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This paper presents a hybrid artificial intelligence (AI) system capable of integrating techniques of case‐based reasoning, rule induction and expert system, using them for knowledge acquisition and problem solving of selecting appropriate retaining wall systems at the project planning stage. The proposed hybrid system can eliminate the bottleneck of knowledge acquisition in developing a knowledge‐based system and improve the solution quality of the AI‐based system. Test results indicate that solutions generated by the proposed hybrid system are better than those generated by using a single technique.

The author argues and explains that the indigenous Eastern epistemological frame of yin-yang balancing can be taken as a unique system of thinking toward a meta-perspective. It is not only deeply rooted in the indigenous Eastern culture traditions, but also bears salient global implications, especially in the domain of paradox management. The purpose and contribution of this chapter are twofold: (1) to explain the unique and salient features of yin-yang balancing (the “either/and” system to reframe paradox into duality as partially conflicting and partially complementary, both spatially and temporarily) as compared with the Western logic systems (the “either/or” and “both/or” or “both/and” systems); and (2) to explore the global implications of the “either/and” system for future paradox research, including the three unique themes of overlap between opposites with the “seed” of one opposite inside the other; threshold from the contingent balance between partial separation and partial integration in line with specific contexts through three operating mechanisms, and knot for the special role of third-party to shift paradox from a dyadic level to a triadic and even a multiplex level.

As returns to scale (RTS) describes the long run connection of the changes of outputs relative to increases in the inputs, the purpose of this study is to answer the following questions: If the proportionate changes exist in the inputs, what is the rate of changes in outputs with respect to the inputs’ variations in the two-stage networks over the long term? How can the authors investigate quantitative RTS in the two-stage networks? In other words, the purpose of this research is to introduce a different approach to estimate the performance, RTS and scale economies (SE) in network structures.

This paper proposes a novel non-radial approach based on data envelopment analysis to analyze the performance and to investigate RTS and SE in two-stage processes.

The findings show that the range adjusted measure (RAM)/RTS approach can identify reference sets for overall systems and each stage. In addition, the models presented in this paper can classify decision-making units and determine the increasing/decreasing trends of RTS.

The majority of previous RTS studies have been examined in black-box structures and have been discussed in a radial framework. Therefore, in this study, RTS and SE in the two-stage networks are dealt with using an extended RAM approach. Actually, the efficiency and RTS for each stage and the overall model are calculated using the proposed technique.

This paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks.

An extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers.

Disasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers.

Based on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.

Selecting the ‘best’ main contractor is a complex decision process for construction clients. It requires a large number of criteria to be simultaneously measured and evaluated. Many of these criteria are related to one another in a complex way and therefore, they very often conflict insofar as improvement in one often results in decline of another(s). Furthermore, as contractors' attributes are expressed in both quantitative and qualitative terms, decision‐makers have to base their judgements on both quantitative data and experiential subjective assessments. In this paper, the evidential reasoning (ER) approach (which is capable of processing both quantitative and qualitative measures) is applied as a means of solving the contractor selection problem (CSP). The process of building a multiple criteria decision model of a hierarchical structure is presented, in which both quantitative and qualitative information is represented in a unified manner. The CSP is then fully investigated using the ER approach. Both the advantages of applying this model in practice and the analysis process itself are discussed.

In this paper, the authors aim to propose an effective method to indirectly determine nonlinear elastic shear stress-strain constitutive relationships for nonlinear elasticity materials, and then study the nonlinear free torsional vibration of Al–1%Si shaft.

In this study the authors use BoxLucas1 model to fit the determined-experimentally nonlinear elastic normal stress–strain constitutive relationship curve of Al–1%Si, a typical case of isotropic nonlinear elasticity materials, and then derive its nonlinear shear stress-strain constitutive relationships based on the fitting constitutive relationships and general equations of plane-stress and plane-strain transformation. Hamilton’s principle is utilized to gain nonlinear governing equation and boundary conditions for free torsional vibration of Al–1%Si shaft. Differential quadrature method and an iterative algorithm are employed to numerically solve the gained equations of motion.

The effect of four variables, namely dimensionless fundamental vibration amplitude ϑmax, radius α and length β, and nonlinear-elasticity intensity factor δ, on frequencies and mode shapes of the shafts is obtained. Numerical results are in good agreement with reference solutions, and show that compared with linearly elastic shear stress-strain constitutive relationships of the shafts made of the nonlinear elasticity materials, its actual nonlinearly elastic shear stress-strain constitutive relationships have smaller torsion frequencies. In addition, but β having opposite hardening effect, the rest of the four variables have softening effect on nonlinearly elastic torsion frequencies. Eventually, taking into account nonlinearly elastic shear stress-strain constitutive relationships, changes of the four factors, i.e. ϑmax, α, β and δ, cause inflation and deflation behaviors of mode shapes in nonlinear free torsional vibration.

