotion analysis methods are effective in analyzing classroom learning emotions, current research focuses on facial expressions and does not consider the fact that expressions in different postures do not represent the same emotions. To provide a continuous and deeper understanding of students' learning emotions, this study proposes an algorithm to characterize learning emotions based on classroom time-series image data. First, face expression data for classroom scenarios are established to address the lack of expression databases in real teaching environments. Second, to improve the accuracy of facial expression recognition, a residual channel cross transformer masking net expression recognition model is proposed in this paper. Finally, to address the problem that the existing research dimension of learning emotion is too single, this paper uses the facial expression and head posture data obtained from deep learning models for fusion analysis and innovatively proposes a Dempster–Shafer evidence-theoretic fusion model to characterize the learning emotion within the lecture duration of knowledge points. The experiments show that both the proposed expression recognition model and the learning sentiment analysis algorithm have good performance, with the expression recognition model achieving an accuracy of 73.58% on the FER2013 dataset. The proposed learning emotion analysis method provides technical support for holistic analysis of student learning effects and evaluation of students' level of understanding of the knowledge points. [ABSTRACT FROM AUTHOR]

The variation of frequency is a significant indicator of the operation status of rotating machinery. Generally, the frequency is extracted and tracked in real-time based on the vibration signal produced by the rotating machinery. However, various interferences generated from contact sampling and transmitting result in difficulty in obtaining the frequency correctly in real-time from the vibration. To solve this problem, this paper presents an interpolated Kalman filter (IKF) based on the vibration and surface noise signals for real-time frequency tracking. First, the cross-correlation operation is performed on the vibration and surface noise signals sampled synchronously to enhance the energy concentration. After that, a frequency search procedure is carried out to calculate the input of the tracking task. Finally, an IKF-based frequency lock procedure is applied to eliminate the interferences and track the frequency in real time. Besides, a correction procedure is added to prevent the measurement process from tracking the frequency incorrectly. The performance of the proposed method is verified by the experiments based on an ARM-based test bench using standard signals and actual test signals. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have the advantages of resilience, global optimization capacity, and coding flexibility, making them helpful in tackling difficult optimization issues. The enhanced wind-driven optimization (CHTWDO) that was proposed in this paper coupled the chaotic map approach and the hyperbolic tangent with the T-distribution mutation method. The initial air particles are evenly distributed in the system space through a tent mapping strategy. Meanwhile, the variation probability of the hyperbolic tangent model and the T-distribution variation method are used to improve the comprehensive performance of the algorithm. In this way, the global search accuracy and the ability of avoiding the extreme value of the algorithm can be taken into account. Combining the three strategies, CHTWDO had higher global search accuracy and a stronger ability to jump out of local extremum. Comparing with the eight meta-heuristic algorithms (including WDO) and the single strategy improved WDO on 24 test functions, the experimental results show that CHTWDO with two improved strategies has better convergence precision and faster convergence speed. Statistical tests such as Friedman's and Wilcoxon's rank-sum tests are used to determine significant differences between these comparison algorithms. Finally, CHTWDO also obtains the best results on four classical optimization problems in engineering applications, which verifies its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

As a kind of thermal control device, fluid loop systems must operate with the demands of high safety, high reliability, and long life. In order to accurately assess the health status of fluid loop systems, a hierarchical multi-information fusion (HMIF) method is proposed in this paper. Considering that fluid loop systems generally have distinct structural hierarchies, the health evaluation process in this method is divided into three levels, which are the indicator level, the component level, and the system level. In the evaluation process, the health indices are, respectively, constructed to quantify the health status at the three levels. At the indicator level, One-Class support vector machine algorithm is used to obtain the distribution space of each state monitoring indicator under a normal state. The indicator-level health indices are evaluated by calculating the ratio of the data located in the distribution space to the overall data. At the component level, a fuzzy theory is used to calculate the health indices of the component level. Health indices at the indicator level are first converted to membership degree by membership degree function. Then, the evaluation fusion strategy is used to deduce the membership degree of the component level. The health indices at the component level are obtained from the mapping relationship between the membership degree and the health index. At the system level, an adaptive weight adjustment strategy is proposed to fuze all component-level health indices. Taking a practical fluid loop system as an example, health indices at the three levels evaluated by the HMIF method are compared with the actual status. The results indicate that the proposed method can correctly judge the health state of the system and provide a reference for the maintenance and fault diagnosis of fluid loop systems. [ABSTRACT FROM AUTHOR]

