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The purpose of this paper is to propose a new hybrid algorithm, named improved plant growth simulation algorithm and genetic hybrid algorithm (PGSA-GA), for solving structural optimization problems.

PGSA-GA is based on PGSA and three improved strategies, namely, elitist strategy of morphactin concentration calculation, strategy of intelligent variable step size and strategy of initial growth point selection based on GA. After a detailed formulation and explanation of its implementation, PGSA-GA is verified using the examples of typical truss and single-layer lattice shell.

Improved PGSA-GA was implemented and optimization was carried out for two typical optimization problems; then, a comparison was made between the PGSA-GA and other methods. The results show that the method proposed in the paper has the advantages of high efficiency and rapid convergence, which enable it to be used for the optimization of various types of steel structures.

Through the examples of typical truss and single-layer lattice shell, it shows that the optimization efficiency and effect of PGSA-GA are better than those of other algorithms and methods, such as GA, secondary optimization method, etc. The results show that PGSA-GA is quite suitable for structural optimization.

The purpose of this paper is to propose a new hybrid algorithm, named improved plant growth simulation algorithm and particle swarm optimization hybrid algorithm (PGSA–PSO hybrid algorithm), for solving structural optimization problems.

To further enhance the optimization efficiency and precision of this algorithm, the optimization solution process of PGSA–PSO comprises two steps. First, an excellent initial growth point is selected by PSO. Then, the global optimal solution can be obtained quickly by PGSA and its improved strategy called growth space adjustment strategy. A typical mathematical example is provided to verify the capacity of the new hybrid algorithm to effectively improve the global search capability and search efficiency of PGSA. Moreover, PGSA–PSO is applied to the optimization design of a suspended dome structure.

Through typical mathematical example, the improved strategy can improve the optimization efficiency of PGSA considerably, and an initial growth point that falls near the global optimal solution can be obtained. Through the optimization of the pre-stress of a suspended dome structure, compared with other methods, the hybrid algorithm is effective and feasible in structural optimization.

Through the examples of suspended dome structure, it shows that the optimization efficiency and precision of PGSA–PSO are better than those of other algorithms and methods. PGSA–PSO is effective and feasible in structural optimization problems such as pre-stress optimization, size optimization, shape optimization and even topology optimization.

Briefly reviews previous literature by the author before presenting an original 12 step system integration protocol designed to ensure the success of companies or countries in their efforts to develop and market new products. Looks at the issues from different strategic levels such as corporate, international, military and economic. Presents 31 case studies, including the success of Japan in microchips to the failure of Xerox to sell its invention of the Alto personal computer 3 years before Apple: from the success in DNA and Superconductor research to the success of Sunbeam in inventing and marketing food processors: and from the daring invention and production of atomic energy for survival to the successes of sewing machine inventor Howe in co‐operating on patents to compete in markets. Includes 306 questions and answers in order to qualify concepts introduced.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

In shopping, for selecting the appropriate garments, people have to try on multiple garments. This problem is due to lack of a sizing system based on updated anthropometric data and the classification system that introduces the appropriate size from the sizing chart to each person. To solve this problem, as a first study in the literature, a hybrid intelligent classification model as a size recommendation expert system is proposed. The paper aims to discuss these issues.

Three stages for developing a hybrid intelligent classification system based on data clustering and probabilistic neural network (PNN) are proposed. In the first stage, the clustering algorithm is used for specifying the sizing chart. In the second stage, the resulting sizing chart is used as a reference for developing a new intelligent classification system by using a PNN. At the last stage, the accuracy of the proposed model is evaluated by using the Iranian male's body type data set.

Experimental results show that the proposed model has a good accuracy and can be used as a size recommendation expert system to specify the right size for the customers. By using the proposed model and designing an interface for it, a decision support system was developed as a size recommendation expert system that was used by an apparel sales store. The results were time saving and more satisfying for the customers by selecting the appropriate apparel size for them.

In this paper, as a first study in literature, a hybrid intelligent model for developing a size recommendation expert system based on data clustering and a PNN to enable the salesperson to help the consumer in choosing the right size is proposed. In the first stage, the clustering algorithm is used for specifying the sizing chart. In the second stage, the resulting sizing chart is used as a reference to develop a new intelligent classification system by using a PNN. In the last stage, the accuracy of the proposed model is evaluated by using testing data. The proposed model achieved an 87.2 percent accuracy rate that is very promising.

