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This study aims to suggest and develops a global sensitivity analysis-assisted multi-level sequential optimization method for the heat transfer problem.

Compared with other surrogate-assisted optimization methods, the distinctive characteristic of the suggested method is to decompose the original problem into several layers according to the global sensitivity index. The optimization starts with the several most important design variables by the support vector regression-based efficient global optimization method. Then, when the optimization process progresses, the filtered design variables should be involved in optimization one by one or the setting value. Therefore, in each layer, the design space should be reduced according to the previous optimization result. To improve the accuracy of the global sensitivity index, a novel global sensitivity analysis method based on the variance-based method incorporating a random sampling high-dimensional model representation is introduced.

The advantage of this method lies in its capability to solve complicated problems with a limited number of sample points. Moreover, to enhance the reliability of optimum, the support vector regression-based global efficient optimization is used to optimize in each layer.

The developed optimization tool is built by MATLAB and can be integrated by commercial software, such as ABAQUS and COMSOL. Lastly, this tool is integrated with COMSOL and applied to the plant-fin heat sink design. Compared with the initial temperature, the temperature after design is over 49°. Moreover, the relationships among all design variables are also disclosed clearly.

The D-MORPH-HDMR is integrated to obtain the coupling relativities among the design variables efficiently. The suggested method can be decomposed into multiplier layers according to the GSI. The SVR-EGO is used to optimize the sub-problem because of its robustness of modeling.

The purpose of study is to overcome the error estimation of standard deviation derived from Expected improvement (EI) criterion. Compared with other popular methods, a quantitative model assessment and analysis tool, termed high-dimensional model representation (HDMR), is suggested to be integrated with an EI-assisted sampling strategy.

To predict standard deviation directly, Kriging is imported. Furthermore, to compensate for the underestimation of error in the Kriging predictor, a Pareto frontier (PF)-EI (PFEI) criterion is also suggested. Compared with other surrogate-assisted optimization methods, the distinctive characteristic of HDMR is to disclose the correlations among component functions. If only low correlation terms are considered, the number of function evaluations for HDMR grows only polynomially with the number of input variables and correlative terms.

To validate the suggested method, various nonlinear and high-dimensional mathematical functions are tested. The results show the suggested method is potential for solving complicated real engineering problems.

In this study, the authors hope to integrate superiorities of PFEI and HDMR to improve optimization performance.

Reflow soldering process is an important step of the surface mount technology. The purpose of this paper is to minimize the maximum warpage of shielding frame by controlling reflow soldering control parameters.

Compared with other reflow-related design methods, both time and temperate of each extracted time region are considered. Therefore, the number of design variable is increased. To solve the high-dimensional problem, a surrogate-assisted optimization (SAO) called adaptive Kriging high-dimensional representation model (HDMR) is used.

Therefore, the number of design variable is increased. To solve the high-dimensional problem, a surrogate-assisted optimization (SAO) called HDMR is used. The warpage of shield frame is significantly reduced. Moreover, the correlations of design variables are also disclosed.

Compared with the original Kriging HDMR, the expected improvement (EI) criterion is used and a new projection strategy is suggested to improve the efficiency of optimization method. The application suggests that the adaptive Kriging HDMR has potential capability to solve such complicated engineering problems.

For global optimization, an important issue is a trade-off between exploration and exploitation within limited number of evaluations. Efficient Global Optimization (EGO) is an important algorithm considering such condition termed as Expected Improvement (EI). One of major bottlenecks of EGO is to keep the diversity of samples. Recently, Multi-Surrogate EGO (MSEGO) uses more samples generated by multiple surrogates to improve the efficiency. However, the total number of samples is commonly large. To overcome this bottleneck, a bi-direction multi-surrogate global optimization is suggested.

As the name implies, two different ways are used. The first way is to EI criterion to find better samples similar to EGO. The second way is to use the second term of EI to find accurate regions. Sequentially, the samples in these regions should be evaluated by multiple surrogates instead of exact function evaluations. To enhance the accuracy of these samples, Bayesian inference is employed to predicted the performance of each surrogate in each iteration and obtain the corresponding weight coefficients. The predicted response value of a cheap sample is evaluated by the weighted multiple surrogates combination. Therefore, both accuracy and efficiency can be guaranteed based on such frame.

According to the test functions, it empirically shows that the proposed algorithm is a potentially feasible method for complicated underlying problems.

A bi-direction sampling strategy is suggested. The first way is to use EI criterion to generate samples similar to the EGO. In this way, new samples should be evaluated by real functions or simulations called expensive samples. Another way is to search accurate region according to the second term of EI. To guarantee the reliability of samples, a sample selection scenario based on Bayesian theorem is suggested to select the cheap samples. We hope this strategy help us to construct more accurate model without increasing computational cost.

The study aims to contribute to the debate on how to identify and manage an organization’s sustainability-related resources and processes by understanding the impact of business activities on the environment and evaluating actions to ameliorate their impacts. Within this debate, and specifically taking into consideration the opportunity to support circular economy actions and initiatives, the study focuses on integrated reporting (IR) practices. In detail, this study advocates the joint use of IR principles with the dynamic resource-based view (DRBV) of the firm, adopting their representation of resources and impact of the business activities to identify environmental friendly “hot spots” in organizations. The framework is illustrated through two exploratory case studies.

