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This chapter studies the consequences of firm delayering on wages and the wage distribution inside firms. I consider a market-based tournament model with asymmetric information to endogenize firms’ delayering decisions. My model predicts that when the CEO becomes more productive, firms grow in size. When the CEO becomes sufficiently productive, firms delayer. After delayering, wages at all levels rise and the wage gap between the CEO and the laborers widens. These predictions capture the dynamic process of firms’ structure and size changes and match a set of empirical findings in recent studies that are not well explained by existing theories.

The focus of industrial cluster innovation lies in the cooperation between enterprises and universities/scientific research institutes to make a theoretical breakthrough in the system and mechanism of industrial cluster network. Under the theoretical framework of cluster network, industrial structure can be optimized and upgraded, and enterprise benefit can be improved. Facing the increasing proliferation and multi-structured enterprise data, how to obtain potential and high-quality innovation features will determine the ability of industrial cluster network innovation, as well as the paper aims to discuss these issues.

Based on complex network theory and machine learning method, this paper constructs the structure of “three-layer coupling network” (TLCN), predicts the innovation features of industrial clusters and focuses on the theoretical basis of industrial cluster network innovation model. This paper comprehensively uses intelligent information processing technologies such as network parameters and neural network to predict and analyze the industrial cluster data.

From the analysis of the experimental results, the authors obtain five innovative features (policy strength, cooperation, research and development investment, centrality and geographical position) that help to improve the ability of industrial clusters, and give corresponding optimization strategy suggestions according to the result analysis.

Building a TLCN structure of industrial clusters. Exploring the innovation features of industrial clusters. Establishing the analysis paradigm of machine learning method to predict the innovation features of industrial clusters.

The purpose of this paper is to focus on the development of a thorough method for the macromechanical analysis of fabrics.

The homogenization method was implemented for the generation of continuum equivalent model for the plain woven structure. Keystone of the method is the mesomechanical analysis of the textile unit cell for the evaluation of the apparent properties and the generation of an equivalent macromechanical model supporting the mechanical performance of the structure. The finite element method (FEM) using beam elements was applied for the mechanical analysis of the discrete model of the unit cell and the FEM using shell elements was applied for the analysis of the continuum macromechanical model.

The tensile, shear and bending test of the unit cell were simulated. The constitutive equations of the continuum model were formed considering equivalent performance with the discrete model.

The reliability of the equivalent model in tensile, shear (in‐plane) and bending (out‐of‐plane) deformation was achieved even for asymmetric woven structures. The low computational power demanded for the meso‐ and macro‐mechanical modelling and analysis is a beneficial feature of the proposed method.

On-orbit assembly technology is a promising research topic in spaceflight field. For purposes of studying the dynamic performance and reducing weight of an on-orbit assembly satellite structure frame, this paper aims to propose a structural optimization design method based on natural frequency.

The dynamic stability of the satellite under working condition depends on the mechanical properties of the structure matrix. A global structural optimization model is established, with the objective of mass minimization and the constraints of given natural frequencies and given structure requirements. The structural optimization and improvement design method is proposed using sequential quadratic programming calculation.

The optimal result of objective function is effectively obtained, and the best combination of structural geometric parameters is configurated. By analyzing the relationship between the structural variables and optimization parameters, the primary and secondary factors to the mass optimization process of the microsatellite satisfying the dynamic performance requirements are obtained, which improves the effectiveness and accuracy of the system optimization design.

This method can coordinate the relation between satellite vibration stability and weight reduction, which provides an effective way for the optimization design of on-orbit assembly microsatellite. It has reference significance for the similar spacecraft framework structure design.

A convenient form of spatial‐domain Green’s function for a point charge in multilayered dielectric medium enclosed by a conducting cylinder of circular cross‐section is presented in this paper. Green’s function expression is obtained by solving Poisson differential equation in cylindrical coordinates and applying analogies with multistep electrical transmission lines. Convergence of the proposed expression obtained in the form of a double infinite sum is investigated and compared with triple‐sum solution for the same problem obtained by standard variable separation method. The numerical investigation has shown that the proposed expression has much faster convergence than the standard solution. Also, contrary to the variable separation method, increasing the number of dielectric layers is not an obstacle in determining of proposed Green’s functions, as it is shown for the three layers dielectric structure.

The purpose of this paper is to explore the significance of the internet of things (IoT) system for smart cities and deliberate on the technological aspects involved in developing smart cities along with the framework, impact and benefits of IoT for smart cities.

