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The world is having a vast collection of text with abandon of knowledge. However, it is a difficult and time-taking process to manually read and recognize the text written in numerous regional scripts. The task becomes more critical with Gurmukhi script due to complex structure of characters motivated from the challenges in designing an error-free and accurate classification model of Gurmukhi characters. In this paper, the author has customized the convolutional neural network model to classify handwritten Gurmukhi words. Furthermore, dataset has been prepared with 24000 handwritten Gurmukhi word images with 12 classes representing the month’s names. The dataset has been collected from 500 users of heterogeneous profession and age group. The dataset has been simulated using the proposed CNN model as well as various pretrained models named as ResNet 50, VGG19, and VGG16 at 100 epochs and 40 batch sizes. The proposed CNN model has obtained the best accuracy value of 0.9973, whereas the ResNet50 model has obtained the accuracy of 0.4015, VGG19 has obtained the accuracy of 0.7758, and the VGG16 model has obtained value accuracy of 0.8056. With the current accuracy rate, noncomplex architectural pattern, and prowess gained through learning using different writing styles, the proposed CNN model will be of great benefit to the researchers working in this area to use it in other ImageNet-based classification problems.

Acoustic metamaterials have been widely concerned by researchers because of their excellent sound absorption properties. Traditional uncertainty analysis methods for response analysis of acoustic metamaterials with evidence variables have limitations. The excessive time spent on repetitive extreme analysis of focal elements severely impedes the practical application of evidence theory. To reduce the computational costs of uncertainty quantification for acoustic metamaterials under the evidence theory, a reduced space optimization-based evidence theory method (RSO-ETM) is proposed. In RSO-ETM, a simplified surrogate model is first constructed by a modified adaptive arbitrary orthogonal polynomial (MAAOP) expansion method, and then, the monotonicity of the response surface is examined to reduce the space. Subsequently, the Newton iteration technique and a transformation boundary method are used to obtain the extreme points in the reduced space, through which the boundary of the response over each focal element can be readily obtained. By using RSO-ETM, the optimization for each focal element can be avoided, and correspondingly the computational costs are reduced. Two mathematical examples and acoustic problems are employed to demonstrate the practicality of the methods.

With the reform of the higher education system, schools and universities have transitioned from risk-free management to current risk management, and financial risk has become a concern that every college and university must address. In recent years, how to effectively forewarn colleges and universities for financial risk, as well as how to prevent and control financial risk at colleges and universities, has been a hot topic. Firstly, this study analyzes the process of model construction, including introducing the basic information of the model, determining the factor level, establishing the weight set and the alternative set, the first-grade fuzzy comprehensive evaluation, and the second-grade fuzzy comprehensive evaluation. Secondly, with the financial data of our school in 2020 and 2021 as samples, the fuzzy comprehensive evaluation and early warning model of university financial risk is used to comprehensively evaluate its financial status. Finally, according to the causes and types of financial risks in universities and combined with the analysis results of the fuzzy comprehensive evaluation model of our school’s financial situation, the study gives the relevant measures to prevent and control the financial risks in universities and carries on a detailed analysis and explanation of each measure. The construction and implementation of a financial early warning model in colleges and universities can effectively avoid and reduce the financial risk of colleges and universities, which has certain research value.

In this study, we have utilized two study variables and one auxiliary variable. The auxiliary variable is used as the stratification variable, and we selected the sample using the stratification variable with a mixture of ratio and product estimators. Under super population set-up, minimal equations have been obtained through minimization of the aggregated variance with the help of the variables under study. The objective function is minimized with respect to the constraints under consideration. The dynamic programming approach has been used to minimize the variance and obtain the optimum strata boundaries. Empirical studies have also been made on the proposed rule utilizing different distributions. A simulation study has been done which shows the gain in precision using the proposed method.

In order to meet the optimization needs of physical education curriculum resource allocation, the author proposes a deep belief-based physical education curriculum resource allocation technology. The efficient feature abstraction and feature extraction capabilities of deep belief technology fully explore the interests and preferences of learners on course resources. Because deep belief has strong capabilities in feature detection and feature extraction, it has unique and efficient feature abstraction capabilities for different dimensional attributes of input data; the author proposes a DBN-MCPR model optimization method based on deep belief classification in the MOOC environment. Experimental results show that when the number of iterations reaches about 80, the RMSE of DBN-MCPR trained with the training dataset without learner feature vector is 77.94%, while the RMSE of DBN-MCPR trained with the dataset with learner feature vector is 77.01; DBN-MCPR with full eigenvectors tends to converge after about 40 iterations, while DBN-MCPR without learner eigenvectors starts to converge after about 15 iterations; this result is in line with the characteristics of the internal network structure of DBN. Conclusion. This application proves that the technical research based on deep belief can effectively meet the needs of the optimization of physical education curriculum resource allocation.