Employee Stock Options are the most widely used incentive compensation tool, and prior research has shown their advantages. However, research among different peer groups, different time frames, different research methodologies, and the constantly changing public opinion prevents unanimous agreements on the various benefits of Employee Stock Options. In this paper we apply a number of research hypotheses tested in recent US studies to a European sample of EuroStoxx 50 companies. Due to the globalisation, the similar accounting regulations and the IT and telecommunications revolu tions, Europe and the United States have grown closer together than ever before and are expected to display similar business practices. This assessment should be especially relevant for the large European companies, which mostly have a dual listing in the United States and are therefore essentially forced to manage according to American practices. How ever, the results differ significantly from the existing US research, providing insufficient grounds to accept previous findings for European companies.

The purpose of this paper is to present one modification of the fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and to develop a corresponding computer program which could be used for the multicriteria decision making for problems in practice.

This method is based on the uncertainties and probabilities of input data for ratings of alternatives with respect to criteria and weights of criteria that are presented by triangular fuzzy numbers as probabilistic fuzzy values. These input data are transformed in the procedure into output data that are relevant for the ranking of alternatives and decision making.

The proposed method is based on the generalized mean and spread of fuzzy numbers that are calculated according to probability of fuzzy events due to Zadeh. Ranking of alternatives for relevant criteria performs according to relative expected closeness, coefficient of variation and relative standard deviation of distance of alternatives to the ideal solutions. The most acceptable rule is related to the minimal value of the expected relative distance to positive ideal solution, especially when the coefficient of variation of distance to this solution is small. The attached example, related to a real project, confirms these findings.

This paper proposes three novel contributions in this area. Unlike the methods proposed by other authors, the weighted fuzzy decision matrix is expressed by the matrix of generalized expected values and matrix of generalized variances. To compute elements of these two matrices, exact formulae are derived and then the modified fuzzy TOPSIS procedure is carried out.

This paper aims to discuss the individual participation and involvement affecting the user engagement in social media and to answer the following research questions: Is it possible to measure the individual participation and involvement of social media within organizations? Which factors should be analysed in order to increase the individual participation in social media? Which KPIs should be selected in order to increase the user ' s engagement and increase individual participation in social media? Can social media in a company be measured in terms of their impact on KM?

This paper presents a case study that describes how Key Performance Indicators (KPIs) are used to monitor and manage the applications of social technologies, which include many tools facilitating the participation and collaboration on the web. The case study was applied to the information and communication technology area of Eni S.p.A., which is an integrated energy company active in over 70 countries in the world.

Based on the indications obtained from the case study, a methodology is proposed to select and develop the appropriate KPIs in order to manage and monitor the application of social technologies. The methodology turned out to be able to monitor collaboration and knowledge sharing activities among employees and to incentivize participation and involvement of employees who use the company ' s social media.

Organizations can use the suggested methodology as a guideline for managing and monitoring social media inside a company. The possibility of continuously modifying the adopted social media tool by means of corrective actions together with the possibility of adapting the KPIs to new situations make the present methodology an efficient management approach to take on the multifaceted activities of a social media environment.

Few case studies dealing with the applications regarding the implementation and management of social technologies within organizations have been carried out. Similarly, even if some empirical studies have been proposed to analyse what motivates and prevents employees from sharing their knowledge through social media, there appears to be a lack of studies which have taken into consideration the evaluation of the actual benefits in terms of individual involvement and participation, knowledge sharing and increase in performance.

This study aims to investigate the awareness of extensible business reporting language (XBRL) and the perception of chartered accountants of India concerning the inhibitors of XBRL adoption, namely, environmental, organizational and innovation factors developed by Troshani and Rao (2007) from Rogers’ innovation diffusion theory. In addition, the analysis also investigated the relationship between the perception of issues regarding XBRL adoption and individual characteristics (training, age, gender and professional experience).

A Web-based questionnaire was circulated through e-mail to chartered accountants registered with the Institute of Chartered Accountants India (ICAI) and 233 chartered accountants responded to the questionnaire. The data was analyzed using reliability statistics and multivariate regression analyses.

The results indicate that accountants perceived that environmental, organizational and innovational factors were challenging in adopting XBRL. Interestingly, training and experience were significant factors in explaining respondents’ perceptions.

From a practical panorama, the significance of issues implies that associations such as XBRL International, XBRL India, ICAI and the Ministry of Corporate Affairs should collectively take the appropriate steps to sustain and ameliorate the reliability and adoption of XBRL.

The results can motivate ICAI/Institute of Company Secretaries of India (ICSI) courses to teach academic content about XBRL.

The present study differs from previous research because it examines the inhibitors in adopting XBRL, namely, environmental, organizational and innovation factors, in an empirical setting. Moreover, to the best of the author’s knowledge, this is the first study to analyze the influence of individual factors on accountants’ perceptions about inhibitors of XBRL adoption.

This paper aims at the improvement of the generating capability of a hybrid excited brushless claw pole alternator (HEBCPA).

Following the identification of the HEBCPA influent sizing parameters, the investigation of their impact on the back‐EMF production capability is carried out considering a 3D‐finite element analysis (FEA).

The HEBCPA Back‐EMF production capability is highly affected by the variation of the influent sizing parameters. A selected combination of these parameters has led to an optimized generating capability.

An experimental validation of the generating performance computed by 3D‐FEA shall be treated in the future.

The optimized HEBCPA is of great interest for the automotive applications as well as for wind energy generating systems.

The optimized HEBCPA is a new concept that has been proposed by the authors in a recent work.