Venture capitalists invest not only in the business aspect of a deal but also in its contractual terms. Therefore, the selection of deals and the combination of contractual terms pose challenging decisions for them. This paper consists of two main sections. The first section introduces a novel framework for the valuation of venture capital (VC) deals, including startups and their contractual terms. By taking into account risk situations, this section presents the valuation of combined contractual terms, including call options, liquidity preference, and participant rights. In the second section, a new multiobjective mathematical model for VC deals and contractual terms portfolio selection is developed using right-tail probability, strategy alignment, and a utility function. To solve the proposed model, three metaheuristic algorithms—Non-Dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Binary Harmony Search Algorithm, and Dynamic Tuning Parameter Binary Harmony Search Algorithm (DTPBHS)—are applied. Based on numerical examples, DTPBHS outperforms other algorithms in the "Mean Ideal Distance" index, but NSGA-II demonstrates the best performance in the "Rate of Achievement of two objectives simultaneously" index. Furthermore, we demonstrate that the proposed utility function is more robust than the right-tail probability function under default deals conditions. [ABSTRACT FROM AUTHOR]

This paper addresses the preprocessing of event sequences issued from cyclic discrete event processes, which perform activities continuously whose delimitation of jobs or cases is not explicit. The sequences include several occurrences of the same events due to the iterative behaviour, such that discovery methods conceived for workflow nets (WFN) cannot process such sequences. In order to handle this issue, a novel technique for splitting a set of long event traces S = {Sk} (|S| ≥ 1) exhibiting the behaviour of cyclic processes is presented. The aim of this technique is to obtain from S a log λ = {σi} of event traces representing the same behaviour, which can be processed by methods that discover WFN. The procedures derived from this technique have polynomial-time complexity. [ABSTRACT FROM AUTHOR]

In this paper, we consider the stabilization of the coupled heat and wave equations under the static feedback or the dynamic feedback. Moreover, we make the coupled systems discretized by using the finite-volume approach, and then we consider the stabilized properties of the discrete systems. First, for the coupled system under the static feedback, it is shown that the system is exponentially stable by using the Lyapunov method, and then the corresponding discrete system can be shown to be exponentially stable by constucting the discretized Lyapunov function. Second, for the coupled system under the dynamic feedback, we also show that both of the system and its discrete scheme are exponentially stable. Third, numerical simulations are given to show the effectiveness of the stable controllers. [ABSTRACT FROM AUTHOR]

This paper proposes an interactive two-archive method to solve many-objective optimization problems. Two updating strategies based on the aggregation-based framework are presented and incorporated into a two-archive framework. In addition, we further extend this method by introducing an interactive mechanism in which evolutionary information is passed from the diversity archive to the convergence archive. Given the requirement to balance convergence and diversity, a mating selection method is proposed to regulate the evolutionary speed of these two archives collaboratively. The proposed algorithm has been tested extensively on several problems with different peer algorithms to validate its effectiveness. The results show that the proposed method can outperform several state-of-the-art evolutionary algorithms for handling many-objective optimization. [ABSTRACT FROM AUTHOR]

