Reasoning over knowledge-based generation of situations in context spaces to reduce food waste

With the ever-growing amount of connected sensors (IoT), making sense of sensed data becomes even more important. Pervasive computing is a key enabler for sustainable solutions, prominent examples are smart energy systems and decision support systems. A key feature of pervasive systems is situation awareness which allows a system to thoroughly understand its environment. It is based on external interpretation of data and thus relies on expert knowledge. Due to the distinct nature of situations in different domains and applications, the development of situation aware applications remains a complex process. This thesis is concerned with a general framework for situation awareness which simplifies the development of applications. It is based on the Situation Theory Ontology to provide a foundation for situation modelling which allows knowledge reuse. Concepts of the Situation Theory are mapped to the Context Space Theory which is used for situation reasoning. Situation Spaces in the Context Space are automatically generated with the defined knowledge. For the acquisition of sensor data, the IoT standards O-MI/O-DF are integrated into the framework. These allow a peer-to-peer data exchange between data publisher and the proposed framework and thus a platform independent subscription to sensed data. The framework is then applied for a use case to reduce food waste. The use case validates the applicability of the framework and furthermore serves as a showcase for a pervasive system contributing to the sustainability goals. Leading institutions, e.g. the United Nations, stress the need for a more resource efficient society and acknowledge the capability of ICT systems. The use case scenario is based on a smart neighbourhood in which the system recommends the most efficient use of food items through situation awareness to reduce food waste at consumption stage.

New biomass derived carbon catalysts for biomass valorization

Due to diminishing petroleum reserves, unsteady market situation and the environmental concerns associated with utilization of fossil resources, the utilization of renewables for production of energy and chemicals (biorefining) has gained considerable attention. Biomass is the only sustainable source of organic compounds that has been proposed as petroleum equivalent for the production of fuels, chemicals and materials. In fact, it would not be wrong to say that the only viable answer to sustainably convene our future energy and material requirements remain with a bio-based economy with biomass based industries and products. This has prompted biomass valorization (biorefining) to become an important area of industrial research. While many disciplines of science are involved in the realization of this effort, catalysis and knowledge of chemical technology are considered to be particularly important to eventually render this dream to come true. Traditionally, the catalyst research for biomass conversion has been focused primarily on commercially available catalysts like zeolites, silica and various metals (Pt, Pd, Au, Ni) supported on zeolites, silica etc. Nevertheless, the main drawbacks of these catalysts are coupled with high material cost, low activity, limited reusability etc. – all facts that render them less attractive in industrial scale applications (poor activity for the price). Thus, there is a particular need to develop active, robust and cost efficient catalytic systems capable of converting complex biomass molecules. Saccharification, esterification, transesterification and acetylation are important chemical processes in the valorization chain of biomasses (and several biomass components) for production of platform chemicals, transportation fuels, food additives and materials. In the current work, various novel acidic carbons were synthesized from wastes generated from biodiesel and allied industries, and employed as catalysts in the aforementioned reactions. The structure and surface properties of the novel materials were investigated by XRD, XPS, elemental analysis, SEM, TEM, TPD and N2-physisorption techniques. The agro-industrial waste derived sulfonic acid functionalized novel carbons exhibit excellent catalytic activity in the aforementioned reactions and easily outperformed liquid H2SO4 and conventional solid acids (zeolites, ion-exchange resins etc). The experimental results indicated strong influence of catalyst pore-structure (pore size, pore-volume), concentration of –SO3H groups and surface properties in terms of the activity and selectivity of these catalysts. Here, a large pore catalyst with high –SO3H density exhibited the highest esterification and transesterification activity, and was successfully employed in biodiesel production from fatty acids and low grade acidic oils. Also, a catalyst decay model was proposed upon biodiesel production and could explain that the catalyst loses its activity mainly due to active site blocking by adsorption of impurities and by-products. The large pore sulfonated catalyst also exhibited good catalytic performance in the selective synthesis of triacetin via acetylation of glycerol with acetic anhydride and out-performed the best zeolite H-Y with respect to reusability. It also demonstrated equally good activity in acetylation of cellulose to soluble cellulose acetates, with the possibility to control cellulose acetate yield and quality (degree of substitution, DS) by a simple adjustment of reaction time and acetic anhydride concentration. In contrast, the small pore and highly functionalized catalysts obtained by hydrothermal method and from protein rich waste (Jatropha de-oiled waste cake, DOWC), were active and selective in the esterification of glycerol with fatty acids to monoglycerides and saccharification of cellulosic materials, respectively. The operational stability and reusability of the catalyst was found to depend on the stability of –SO3H function (leaching) as well as active site blocking due to adsorption of impurities during the reaction. Thus, our results corroborate the potential of DOWC derived sulfated mesoporous active carbons as efficient integrated solid acid catalysts for valorization of biomass to platform chemicals, biofuel, bio-additive, surfactants and celluloseesters.

