Showing papers in "Mathematical Problems in Engineering in 2023"

Fuzzy Inventory Model for Imperfect Items with Price Discount and Penalty Maintenance Cost

Abstract: The present paper considers the fuzzy economic manufacturing model (FEMM) for an inventory model with an imperfect production process that has been studied along with rework. During the pandemic, it is evident that the products accumulated without a sale, which has increased the maintenance cost of the products. This research paper compares a special sale of products with discount and without discount prices both in the fuzzy environment and in the crisp case. New computing methods based on fuzzy logic are being utilized to enhance identification, decision making, and optimization. A triangular fuzzy number is applied in the economic production quantity to emphasize the importance of optimal manufacturing. The EPQ model’s optimal total cost is obtained in the crisp version. It is to be noted that this model is developed in the fuzzy sense by using the deterioration as a triangular fuzzy number. The applications of this model in the fields are constructing customized industrial machinery or heavy-duty construction equipment, specific chemicals, and processed food. By using MATLAB R2021, a numerical example of the optimal solution is provided. Finally, the present research discusses how changing several parameters affects the optimum total cost.

Inverse Matrix Problem in Regression for High-Dimensional Data Sets

Abstract: Forhigh-dimensional chemometric data, the inverse matrix X t X − 1 problem in regression models is a difficulty. Multicollinearity and identification result from the inverse matrix problem. The usage of the least absolute shrinkage and selection operator (LASSO) and partial least squares are two existing ways of dealing with the inverse matrix problem (PLS). For regressing the chemometric data sets, we used extended inverse and beta cube regression. The existing and proposed methods are compared over near-infrared spectra of biscuit dough and Raman spectra analysis of contents of polyunsaturated fatty acids (PUFA). For reliable estimation, Monte Carlo cross-validation has been used. The proposed methods outperform based on the root mean square error, indicating that cube regression and inverse regression are reliable and can be used for diverse high-dimensional data sets.

A Simulation-Based Optimization Method for Truck-Prohibit Ramp Placement along Freeways

Abstract: The increasing number of trucks has a negative impact pertinent to efficiency and safety concerns on the operation of mixed traffic flow along freeways, especially at freeway entrance and exit ramps. To address such issue, this study proposes a simulation-based method for truck-prohibit ramp placement along freeways. The method framework contains two parts: the first part is to generate a set of new truck restriction schemes using simulation experiments, and the second part is to evaluate the generated schemes and find out the current optimal location of truck-prohibit ramps based upon the AHP-TOPSIS method. Three patterns of evaluation indicators are utilized to estimate the performance of freeway service in terms of traffic efficiency, road safety, and accessibility. A case study of the Beijing-Hong Kong-Macao freeway within Henan Province, China, is conducted to verify the effectiveness of the proposed method. Results could provide beneficial insights on the optimal location setting of truck-prohibit ramps to enhance the entire performance of mixed traffic flow along freeways.

Accelerating Flow of Carbon Nanotubes with Carboxymethyl Cellulose and Blood Base Materials with Comparative Thermal Features: Prabhakar Fractional Model

Abstract: A fractional model is developed to investigate the thermal onset of carbon nanotubes containing single-wall carbon nanotubes (SWCNTs) and multiwall carbon nanotubes (MWCNTs). The blood and carboxymethyl cellulose (CMC) are utilized to report the characteristics of the base material. The thermal phenomenon is further supported with inclined magnetic force and mixed convection features. The vertical plate with an oscillatory nature induced the flow. After formulating the problem in view of flow assumptions, the fractional framework is carried out via the Prabhakar technique. The validation of the fractional model is ensured in view of previous studies. The comparative thermal aspect of carbon nanotubes and base materials by varying flow parameters is tested.

