Extended Producer Responsibility for the Management of Waste from Mobile Phones

This thesis explores the functionality of Extend Producer Responsibility (EPR) in the management of Electrical and Electronic waste (e-waste) in Kenya using a case study on manufacturer involvement in end-of-life management. To achieve the purpose of the study the analytical framework used incorporates Environmental effectiveness, Economic efficiency, Political acceptability, Administrabilty and Innovative advancement in discussing the EPR policy instrument used by the manufacturer. On the practical front the data on the take-back scheme was discussed under the following factors that affect the efficiency and effectiveness of a take-back scheme: economic incentives, disincentives, convenience, inconvenience and information. On the other hand the thesis provides preliminary insights into the overall ewaste management scenario in Kenya. Literature and practical knowledge were used to explore and establish a picture of the dynamics of EPR in e-waste management under the ICT sector with special focus on mobile telephony and the actors in the sector. Suggested policy directions are based on the gaps identified through an analysis of the materials and information collected while in the field. The research confirms that there is need to develop waste management policies and regulations in Kenya structured and guided by EPR principles. The thesis emphasizes that EPR is a necessity in the management of e-waste in Kenya and the developing countries at large. Further it notes that there is need for knowledge transfer and exchange from the developed countries to the developing countries grappling with e-waste management in formulation of appropriate institutional and legislative frameworks customized to the ground realities

Transferable knowledge for Low-cost Decision Making in Cloud Environments

Users of Infrastructure as a Service (IaaS) are increasingly overwhelmed with the wide range of providers and services offered by each provider. As such, many users select services based on description alone. An emerging alternative is to use a decision support system (DSS), which typically relies on gaining insights from observational data in order to assist a customer in making decisions regarding optimal deployment of cloud applications. The primary activity of such systems is the generation of a prediction model (e.g. using machine learning), which requires a significantly large amount of training data. However, considering the varying architectures of applications, cloud providers, and cloud offerings, this activity is not sustainable as it incurs additional time and cost to collect data to train the models. We overcome this through developing a Transfer Learning (TL) approach where knowledge (in the form of a prediction model and associated data set) gained from running an application on a particular IaaS is transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures. In this paper, we present our approach and evaluate it through extensive experimentation involving three real world applications over two major public cloud providers, namely Amazon and Google. Our evaluation shows that our novel two-mode TL scheme increases overall efficiency with a factor of 60% reduction in the time and cost of generating a new prediction model. We test this under a number of cross-application and cross-cloud scenario

Technology and carbon mitigation in developing countries: Are cleaner coal technologies a viable option?

human development, climate change

Fostering shared knowledge with active graphical representation in different collaboration scenarios

This study investigated how two types of graphical representation tools influence the way in which learners use shared and unshared knowledge resources in two different collaboration scenarios, and how learners represent and transfer shared knowledge under these different conditions. Moreover, the relation between the use of knowledge resources, representation, and the transfer of shared knowledge was analyzed. The type of graphical representation (content-specific vs. content-unspecific) and the collaboration scenario (video conferencing vs. face-to-face) were varied. 64 university students participated. Results show that the learning partners converged in their profiles of resource use. With the content-specific graphical representation, learners used more appropriate knowledge resources. Learners in the computer-mediated scenarios showed a greater bandwidth in their profiles of resource use. A relation between discourse and outcomes could be shown for the transfer but not for the knowledge representation aspectIn dieser Studie werden die Wirkungen von verschiedenen Arten graphischer Repräsentation auf die Nutzung geteilter und ungeteilter Wissensressourcen in zwei verschiedenen Kooperationsszenarien untersucht. Des Weiteren wird analysiert, wie Lernende geteiltes und ungeteiltes Wissen unter diesen verschiedenen Bedingungen repräsentieren und transferieren. Schließlich wird die Beziehung zwischen der Nutzung von Wissensressourcen auf der einen Seite sowie der Repräsentation und dem Transfer geteilten Wissens auf der anderen Seite geprüft. Mit der Art der graphischen Repräsentation (inhaltsspezifisch vs. inhaltsunspezifisch) und dem Kooperationsszenario (Videokonferenz vs. face-to-face) werden zwei Faktoren experimentell variiert. 64 Studierende nahmen an der Studie teil. Ergebnisse zeigen, dass die Lernpartner in ihren Profilen der Ressourcennutzung konvergierten. Lernende, die durch die inhaltsspezifische graphische Repräsentation unterstützt wurden, verwendeten angemessenere Wissensressourcen. Lernende in den computervermittelten Szenarien weisen eine größere Bandbreite in ihren Profilen der Ressourcennutzung auf. Eine direkte Wirkung vom Diskurs der Lernenden auf die Entwicklung geteilten Wissens konnte für den Transfer, aber nicht für die Wissensrepräsentation gezeigt werde

Joint Transmission and Energy Transfer Policies for Energy Harvesting Devices with Finite Batteries

One of the main concerns in traditional Wireless Sensor Networks (WSNs) is energy efficiency. In this work, we analyze two techniques that can extend network lifetime. The first is Ambient \emph{Energy Harvesting} (EH), i.e., the capability of the devices to gather energy from the environment, whereas the second is Wireless \emph{Energy Transfer} (ET), that can be used to exchange energy among devices. We study the combination of these techniques, showing that they can be used jointly to improve the system performance. We consider a transmitter-receiver pair, showing how the ET improvement depends upon the statistics of the energy arrivals and the energy consumption of the devices. With the aim of maximizing a reward function, e.g., the average transmission rate, we find performance upper bounds with and without ET, define both online and offline optimization problems, and present results based on realistic energy arrivals in indoor and outdoor environments. We show that ET can significantly improve the system performance even when a sizable fraction of the transmitted energy is wasted and that, in some scenarios, the online approach can obtain close to optimal performance.Comment: 16 pages, 12 figure

Communication Theoretic Data Analytics

Widespread use of the Internet and social networks invokes the generation of big data, which is proving to be useful in a number of applications. To deal with explosively growing amounts of data, data analytics has emerged as a critical technology related to computing, signal processing, and information networking. In this paper, a formalism is considered in which data is modeled as a generalized social network and communication theory and information theory are thereby extended to data analytics. First, the creation of an equalizer to optimize information transfer between two data variables is considered, and financial data is used to demonstrate the advantages. Then, an information coupling approach based on information geometry is applied for dimensionality reduction, with a pattern recognition example to illustrate the effectiveness. These initial trials suggest the potential of communication theoretic data analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan. 201