Landslides occur in most countries. As one of the most serious geological hazards, landslides affect infrastructure construction. Thus, it is vital to prepare reliable landslide susceptibility evaluation maps to avoid landslide-prone areas for various construction projects. In recent years, supervised machine learning algorithms have been widely used in landslide susceptibility evaluation, but many flaws remain in the selection of nonlandslide point samples for comparative analysis. It is significant to improve the authenticity of sample data and reduce the impact of noise. China’s Funing County was used as a case study in this paper, which first identified 122 landslide incidents based on historical data, fieldwork, and remote sensing images to create a landslide inventory in the research area. In addition, 12 causal factors of landslides were determined, including elevation, slope, aspect, plan curvature, profile curvature, distance to roads, distance to rivers, distance to faults, rainfall, normalized difference vegetation index (NDVI), lithology, and land cover. K-means clustering was used to purify the factor data factors before data-driven certainty factor (CF) and frequency ratio (FR) models, and machine learning models, random forest (RF) and artificial neural network (ANN), were used for a comparative study on landslide susceptibility evaluation in Funing County. The results show that the selection method of nonlandslide sample data will affect the accuracy of different evaluation models. The purified sample data improved the prediction accuracy of the four models, with significant prediction accuracy improvements observed in the ANN model. The purification of nonlandslide sample data by K-means method is of great significance for the drawing of landslide sensitivity map.

With the sharply increased development of variable renewable energy resources (VRERs) in recent years, the hydro-wind-photovoltaic (PV) hybrid system (HWPHS) has the prospective to enhance the grid integration of VRERs. Nevertheless, the intense variation associated with wind and PV generation causes uncertainties in the long-term operation of the HWPHS. To overcome this drawback, this paper develops a novel method to derive adaptive operating rules for a cascade HWPHS. First, a scenario-generating method coupling Kernel density estimation with the copula function is proposed to characterize the wind and PV forecast errors. Second, based on the power generation scenarios, an optimal scheduling model for the cascade HWPHS considering transmission section constraints is proposed to simulate the hydro-wind-PV complementary operation; finally, the long-term operating rules for the cascade HWPHS are extracted by grey relational analysis and BP neural network. As a case study, the HWPHS of the Wu River basin in China is chosen. Results demonstrate that the proposed model can effectively utilize the flexibility of cascade hydropower stations, improve transmission section utilization efficiency, and promote clean energy absorption.

Innovation has been made in the caisson foundation to support a very large structure resting over a soft soil stratum known as the root-caisson foundation. This technique was executed in China-Yangtze River Bridge. The root caisson foundation was first implemented in the Yangtze River bridge and discovered that the root improves soil structure interaction and increases vertical bearing capacity by 100%. In the present research, a numerical study of root-caisson foundation under combined vertical and horizontal loading was performed using ABAQUS software. Analysis was performed by varying the parameters of root caisson such as the inclination of roots (30°, 45°, 60°, and 90°) spacing(S) between the root floors with respect to diameter (D) of the caisson as (0.5°D, 0.6°D, and 0.75°D) with different loading conditions and results were compared with normal traditional caisson with the same length (L) to diameter (D) ratio. From the test results, the load-settlement behavior of caisson is nonlinear for individual vertical, and lateral load tests and also the same for the combined loading conditions. It is found that the root-caisson has a higher load bearing capacity as compared to that of the traditional caisson. Moreover, in combined loading, the load bearing capacity increases considerably, compared with the ultimate vertical and lateral load capacity.

The surface crack on track slab is the main cause of the service reliability deterioration of the high-speed rail (HSR) slab track. To realize the rapid and accurate detection of these cracks, infrared thermography (IRT) can be regarded as an effective way in HSR application. However, the performance of IRT can be significantly affected by the testing environment. This study aims to analyze the feasibility of IRT in crack detection for HSR slab track by investigating the key factors affecting the detection accuracy. Firstly, the principle of IRT in detecting surface crack of track slab is given, and the key factors influencing the detection performance are discussed. Considering the difficulty in conducting field tests for slab tracks, this study establishes a finite element (FE) model to simulate the temperature field of track slab with surface cracks. This model is verified by using the field measurement results of track slab temperature. With the FE model, the effects of detection time, ambient temperature, and sensitivity of the thermal imager on the detection accuracy are revealed. The results show that, during the window time for track maintenance (0:00 am–3:00 am), the difference of temperature between crack areas and noncrack areas gradually increases with time, so it is recommended to conduct detection during 2:00–3:00 am; under the condition of no auxiliary heat source, the crack detection is suggested to be arranged in summer with a high incidence of crack; the minimum limit of thermal sensitivity of thermal imager is below 108mk@25°C. The findings in this research can offer guidance for the implementation of IRT-based crack detection for HSR slab crack.

In this study, we investigated the effects of slip boundary and magnetic field on the flow of two immiscible couple stress fluids in an inclined porous media channel. The Stokes model was used to define the two couple stress fluids. The fluids in both the regions are assumed to be incompressible and immiscible and the transport properties in the two regions are assumed to be constant. The governing coupled linear ordinary differential equations are made dimensionless by using appropriate fundamental quantities. The expressions for the velocity components in both the regions are obtained in closed form using the undetermined coefficient method; by applying slip boundary condition at the boundary of the channel and at fluid-fluid interface, the continuity of velocities, vorticities, shear stresses, and couple stresses are applied. The effects of the nondimensional parameters including the couple stress parameters, slip parameter, Hartmann number, Darcy number, and the ratio of basic fluid properties on the velocity components are presented graphically; the flow rate and shear stress are presented in table and discussed briefly. It is reported that the presence of couple stresses and magnetic field have a declining impact while slip boundary has an enhancement impact on the fluid velocity and the volumetric flow rate. In the absence of couple stress, the results for the liming case as s ⟶ ∞ η ⟶ 0 for Newtonian flow are achieved and further limiting cases are established and briefly addressed.

