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This paper aims to develop a novel trajectory optimization algorithm which is capable of producing high accuracy optimal solution with superior computational efficiency for the hypersonic entry problem.

A two-stage trajectory optimization framework is constructed by combining a convex-optimization-based algorithm and the pseudospectral-nonlinear programming (NLP) method. With a warm-start strategy, the initial-guess-sensitive issue of the general NLP method is significantly alleviated, and an accurate optimal solution can be obtained rapidly. Specifically, a successive convexification algorithm is developed, and it serves as an initial trajectory generator in the first stage. This algorithm is initial-guess-insensitive and efficient. However, approximation error would be brought by the convexification procedure as the hypersonic entry problem is highly nonlinear. Then, the classic pseudospectral-NLP solver is adopted in the second stage to obtain an accurate solution. Provided with high-quality initial guesses, the NLP solver would converge efficiently.

Numerical experiments show that the overall computation time of the two-stage algorithm is much less than that of the single pseudospectral-NLP algorithm; meanwhile, the solution accuracy is satisfactory.

Due to its high computational efficiency and solution accuracy, the algorithm developed in this paper provides an option for rapid trajectory designing, and it has the potential to evolve into an online algorithm.

The paper provides a novel strategy for rapid hypersonic entry trajectory optimization applications.

Most of the redundant dual-arm robots are singular free, dexterous and collision free compared to other robotic arms. This paper aims to analyse the workspace of redundant arms to study the manipulability. Furthermore, multi-layer perceptron (MLP) algorithm is used to determine the various joint parameters of both the upper body redundant arms. Trajectory planning of robotic arms is carried out with the help of inverse solutions obtained from the MLP algorithm.

In this paper, the kinematic equations are derived from screw theory approach and inverse kinematic solutions are determined using MLP algorithm. Levenberg–Marquardt (LM) and Bayesian regulation (BR) techniques are used as the backpropagation algorithms. The results from two backpropagation techniques are compared for determining the prediction accuracy. The inverse solutions obtained from the MLP algorithm are then used to optimize the cubic spline trajectories planned for avoiding collision between arms with the help of convex optimization technique. The dexterity of the redundant arms is analysed with the help of Cartesian workspace of arms.

Dexterity of redundant arms is analysed by studying the voids and singular spaces present inside the workspace of arms. MLP algorithms determine unique solutions with less computational effort using BR backpropagation. The inverse solutions obtained from MLP algorithm effectively optimize the cubic spline trajectory for the redundant dual arms using convex optimization technique.

Most of the MLP algorithms used for determining the inverse solutions are used with LM backpropagation technique. In this paper, BR technique is used as the backpropagation technique. BR technique converges fast with less computational time than LM method. The inverse solutions of arm joints for traversing optimized cubic spline trajectory using convex optimization technique are computed from the MLP algorithm.

The present study offers insight to the current literature regarding digital uncertainty and the hypothesis of portfolio optimisation by risk estimation index of the geopolitical risks (GPR). The examination investigates the effect of Geopolitical Risk Index which as of late was explored by Caldara and Iacoviello (2018) to shine a light to the impact of worldwide strain and struggle on excellent portfolio weights, and the link between Convex Risk Measures. Moreover, it investigates the way corporate administration, bank explicit indicators influence China banks' marketing profitability. Furthermore, we explored the idea of a directed linear space and given some sets of mathematical objects whose structure is represented by the concept of linear spaces.

The issues of radiating sources in the existence of smooth convex matters by such objects are of huge significance in the modeling of antennas on structures. Conformal antenna arrays are necessary when an antenna has to match to certain platforms. A fundamental problem in the design is that the possible surfaces for a conformal antenna are infinite in number. Furthermore, if there is no symmetry, each element will see a different environment, and this complicates the mathematics. As a consequence, the element factor cannot be factored out from the array factor.

