Survey of Rough and Fuzzy Hybridization

In this research existing barriers and the influence of product’s functional lifecycle on the adoption of circular revenue models in the civil and non-residential building sector was investigated. A revenue model, i.e. how revenues are generated in a business model, becomes circular if it is used to extend producer responsibility to create financial incentives for producers to benefit from making their product more circular. For example, leasing or a buy-back scheme in theory creates an incentive for producers to, amongst others, make the product last longer, to be maintained more easily and to be returned. In the Dutch national policy documents there is a call for the development of circular revenue models to extend producer responsibility in the construction sector, as the construction sector is highlighted as a key sector in terms of environmental impact. Adopting circular revenue models in the construction has so far not been research, however expectations about barriers towards adopting circular revenue models can be derived from related literature. The civil and non-residential building sub-sector of the construction sector is of special interest as this subsector has specific characteristics that were expected to create barriers towards adopting circular revenue models: ownership rights and the long functional lifecycle of products (e.g. buildings). This led to the main research question: “What are the barriers to the adoption of circular revenue models in the civil- and non-residential building sector?” The long functional lifecycle of buildings is of special interest as literature suggests that buildings are made from products with different functional lifecycles. This led to led to an additional sub question: “What is the influence of product’s functional lifecycle on the adoption of circular revenue models in the civil and non-residential building sector?” To answer both research questions, the research was split up into three phases. First, semi-structured interviews were held with practitioners, e.g. companies that have adopted, or are working on adopting, circular revenue models. Based upon the results, a second round of interviews was held with experts to better understand the barriers and gather more in-depth insights. The topics chosen for this round were based on the results from the practitioners. The third research phase was a focus group session held primarily with respondents from the expert and practitioner interviews. During the focus group preliminary results were presented and several topics were discussed. During this research 25 barriers, such as a maximum duration for contracts, short-term thinking and the adoption of measurement methods, towards adopting circular revenue models in the civil and non-residential building sector were found, which fit under five main categories in order of importance: financial, sector-specific, regulatory, organisational and technical barriers. Furthermore, seven additional barriers were found when adopting circular revenue models in which producers retain ownership. This shows that there are many barriers that hinder the adoption of circular revenue models in the civil and non-residential building sector, especially when adopting circular revenue models where producers retain ownership. Furthermore, during this research it was found that the shorter the functional lifecycle of building layers, the more easy the adoption of circular revenue models becomes, because, amongst others, financing for longer that 15 years is difficult and two parties to not like to be mutually dependents upon each other over long time periods. In increasing order of difficulty circular revenue models can be adopted to the building layers with longer functional lifecycles: space plan, services, skin and structure. During the research a consensus amongst respondents was identified that circular revenue models should not be adopted to the structure, as the functional lifecycle was too long. In addition to the functional lifecycle, four additional variables were identified that emphasise why the adoption of circular revenue models to building layers with shorter functional lifecycles is more interesting: ratio CAPEX/OPEX, flexibility of products, focus on investor or user and complexity of products

Fuzzy c-means clustering of web users for educational sites

Publisher's version/PDFCharacterization of users is an important issue in the design and maintenance of websites. Analysis of the data from the World Wide Web faces certain challenges that are not commonly observed in conventional data analysis. The likelihood of bad or incomplete web usage data is higher than in conventional applications. The clusters and associations in web mining do not necessarily have crisp boundaries. Researchers have studied the possibility of using fuzzy sets for clustering of web resources. This paper presents clustering using a fuzzy c-means algorithm, on secondary data consisting of access logs from the World Wide Web. This type of analysis is called web usage mining, which involves applying data mining techniques to discover usage patterns from web data. The fuzzy c-means clustering was applied to the web visitors to three educational websites. The analysis shows the ability of the fuzzy c-means clustering to distinguish different user characteristics of these sites

Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets

Publisher's version/PDFWeb usage mining involves application of data mining techniques to discover usage patterns from the web data. Clustering is one of the important functions in web usage mining. The likelihood of bad or incomplete web usage data is higher than the conventional applications. The clusters and associations in web usage mining do not necessarily have crisp boundaries. Researchers have studied the possibility of using fuzzy sets in web mining clustering applications. Recent attempts have adapted the K-means clustering algorithm as well as genetic algorithms based on rough sets to find interval sets of clusters. The genetic algorithms based clustering may not be able to handle large amounts of data. The K-means algorithm does not lend itself well to adaptive clustering. This paper proposes an adaptation of Kohonen self-organizing maps based on the properties of rough sets, to find the interval sets of clusters. Experiments are used to create interval set representations of clusters of web visitors on three educational web sites. The proposed approach has wider applications in other areas of web mining as well as data mining

Relationship between product based loyalty and clustering based on supermarket visit and spending patterns

Loyalty of customers to a supermarket can be measured in a variety of ways. If a customer tends to buy from certain categories of products, it is likely that the customer is loyal to the supermarket. Another indication of loyalty is based on the tendency of customers to visit the supermarket over a number of weeks. Regular visitors and spenders are more likely to be loyal to the supermarket. Neither one of these two criteria can provide a complete picture of customers’ loyalty. The decision regarding the loyalty of a customer will have to take into account the visiting pattern as well as the categories of products purchased. This paper describes results of experiments that attempted to identify customer loyalty using thes e two sets of criteria separately. The experiments were based on transactional data obtained from a supermarket data collection program. Comparisons of results from these parallel sets of experiments were useful in fine tuning both the schemes of estimating the degree of loyalty of a customer. The project also provides useful insights for the development of more sophisticated measures for studying customer loyalty. It is hoped that the understanding of loyal customers will be helpful in identifying better marketing strategies

Building Cross-Platform Mobile and Web Apps for Engineers and Scientists : An Active Learning Approach

Cross-platform application design provides an excellent starting point for mastering application development in this new book. You can introduce today's most popular technologies, including HTML5, CSS3, JavaScript, jQuery Mobile, Node.js, JSON, localStorage, sessionStorage, NoSQL using MongoDB, SQL using MySQL, templating using handlebars, and maps. An app-centric view emphasizes subsets of these technologies to guide students in developing non-trivial apps. The apps serve as models for numerous projects from various application domains, while detailed outlines present potential course projects. Apps continue to evolve, but the technologies in this book form the backbone for future cross-platform app development. Students learn to work with all major mobile and web platforms as this book's active learning approach asks students to type code in parallel as the apps are developed. Meaningful exercises further encourage students to change code and evaluate resulting app behavior.Award Winning The award recognizes excellence in 1st edition textbooks and learning materialsix+355hlm.;21,5x27,5c