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Diploma thesis is focused on study of compounds of terpenic nature in mint plants. Terpenic compounds are volatile hydrocarbons formed by several isoprenoid units with low molecular weight which cause the typical smell of plants. Presence of these compounds cause that plants seem to be fungicidal, bactericidal and insecticidal. Due to these properties, they can be used as botanical pesticides which are not toxic and no resistant organisms occur. Each plant contains relatively low concentrations of these compounds and therefore elicitors must be used for the increasing of terpenes amount. Elicitors activate defensive mechanisms in the plant leading to higher defense abilities and production of secondary metabolites. The identification and quantification of analytes was determined by gas chromatography in connection with mass spectrometry.

Au cours de ces dernières années, la sécurité des Systèmes d'Information (SI) est devenue une préoccupation importante, qui doit être prise en compte dans toutes les phases du développement du SI, y compris dans la phase initiale de l'ingénierie des exigences (IE). Prendre en considération la sécurité durant les premières phases du développement des SI permet aux développeurs d'envisager les menaces, leurs conséquences et les contre-mesures avant qu'un système soit mis en place. Les exigences de sécurité sont connues pour être "les plus difficiles des types d’exigences", et potentiellement celles qui causent le plus de risque si elles ne sont pas correctes. De plus, les ingénieurs en exigences ne sont pas principalement intéressés à, ou formés sur la sécurité. Leur connaissance tacite de la sécurité et leur connaissance primitive sur le domaine pour lequel ils élucident des exigences de sécurité rendent les exigences de sécurité résultantes pauvres et trop génériques. Cette thèse explore l'approche de l’élucidation des exigences fondée sur la réutilisation de connaissances explicites. Tout d'abord, la thèse propose une étude cartographique systématique et exhaustive de la littérature sur la réutilisation des connaissances dans l'ingénierie des exigences de sécurité identifiant les différentes formes de connaissances. Suivi par un examen et une classification des ontologies de sécurité comme étant la principale forme de réutilisation. Dans la deuxième partie, AMAN-DA est présentée. AMAN-DA est la méthode développée dans cette thèse. Elle permet l’élucidation des exigences de sécurité d'un système d'information spécifique à un domaine particulier en réutilisant des connaissances encapsulées dans des ontologies de domaine et de sécurité. En outre, la thèse présente les différents éléments d'AMAN-DA : (I) une ontologie de sécurité noyau, (II) une ontologie de domaine multi-niveau, (iii) des modèles syntaxique de buts et d’exigences de sécurité, (IV) un ensemble de règles et de mécanismes nécessaires d'explorer et de réutiliser la connaissance encapsulée dans les ontologies et de produire des spécifications d’exigences de sécurité. La dernière partie rapporte l'évaluation de la méthode. AMAN-DA a été implémenté dans un prototype d'outil. Sa faisabilité a été évaluée et appliquée dans les études de cas de trois domaines différents (maritimes, applications web, et de vente). La facilité d'utilisation et l’utilisabilité de la méthode et de son outil ont également été évaluées dans une expérience contrôlée. L'expérience a révélé que la méthode est bénéfique pour l’élucidation des exigences de sécurité spécifiques aux domaines, et l'outil convivial et facile à utiliser. / In recent years, security in Information Systems (IS) has become an important issue that needs to be taken into account in all stages of IS development, including the early phase of Requirement Engineering (RE). Considering security during early stages of IS development allows IS developers to envisage threats, their consequences and countermeasures before a system is in place. Security requirements are known to be “the most difficult of requirements types”, and potentially the ones causing the greatest risk if they are not correct. Moreover, requirements engineers are not primarily interested in, or knowledgeable about, security. Their tacit knowledge about security and their primitive knowledge about the domain for which they elicit security requirements make the resulting security requirements poor and too generic. This thesis explores the approach of eliciting requirements based on the reuse of explicit knowledge. First, the thesis proposes an extensive systematic mapping study of the literature on the reuse of knowledge in security requirements engineering identifying the different knowledge forms. This is followed by a review and classification of security ontologies as the main reuse form. In the second part, AMAN-DA is presented. AMAN-DA is the method developed in this thesis. It allows the elicitation of domain-specific security requirements of an information system by reusing knowledge encapsulated in domain and security ontologies. Besides that, the thesis presents the different elements of AMANDA: (I) a core security ontology, (II) a multi-level domain ontology, (III) security goals and requirements’ syntactic models, (IV) a set of rules and mechanisms necessary to explore and reuse the encapsulated knowledge of the ontologies and produce security requirements specifications. The last part reports the evaluation of the method. AMAN-DA was implemented in a prototype tool. Its feasibility was evaluated and applied in case studies of three different domains (maritime, web applications, and sales). The ease of use and the usability of the method and its tool were also evaluated in a controlled experiment. The experiment revealed that the method is beneficial for the elicitation of domain specific security requirements, and that the tool is friendly and easy to use.

