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Domain integrals, known as volume potentials in 3D elasticity problems, exist in many boundary-type methods, such as the boundary element method (BEM) for inhomogeneous partial differential equations. The purpose of this paper is to develop an accurate and reliable technique to effectively evaluate the volume potentials in 3D elasticity problems.

An adaptive background cell-based domain integration method is proposed for treatment of volume potentials in 3D elasticity problems. The background cells are constructed from the information of the boundary elements based on an oct-tree structure, and the domain integrals are evaluated over the cells rather than volume elements. The cells that contain the boundary elements can be subdivided into smaller sub-cells adaptively according to the sizes and levels of the boundary elements. The fast multipole method (FMM) is further applied in the proposed method to reduce the time complexity of large-scale computation.

The method is a boundary-only discretization method, and it can be applied in the BEM easily. Much computational time is saved by coupling with the FMM. Numerical examples demonstrate the accuracy and efficiency of the proposed method..

Boundary elements are used to create adaptive background cells, and domain integrals are evaluated over the cells rather than volume elements. Large-scale computation is made possible by coupling with the FMM.

Guided by social exchange theory and signaling theory, this chapter investigates the relationship between individual perceptions of high-performance work systems (HPWS), union instrumentality, and employees’ turnover intention. The results obtained from a multilevel and multisource sample of more than 1,300 employees in 37 multinational corporation based in China show that, in contrast to our hypothesis, union instrumentality is not directly related to turnover intention; rather, the results from the post hoc mediation analysis show that union instrumentality is indirectly and negatively related to turnover intention through affective organizational commitment. Consistent with our hypothesis, the results of our analysis show that union instrumentality serves as an important contingent factor in the relationship between HPWS and employee turnover intention. The relationship between HPWS and turnover intention becomes positive when employee union instrumentality is low.

Emergence is the kernel concept of complexity science. Lack of precision when people refer to “emergent properties” hinders the research of complex systems. The purpose of this paper is to develop a formal definition of emergence to make it intrinsic to a system and to integrate different views on emergence.

Based on the modeling framework of entity grammar systems (EGS), a formal definition of emergence is proposed and a theorem is obtained for exploring the producing conditions of emergence. With the definition and theorem, three views on emergence are unified using the same formalism of EGS.

The concept of emergence can be formally defined in the framework of EGS to integrate the “downward causation” and “upward causation” views of emergence and makes emergent properties intrinsic to a system. It is possible to control the production of emergence when the system is analyzed using the formalism of EGS.

A formal definition of emergence is proposed in this paper. This work combines the modeling power of EGS with the formal analysis of emergence, which will prompt the further application of EGS in modeling, simulation, and analysis of complex systems in many fields and will provide practical tools for complexity research.

This chapter investigates the environmental behaviours of three, four- and five-star hotels in Azuay (Ecuador). The methodology involved a quantitative research that measured the relationship between environmental responsibility (Gallardo, Sanchez, & Corchuelo, 2013) and stakeholder engagement (Kostova & Roth, 2002; Llamas-Sanchez, García-Morales, & Martin-Tapia, 2013; Vargas-Sánchez & Riquel-Ligero, 2012). The main findings suggest that the managers of the hotels implement environmental practices as they reduce waste, gas emissions and recycle materials. These practices are aligned with Ecuador’s extant legislation and regulations. In conclusion, this contribution implies that the hotels’ managers ought to communicate about their environmental responsibility with their stakeholders, including the employees, suppliers and customers.

This chapter discusses key training challenges that organizations need to confront with the objective of building a robust human resource management system. Given the dynamics of the current business environment, training and development has become an indispensable function in global organizations. Building an effective human capital that contributes to continual organizational growth has become the established norm to survive in a competitive business landscape. However, the training and development function is often rendered ineffective, on account of various bottlenecks existing in the organization. Addressing these bottlenecks is quintessential in ensuring the creation of a performance-driven human capital. The goal of this chapter is to draw attention to the training impediments that hinder organizational growth and to diagnose the underlying causes for the same. This chapter concludes with recommendations that organizational decision-makers can leverage in their quest to strengthen the human capital, by utilizing their training and development infrastructure optimally.

This study aims to explore the role of equity investment in knowledge-driven business model innovation (BMI) in context of open modes according to the evidence from China’s primary market.

Based on the database of China’s private market and data set of news clouds, the statistic approach is applied to explore and explain whether equity investment promotes knowledge-driven BMI. Machine learning method is also used to prove and predict the performance of such open innovation.

The results of logistic regression show that explanatory variables are significant, providing evidence that knowledge management (KM) promotes BMI through equity investment. By further using back propagation neural network, the classification learning algorithm estimates the possibility of BMI, which can be regarded as a score to quantify the performance of knowledge-driven BMI

The quality of secondhand big data is not very ideal, and future empirical studies should use first-hand survey data.

This study provides new insights into the link between KM and BMI by highlighting the important roles of external investments in open modes.

From the perspective of investment, the findings of this study suggest the importance for stakeholders to share knowledge and strategies for entrepreneurs to manage innovation.