This paper intends to enhance the design of the conformal antenna. Here, the main objective of this task is to maximize the antenna gain and directivity from the first-side lobe and other side-lobes in the two way radiation pattern. Thus the adopted model is designed as a multiobjective concern. In order to attain this multiobjective function, both the element spacing and the radius of each antenna element should be optimized based on the probability of the Crow Search Algorithm (CSA). Thus the proposed method is named Probability Improved CSA (PI-CSA). Here, the First Null Beam Width (FNBW) and Side-Lobe Level (SLL) are minimized. Moreover, the adopted scheme is compared with conventional algorithms, and the results are attained.

From the analysis, the gain of the presented PI-CSA scheme in terms of best performance was 52.68% superior to ABC, 25.11% superior to PSO, 13.38% superior to FF and 3.21% superior to CS algorithms. Moreover, the mean performance of the adopted model was 62.94% better than ABC, 13.06% better than PSO, 24.34% better than FF and 10.05% better than CS algorithms. By maximizing the gain and directivity, FNBW and SLL were decreased. Thus, the optimal design of the conformal antenna has been attained by the proposed PI-CSA algorithm in an effective way.

This paper presents a technique for enhancing the design of the conformal antenna using the PI-CSA algorithm. This is the first work that utilizes PI-CSA-based optimization for improving the design of the conformal antenna.

Artificial intelligence (AI) has become an input to the production of goods and services. Therefore, a general question is there that “How the labor hour/human resource will be replaced by the artificial intelligence?” To answer this question, the paper considers that both AI and the human resources (HR) are the inputs to the firm and explains the choice between the two with reference to the customer relationship management. The paper derives the individual firms and the industry demand functions of the AI and the HR when both are present in the production of the identical or closely related goods and services. Moreover, the paper also shows the strategic behavior of an individual firm with the industry in selecting the AI and the HR. It has been shown that the individual firm's choice in the industry depends on the choice of the industry leader. The paper explains the supermodular game between the firms in an industry.

Game theory, industrial organization and non-convexity theories have been used in this paper to identify the choice between the HR and the AI in the customer relationship management.

The paper explains analytically the preference and demand for AI in the industry. Individual firm's strategic behavior and decision on choosing AI and the industry equilibrium have been studied logically. Moreover, the paper gives some light on the question of employment in presence of AI. The paper proves that in the presence of AI, labor demand will not be reduced but both will be used.

This work proves for the first time using some logical derivation that AI will not crowd out labor from the market. Moreover, to run AI, labor should also be used. It has been proved that to complete a job with speed and quality, both AI and HR are to be used.

For robot motion planning there exists a large number of different algorithms, each appropriate for a certain domain, and the right choice of planner depends on the specific use case. The purpose of this paper is to consider the application of bin picking and benchmark a set of motion planning algorithms to identify which are most suited in the given context.

The paper presents a selection of motion planning algorithms and defines benchmarks based on three different bin-picking scenarios. The evaluation is done based on a fixed set of tasks, which are planned and executed on a real and a simulated robot.

The benchmarking shows a clear difference between the planners and generally indicates that algorithms integrating optimization, despite longer planning time, perform better due to a faster execution.

The originality of this work lies in the selected set of planners and the specific choice of application. Most new planners are only compared to existing methods for specific applications chosen to demonstrate the advantages. However, with the specifics of another application, such as bin picking, it is not obvious which planner to choose.

The inverse problem in the eddy current (EC) imaging of metallic parts is an ill‐posed problem. The purpose of the paper is to compare the performances of regularized algorithms to estimate the 3D geometry of a surface breaking defect.

The forward problem is solved using a mesh‐free semi‐analytical model, the distributed point source method, which allows EC data to be simulated according to the shape of the considered defect. The inverse problem is solved using two regularization methods, namely the Tikhonov (l2) and the 3D total variation (tv) methods, implemented with first‐ and second‐order algorithms. The inversion performances were evaluated in terms of both mean square error (MSE) and computation time, while considering additive white and colored noise, respectively, standing for acquisition errors and model errors.