Supply chain finance plays a crucial role as a financing channel for small- and medium-sized enterprises (SMEs). However, issues such as financial problems and credit defaults have led to disruptions in this channel. To address credit risk control in SME financing within the field of supply chain finance, this paper focuses on a sample of 506 equipment manufacturing companies listed on the SME board of the Shenzhen Stock Exchange from 2016 to 2020. Taking into consideration, the overall risks faced by these enterprises, the study establishes seven first-level indicators and identifies 84 candidate second-level indicators. Partial correlation and variance analysis are then used for the first round of indicator screening, followed by the use of a BP neural network for the second round of selection. As a result, a system of 26 indicators for supply chain financial risk is constructed. The XGBoost model is employed to evaluate the constructed risk index system, while SVM and random forest models are used as comparison models. Bayesian optimization is utilized for parameter tuning of the three models. Empirical results demonstrate that the BO-XGBoost model reduces prediction errors in comparison to the control models. Furthermore, statistical tests reveal that the predicted values of the BO-XGBoost model significantly differ from those of the other control models. Compared to other individual models, the BO-XGBoost model exhibits increased accuracy in credit risk prediction and a significant discriminative effect. These findings highlight the effectiveness of constructing an efficient risk indicator system and utilizing Bayesian optimization for parameter tuning in XGBoost to better differentiate between risky and normal enterprises, thereby minimizing default losses. The research results underscore the advantages of employing Bayesian optimization in XGBoost, which can be applied in credit default prediction for SMEs and serves as a valuable tool in financial risk management and control. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has been used in smart cities, agriculture, weather forecasting, smart grids, and waste management. The IoT has huge potential but needs refinement. The paper focuses on lowering IoT sensor power consumption to improve network life. This work selects the best IoT cluster header (CH) to maximize energy consumption. The suggested technique uses particle swarm optimization (PSO) with artificial neural networks (ANNs). The optimal CH in an IoT network cluster was identified by taking into account the number of active nodes, the load, the residual energy, and the cost function. This work compares the suggested method with artificial bee colony, genetic, and adaptive gravity search algorithms. The hybrid solution beats conventional methods. [ABSTRACT FROM AUTHOR]

Circuit design plays a pivotal role in engineering, ensuring the creation of efficient, reliable, and cost-effective electronic devices. The complexity of modern circuit design problems has led to the exploration of multi-objective optimization techniques for circuit design optimization, as traditional optimization tools fall short in handling such problems. While metaheuristic algorithms, especially genetic algorithms, have demonstrated promise, their susceptibility to premature convergence poses challenges. This paper proposes a pioneering approach, the chaotic multi-objective Runge–Kutta algorithm (CMRUN), for circuit design optimization, building upon the Runge–Kutta optimization algorithm. By infusing chaos into the core RUN structure, a refined balance between exploration and exploitation is obtained, critical for addressing complex optimization landscapes, enabling the algorithm to navigate nonlinear and nonconvex optimization challenges effectively. This approach is extended to accommodate multiple objectives, ultimately generating Pareto Fronts for the multiple circuit design goals. The performance of CMRUN is rigorously evaluated against 11 multiobjective algorithms, encompassing 15 benchmark test functions and practical circuit design scenarios. The findings of this study underscore the efficiency and real-world applicability of CMRUN, offering valuable insights for tailoring optimization algorithms to the real-world circuit design challenges. [ABSTRACT FROM AUTHOR]

DSS has low machinability characteristics due to its high strength, machining is complicated, and careful attention is required when selecting machining parameters. The main criteria discussed in this paper concern the turning optimization parameters and machining time reduction of DSS 2205 as the work material. The input parameters are cutting velocity, feeds, cutting depth, and tooltip nose radius of the cutting tool. The design of experiments methodology is employed to design the experiments using Design-Expert V12 software. The second-order mathematical model was developed, and analysis of variance was performed to analyze the performance characteristics to recognize the critical variables influencing the output parameter. An artificial neural network (ANN) backpropagation algorithm using MATLAB software was used to develop the mathematical model and optimize the output. The model was developed, and the results were optimized using MATLAB software's ANN back propagation method to find the best possible solutions. The generated models were significant based on the analysis of variance and the R-squared value, and these results indicate that the cutting velocity is the most critical factor. For a low machining time, the cutting velocity should be between 100 and 140 m/min, and the tooltip nose radius should be 2.8 mm. The optimal parameter settings are validated by performing a lower is better confirmation test using gray relational analysis (GRA). The GRA exposed the lower machining time at a cutting velocity of 140 m/min, rate of feed of 0.5 mm/rev, cutting depth of 0.5 mm, and tooltip nose radius of 2.4 mm. The predicted values were close to the experimental values, and the result indicates the optimal level of the highest GRA grade of the machining variable. [ABSTRACT FROM AUTHOR]

