Decentralised energy and its performance assessment models

Energy development concerns not only the development of renewable energies but also the shift from centralised to clean, decentralised power generation. The development of decentralised energy (DE) is a core part of the energy and economic strategies being adopted around the world that drives the progress toward a highly sustainable future. This paper reviews the concepts, development status, trends, benefits and challenges of DE systems and analyses the existing models and methods for assessing the performance of these systems. A hierarchical decision model for evaluating the performance of DE systems is also constructed based on the framework of multiple criteria decision analysis, which considers the identification, definition and assessment grade of decision criteria. The evidential reasoning approach is applied to aggregate assessment information in a case study of the implementation of an intelligent decision system. Sensitivity and trade-off analyses are also conducted to show how the proposed model can be used to support decision making in DE systems.

Technology Adoption

Cryptocurrency has emerged in recent years as a new form of money. With the wave of cryptocurrency developments and wild attention, it is essential to understand consumer attitudes toward the adoption of the cryptocurrency and the factors influencing their decisions. Thus, the chapter aims to fill a gap in the current literature by examining the factors that influence the user's intention to adopt cryptocurrency. This adoption is a purchasing decision where users make a decision based on a set of factors that matters to them. This chapter uses a hierarchical decision model (HDM) to understand the user decision to adopt cryptocurrency. The model proposes four main perspectives that influence the adoption decision: economic, technical, social, and personal. This study aims at providing an in-depth analysis of the factors influencing the adoption of cryptocurrency as well as the ranking of these influencing factors based on the quantification of the users' judgments.

User Audit Model Based on Attribute Measurement and Similarity Measurement

The Internet of Things (IoT) is an open network. And, there are a large number of malicious nodes in the network. These malicious nodes may tamper with the correct data and pass them to other nodes. The normal nodes will use the wrong data for information dissemination due to a lack of ability to verify the correctness of the messages received, resulting in the dissemination of false information on medical, social, and other networks. Auditing user attributes and behavior information to identify malicious user nodes is an important way to secure networks. In response to the user nodes audit problem, a user audit model based on attribute measurement and similarity measurement (AM-SM-UAM) is proposed. Firstly, the user attribute measurement algorithm is constructed, using a hierarchical decision model to construct a judgment matrix to analyze user attribute data. Secondly, the blog similarity measurement algorithm is constructed, evaluating the similarity of blog posts published by different users based on the improved Levenshtein distance. Finally, a user audit model based on a security degree is built, and malicious users are defined by security thresholds. Experimental results show that this model can comprehensively analyze the attribute and behavior data of users and have more accurate and stable performance in the practical application of the network platforms.

Prone Regions of Zoonotic Cutaneous Leishmaniasis in Southwest of Iran: Combination of Hierarchical Decision Model (AHP) and GIS

Background: Cutaneous leishmaniasis due to Leishmania major is an important public health problem in the world. Khuzestan Province is one of the main foci of zoonotic cutaneous leishmaniasis (ZCL) in the southwest of Iran. We aimed to predict the spatial distribution of the vector and reservoir(s) of ZCL using decision-making tool and to pre­pare risk map of the disease using integrative GIS, RS and AHP methods in endemic foci in Shush (plain area) and Khorramshahr (coastal area) counties of Khuzestan Province, southern Iran from Mar 2012 to Jan 2013. Methods: Thirteen criteria including temperature, relative humidity, rainfall, soil texture, soil organic matter, soil pH, soil moisture, altitude, land cover, land use, underground water depth, distance from river, slope and distance from human dwelling with the highest chance of the presence of the main vector and reservoir of the disease were chosen for this study. Weights of the criteria classes were determined using the Expert choice 11 software. The pres­ence proba­bility maps of the vector and reservoir of the disease were prepared with the combination of AHP method and Arc GIS 9.3. Results: Based on the maps derived from the AHP model, in Khorramshahr study area, the highest probability of ZCL is predicted in Gharb Karoon rural district. The presence probability of ZCL was high in Hossein Abad and Benmoala rural districts in the northeast of Shush. Conclusion: Prediction maps of ZCL distribution pattern provide valuable information which can guide policy mak­ers and health authorities to be precise in making appropriate decisions before occurrence of a possible disease out­break.

Assessment of Arms Import Policies Using a Hierarchical Decision Model and Expert Judgments: Case Study of South Korean Arms Procurement Organizations

Arms import decisions have a significant impact on certain nations. Priorities based on diverse policies are a major cause of conflict and dissent among policy decision makers and practitioners. This is because there are many competing qualitative and quantitative decision elements from varying perspectives. Hence, it has been difficult to compare them and rank their contributions to the final decision. Instead, a common approach has been to make decisions reacting to the situation at hand and on a case by case basis. Another approach is to use a rational decision model and expert judgments to assess arms import policies. For this, a hierarchical decision model (HDM) is used to identify the decision elements. Once a model framework is established many aspects of the results can be studied. In this research, a decision model based on technology, economics, politics, industry, and military (TEPIM) perspectives is developed for assessment of arms import policies. The case of the Republic of Korea is considered. The perspectives and their composite criteria are then ranked using the consensus of panels of experts and decision makers. An important aspect of expert judgment consensus is the management and analysis of disagreements. This study includes hierarchical cluster analyses and MANOVA of the disagreements between the decision makers and practitioners. We have confirmed that the stakeholder arms procurement organizations are guided by their organizational mission and this has an impact on their policy priorities and hence can result in disagreements.