EDAS Method for Single-Valued Neutrosophic Number Multiattribute Group Decision-Making and Applications to Physical Education Teaching Quality Evaluation in Colleges and Universities

Abstract: The evaluation of undergraduate teaching levels by the Ministry of Education has greatly promoted the construction of software and hardware in universities. After the evaluation, it will be a long-term task to build a long-term decision-making mechanism to guarantee the continuous improvement of teaching quality. As an important part of university education, physical education (PE) has always played an irreplaceable role in improving current students’ physical quality and cultivating their awareness of lifelong physical exercise. How to establish and improve the quality evaluation information system of PE to guarantee the quality of PE is not only related to the awareness of physical education workers about their own teaching work but also to the effective means to regulate the quality monitoring of PE. An effective PE quality evaluation system depends not only on the teaching management department but also on the evaluation of teaching by students and teachers themselves. Only in this way can PE classroom teaching quality be comprehensively and truly reflected. The evaluation of PE quality in higher education is often considered as a multiattribute group decision-making (MAGDM) problem. In this article, the EDAS model is extended to the single-valued neutrosophic sets (SVNSs) setting to deal with MAGDM and the computational steps, for all designs are listed. Finally, the PE quality evaluation is given to prove the SVNN-EDAS model and some good comparative analysis is done to demonstrate the advantages of SVNN-EDAS. It is shown that the SVNN-EDAS method emphasizes the expectation value of the SVNN average alternative. Compared with different methods mentioned, the SVNN-EDAS method is more practical and efficient because the calculation steps are simpler and easier to apply in practice.

Trajectory Tracking Control of Intelligent Driving Vehicles Based on MPC and Fuzzy PID

Abstract: To improve the stability and accuracy of quintic polynomial trajectory tracking, an MPC (model predictive control) and fuzzy PID (proportional-integral-difference)- based control method are proposed. A lateral tracking controller is designed by using MPC with rule-based horizon parameters. The lateral tracking controller controls the steering angle to reduce the lateral tracking errors. A longitudinal tracking controller is designed by using a fuzzy PID. The longitudinal controller controls the motor torque and brake pressure referring to a throttle/brake calibration table to reduce the longitudinal tracking errors. By combining the two controllers, we achieve satisfactory trajectory tracking control. Relative vehicle trajectory tracking simulation is carried out under common scenarios of quintic polynomial trajectory in the Simulink/Carsim platform. The result shows that the strategy can avoid excessive trajectory tracking errors which ensures a better performance for trajectory tracking with high safety, stability, and adaptability.

Two-Stage Hybrid Optimization Algorithm for Silicon Single Crystal Batch Scheduling Problem under Fuzzy Processing Time

Abstract: Considering the widely existing processing time uncertainty in the real-world production process, this paper constructs a fuzzy mathematical model for the silicon single crystal production batch scheduling problem to minimize the maximum completion time. In this paper, a two-stage hybrid optimization algorithm (TSHOA) is proposed for solving the scheduling model. Firstly, the improved differential evolution algorithm (IDE) is used to solve the order quantity allocation problem of silicon single crystal with different sizes to obtain the quantity of silicon single crystal rods with different sizes produced by different types of single crystal furnaces. Secondly, the variable neighborhood search (VNS) algorithm is adopted to optimize the order quantity sequencingof batch production processes. Finally, simulations and comparisons demonstrate the feasibility of the model and the effectiveness of TSHOA.

Corrigendum to “The Reward-Penalty Mechanism in a Closed-Loop Supply Chain with Asymmetric Information of the Third-Party Collector”

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Trajectory Planning and Collision Control of a Mobile Robot: A Penalty-Based PSO Approach

Abstract: In this paper, trajectory planning and navigation control problems have been addressed for a mobile robot. To achieve the objective of the research, an adaptive PSO (Particle Swarm Optimization) motion algorithm is developed using a penalty-based methodology. To deliver the best or collision-free position to the robot, fitness values of the all-random-positioned particles are compared at the same time during the target search action. By comparing the fitness values, the robot occupies the best position in the search space till it reaches the target. The new work integrated with conventional PSO is varying a velocity event that plays a vital role during the position acquisition (continuous change in position during the obstacle negotiation with the communication through random-positioned particles). The obstacle-negotiating angle and positional velocity of the robot are considered as input parameters of the controller whereas the robot's best position according to the target position is considered as the output of the controller. Simulation results are presented through the MATLAB environment. To validate simulation results, real-time experiments have been conducted in a similar workspace. The results of the adaptive PSO technique are also compared with the results of the existing navigational techniques. Improvements in results between the proposed navigation technique and existing navigation techniques are found to be 4.66% and 11.30%, respectively.