An optimized matter element decision method is proposed based on intuitionistic fuzzy set (IFS) and Choquet integral to solve the problems of equipment development scheme decision (e.g., the correlation of scheme attributes and the low decision reliability of evaluation results). First, the nonlinear programming problem of expert intuition fuzzy subjective and objective decision information difference is constructed using the Choquet integral method, and the measure values and related factors of decision attributes are solved using the optimization algorithm. Second, given the comprehensive contribution of attributes, the fuzzy measure Shapley value is employed to represent the attribute weight. Last, the matter element extension model is optimized using the attribute weight information and intuitionistic fuzzy score value, and a two-level evaluation strategy is formulated to determine the ranking and grade. On that basis, scientific decisions are made for several different schemes to be selected.

This research study proposes the inclusion of randomness or an error term in the modified Lee–Carter model, which improves the traditional Lee–Carter model for modeling and forecasting mortality risk for years in the actuarial science field. While the modified Lee–Carter model points out some of its common shortcomings , it has no distributional assumption that has been placed on the error/disturbance term. Incorporating a Gaussian distributional assumption on the error term is proposed, and then, the deep learning technique is used to obtain the parameter estimates. It is a departure from the traditional singular value decomposition estimation technique for estimating the parameters in the model. Finally, the Bühlmann credibility approach is incorporated into the model to determine its forecasting precision compared to the classical Lee–Carter model before applied in actuarial valuations.

Based on boundary rationality and relative profit maximization, quantum game theory is applied to develop a dynamic model of the quantum Cournot duopoly game. We investigate the effect of quantum properties on game stability and nonlinear dynamics. The results show that quantum entanglement can improve the model’s stability and control the generation of bifurcation and chaos when comparing the classical game and the game without considering relative profit maximization. It facilitates the player’s choice of quantum entanglement to control the chaos that emerges in the production output. Numerical simulations verify the chaotic properties of the game by means of bifurcations, maximum Lyapunov exponents, and phase diagrams. The results show that quantum entanglement has different effects on different games.

There are many measurable dimensions for products in industries, and their quality should not deviate too much from the established norm. Therefore, a big part of the consignment’s acceptance is determined by the variance of distributions. In the production of surgical or critical care equipment, measurable dimensions are very important, since each part of the equipment should meet the indicated specification with tolerable variance. Therefore, a procedure for constructing a combined (mixed) sampling plan with a variance measure is provided in this module. This sampling plan supports the medical device engineer in establishing the target dimensions for the quality control of medical instruments because many variations in dimensions result in heavy losses for manufacturers. The relational complete chain sampling plan (RCCSP) is utilized in the II phase inspection to have a significant impact on production quality. The performance measures, algorithm, and design of the mixed sampling plan are provided. The plan is indexed through AQL. In order to choose the plan easily, the table is set up with an example.

In the process of modern industrial production equipment developing towards the direction of structure, automation, and intelligence, motor is still the main power output equipment. If the data flow classification occurs during the operation of the motor, it will lead to problems such as the reduction of its operation efficiency and the increase in system energy consumption. In serious cases, it will even cause motor damage, and the overall system equipment will be shut down for maintenance for a long time, resulting in serious economic losses. Therefore, the research on intelligent multilabel data stream classification technology of motor is of great significance to ensure the stability and reliability of efficient operation of production equipment. In order to improve the recognition efficiency and accuracy of ms-1dcnn’s multilabel data stream classification method in the environment of variable motor conditions and strong noise interference, a multiscale feature fusion framework is constructed based on the residual network structure. The implementation principles of two kinds of attention mechanism algorithms, squeeze and excitation module and convolution attention module, are studied, respectively. The attention module suitable for one-dimensional residual network is designed and embedded into the residual module to build a multiscale attention residual network model. Finally, the effectiveness and superiority of the proposed model are verified by using the experimental platform data.

In this paper, the concepts of plaque expansivity, topological quasi-stability, and quasi-shadowing property for Borel measures are considered. It is proved that every plaque expansive measure with the quasi-shadowing property is topologically quasi-stable with respect to its continuous foliation. At the same time, some other properties of the topological quasi-stable measures, the plaque expansive measures, and measures with the quasi-shadowing on a compact Riemannian manifold are investigated.

Using the sample of A-share listed firms in China from 2008 to 2019, we empirically test the spillover effect of violation punishment on the total factor productivity of director-interlocked firms. We find that violation punishment can significantly aggravate the violation of director-interlocked firms and preliminarily verify the spillover effect of violation punishment among director-interlocked firms. We also find that violation punishment will significantly reduce the total factor productivity of director-interlocked firms. The result is still stable after replacing the measurement method of total factor productivity, and its impact has a certain continuity, which can affect for two consecutive years. Further research shows that the R&D investment plays a mediation role between the violation punishment and the total factor productivity of the director interlocked firms. The violation punishment can reduce the R&D investment of the director-interlocked firms and then reduce the total factor productivity of the director-interlocked firms. In addition, after distinguishing the type of firm ownership, we find that compared with state-owned enterprises, violation punishment has a more significant inhibitory effect on the total factor productivity of private enterprises’ director-interlocked firms. Based on the perspective of the director network, we investigate the spillover effect of violation punishment on the total factor productivity of director-interlocked firms, which provides a new theoretical perspective for the in-depth understanding of the economic consequences of violation punishment, improves the total factor productivity of listed firms, and has important theoretical and practical significance.