A Belief rule-based environmental responsibility assessment system for small and medium-sized enterprises

Various environmental management systems, standards and tools are being created to assist companies to become more environmental friendly. However, not all the enterprises have adopted environmental policies in the same scale and range. Additionally, there is no existing guide to help them determine their level of environmental responsibility and subsequently, provide support to enable them to move forward towards environmental responsibility excellence. This research proposes the use of a Belief Rule-Based approach to assess an enterprise’s level commitment to environmental issues. The Environmental Responsibility BRB assessment system has been developed for this research. Participating companies will have to complete a structured questionnaire. An automated analysis of their responses (using the Belief Rule-Based approach) will determine their environmental responsibility level. This is followed by a recommendation on how to progress to the next level. The recommended best practices will help promote understanding, increase awareness, and make the organization greener. BRB systems consist of two parts: Knowledge Base and Inference Engine. The knowledge base in this research is constructed after an in-depth literature review, critical analyses of existing environmental performance assessment models and primarily guided by the EU Draft Background Report on "Best Environmental Management Practice in the Telecommunications and ICT Services Sector". The reasoning algorithm of a selected Drools JBoss BRB inference engine is forward chaining, where an inference starts iteratively searching for a pattern-match of the input and if-then clause. However, the forward chaining mechanism is not equipped with uncertainty handling. Therefore, a decision is made to deploy an evidential reasoning and forward chaining with a hybrid knowledge representation inference scheme to accommodate imprecision, ambiguity and fuzzy types of uncertainties. It is believed that such a system generates well balanced, sensible and Green ICT readiness adapted results, to help enterprises focus on making improvements on more sustainable business operations.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Adaptability, Transformation and Complex Changes in Namibia and Tanzania: Resilience and Innovation System Development in Local Communities

This thesis is a research about the recent complex spatial changes in Namibia and Tanzania and local communities’ capacity to cope with, adapt to and transform the unpredictability engaged to these processes. I scrutinise the concept of resilience and its potential application to explaining the development of local communities in Southern Africa when facing various social, economic and environmental changes. My research is based on three distinct but overlapping research questions: what are the main spatial changes and their impact on the study areas in Namibia and Tanzania? What are the adaptation, transformation and resilience processes of the studied local communities in Namibia and Tanzania? How are innovation systems developed, and what is their impact on the resilience of the studied local communities in Namibia and Tanzania? I use four ethnographic case studies concerning environmental change, global tourism and innovation system development in Namibia and Tanzania, as well as mixed-methodological approaches, to study these issues. The results of my empirical investigation demonstrate that the spatial changes in the localities within Namibia and Tanzania are unique, loose assemblages, a result of the complex, multisided, relational and evolutional development of human and non-human elements that do not necessarily have linear causalities. Several changes co-exist and are interconnected though uncertain and unstructured and, together with the multiple stressors related to poverty, have made communities more vulnerable to different changes. The communities’ adaptation and transformation measures have been mostly reactive, based on contingency and post hoc learning. Despite various anticipation techniques, coping measures, adaptive learning and self-organisation processes occurring in the localities, the local communities are constrained by their uneven power relationships within the larger assemblages. Thus, communities’ own opportunities to increase their resilience are limited without changing the relations in these multiform entities. Therefore, larger cooperation models are needed, like an innovation system, based on the interactions of different actors to foster cooperation, which require collaboration among and input from a diverse set of stakeholders to combine different sources of knowledge, innovation and learning. Accordingly, both Namibia and Tanzania are developing an innovation system as their key policy to foster transformation towards knowledge-based societies. Finally, the development of an innovation system needs novel bottom-up approaches to increase the resilience of local communities and embed it into local communities. Therefore, innovation policies in Namibia have emphasised the role of indigenous knowledge, and Tanzania has established the Living Lab network.