DOF Calculation Method of Planar Mechanisms Based on Constraint Normal Line

Abstract: In the analysis and calculation of the degree of freedom (DOF) of mechanisms, it is generally a complicated problem to judge the virtual constraint correctly. Under what conditions do virtual constraints exist, and how many virtual constraints are there? Understanding these problems can contribute to effectively tackling the difficulty of calculating the DOF. With planar mechanisms as a research object, the constraints among various components are simplified into point constraints, and the common normal at a constraint point is referred to as normal. According to whether a constraint point can move relative to the frame in its normal direction, normals are divided into different categories. Based on the geometric theorem for judging the DOF of a workpiece, a set of geometric theorems for judging the DOF of components and their nature, over-constraint, and quantity are established. After the correlation between virtual constraints and over-constraints is clarified, the total number of virtual constraints in a mechanism is calculated. The analysis and calculation of the DOF of several typical planar mechanisms demonstrate that the new method is logically rigorous, simple, and intuitive, and the DOF of a mechanism can be accurately calculated.

A Tool for Visualization and Analysis of Neighbourhoods, Clusters, and Indicators during the COVID-19 Pandemic

Abstract: Visual analytics tools for spatiotemporal analysis can be used to manage and monitor the propagation of an epidemic. The problem is that dashboards encountered in the literature do not take into consideration how the geolocation characteristics, such as socioeconomic indicators, influence the infection risk or other epidemic variables. This analysis can support health officials in managing the outbreak to consider information about indicators in compartment models for propagation prediction and intervention simulation. The objective of this work was to bring widgets that offer a more profound exploration and analysis of the impact of the pandemic on socioeconomic indicators. In our approach, the association of epidemic variables and indicators can be explored with commonly adopted visualization plots. Also, we propose a way to gather the specialist’s risk perception, and as a result, a risk heatmap is produced, allowing the reduction of time series data cluttering. Finally, it is possible to use the risk heatmap information to compare neighbourhoods and socioeconomic indicators by ranking them according to a severity score. Some use cases were performed to demonstrate the use and capability of the proposed widgets.

Two Statistical Approaches to Justify the Use of the Logistic Function in Binary Logistic Regression

Abstract: Logistic regression is a commonly used classification algorithm in machine learning. It allows categorizing data into discrete classes by learning the relationship from a given set of labeled data. It learns a linear relationship from the given dataset and then introduces nonlinearity through an activation function to determine a hyperplane that separates the learning points into two subclasses. In the case of logistic regression, the logistic function is the most used activation function to perform binary classification. The choice of logistic function for binary classifications is justified by its ability to transform any real number into a probability between 0 and 1. This study provides, through two different approaches, a rigorous statistical answer to the crucial question that torments us, namely where does this logistic function on which most neural network algorithms are based come from? Moreover, it determines the computational cost of logistic regression, using theoretical and experimental approaches.

Waveform Prediction of Blade Tip-Timing Sensor Based on Kriging Model and Static Calibration Data

Abstract: Blade tip clearance is an important parameter affecting the efficiency, stability, and safety of aero-engines. During the high-speed rotation of the blade, the blade tip clearance changes, which leads to changes in signal amplitude collected by the tip timing sensor. When the rotor is rotating at high speed, it is impractical to measure the tip-timing signal under each tip clearance. Aiming at the previous problems, a prediction method of blade tip-timing sensor waveform based on the combination of the Kriging model and static calibration data are proposed. The relationship between the output voltage of the tip timing sensor and the blade tip clearance and the angle of the blade tip cutting magnetic line is obtained by collecting the data of static calibration. Based on the collected static calibration experimental data and compared with the polynomial fit method and the RBF model, the accuracy of the Kriging model in predicting the waveform of the blade tip timing sensor was verified. The results show that the prediction accuracy of the Kriging model is basically the same as that of the RBF model, but the Kriging model has more advantages in predicting the waveform when the blade tip clearance is unknown. In contrast, the prediction accuracy of the polynomial fit is lower than that of the Kriging and RBF models, and the polynomial fit is prone to significant prediction errors.