This paper concentrates on the properties of estimators in a semiparametric EV model, particularly considering the effects of missing data and linear process errors according to the actual situation. The missing data are processed by three different methods: the direct deletion method, imputation (interpolation fill) method, and regression surrogate method. Also, the corresponding estimators of the slope parameter β and the nonparameter variable g ⋅ are obtained. All the estimators are asymptotically normal, and the consistency rates for which can achieve o n − 1 / 6 log n . Besides, the performance of the estimators is investigated by one sample experiment.

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.

Chemical descriptors are numeric numbers that contain a basic chemical structure and describe the structure of a graph. A graph’s topological indices are linked to its chemical characteristics. Biological activity of chemical compounds can be predicted using topological indices. Numerous chemical indices have been developed in theoretical chemistry, including the Zagreb index, the Randić index, the Wiener index, and many others. In this paper, we compute the exact results for the Randić, Zagreb, Harmonic, augmented Zagreb, atom-bond connectivity, and geometric-arithmetic indices for the Benzenoid networks theoretically.

The online freight platform, also called freight resource sharing platform or freight O2O platform, developed rapidly lately, has gradually become the leaders in China’s road freight market in recent years, especially in the intracity freight market. The platform’s revenue mainly comes from the registration fee and commission, of which the commission is extracted from the freight rate. Therefore, how to determine the registration fee and freight rate becomes a key issue for the platform. Our research focuses on analyzing the pricing strategy by considering the charging mode, game model, and affecting factors. Based on the two-sided market theory and the classic Hotelling model, a tripartite game model of online freight platform-carrier-consignor is constructed. Furthermore, a game model of consignor and alternative carriers is built by introducing the platform user’s psychological pressure and satisfaction priority into the model. Finally, through mathematical derivation and numerical experiment, we analyze the effects of various factors on platform profits, platform registration fees, and freight rate, and propose some suggestions to promote the orderly development of online freight platforms for the intracity freight market.

This article developed a significant improvement of a Galerkin-type approximation to the regularized long-wave equation (RLW) solution under homogeneous Dirichlet boundary conditions for achieving higher accuracy in time variables. First, a basis derived from cubic B-splines and limit conditions is used to perform a Galerkin-type approximation. Then, a Crank–Nicolson and fourth-order 4-stage improved Runge–Kutta scheme (IRK4) is used to discretize time. Both a strong stability analysis of a fully discrete IRK4 scheme and the evaluation of Von Neumann stability of the proposed Crank–Nicolson technique are examined. We demonstrate the efficiency of our method with two test problems. The analytical and numerical solutions found in the literature are then contrasted with the approximate solutions produced by the suggested method. The validated numerical results illustrate that the provided technique is more efficient and converges faster than earlier research, resulting in less computational time, smaller space dimensions, and storage. As a result, the proposed numerical approach is appealing for approximating PDEs whose explicit solution is unknown for a variety of boundary conditions.

Nonlinear systems constantly suffer from time-varying delays, which cover slow delays and nonslow delays. The existing results were used to impose slow delay conditions and were used to study the control problems, but few pieces of research studies have discussed the case of systems with nonslow delay. In this work, we remove the slow time-delay condition and consider the nonlinear delayed system with complicated polynomial terms. By proposing a dynamic gain method and constructing a new Lyapunov–Razumikhin (L-R) function, we successfully construct a stable controller, which guarantees that the plant is globally asymptotically stable (GAS). An example is utilized to verify the raised control scheme.

In this paper, a data-driven differential dynamic programming (DDP) algorithm is presented for the optimal tracking problems of nonlinear affine systems with unknown drift dynamics. Optimal tracking dynamics are established between the system states and expected trajectory. By using the DDP method and test data, a second-order data-driven framework of the Hamilton–Jacobi–Bellman (HJB) equation is constructed, in which system approximation and value function approximation are used. By using this framework, a data-driven iteration algorithm is proposed to achieve the optimal tracking controller. A simulation example is provided to verify the effectiveness of the proposed approach.

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.

In this work, a novel nonlinear control theory design for first-order systems is developed, contributing to the improvement of the existing theory. The theory will allow a design of the open loop and closed-loop controllers that ensure the tracking of any reference, constant, or variant in time with a free initial condition where the Laplace transform was used to find all the analytical solutions, avoiding the transfer function theory. Moreover, the closed-loop control will be the best option to speed up or slow down the reference convergence rate in the desired finite time. Then, an algorithm indicating the steps for designing a closed-loop controller and achieving proper tuning for a real-time application is shown. Finally, this manuscript presents the results and discussions of the theory implemented in a prototype tank of a liquid temperature control system, where the effectiveness of the applied temperature control can be seen.

