Application of STEEP and Interpretive Structural Modeling in the Design Imagery of Taiwan Public Ceramic Relief Murals

Ceramic relief mural is a contemporary landscape art that is carefully designed based on human nature,culture,and architectural wall space,combined with social customs,visual sensibility,and art.It may also become the main axis of ceramic art in the future.Taiwan public ceramic relief murals(PCRM)are most distinctive with the PCRM pioneered by Pan-Hsiung Chu of Meinong Kiln in 1987.In addition to breaking through the limitations of traditional public ceramic murals,Chu leveraged local culture and sensibility.The theme of art gives PCRM its unique style and innovative value throughout the Taiwan region.This study mainly analyzes and understands the design image of public ceramic murals,taking Taiwan PCRM’s design and creation as the scope,and applies STEEP analysis,that is,the social,technological,economic,ecological,and political-legal environments are analyzed as core factors;eight main important factors in the artistic design image of ceramic murals are evaluated.Then,interpretive structural modeling(ISM)is used to establish five levels,analyze the four main problems in the main core factor area and the four main target results in the affected factor area;and analyze the problem points and target points as well as their causal relationships.It is expected to sort out the relationship between these factors,obtain the hierarchical relationship of each factor,and provide a reference basis and research methods.

COMPLEXITY OF SYSTEM MAINTAINABILITY ANALYSIS BASED ON THE INTERPRETIVE STRUCTURAL MODELING METHODOLOGY： TRANSDISCIPLINARY APPROACH

This paper outlines a diagnostic approach to quantify the maintainability of a Commercial off-the-Shelf （COTS）-based system by analyzing the complexity of the deployment of the system components. Interpretive Structural Modeling （ISM） is used to demonstrate how ISM supports in identifying and understanding interdependencies among COTS components and how they affect the complexity of the maintenance of the COTS Based System （CBS）. Through ISM analysis we have determined which components in the CBS contribute most significantly to the complexity of the system. With the ISM, architects, system integrators, and system maintainers can isolate the COTS products that cause the most complexity, and therefore cause the most effort to maintain, and take precautions to only change those products when necessary or during major maintenance efforts. The analysis also clearly shows the components that can be easily replaced or upgraded with very little impact on the rest of the system.

Toward equation structural modeling:an integration of interpretive structural modeling and structural equation modeling

Interpretive structural modeling(ISM)is an interactive process in which a malformed(bad structured)problem is structured into a comprehensive systematic model.Yet,despite many advantages that ISM provides,this method has some shortcomings,the most important one of which is its reliance on participants’intuition and judgment.This problem undermines the validity of ISM.To solve this problem and further enhance the ISM method,the present study proposes a method called equation structural modeling(ESM),which draws on the capacities of structural equation modeling(SEM).As such,ESM provides a statistically verifiable framework and provides a graphical,hierarchical and intuitive model.

Analysis of Ecosystem Degradation Factors in Yuanmou Arid-Hot Valleys Based on Interpretative Structural Model

For ecological restoration and reconstruction of the degraded area, it is an important premise to correctly understand the degradation factors of the ecosystem in the arid-hot valleys. The factors including vegetation degradation, land degradation, arid climate, policy failure, forest fire, rapid population growth, excessive deforestation, overgrazing, steep slope reclamation, economic poverty, engineering construction, lithology, slope, low cultural level, geological hazards, biological disaster, soil properties etc, were selected to study the Yuanmou arid-hot valleys. Based on the interpretative structural model （ISM）, it has found out that the degradation factors of the Yuanmou arid-hot valleys were not at the same level but in a multilevel hierarchical system with internal relations, which pointed out that the degradation mode of the arid-hot valleys was ＂straight （appearance）-penetrating-background＂. Such researches have important directive significance for the restoration and reconstruction of the arid-hot valleys ecosystem.

Knowledge-Based Multifaceted Modeling Methodology for Open Complex Giant Systems

In this paper, the structure characteristics of open complex giant systems are concretely analysed in depth, thus the view and its significance to support the meta synthesis engineering with manifold knowledge models are clarified. Furthermore, the knowledge based multifaceted modeling methodology for open complex giant systems is emphatically studied. The major points are as follows: (1) nonlinear mechanism and general information partition law; (2) from the symmetry and similarity to the acquisition of construction knowledge; (3) structures for hierarchical and nonhierarchical organizations; (4) the integration of manifold knowledge models; (5) the methodology of knowledge based multifaceted modeling.

ISM-based Correlation Analysis of Development Constraints of Forest Resource-exhausted Cities

Forest resource-exhausted cities have to face with various constraints in the acceleration of its urbanization.This paper analyzed major development constraints of these cities,such as unitary economic structure,weakened forest ecological functions,and geographical barriers,and applied ISM method(Interpretive Structural Modeling) to analyze the correlation among the constraints,and gave suggestions for promoting the development of forest resource-exhausted cities.