BSV System with MSC and Its Application in the Measurement of Surface Cracks

Abstract: In this paper, we have proposed a flexible noncontact crack-size measurement method that can realize binocular stereo vision measurement with only a single camera. On the premise that the camera’s intrinsic parameters have been accurately calibrated, we use a camera to collect the image of the crack from two directions. Then, we calculate the motion parameters using the collected images from the camera in different positions. In addition, Canny algorithm is used to extract the edge pixels of crack images. Finally, we establish the binocular stereo vision model for crack measurement according to the camera parameters, the motion parameters, and the edge information of crack images. Thus, we can measure the crack size through this model. Experimental results show that the measurement error is less than 5% under a distance of 2 meters, which can effectively prove the precision of the proposed method. In addition, our method only uses a single camera. Compared with the traditional binocular stereo vision method, this method is not only flexible but also more economical.

Simplified Calculation of Lateral Displacement of a Cement-Soil Gravity Retaining Wall

Abstract: A common retaining structure in Shanghai’s soft soil foundation pit is the cement-soil gravity retaining wall. In this research, a streamlined calculation method is suggested to examine the lateral displacement of the cement-soil gravity retaining wall’s deformation features. First, the lateral pressure of the retaining wall in a nonlimit passive state is calculated by separately estimating water and soil pressure. Then, the work performed by the lateral pressure of the retaining wall and the internal energy of deformation is calculated and analyzed according to the law of energy conservation to obtain the maximum lateral displacement at the top of the retaining wall. At last, the influences of wall insertion ratio, wall width, cement mixing ratio, and soil internal friction angle on the lateral displacement of retaining wall are analyzed with finite element numerical simulation. The results of the streamlined calculations are contrasted with the measured findings when paired with engineering scenarios, which demonstrates that the predicted lateral displacement is reasonably accurate with an average error of less than 15%.

A Chaos-Based Image Encryption Scheme Is Proposed Using Multiple Chaotic Maps

Abstract: Everybody wants to maintain solitariness to some extent or entirely in his dealings with other people during different modes of communication. To retain privacy, researchers materialized distinct image encryption algorithms using chaotic maps. Due to their extraordinary features, most researchers employed multidimensional chaotic maps to barricade clandestine information or digital images from potential invaders. Still, multidimensional chaotic maps have many impediments conferred in the literature review. In this paper, we developed a cryptosystem utilizing multiple chaotic maps to mitigate the shortcoming of multidimensional chaotic maps. A distinctive approach is adopted to sire a key stream using a combination of chaotic maps and create a sequence of random integers linked with the pixels of the plain image to shatter the association between neighboring pixels of a plain image. Finally, diffusion is accomplished using the previously diffused pixels at a decimal level. Security and statistical analysis demonstrate that the presented encryption algorithm is robust against well-known attacks. An ample key space indicates that it is best suited for secure communication.

A Hybrid Gravitational Emulation Local Search-Based Algorithm for Task Scheduling in Cloud Computing

Abstract: The flexibility of cloud computing to provide a dynamic and adaptable infrastructure in the context of information technology and service quality has made it one of the most challenging issues in the computer industry. Task scheduling is a major challenge in cloud computing. Scheduling tasks so that they may be processed by the most effective cloud network resources has been identified as a critical challenge for maximizing cloud computing’s performance. Due to the complexity of the issue and the size of the search space, random search techniques are often used to find a solution. Several algorithms have been offered as possible solutions to this issue. In this study, we employ a combination of the genetic algorithm (GA) and the gravitational emulation local search (GELS) algorithm to overcome the task scheduling issue in cloud computing. GA and the particle swarm optimization (PSO) algorithms are compared to the suggested algorithm to demonstrate its efficacy. The suggested algorithm outperforms the GA and PSO, as shown by the experiments.

A Novel FRBF-Type Model for Nonlinear Time Series Prediction

Abstract: Accurate prediction of time series is complex due to nonlinear characteristics but can play a significant role in practical problem. In this paper, a novel varying-coefficient hybrid model is proposed to accurately predict the nonlinear time series. A set of fuzzy radial basis function (FRBF) neural networks is used to approximate the varying functional coefficients of the state-dependent autoregressive model with exogenous variables (SD-ARX). The obtained model is called the fuzzy radial basis function network-based autoregressive model with exogenous variables (FRBF-ARX), which combines the advantages of the FRBF in function approximation and the SD-ARX model in nonlinear dynamics description. Then, a structured nonlinear parameter optimization method (SNPOM) and the modified multifold cross-validation criterion are used to estimate the parameters of the proposed varying-coefficient FRBF-ARX model. The performances of the FRBF-ARX model are used to predict the PM2.5 concentration and simulated SISO nonlinear process, respectively, and the performances of the proposed model are also compared and discussed. The experimental results show that the FRBF-ARX model has better performances of accuracy on nonlinear time series forecasting than that of other models.