In the process of multiscale network traffic prediction using a single model, the results are often single, resulting in a decline in the accuracy of multiscale network traffic prediction. In order to solve this problem effectively, a mathematical modeling method of multiscale network traffic combination prediction based on fuzzy SVM is proposed. Firstly, according to the multiscale network approximation signal, the multiscale network traffic feature function is constructed to complete the multiscale network traffic feature extraction. Secondly, according to the feature extraction results, the fuzzy membership function is introduced into the SVM, and the fuzzy SVM is used to classify the multiscale network traffic. Finally, based on the traffic classification results, the combination prediction of multiscale network traffic is completed by combining the grey Verhulst prediction model with the GNN model. The experimental results show that the prediction accuracy of this method for multiscale network traffic is higher, and the prediction accuracy can always maintain above 95%, and the MSE and MAE values are relatively low.

After mining, upward fractures develop from bottom to top, and they are key channel for groundwater and gas flow and also sand burst; therefore, it is important to study the development mechanism and criterion of upward fractures. Combining with physical simulation and theoretical analysis, the development law of mining-induced upward fractures was revealed, and the mechanism and criterion of fracture propagation was studied. The results show that the development of mining-induced upward fractures can be divided into the following three stages: with the face advances from the open cut, the vertical fractures are generated at the mining boundary, and the roof is flexural but not caved; it is “Fractures generate stage.” After roof caves, the upward fractures extend and develop upward along the caving angle with continue advances; it is “Fractures develop and extend stage.” When the face reaches critical mining, the development height of the upward fractures is at its maximum and is basically invariable; it is “Fractures stabilization stage.” Mining-induced fractures can be analyzed as mixed-type fractures in fracture mechanics; when the combined stress exceeds the critical strength of stratum, it will extend downward until penetrate through the stratum, and the upward fracture develops and extends upwards. The parameter controlling the fracture of the rock stratum is the maximum hoop tensile stress σ(θ)max, at the fracture end, the theoretical model of fracture propagation was established, and the criterion of upward fractures was proposed.

High-resolution millimeter-wave (MMW) radar is viewed as a low-cost and highly reliable sensor compared to camera, lidar, etc., in moving scenarios and thus has been selected by highway stakeholders as an important roadside detector to detect the movement of traffic vehicles and monitor traffic flow in real time. However, the echo signal of MMW radar in complex highway environment contains not only the signal reflected by target but also spurious signals and other interference signals, which significantly affects the estimation of the target movement state. To solve this problem, an improved vehicle tracking method is designed to simultaneously estimate the polar angle and polar radius in coordinator of MMW radar. Moreover, considering the movement patterns of target vehicles in dynamic uncertain traffic situations, a set of state space models, such as CA, CV, and CT are combined to represent the vehicle movement. In addition, based on the enhanced detection performance of a single radar, the combination of multiple MMW radars’ information was performed to determine the sequential trajectory of the target vehicle on the continuous road sections; then, the historical trajectory of the target vehicle was correlated and fused. Real experiments in highway scenarios show that the method used in this study is effective in deriving the trajectory of the vehicle and improving the positioning accuracy and reliability when the vehicle performs heavy maneuvers.

The effectiveness of evolutionary algorithms (EAs) such as differential search algorithm (DSA), Real-Coded genetic algorithm with simulated binary crossover (RGA-SBX), particle swarm optimization (PSO), and chaotic gravitational search algorithm (CGSA) on the optimal design of cross-coupled nonlinear PID controllers is compared in this paper. A cross-coupled multivariable PID controller structure for the binary distillation column was developed with two inputs and two outputs. EA simulations are run to lower IAE using two stopping criteria, namely, maximum number of functional evaluations (Fevalmax) and Fevalmax plus PID parameter and IAE tolerance. Over 20 separate trials were used to compare the performances of several EAs using statistical measures such as best, mean, standard deviation of outcomes, and average calculation time. This article presents the design of a cross-coupled nonlinear PID controller using single-objective evolutionary algorithms. Using evolutionary algorithms (EAs) with a multicrossover strategy, the results achieved by various EAs are compared to previously reported results. The results of a multivariable cross-coupled system clearly show that a single-objective nonlinear PID controller performs better. Simulations further show that all four techniques evaluated are suitable for PID controller tweaking off-line. However, only the single-objective evolutionary algorithms are acceptable for online PID tuning due to their higher consistency and shorter computation time.

In this work, a lower limb exoskeleton with uncertainties and external disturbances under constrained motion has been controlled by developing a model-free adaptive optimal fractional-ordersuper-twisting sliding mode (AOFSTSM) controller. Contrary to popular existing modeling methods, the modeling of the lower limb exoskeleton has been accomplished in the case of contact with the ground. A fractional-order operator and super-twisting sliding mode control have been combined to achieve an excellent tracking performance, chatter-free control inputs, and robustness to external disturbances and uncertainties. The controller structure is model-independent which has been derived without needing to know the dynamics of the system. An adaptive control strategy has been adopted as an approximating method to evaluate the uncertain dynamics of the system without the necessity for knowing the prior knowledge of the upper bounds. A grey wolf optimization technique has been used to find the optimal values of controller parameters. The stability of the overall system has been investigated and derived from the Lyapunov stability criterion. To validate the effectiveness of our proposed controller structure, a series of comparative simulations have been conducted with optimal fractional-order super-twisting sliding mode (OFSTSM) controller and fractional-order super-twisting sliding mode (FSTSM) controller. The comparative simulations have been also carried out with other types of recently existing control methods and recently existing optimization algorithms. The results of numerical simulations indicate the superiority of the AOFSTSM controller over other types of recently existing optimization algorithms and controllers in terms of tracking error and robustness towards the uncertainties and external disturbances.