The tubing used in a gas well rarely collapses and fails during applying annulus pressure. In this study, the failure causes of tubing collapse were analyzed by means of data verification, macroscopic observation, magnetic particle inspection, physical and chemical inspection, optical microscopy, and tubing collapse test. Mechanical analysis of the string and full-scale physical simulation test simulating downhole working conditions. Finally, the verification analysis of the collapse test is carried out by the finite element analysis (FEA). The results showed that (1) the physical dimension, physical and chemical properties, and collapse resistance of this batch of tubing met the requirements of the tubing ordering technical standard. (2) Assuming that the well packer slip was unsealed and could slide freely, the mechanical theoretical analysis of collapsed tubing string and collapse test under simulated working condition load was carried out, which reproduced the load when the tubing collapsed. It can be seen from this that the packer did fail. (3) The FEA calculation results showed that when the external pressure was greater than 30.75 MPa, it would inevitably lead to collapse failure in case of packer unsealed. In conclusion, the root cause for the collapse failure of the 105th underground tubing string was that the packer lost its sealing function, resulting in an abnormal axial load. While under the action of external pressure, the tubing was overloaded and collapsed. It is recommended to carry out verification tests on the material performance of packer slip, the dimensional changes of packer tool outer diameter and inner diameter under actual well conditions, the creep behavior of packer seal, and the performance of shear pin under actual working conditions, especially in the well containing H2S, so as to prevent the pressure leakage of gas well annulus caused by packer unsealing and the reoccurrence of such downhole string collapse accidents. The first collapse test under simulated working condition load is conducted in this paper. Analyzing the collapse failure work and putting forward suggestions to effectively prevent similar failures from happening again are of great significance to the oilfield. [ABSTRACT FROM AUTHOR]

This paper proposes a load shedding model for the island microgrid based on the ranking of loads and the power stability index (PSI). Loads are ranked based on the improved analytic hierarchy process (AHP) algorithm. Real-time measurement systems have the function of collecting data for very important, important, and less-important loads at each bus load. From this data, the improved AHP method is applied to rank the loads. The advantage of this method is that the subjectivity is eliminated and not depending on the expertise of the system operator when implementing the traditional AHP method. Besides, the minimum amount of load shedding power is calculated, taking into account both primary and secondary control methods. The objective is to minimize the impact on power consumers and ensure that the frequency returns to an acceptable range. In addition, when implementing load shedding, voltage quality, and stability are considered. The PSI serves as a crucial parameter for assessing the voltage stability of microgrid buses. This index is combined with load ranking weights to obtain combined weights for the load shedding plan. Consequently, the proposed load shedding plan prioritizes minimizing damage to customers, improving voltage quality and stability, and ensuring frequency is within permissible limits. The 16-bus microgrid system is applied to compare with traditional methods and to prove the efficiency of the suggested technique. [ABSTRACT FROM AUTHOR]

The nature of construction and formulation of the multi-objective functions in the flexible job shop (FJS) provides great insights about the study of the technique and the mechanism in scheduling helping to prioritize efforts for optimizing the manufacturing execution performance, which draw the focus of the researchers at home and abroad. From the perspective of the model, the solution method and the objective function, this paper reviews and analyzes the related academic papers and conferences articles during past few years. As a result, the construction forms of the multi-objective function and the scheduling scenarios show strongly positive correlation with the Makespan, the delivery tardiness, the energy consumption or the cost. Consequently, facing with the characteristic in a general way, the detailed construction analysis on the function for the flexible job shop scheduling problem (FJSP) is critical reviewed, and a total 6 types with 49 kinds classification are discussed. Obviously, the main goals are to summarize, analyze, discuss, and synthesize the existing achievements, the current research status, and the ongoing studies on the FJSP functions construction, and additionally to give useful insight into future research. Combined with the development trend of the intelligent manufacturing and the operations research, arguing for the formulation of the multi-obj. function, this review is expected to contribute to the future research of FJSP. [ABSTRACT FROM AUTHOR]

The choice and the dimensioning of the controller for the maximum power point tracking (MPPT) are determined for the ideal energy efficiency of the photovoltaic (PV) systems. Many works have been developed in the field of MPPT methods, especially fuzzy logic controllers. However, these are robust if the parameters of the membership functions have been well designed. In this paper, the performances of an intelligent fuzzy logic controller (FLC)-based MPPT method have been optimized by an evolutionary genetic algorithm (GA). The works presented in the literature have shown the efficiency of the proposed method compared to classical methods. In our paper, the validation of the experimental results obtained is given with respect to a reference signal. The control of the simulated PV source and the proposed method are built using the Simulink/Matlab environment and implemented on the dSPACE DS1104 controller to validate the practical execution of the suggested method. The standalone PV system has been tested in an emulated test bench experimentation. Experimental results confirm the efficiency of the proposed method and its high accuracy in handling fast varying load conditions. [ABSTRACT FROM AUTHOR]