Enhanced Estimation of the Population Mean Using Two Auxiliary Variables under Probability Proportional to Size Sampling

Abstract: In some situations, the population of interest differs significantly in size, for example, in a medical study, the number of patients having a specific disease and the size of health units may vary. Similarly, in a survey related to the income of a household, the household may have a different number of siblings, and then in such situations, we use probability proportional to size sampling. In this article, we have proposed an improved class of estimators for the estimation of population mean on the basis of probability proportional to size (PPS) sampling, using two auxiliary variables. The mathematical expressions of the bias and mean square error (MSE) are derived up to the first order of approximation. Four real datasets and a simulation study are conducted to assess the efficiency of the improved class of estimators. It is found from the real datasets and a simulation study, that the proposed generalized class of estimators produced better results in terms of minimum MSE and higher PRE, as related to other considered estimators. An empirical study is given to support the theoretical results. The theoretical study also demonstrates that the proposed generalized class of estimators outperforms the existing estimators.

Integrated Process for Developing and Selecting More or Less Better Products Based on QFD and MCGP

Abstract: As consumer needs for product features change rapidly, companies must develop new products (NPD) that satisfy these needs in the shortest possible time. Consumer product selection behavior is generally overlooked when developing new products. This is a more complicated problem for consumers belonging to multiple types, such as hospitals and patients. Quality function deployment (QFD) is a good and commonly used method for product design and selection. Choosing the right product design for a company is a crucial strategic consideration to satisfy the needs of consumers and the company’s technical requirements. Designing a new product is multicriteria decision-making (MCDM). In this study, the analytical hierarchy process (AHP), QFD, and multichoice goal programming (MCGP) were combined to address the design and selection of new products. This integrated method considers both tangible (qualitative) and intangible (quantitative) factors, as well as “the higher criterion is better” and “the lower criteria is better” when selecting new design projects. To prove the practicality and sustainable value of the model, we considered a Taiwanese company that developed medical aid products as an example. This paper suggests a good decision-making tool for selecting a new product design based on “more is better/less is better” criteria.

A Quantitative Model of the Multisubject Quality Responsibility of Construction Projects Based on an IPSO

Abstract: In order to solve the problem of the quantitative division of multisubject quality responsibility in construction project quality disputes, this article proposes a quantitative model of multisubject quality responsibility division in construction projects based on an improved particle swarm optimization (IPSO). First, this article proposes a set of classification guidelines for quality risk behaviors based on the theory of organizational behavior. Through these, the interconnections between different types of risk behaviors and quality defects were explored. Following this, this article explored potential laws among 84 practical judicial cases from China using the IPSO. The category coefficients of the three types of quality risk behaviors, namely, technical defects, management violations, and irregularities, were obtained in this analysis. This article also deduced the mathematical expression of the division of engineering quality responsibility using fuzzy mathematical theory and established a multisubject quality responsibility quantitative model. It was then simulated and applied in four practical judicial cases. The simulation results revealed that the multisubject quality responsibility quantitative model based on quality risk behavior has good applicability.

An Experimental Testing of Optimized Fuzzy Logic-Based MPPT for a Standalone PV System Using Genetic Algorithms

Abstract: 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.

Deviation Correction Path Planning Method of Full-Width Horizontal Axis Roadheader Based on Improved Particle Swarm Optimization Algorithm