This study discusses the practical engineering problem of determining random load sources on coal-rock structures. A novel combined regularization technique combining mollification method (MM) and discrete regularization (DR), which was called MM-DR technique, was proposed to reconstruct random load sources on coal-rock structures. MM-DR technology is compared with DR, Tikhonov regularization (TR), and maximum entropy regularization (MER) in load reconstruction. The results show that the reconstructed random load sources are more consistent with the real load sources using MM-DR technique combined with particle swarm optimization (PSO) and L-curve method, which was named as PSO-L method, and selecting optimal ω value of kernel function is beneficial to overcome the ill-posed of random load sources reconstruction and to obtain the stable and approximate solutions. The method proposed in this study provides a theoretical basis for the recognition of coal-rock interface.

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.

Multilevel inverters have great scope in current developments of grid-connected solar PV systems. Two-level inverters are the simplest kind of multilevel inverter available (MLI). As the number of output levels is raised, the total harmonic distortion decreases. In classic MLI topologies, more electronic components are utilized to get higher-level outputs, which raise the cost, complexity, and volume of typical MLI installations. By reducing the design components, the cost of the system will be reduced. Furthermore, the two- and three-level inverters produce constant dv/dt output, which increases the stress on the power switches. This research proposes an asymmetric MLI topology that is suitable for PV applications and utilizes less number of DC sources and switches. The proposed inverter is controlled by selective harmonic elimination-based pulse width modulation (SHEPWM) to eliminate the lower-order dominant harmonics. The nonlinear equations produced by the SHEPWM are solved for the switching angles of the proposed inverter using the Newton-Raphson (NR) method and particle swarm optimization (PSO) method for various modulation indexes. The performance of the proposed inverter is analyzed based on the total harmonic distortion (THD) of the output for different operating levels of the inverter by comparing similar topologies in the literature. The THD obtained by the NR method is 7.3% and by using PSO is 4.23% at 0.9 modulation index.

The aim of the present work is to apply the fractional derivative to the heat transformation of a nanofluid along with ramped wall temperature. The flow is analyzed under the effect of magnetohydrodynamic together with heat transfer. A nanofluid under the application of fractional-order differential equations by Caputo–Fabrizio derivatives with respect to time has the ability to explain the behavior of nanofluid under the influence of memory concept. For the same purpose, Caputo–Fabrizio time-fractional derivative is applied to investigate the behavior of nanoparticles on the thermal conductivity of a fluid. Appropriate nondimensional variables are engaged in the equation which governs the problem and guides us to obtain the exact solutions for the fields of velocity and temperature. These obtained solutions for the nondimensional set of governing equations are found by extracting them from the governing equations by applying Laplace transform techniques along with Caputo–Fabrizio time-fractional derivative. The influence of the fractional variable on the velocity, temperature, and Nusselt number is graphically exposed and discussed. The velocity for the state of wall temperature as ramped falls down with the enlarging values of the fractional parameter. Variation in Nusselt number is shown in the tabular form. Solutions are visualized graphically to make an analysis of how the variation is taking place in the physical behavior of the nanofluid flow with respect to the change in distinct physical parameters. The obtained results here will have useful industrial and engineering implementations. It is found that fluid velocity in the flow direction decreases with the increase in the magnetic parameter. The relationship of fractional parameter with the velocity and temperature of the nanofluid is found as direct proportional for a smaller time. However, this direct proportionality converts into inverse proportionality for larger values of time. It is observed that the increase in the nanoparticles volume fraction causes an increase in temperature distribution. It is due to lower specific heat of nanoparticles and its higher thermal conductivity than that of the base fluid.

The transportation mode of the railway cold chain has an essential impact on operational efficiency and cost. Under the uncertainty of demand and limited transportation capacity, an evolutionary game model of railway cold chain logistics is constructed to explain and analyze the dynamic decision-making processes and mechanisms between cargo owners and railway transport enterprises. The results show that the evolutionary path and convergence speed of the model are influenced by the initial value and its allocation coefficient, cargo volume, additional benefit, and default cost. By using Matlab, numerical simulation is conducted to verify the theoretical assumptions and results. The conclusion provides a basis for studying and managing railway cold chain logistics.

High utility itemset mining is an interesting research in the field of data mining, which can find more valuable information than frequent itemset mining. Several high-utility itemset mining approaches have already been proposed; however, they have high computational costs and low efficiency. To solve this problem, a high-utility itemset mining algorithm based on the particle filter is proposed. This approach first initializes a population, which consists of particle sets. Then, to update the particle sets and their weights, a novel state transition model is suggested. Finally, the approach alleviates the particle degradation problem by resampling. Substantial experiments on the UCI datasets show that the proposed algorithm outperforms the other previous algorithms in terms of efficiency, the number of high-utility itemsets, and convergence.