The concepts and indicators related to business models are difficult to quantify currently, while this study provides feasible and practical methods to estimate knowledge-driven BMI with secondhand data from the primary market. The mechanism of knowledge and innovation bridged by the experience from investors is introduced and analyzed.

The purpose of this paper is to develop a compressive sensing (CS) algorithm for noisy solder joint imagery compression and recovery. A fast gradient-based compressive sensing (FGbCS) approach is proposed based on the convex optimization. The proposed algorithm is able to improve performance in terms of peak signal noise ratio (PSNR) and computational cost.

Unlike traditional CS methods, the authors first transformed a noise solder joint image to a sparse signal by a discrete cosine transform (DCT), so that the reconstruction of noisy solder joint imagery is changed to a convex optimization problem. Then, a so-called gradient-based method is utilized for solving the problem. To improve the method efficiency, the authors assume the problem to be convex with the Lipschitz gradient through the replacement of an iteration parameter by the Lipschitz constant. Moreover, a FGbCS algorithm is proposed to recover the noisy solder joint imagery under different parameters.

Experiments reveal that the proposed algorithm can achieve better results on PNSR with fewer computational costs than classical algorithms like Orthogonal Matching Pursuit (OMP), Greedy Basis Pursuit (GBP), Subspace Pursuit (SP), Compressive Sampling Matching Pursuit (CoSaMP) and Iterative Re-weighted Least Squares (IRLS). Convergence of the proposed algorithm is with a faster rate O(k*k) instead of O(1/k).

This paper provides a novel methodology for the CS of noisy solder joint imagery, and the proposed algorithm can also be used in other imagery compression and recovery.

According to the CS theory, a sparse or compressible signal can be represented by a fewer number of bases than those required by the Nyquist theorem. The new development might provide some fundamental guidelines for noisy imagery compression and recovering.

Despite the wide dissemination and application of current signature analysis (CSA) in general industry, CSA is not commonly used in the wind industry, where the use of vibration signals predominates. Therefore, the purpose of this paper is to review the use of generator CSA (GCSA) in the online fault detection and diagnosis of wind turbines (WTs).

This is a bibliographical investigation in which the use of GCSA for the maintenance of WTs is analyzed. A section is dedicated to each of the main components, including the theoretical foundations on which GCSA is based and the methodology, mathematical models and signal processing techniques used by the proposals that exist on this topic.

The lack of appropriate technology and mathematical models, as well as the difficulty involved in performing actual studies in the field and the lack of research projects, has prevented the expansion of the use of GCSA for fault detection of other WT components. This research area has yet to be explored, and the existing investigations mainly focus on the gearbox and the doubly fed induction generator; however, modern signal treatment and artificial intelligence techniques could offer new opportunities in this field.

Although literature on the use of GCSA for the detection and diagnosis of faults in WTs has been published, these papers address specific applications for each of the WT components, especially gearboxes and generators. For this reason, the main contribution of this study is providing a comprehensive vision for the use of GCSA in the maintenance of WTs.

This paper aims to identify important factors in green public procurement (GPP) implementation and then to clarify how these factors affect GPP implementation.

The authors applied the Delphi method first and then conducted a focused and constrained multiple case study at 18 government procurement centers across China.

The authors identified four clusters of factors for successful GPP implementation: more clear, consistent and operational policy goals; a nation-wide green procurement campaign to enhance social capital and cultural resources; promoting staff’s ethics, professionalism, capacity and knowledge; and establishing checks and balances among organizations involved in the whole purchasing process.

GPP can significantly improve environmental protection and sustainable development.

Based on key insights from systems theory and agency theory, the authors emphasize that GPP implementation must take down its own functional silos and adopt a process approach across organizational tiers to synchronize human resource based and inter-organizational capabilities into a unified whole through information sharing, communications and collaboration.

The purpose of this paper is to present state-of-the-art approaches for precise operation of a robotic manipulator on a macro- to micro/nanoscale.

This paper first briefly discussed fundamental issues associated with precise operation of a robotic manipulator on a macro- to micro/nanoscale. Second, this paper described and compared the characteristics of basic components (i.e. mechanical parts, actuators, sensors and control algorithm) of the robotic manipulator. Specifically, commonly used mechanisms of the manipulator were classified and analyzed. In addition, intuitive meaning and applications of its actuator explained and compared in details. Moreover, related research studies on general control algorithm and visual control that are used in a robotic manipulator to achieve precise operation have also been discussed.

Remarkable achievements in dexterous mechanical design, excellent actuators, accurate perception, optimized control algorithms, etc., have been made in precise operations of a robotic manipulator. Precise operation is critical for dealing with objects which need to be manufactured, modified and assembled. The operational accuracy is directly affected by the performance of mechanical design, actuators, sensors and control algorithms. Therefore, this paper provides a categorization showing the fundamental concepts and applications of these characteristics.

This paper presents a categorization of the mechanical design, actuators, sensors and control algorithms of robotic manipulators in the macro- to micro/nanofield for precise operation.