Abstract: Aiming at the problem of optimal path planning for deviation correction of full-width horizontal axis roadheader in the confined roadway of the coal mine, a deviation correction path planning method of full-width horizontal axis roadheader based on improved particle swarm optimization (I-PSO) algorithm is proposed. First, according to the driving characteristics of roadheader and deviation correction strategy, the mathematical model of full-width horizontal axis roadheader is established, and the constraints based on pose analysis are determined. Then, taking the shortest total distance from the initial position to the terminal position of roadheader as the objective function of path planning, an optimization model of deviation correction path planning is established. Finally, the I-PSO algorithm is proposed to quickly and accurately solve the deviation correction path model, in order to plan a safe and shorter distance path of full-width horizontal axis roadheader. In the I-PSO algorithm, the fitness difference of particles is used to dynamically adjust the inertia weight parameters to optimize the exploration of the whole or part of activity space, and the learning factor is adjusted to speed up particle convergence and optimization of particles, and a random factor is introduced to update the position of particles to avoid local traps as much as possible. The performance test of the I-PSO algorithm shows that the I-PSO algorithm is superior to other algorithms. The EJM340/4-2 full-width horizontal axis roadheader is used as the simulation object to verify the effectiveness of the I-PSO algorithm in the deviation correction path planning of roadheader. The simulation results show that the I-PSO algorithm proposed can quickly and accurately plan a safe and shorter distance path than the WPSO algorithm and CPSO algorithm and has better convergence and stability. It has laid a good foundation for the intelligent navigation of full-width horizontal axis roadheader in coal mine.

A New Computational Technique for Analytic Treatment of Time-Fractional Nonlinear Equations Arising in Magneto-Acoustic Waves

Abstract: This paper presents the study of time-fractional nonlinear fifth-order Korteweg–de Vries equations by utilizing an adequate novel technique, namely, the q-homotopy analysis transform method. The fifth-order Korteweg–de Vries equation has got its importance in the study of magneto-sound propagation in plasma, capillary gravity water waves, and the motion of long waves under the influence of gravity in shallow water. To justify the effectiveness and pertinence of the contemplated technique, we take a look at three examples of the time-fractional fifth-order Korteweg–de Vries equations. The q-homotopy analysis transform method offers us to modulate the range of convergence of the series solution using ℏ, called the auxiliary parameter or convergence control parameter. The study of the fractional behaviour of the considered equations expresses the originality of the presented work. There is a visible variation in the obtained solutions for different fractional orders and which can lead to different consequences for future work. As a future research direction, readers can use the hybrid methodologies merging with our projected scheme to achieve better consequences. Additionally, to validate the precision and reliability of the proposed method, we organized suitable numerical simulations. The obtained findings show that the proposed method is very gratifying and examines the complex nonlinear challenges that arise in science and innovation.

Retracted: Design of Epidemic Prevention Medical Special Clothing Based on Mathematical Model Analysis

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Research on Body Tube Vibration Response of Small Combat Robots Motion

Abstract: 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.

Active RIS-Assisted Transmission Design for Wireless Secrecy Network with Energy Harvesting

Abstract: We investigated the secrecy transmission design in a simultaneous wireless information and power transfer (SWIPT) system, where an active reconfigurable intelligent surface (RIS) is utilized to enhance the secure information transmission as well as the wireless energy transmission. Specifically, we studied the fairness secrecy rate maximization by jointly optimizing the transmit beamformer and the reflecting coefficients, subject to the transmit power constraint and the nonlinear energy harvesting constraint. To solve the formulated nonconvex problem, we utilize the successive convex approximation to reformulate the problem and then propose an alternating optimization to address the approximated problem. The simulation result showed the performance of the proposed design as well as the superiority of active RIS when compared with other benchmarks.

Retracted: Efficient Object Detection Algorithm in Kitchen Appliance Scene Images Based on Deep Learning

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Retracted: Image Processing Technology Based on Internet of Things in Intelligent Pig Breeding

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Northeast China Urban Network Structure and Reorganization Based on the Coordinated Development Capability of Cities

Abstract: Urban synergy can be assessed based on the development of a given city and the organization and development channels between cities. Using the entropy method, correction gravity model, and social network analysis, our study conducted a comprehensive quantitative analysis of the characteristics of urban network space in three northeast provinces in China. The region exhibited a “core city-peripheral city-marginal city” concentric pattern, with the most developed cities at the core. Nevertheless, our findings indicate that a collaborative development pattern is gradually forming. The three northeastern provinces and urban collaboration networks extended further to the north. Liaoning Province, most of Jilin Province, and most of Heilongjiang Province have gradually established a similar development relationship with other cities. The overall level of urban intermediary centers in the northeastern provinces is declining, and the direct contact between cities is becoming more apparent. These findings, in addition to agglomeration calculations, highlight the need for reorganizing the current collaborative urbanization structure in northeast China.