High-rise buildings fires are far more harmful than ordinary fires. In this regard, fire risk assessment is an important way to control fire risk and reduce losses. This study presents a comprehensive model to electrical fire dynamic risk assessment of high-rise buildings based on a Bayesian network (BN) and a variable fuzzy set theory (VFST). Firstly, electric system, safety management, and other factors were comprehensively analyzed based on three categories: hazard sources identification (HSI), fault tree (FT) analysis, and VFST. A high-rise building electrical fire dynamic risk assessment model was established based on a BN. Secondly, the prior probability of BN root nodes was determined by VFST, and the conditional probability table (CPT) was determined by the analytic hierarchy process (AHP) and decomposition method. On that basis, the quantitative inference and sensitivity analysis can be performed on the electrical fire risks of high-rise buildings in combination with the variable fuzzy Bayesian network (VFBN) inference. Finally, a high-rise building in Wuhan, China, was used as an example for verification. The results show that the proposed method can realize dynamic risk assessment of electrical fires in high-rise buildings. This study provides a new method for fire risk assessment of high-rise buildings to reduce the possibility of fire.

The current work is an analysis of the laminar, two-dimensional, and transient flow over a circular cylinder with a two-branched splitter plate. For complex nonlinear governing equations, the finite element method is used as a computational approach. The objective of the current study is to determine the best conditions using geometrical characteristics along with the impact of Reynolds number R e .The geometric parameters, α which is the angle of separation between the pair of splitter plates of length L and G / D which is the gap from diameter ratio of the splitter plate to cylinder diameter, are the controlling tools for the present study. Due to its little impact on flow characteristics, it was discovered that using a two-branched splitter plate in Reynolds numbers under 100 is not desirable. A complete vortex shedding has been achieved with α = 0 ° and G / D = 2 at R e = 100 and also the periodic behavior of velocity has been controlled. The least drag force is observed at an angle of 30 ° between the two plates compared to another angle of 0 ° . It is not advised to use two splitter plates at angles more than 30 ° because this will not further contribute in the drag reduction. Since the gap to diameter ratio between the splitter plates and the cylinder increased up to a significant value, the overall control on hydrodynamic effects is achieved. It is concluded that the maximum drag reduction of 48% over the cylinder has taken place when the R e i s 200 , α i s 30 ° , a n d G / D i s 2 .

In order to better describe the pressure-sinkage process of deep-sea surface sediments, this article proposes a new four-element model. First, the pressure-sinkage process was divided into four components according to the change in the deep-sea sediment sinkage rate, and the time-dependent mechanical property of deep-sea sediments was described. Then, a new four-element pressure-sinkage model was established by introducing variable-order fractional derivatives into the modelling idea of classic element combination to describe the full pressure-sinkage regions of deep-sea sediments. Furthermore, by comparing with the experimental results from other literature, the proposed model was verified to predict the pressure-sinkage process of deep-sea sediments under static and dynamic loads. Finally, through a sensitivity analysis of model parameters, the effects of ground pressure and time on the law and mechanism of deep-sea sediment pressure sinkage were revealed. Results indicate that the deep-sea sediment pressure-sinkage process has obvious nonlinear characteristics and that the sinkage depth increases directly with ground-specific pressure and time. Through comparative analysis of both experimental and predicted results, the proposed pressure-sinkage constitutive model can describe the stress-strain of the full stages. Thus, the variable order model represents an effective method to predict the pressure-sinkage process of deep-sea sediments.

The rapid growth and development of the logistics industry has brought productivity to commerce and trade and greatly contributed to national economy, but at the expense of vast energy consumption, which significantly affects sustainability. To improve sustainability of the development of the logistics industry (LISD) and identify its potential influencing factors more directly and efficiently, this study proposes a data-driven-based evaluation and optimisation method. First, a comprehensive evaluation index system is constructed for LISD from the aspects of economy, society, and environment (including the logistics industry, degree of specialisation, environmental effects, and innovation capability). Second, considering the diversity of dimensions and units, a min-max standardisation is utilised for data normalisation, providing dimensionless indicators for further weight determination via an entropy value method. Third, two coupling degree models are adopted to evaluate the degree of correlation among subsystems. Subsequently, a degree of obstacle model is applied to analyse the interaction between factors, providing theoretical support for improving regional LISD. Finally, an evaluation of LISD in Anhui Province is used as a case study to validate the practicability and feasibility of the proposed method, establish theoretical basis, and propose policy recommendations for future sustainable development.

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.

With the development of the economy and society, electricity demand is increasing. However, there are some problems on the power grid side, such as the excess capacity of power installed, abandoned power of renewable energy, low overall utilization rate, and high pressure of stable operation. The problem stems from the inconsistency between the peak of electricity production and consumption and the lack of obvious power price signals. In this article, the market development objectives are combined with the time-divided transaction, and two objectives of market development are proposed: (1) Reduce the peak-valley difference of power supply and demand; (2) Expand the peak-valley price difference in the market. Combined with the physical properties of the power market, a multiperiod coupling trading mechanism is designed. The results show that the speculative and profit-seeking behavior of power producers will be stopped under the proposed trading mode, the potential of users for peak-valley filling will be effectively developed, the peak-valley gap of the power system can be controlled within a reasonable range, and the peak-valley gap of power trading can be further amplified.