In presence of colored noise, the authors found out that first‐ and second‐order methods provide approximately the same result according to the SEs obtained while estimating the defect voxels. Nevertheless, in comparison with (l2), the (tv) regularization was proved to decrease the MSE by 10 voxels, at the cost of less than twice the computational effort.

In this paper, an easy to implement mesh‐free model, based on virtual defect current sources, was used to generated EC data relative to a defect positioned at the surface of a metallic part. A 3D total variation regularization approach was used in combination with the proposed model, which appears to be well suited to the reconstruction of volumic defects.

The purpose of this paper is to review the recent studies on opinion polarization and disagreement.

In this work, recent advances in opinion polarization and disagreement and pay attention to how they are evaluated and controlled are reviewed.

In literature, three metrics: polarization, disagreement and polarization-disagreement index are usually adopted and there is a tradeoff between polarization and disagreement. Different strategies have been proposed in literature which can significantly control opinion polarization and disagreement based on these metrics.

This review is of crucial importance to summarize works on opinion polarization and disagreement and to the better understanding and control of them.

Cross-layer approach in media access control (MAC) layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in wireless sensor network (WSN) and Internet of Things (IoT) applications. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes. Game theory optimization for distributed may increase the network performance. The purpose of this study is to survey the various operations that can be carried out using distributive and adaptive MAC protocol. Hill climbing distributed MAC does not need a central coordination system and location-based transmission with neighbor awareness reduces transmission power.

Distributed MAC in wireless networks is used to address the challenges like network lifetime, reduced energy consumption and for improving delay performance. In this paper, a survey is made on various cooperative communications in MAC protocols, optimization techniques used to improve MAC performance in various applications and mathematical approaches involved in game theory optimization for MAC protocol.

Spatial reuse of channel improved by 3%–29%, and multichannel improves throughput by 8% using distributed MAC protocol. Nash equilibrium is found to perform well, which focuses on energy utility in the network by individual players. Fuzzy logic improves channel selection by 17% and secondary users’ involvement by 8%. Cross-layer approach in MAC layer will address interference and jamming problems. Hybrid distributed MAC can be used for simultaneous voice, data transmissions in WSN and IoT applications. Cross-layer and cooperative communication give energy savings of 27% and reduces hop distance by 4.7%. Choosing the correct objective function in Nash equilibrium for game theory will address fairness index and resource allocation to the nodes.

Other optimization techniques can be applied for WSN to analyze the performance.

Game theory optimization for distributed may increase the network performance. Optimal cuckoo search improves throughput by 90% and reduces delay by 91%. Stochastic approaches detect 80% attacks even in 90% malicious nodes.

Channel allocations in centralized or static manner must be based on traffic demands whether dynamic traffic or fluctuated traffic. Usage of multimedia devices also increased which in turn increased the demand for high throughput. Cochannel interference keep on changing or mitigations occur which can be handled by proper resource allocations. Network survival is by efficient usage of valid patis in the network by avoiding transmission failures and time slots’ effective usage.

Literature survey is carried out to find the methods which give better performance.

The newsvendor problem is fundamental to many operations management models. The problem focuses on the trade-off between the gains from satisfying demand and losses from unsold products. The newsvendor model and its extensions have been applied to various areas, such as production plan and supply chain management. This chapter examines the study about newsvendor problem. In this research, there is a review of the contributions for the multiproduct newsvendor problem. It focuses on the current literature concerning the mathematical models and the solution methods for the multiitem newsvendor problems with single or multiple constraints, as well as with the risks. The objective of this research is to go over the newsvendor problem and bring into comparison different newsvendor models applied to the flower industry. A few case studies are described addressing topics related to the newsvendor problem such as discounting and replenishment policies, inventory inaccuracies, or demand estimation. Three newsvendor models are put into practice in the field of flower selling. A full database of the flowers sold by an anonymous retailer is available for the study. Computational experiments for practical example have been conducted with use of the CPLEX solver with AMPL programming language. Models are solved, and an analysis of different circumstances and cases is accomplished.