Analysis on the Structure of Influencing Factors of Sustainable Supply Chain Implementation of Water Diversion Project

The systematic analysis of the hierarchical relationship among the factors affecting the sustainable supply chain implementation of water diversion projects has theoretical value and practical significance for the sustainable development of large-scale water diversion projects. Through the investigation of relevant literature, books, web pages, materials, and discussions with relevant experts and scholars, a total of 23 factors influencing the sustainable supply chain implementation of water diversion projects were identified. Then using ISM (Interpretative Structural Modeling Method) to analyze the causality of each factor, a multi-level hierarchical structure model was obtained. The results showed that: 1) The surface-level influencing factors of the sustainable supply chain implementation of the water diversion project mainly included 8 factors such as water-saving awareness and water-saving intensity in the diversion area, water quality, water pollution and other disasters, effective incentive mechanisms, etc., and surface-level influencing factors were directly related to the sustainable supply chain implementation of water diversion projects. 2) The indirect influencing factors of the sustainable supply chain of water diversion projects included 12 factors such as the water quality and quantity guarantee rate of the supply chain, the government’s enforcement of laws and regulations, water distribution, ecological compensation, and compensation mechanisms for residents in the water source area. Indirect influencing factor scan acts directly on the direct influencing factors, and intervening in the factors that can be controlled by humans is one of the important ways to improve the sustainable operation of water diversion projects. 3) The fundamental influencing factors for the sustainable supply chain implementation of water diversion projects included three factors: Resettlement policy, government financial support, and sound laws and regulations. Deep influencing factors had multi-channel influence and controllability, and intervening in them was the main means to improve the sustainable operation of water diversion projects.

Barriers to big data analytics (BDA) implementation in manufacturing supply chains

Big data analysis(BDA)can increase the capability of supply chain analysis of manufacturing companies.Therefore,many manufacturing companies want to use BDA,but it has been seen that BDA implementation is difficult,especially in developing countries due to the existence of various barriers related to finance,government regulations,etc.This paper aims to investigate the barriers to BDA implementation in Iranian companies.In literature,limited work has been done on identifying barriers to implementing BDA in developing countries.In this regard,34 barriers were identified to BDA adoption in Iran by employing a literature review and feedback received from experts.Then,the most important barriers(14)were analyzed using integrated Interpretive Structural Modeling and MICMAC approach.Results show that two barriers;namely,lack of sufficient knowledge of senior managers and weakness of governance policies,are the most significant.Finally,crucial policy measures and recommendations are proposed to assist managers and government bodies.

Post-evaluation of a water pollution control plan： methodology and case study

China has developed more than 20 water pollution control plans for river basins （RBWPs） since 1996. However, the implementation has generally lagged. This paper proposes a three-step, post-evaluation methodology to analyze the implementation result of a RBWP and its influential factors. First, a multi-attribute evaluation method based on an index system is established to score the enforcement results of a RBWP. Indicators measure how well a RBWP has achieved its objectives, which include water quality compliance, pollution load control, project construction, financial inputs, and related management requirements. Second, an interpretive structural model is used to detect the significant factors that affect RBWP implementation. This model can effectively analyze the cause-effect chain and hierarchical relationship among variables. Five groups of factors were identified, namely, plan preparation, water resource endowment, policy, institution, and management. Both qualitative and quantitative methods are employed in the third step to evaluate the extent to which these factors have influenced the execution result of a RBWP, including pre-post contrast, scenario analysis, and correlation analysis. This research then post-evaluated the implementation of the Huai River Basin water pollution control plans （H-RBWPs） over a period of 10 years as a case study. Results showed that the implementation of the H-RBWPs was unsatisfactory during 2001–2005, although it improved during 2006–2010. The poor execution of these plans was partially caused by the underestimation of regional economic development in combination with ineffective industrial structure adjustment policies. Therefore, this case study demonstrates the feasibility and flexibility of the proposed post-evaluation methodology.

Evaluation of liquefaction-induced lateral displacement using Bayesian belief networks

Liquefaction-induced lateral displacement is responsible for considerable damage to engineered structures during major earthquakes.Therefore,an accurate estimation of lateral displacement in liquefaction-prone regions is an essential task for geotechnical experts for sustainable development.This paper presents a novel probabilistic framework for evaluating liquefaction-induced lateral displacement using the Bayesian belief network(BBN)approach based on an interpretive structural modeling technique.The BBN models are trained and tested using a wide-range casehistory records database.The two BBN models are proposed to predict lateral displacements for free-face and sloping ground conditions.The predictive performance results of the proposed BBN models are compared with those of frequently used multiple linear regression and genetic programming models.The results reveal that the BBN models are able to learn complex relationships between lateral displacement and its influencing factors as cause-effect relationships,with reasonable precision.This study also presents a sensitivity analysis to evaluate the impacts of input factors on the lateral displacement.