This research is based on the review and synthesis of the papers on the broader areas of IoT for the application and implication towards the smart cities. The prime focus of this paper is to realize the IoT systems for smart city’s development and implementation of various technologies in the context of the Indian environment.

The outcome of the paper explores the highlights of the importance of the IoT system, including the technological framework, impact and benefits for smart cities. The outcome also highlights the application of IoT for smart cities. This paper provides direction regarding future degrees, potential conceivable outcomes and issues concerning the technological side of smart cities. IoT can change the lives of the people and support evolving urban areas for developing smart cities in India.

The paper deliberates on the novel techno-managerial approach towards the endeavour of smart cities using the IoT.

This paper presents the results from the evaluation of different types of flexible substrates for high‐density flip chip application. In this work two different types of base materials were used, epoxiglass (EG) and polyimide (PI). According to previous tests the type of conductive particles in the adhesive seems to be one of the key factors in high‐density interconnections. The adhesive selected for these tests was a composite of epoxy matrix and high content of isolated soft metal‐coated polymer particles. Two different test structures with contact areas of 50 × 50μm and 50 × 90μm were compared. The total amount of contacts in one IC was approximately 200 and the effective pitch size was 80μm. The contact resistances were measured by four‐point method and the continuity by daisy chain structure. The reliability of the flip chip interconnections was tested in thermal cycling and humidity tests.

The purpose of this paper is to solve the problem that the analytic solution model of spatial three-dimensional coordinate measuring system based on dual-position sensitive detector (PSD) is complex and its precision is not high.

A new three-dimensional coordinate measurement algorithm by optimizing back propagation (BP) neural network based on genetic algorithm (GA) is proposed. The mapping relation between three-dimensional coordinates of space points in the world coordinate system and light spot coordinates formed on dual-PSD has been built and applied to the prediction of three-dimensional coordinates of space points.

The average measurement error of three-dimensional coordinates of space points at three-dimensional coordinate measuring system based on dual-PSD based on GA-BP neural network is relatively small. This method does not require considering the lens distortion and the non-linearity of PSD. It has simple structure and high precision and is suitable for three-dimensional coordinate measurement of space points.

A new three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA is proposed to predict three-dimensional coordinates of space points formed on three-dimensional coordinate measuring system based on dual-PSD.

Project portfolio risk (PPR) management plays an important role in promoting the smooth implementation of a project portfolio (PP). Accurate PPR prediction helps managers cope with risks timely in complicated PP environments. However, studies on accurate PPR impact degree prediction, which consists of both risk occurrence probabilities and risk impact consequences considering project interactions, are limited. This study aims to model PPR prediction and expand PPR prediction tools.

In this study, the authors build a PPR prediction model based on a genetic algorithm and back-propagation neural network (GA-BPNN) integrated with entropy-trapezoidal fuzzy numbers. Then, the authors verify the proposed model with real data and obtain PPR impact degrees.

The test results indicate that the proposed method achieves an average absolute error of 0.002 and an average prediction accuracy rate of 97.8%. The former is reduced by 0.038, while the latter is improved by 32.1% when compared with the results of the original BPNN model. Finally, the authors conduct an index sensitivity analysis for identifying critical risks to effectively control them.

This study develops a hybrid PPR prediction model that integrates a GA-BPNN with entropy-trapezoidal fuzzy numbers. The authors use this model to predict PPR impact degrees, which consist of both risk occurrence probabilities and risk impact consequences considering project interactions. The results provide insights into PPR management.

The purpose of this work is to describe the effects of the length of cracks and the patch size on the stress intensity factors in a bonded composite repair structure containing multiple site damage.

Finite element method was applied to simulate a bonded repair of a cracked aluminum plate with multiple site damage. A two‐dimensional three‐layer technique was utilized to model damage in a typical aluminum plate with collinear twin cracks.

This research has found that the stress intensity factors of collinear twin cracks can be reduced significantly through bonded composite repair, and their values strongly depend on the relative position of the cracks. Moreover, the composite patch should be 1.5 to two times longer than the crack length and the patch thickness should be 30‐40 percent of the plate thickness for the best repair performance.

Patch debonding can significantly reduce the repair efficiency and should be avoided if possible.

It is seen that, instead of the three‐dimensional finite element model, which is computationally intense, the two‐dimensional three‐layer finite element model has an adequate accuracy to obtain stress intensity factors in a bonded composite repair structure with multiple site damage.