The study could provide a reference for indirectly determining nonlinear elastic shear stress-strain constitutive relationships for nonlinear elasticity materials and for structure design of torsional shaft made of nonlinear elasticity materials.

The purpose of this paper is to develop data envelopment analysis (DEA) models and algorithms for efficiency improvement when the inputs and output weights are restricted and there is fixed availability of inputs in the system.

Limitation on availability of inputs is represented in the form of constant sum of inputs (CSOI) constraint. The amount of excess input of an inefficient decision-making unit (DMU) is redistributed among other DMUs in such a way so that there is no reduction in their efficiency. DEA models have been developed to design the optimum strategy to reallocate the excess input.

The authors have developed the method for reallocating the excess input among DMUs while under CSOI constraint and parameter weight restrictions. It has been shown that in this work to improve the efficiency of an inefficient DMU one needs the cooperation of selected few DMUs. The working of the models and results have been shown through a case study on carbon dioxide emissions of 32 countries.

The limitation of the study is that only one DMU can expect to benefit from the application of these methods at any given time.

Results of the paper are useful in situations when decision maker is exploring the possibility of transferring the excess resources from underperforming DMUs to the other DMUs to improve the performance.

This strategy of reallocation of excess input will be very useful in situations when decision maker is exploring the possibility of transferring the excess resources from underperforming DMUs to the other DMUs to improve the performance. Unlike the existing works on efficiency improvement under CSOI, this work seeks to address the issue of efficiency improvement when the input/output parameter weights are also restricted.

Corporate social responsibility has gradually become an essential enterprise responsibility under stakeholders’ expectations. Employee care strategies involve both qualitative and quantitative factors and are receiving special attention with the advent of the information age. In previous studies, a company’s policy of employee care may not fit with the needs of the employees. Consequently, the purpose of this paper is to investigate enterprises’ employee care from the employee’s perspective by adopting a hybrid multiple attribute decision making (MADM) model.

This study is based on 159 interviews with senior employees and/or department managers using a survey questionnaire. This study uses the MADM model to conduct the analysis. First, this research study used Decision-Making Trial and Evaluation Laboratory (DEMATEL) to construct an influential network relations map of the 4 dimensions and 13 criteria of employee care. Second, this study uses DEMATEL-based Analytic Network Process to conduct a weight analysis for each dimension and criterion. Third, this study uses VIKOR to calculate employees’ level of satisfaction as well as the gap from the “aspired level.”

The results of the study revealed the critical factors influencing employee care and proposed a systematic plan to be used as a reference for improvement. The improvement sequence revealed the following order: Equal employment opportunities→Good industrial relations and benefits→Responsibility to train and educate employees→Occupational health and safety. The empirical results showed there was still 35 percent room for improvement in the enterprises’ implementation policy of employee care.

The implementation of employee care has become an important issue for corporations since it helps to sustain and to increase an enterprise’s competitiveness in the business environment. However, the extant literature on employee care comes from enterprises’ perspectives instead of from employees’ perspectives. This research investigates the key factors of employee care and successfully shows MADM to be an effective model for the planning and implementation of corporate social responsibilities’ employee care from the perspective of employees.

Heritability studies attempt to estimate the contribution of genes (vs. environments) to variation in phenotypes (or outcomes of interest) in a given population at a given time. This chapter scrutinizes heritability studies of adverse health phenotypes, emphasizing flaws that have become more glaring in light of recent advances in the life sciences and manifest most visibly in epigenetics.

Drawing on a diverse body of research and critical scholarship, this chapter examines the veracity of methodological and conceptual assumptions of heritability studies.

The chapter argues that heritability studies are futile for two reasons: (1) heritability studies suffer from serious methodological flaws with the overall effect of making estimates inaccurate and likely biased toward inflated heritability, and, more importantly (2) the conceptual (biological) model on which heritability studies depend – that of identifiably separate effects of genes versus the environment on phenotype variance – is unsound. As discussed, contemporary bioscientific work indicates that genes and environments are enmeshed in a complex (bidirectional, interactional), dynamic relationship that defies any attempt to demarcate separate contributions to phenotype variance. Thus, heritability studies attempt the biologically impossible. The emerging research on the importance of microbiota is also discussed, including how the commensal relationship between microbial and human cells further stymies heritability studies.

Understandably, few sociologists have the time or interest to be informed about the methodological and theoretical underpinnings of heritability studies or to keep pace with the incredible advances in genetics and epigenetics over the last several years. The present chapter aims to provide interested scholars with information about heritability and heritability estimates of adverse health outcomes in light of recent advances in the biosciences.