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

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 Re.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 Re = 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 Re is 200, α is 30°, and G/D is 2.

Although the underwater tunnel is under a rapid development nowadays, the theoretical research methods, including reflection and angle transformation, are still in their infancy. At present, almost all the studies are based on the hypothesis that soil layers are homogeneous, but most of the soil layers are anisotropic in actual soil engineering. Therefore, an analytical derivation method of flow field in heterogeneous media based on the traditional equiangular transformation method is presented. Numerical simulation shows that with the increase of the anisotropic permeability coefficient ratio, the contour line of approximate ellipse becomes flat. On the contrary, the lateral permeability is proportional to the upgrade of the anisotropic permeability coefficient ratio and the permeability appears the same trend.

The numerical simulation is similar to the experimental research, which uses the computer. 3D printing is an experimental study, which is compared with simulation. 3D printing and precision casting of impellers are frequently used in industrial scenarios. To address the problem of low casting yield of aluminium alloy impellers in gypsum‐type casting, three‐dimensional modelling by magics, casting simulation, and parameter optimization method by applying ProCAST software are used to improve the casting yield of impellers. The results show that the optimized structure of the casting system is more reasonable, so that the shrinkage and shrinkage defects are concentrated in the location of the riser; the maximum stress value of the casting and the casing during the solidification stage is 2.71 MPa, which is much smaller than the stress limit of the casing; the maximum horizontal displacement of the casting along the X and Y axes after solidification is completed at 0.025 mm and 0.022 mm, respectively, and the maximum displacement along the Z axis due to the presence of the riser is 0.009 mm, which greatly improves casting accuracy and molding quality.

Hard turning has replaced conventional grinding in production processes in recent years as an emerging technique. Nowadays, coated carbide tools are replacing expensive CBN inserts in turning. Wear is a significant concern when turning with coated carbide; it immediately affects the acceptability of the machined surface, which causes machine downtime and loss due to wastage in machined parts. Online tool condition monitoring (TCM) is required to prevent such critical conditions. Hard turning differs from conventional turning in energy balance during metal cutting, resulting in greater thrust force; hence, the TCM model presented for conventional turning may not be suitable for hard turning. Hence, tool wear prediction for turning is projected based on thrust force using an artificial neural network (ANN). All of the tests were done using a design of experiments called full factorial design (FFD). The specimens were made of AISI 4140 steel that had been hardened to 47 HRC, and the inserts were made of coated carbide. The most impactful input features for wear, selected based on experimental outputs, were given to the neural network and trained. Tool wear is an estimated output from the training set that has been validated with satisfactory results for random conditions. The 5–10–1 network structure with the Levenberg–Marquardt (LM) learning algorithm, R2 values of 0.996602 and 0.969437 for the training and testing data, and mean square error values of 0.000133152 and 0.004443 for the training and testing data, respectively, gave the best results. The MEP values of 0.575407 and 2.977617 are very low (5%). The LM learning algorithm‐based ANN is good at predicting tool wear based on how well it predicts tool wear for both the testing set and the training set.

In order to solve the problem that there are a large number of unknown protocols on the network, which affect the network management and network security in varying degrees, an unknown binary protocol identification method is proposed. On the premise that the center cluster of unknown protocols is obtained by the clustering algorithm, the unknown protocols for network traffic are identified by combining one‐class of classification with one‐dimensional CNN classification technology. At first, a class of classification algorithm is used to select the unknown protocols, and then, the labeled protocol data obtained by clustering are used to train the one‐dimensional CNN model, and the classified binary protocol packets are directly used as the input of the one‐dimensional convolution neural network. After the classification of the CNN model, the unknown protocols are finally identified. The experimental results show that the proposed classification and recognition method is better than the traditional CNN and SVM algorithms, and the maximum frequency pooling is better than the traditional pooling method.

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.