Management accounting plays a greater role in providing business decisions for enterprises. As the core technology of big data processing, data mining technology can process a large amount of complex, structured, and unstructured information very efficiently. In this paper, the nonlinear system is taken as the research object, and the obtained system data is directly used. In the framework of divide and conquer strategy, multi-model method is combined with data clustering and a local modeling algorithm to study the nonlinearity of management accounting data mining. Through online adaptive estimation of parameters, aiming at the zero dynamic problem of the non-minimum phase system, through the decoupling matrix analysis of the nonlinear system, the state of the original strategy is dynamically extended to make the estimated parameters asymptotically approach the true value. The estimated parameters are applied to the nonlinear hypothesis strategy to achieve the goal of linearization control. It is assumed that the generation strategy is applied to the mining of different related types of rules without manually setting parameters, which greatly improves the efficiency of rule discovery. Through case analysis, this paper demonstrates the feasibility of applying nonlinear hypothesis strategy of data mining technology to management accounting and puts forward the relevant application process framework. The validity and feasibility of nonlinear hypothesis strategy data mining technology in management accounting are verified. [ABSTRACT FROM AUTHOR]

Targeting the problem of autonomous navigation of indoor robots in large-scale, complicated, and unknown environments, an autonomous online decision-making algorithm based on deep reinforcement learning is put forward in this paper. Traditional path planning methods rely on the environment modeling, which can cause more workload of calculating. In this paper, the sensors to detect surrounding obstacles are combined with the DDPG (deep deterministic policy gradient) algorithm to input environmental perception and control the action direct output, which enables robots to complete the tasks of autonomous navigation and distribution without relying on environment modeling. In addition, the algorithm preprocesses the relevant data in the learning sample with Gaussian noise, facilitating the agent to adapt to noisy training environment and improve its robustness. The simulation results show that the optimized DL-DDPG algorithm is more efficient on online decision-making for the indoor robot navigation system, which enables the robot to complete autonomous navigation and intelligent control independently. [ABSTRACT FROM AUTHOR]

As a hot topic in military research, small combat robots are always in a dynamic process when traveling, causing the body tube to vibrate which affects its firing accuracy. Different from the traditional two-degree-of-freedom combat robot, this paper establishes a rigid-flexible coupling dynamics model for the new three-degree-of-freedom combat robot to study the response characteristics of its body tube during travel. To accurately reflect the driving conditions of the mobile robot, a road surface unevenness model was established using the sine wave superposition method. The firing accuracy of a small combat robot can be affected by the speed and road conditions, as shown in numerous studies. The effects of arm structure, arm mounting position, and firing angle on the vibration response of the body tube are analyzed in this paper. The results show that a reference for improving the design of small combat robots can be found in a correlation between the structure, mounting position, and firing angle of the robotic arm, and the body tube's vibration response. [ABSTRACT FROM AUTHOR]

The minimum spanning tree (MST) is widely used in planning the most economical network. The algorithm for finding the MST of a connected graph is essential. In this paper, a new algorithm for finding MST is proposed based on a deduced theorem of MST. The algorithm first sorts the edges in the graph according to their weight, from large to small. Next, on the premise of ensuring the connectivity of the graph, it deletes the edges in this order until the number of edges of the graph is equal to the number of vertexes minus 1. Finally, the remaining graph is an MST. This algorithm is equivalent to an inverse Kruskal algorithm. For sparse graphs, the proposed algorithm has higher efficiency than the Kruskal algorithm. Experiments and analysis verify the effectiveness of the algorithm proposed in this paper. The algorithm provides a better choice for finding the MST of the sparse graph. [ABSTRACT FROM AUTHOR]

This paper is intended to introduce the subtractive derivations and study some of their algebraic properties on R l -monoids. Also, we give some characterizations of subtractive derivations on the Gödel center. Moreover, Gödel algebras are characterized by a fixed set of subtractive derivations. Finally, we discuss the relationship between subtractive derivations and other derivations for R l -monoids. These results of the paper can provide the common properties of subtractive derivations in the t-norm-based fuzzy logical algebras. [ABSTRACT FROM AUTHOR]