This paper proposes a novel concept of using defect management technology to measure, predict, and optimize the management of the entire software project cycle. It first utilizes the software defect measurement GQIM model and prediction Gompertz model to track and quantify the defects of the entire project cycle. Then, the specific optimization strategy is proposed to manage the core processes of the project management, including project scope management, project time management, project quality management, project human resource management, project communication management, and project risk management. This study solves the quantification problems that are common in the current software project management process, such as complex testing procedures, high defect repair costs, and high communication costs. Furthermore, it contributes to promote the overall level of project management in the software industry. Taking the SDLM’s electronic banking software project management as an example, the various management quality indicators before and after the optimization of the project management process strongly indicate that using software defect management technology to optimize the software project management process is feasible and effective.

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.

The development of 5G (fifth-generation wireless systems) determines the future direction of technology and economy and has received extensive public attention. Studying the changing rules of public attention to 5G can provide an important guiding significance for the sustainable development of 5G. This paper takes Baidu Index as the measurement index of 5G public attention and analyzes the spatial and temporal evolution characteristics and influencing factors of public attention to 5G from 2011 to 2021 by using the elasticity coefficient, Gini coefficient, geographical concentration index, and panel data model. The results of the study show the following. (1) The public concern to 5G is generally on the rise, but the heat has declined in the past two years. (2) The public’s 5G attention shows a seasonal effect, with the highest attention in March and June. (3) The spatial difference of 5G public attention is obvious. The eastern region has a high degree of attention, the internal differences between the eastern and western regions are obvious, and the central region is relatively balanced. (4) The factors such as local economic level, education level, Internet development, and media attention have significantly affected the public focus on 5G. Also, some suggestions are made for the sustainable development of 5G and the planning of 6G (sixth-generation wireless systems).

This article is concerned with the numerical analysis of reduction of fluid forces (drag and lift) and suppression of vortex shedding around two side-by-side square cylinders using the lattice Boltzmann method. For this purpose, a passive flow control methodology is adopted in which a flat plate (termed as the control plate) is placed at the upstream position of cylinders. The gap ratio ( g ) between the cylinders and the plate is varied from 0 to 4 while the height of the control plate is varied from 0.2 to 0.8 at fixed Reynolds number of 150. Different flow patterns are found in this study depending on the gap ratios and different heights of the control plate. These flow patterns are single bluff body flow, flip-flopping flow, and antiphase vortex shedding flow patterns. The narrow gap ratio ( g = 0.5) is found to have more impact in terms of force reduction and flow control as compared to larger gap ratios. This article also shows that the fluid flow parameters such as the mean drag coefficient, the Strouhal number, and rms values of drag and lift coefficients of both cylinders reduce considerably due to the control plate as compared to without the control plate. In this regard, it is found that the mean drag coefficient of upper cylinder reduces upto 45.94%, the Strouhal number of both cylinders reduces upto 18.66%, while a maximum reduction of 83.47% and 95.39% is observed in root-mean-square values of drag and lift coefficients of upper cylinder, respectively. Another important finding of this article is that the control plate does not suppress forces and control flow at all gap ratios and heights of control plate considered in this article. In some cases, the flow-induced forces were found to be increasing as compared to without the control plate, and this generally happened at higher gap ratios.

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.

The design of sensor fault detection and isolation (FDI) systems has been studied widely by several authors of different fields of science (engineering, mathematics, physics) to ensure the continuous operation of systems or processes. This research presents the design and evaluation of a fault detection and isolation (FDI) system based on discrete fractional-orderhigh-gain observers (DFOHGO) for the outlet temperature sensors of a heat exchanger. The formulation of the discrete fractional-order observers is based on the Grünwald–Letnikov fractional-order derivative. The FDI system is based on a bank of two DFOHGOs to carry out a residual evaluation. The obtained results showed that the DFOHGOs provide accurate estimations of the temperatures. This allows switching the faulty sensor signal to the temperature estimations provided by the DFOHGOs when a fault occurs.

Multistability is a universal nonlinear feature for many areas of physics and technology; for most applications, it is an undesired feature leading to uncertainty of the system’s functioning, and therefore, it should be controlled. We deal with the model of a multistable chopper-fed DC electric drive and study the control of multistability by the periodic modulation of a PWM (pulse-width modulation) carrier signal. The effect of utilizing sinusoidal modulation has been studied employing brute-force bifurcation diagrams as well as those obtained by numerical continuation, symbolic analysis of periodic modes, and normal form approximation of border-collision bifurcation. The result is formulated in terms of the acceptable amplitude of the modulation while the effect of phase shift is taken into account. As an alternative, a modified modulation in the form of the absolute value of the sine wave has been studied; the latter is characterized by a simpler bifurcation structure. Additionally, we propose an implementation of the adaptive control with the objective to drive the system away from a “nonnominal” mode. It has been demonstrated that such a transition should use smooth attenuation of the modulation signal to ensure the desired “nominal” operation of the system.