Using emotionally intelligent leadership (EIL) as the model, the authors identify behaviors that three levels of leaders engage in based on a self-report inventory (Emotionally Intelligent Leadership for Students-Inventory). Three clusters of students are identified: those that are “Less-involved, Less Others-oriented,” “Self-Improvers,” and “Involved Leaders for Others.” EIL behaviors that most differentiate the highest self-ranking group of involved leaders are the extent to which cluster members work to resolve conflicts in a group situation, work to build a sense of team, and consider the needs of others. The underlying constructs of consciousness of context, self, and others are investigated and discussed. Discriminant analysis is used to validate the cluster solution. Cluster analysis is found to be useful tool for helping leadership educators categorize students and by doing so, program architects have an opportunity to design and develop interventions tailored to better meet the needs of individual students.

Even the smartest organizations believe that “good enough is never good enough.” Highly intelligent people may be able to do important things individually; still, it is their accumulated brainpower that allows them to do great things. Collective intelligence means when a group of people do things that seem intelligent. On the other hand, when intelligent people are gathered or hired in an organization, they tend toward collective stupidity and slow-wittedness. Therefore, the purpose of the present study is to develop a model of the factors affecting and affected by collective stupidity.

This study was conducted using a mixed-method approach and in two phases: qualitative and quantitative. First, in the qualitative phase and in order to achieve new findings, semi-structured interviews with experts from 12 knowledge-based companies were used to design a conceptual model and formulate the hypotheses. At the end of the qualitative phase, the conceptual model and relationships between variables were drawn. Then, in the quantitative phase, by running structural equation modeling, the antecedents and consequences of collective stupidity derived from the qualitative phase findings were analyzed and the research hypotheses were tested in 110 industrial knowledge-based companies.

The results of the qualitative phase revealed that individual, group and organizational factors were the antecedents of collective stupidity, with individual factors having three dimensions, collective ones including two dimensions and organizational ones focusing on the characteristics of managers/companies. It was also found that collective stupidity had individual consequences in three dimensions and organizational consequences in five dimensions; and one-sidedness, non-strategic thinking, organizational injustice and weakness in the management of key personnel were extracted as mediators. The results of the quantitative phase confirmed the research model and showed that individual factors and organizational consequences had the lowest (0.037) and highest (1.084) effect sizes on collective stupidity, respectively.

The study of the phenomenon in a particular context and the difficulty of generalizing the findings to other situations, the small size of the study population due to the disproportionate number of experts to the total staff in the qualitative phase, and the limited participation of experts due to the negative nature of the issue in the quantitative phase. Taking advantage of the diversity of experts' mental abilities through equipping the organization with tools for recognizing collective stupidity, improving collective decision-making, enhancing the efficiency of think tanks and organizational prosperity in the age of knowledge economy, preventing damage to the body of knowledge of the company and reducing social loafing can be the main operational implications of this study.

Using a mixed-method approach for analyzing the antecedents and consequences of collective stupidity in this study and examining such a phenomenon in a knowledge-based organizational context and its implementation in a developing country can be among the innovations of the present research. By following a few studies conducted in this field in addition to the organizational pathology of the phenomenon using a fundamental method, this study obtained deep results on how to make the maximum use of experts’ capacities. This was to the extent that in all of the three sections, i.e. the antecedents and consequences of collective stupidity, a new factor emerged.

Examines the thirteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

Examines the fourteenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

This paper addresses two fundamental issues with regard to the production of printed circuit boards (PCBs). First, the paper presents a statistically‐based, four‐phase strategy aimed at better organising and facilitating the characterisation of a manufacturing process. The fact that the case study addresses copper plating thickness of PCBs is secondary to the characterisation strategy. In this sense, the strategy is universal by nature. Second, the paper provides analytical insight into a new statistical methodology aimed at establishing coupon‐to‐board correlation. The general concepts surrounding these two issues are embodied within the given PCB case study. Essentially, the case analysis systematically progresses through all four phases of the parameter characterisation strategy, highlighting the key aspects of each phase. In addition, the aforementioned technique for assessing coupon‐to‐board correlation is given substantiative discussion. However, it should be pointed out that a thorough narration pertaining to many of the statistical and engineering details of the case analysis has been deliberately omitted for the sake of brevity and reading ease. As a result of such considerations, it is assumed that the reader has a working knowledge of descriptive and inferential statistics, as well as experiment design. The benefit of this approach is simple—focus is easily given to the analytical strategy and order of execution without the usual clouding imposed by mathematical explanations.