Development, environmental sustainability, agriculture and livelihoods are dimensions that are often considered antagonistic. By thinking at the landscape level however, innovative opportunities arise for simultaneity as these entities manifest spatially and require communication across disciplines. Trans-frontier Conservation Areas (TFCAs) embrace this thinking. These are large areas that cut across two or more international boundaries, include within them at least one Protected Area (PA) and other multiple resource use areas, including human dwellings and cultivated areas. Similarly, ecoagriculture is an innovative approach to land use management as it seeks to spatially synergise agriculture, livelihoods and biodiversity conservation across space and requires an awareness of landscape-level issues by land users, a condition which is not necessarily met. Such landscape thinking stems from the fact that if a piece of land is subject to rigorous conservation, it will fail if the surrounding areas are degraded. Additionally, it has been shown that agriculture often benefits from the nearby presence of natural areas for ecosystem services such as pollination, pest management, and erosion control. As such, multifunctional landscape mosaics together with small scale farmers, not large scale monocultures, are the key to global food security, as the former more effectively links agricultural intensification to hunger reduction. In order to ascertain an integrated understanding of the landscape concept, necessary for the formalisation of ecoagriculture, this study assessed the landscape perceptions and understandings held by local people residing within a TFCA. We employed participatory methods within the Mathenjwa Tribal Area (MTA), an area falling within the Lubombo TFCA and identified as holding ecoagriculture potential. Results revealed that local people perceive landscape as a function of subsistence utility. Local people perceive land-use multifunctionality, necessary for the formalisation of ecoagriculture, but at a smaller scale than expected depending on both social and biophysical interpretations. Landscape scale projects should incorporate local landscape understandings. / Dissertation (MA)--University of Pretoria, 2013. / Geography, Geoinformatics and Meteorology / MA / Unrestricted

<p>Commercial buildings consume more than 19% of the total energy consumption in the United States. Most of this energy is consumed by the HVAC and shading/lighting systems inside these buildings. The main purpose of such systems is to provide satisfactory thermal and visual environments for occupants working inside these buildings. Providing satisfactory thermal/visual conditions in indoor environments is critical since it directly affects occupants’ comfort, health and productivity and has a significant effect on energy performance of the buildings. </p> <p>Therefore, efficiently learning occupants’ preferences is of prime importance to address the dual energy challenge of reducing energy usage and providing occupants with comfortable spaces at the same time. The objective of this thesis is to develop robust and easy to implement algorithms for learning and eliciting thermal and visual preferences of office occupants from limited data. As such, the questions studied in this thesis are: 1) How can we exploit concepts from utility theory to model (in a Bayesian manner) the hidden thermal and visual utility functions of different occupants? Our central hypothesis is that an occupant’s preference relation over different thermal/visual states of the room can be described using a scalar function of these states, which we call the “occupant’s thermal/visual utility function.” 2) By making use of formalisms in Bayesian decision theory, how can we learn the maximally preferred thermal/visual states for different occupants without requiring unnecessary or excessive efforts from occupants and/or the building engineers? The challenge here is to minimize the number of queries posed to the occupants to learn the maximally preferred thermal/visual states for each occupant. 3) Inferring preferences of occupants based on their responses to the thermal/visual comfort-based questionnaire surveys is intrusive and expensive. Contrary to this, how can we learn the thermal/visual preferences of occupants from cheap and non-intrusive human-building interactions’ data? 4) Lastly, based on the observation that the occupant population decompose into different clusters of occupants having similar preferences, how can we exploit the collective information obtained from the similarities in the occupants’ behavior? This thesis presents viable answers to the aforementioned questions in the form of probabilistic graphical models/frameworks. In future, I hope that these frameworks would prove to be an important step towards the development of intelligent thermal/visual systems which would be able to respond to occupants’ personalized comfort needs. Furthermore, in order to encourage the use of these frameworks and ensure reproducibility in results,various implementations of this work (namely GPPref, GPElicit and GPActToPref) are published as open-source Python packages.</p><br>

The demand on research libraries to digitize theircollections as a means of increasing the availabilityof said collections are increasing. However, a prerequisite for this is the cataloging of the collections – a task commonly associated with large demands on time and other resources. One way of handling this might be efforts in applying automatization as a part of the cataloging process. This thesis examines the possibilities of using automatization when catalog- ing commercial ephemera. For this, focus is directed towards the features of the material; the process of cataloging; and the demands on the catalogued mate- rial from its various users using a theory based on Monica J. Bates (2002) Cascade-model. By conducting a case study, consisting of observations based on contextual inquiry and interviews partly using photo elicitation, automatization of cataloging is found to be a possible way to improve availability, but not without its own complications and demands on re- sources. In conclusion, suggestions are made for considerations libraries should be aware of before automatization might be implemented at research libraries.

Mothering is a complex and multifaceted occupation that encompasses the nurturing work that women engage in. It addition, it has been established that ethnicity, class, and gender have effects on motherhood that need to be taken into account when looking at the occupations of motherhood across cultures. One important task that occurs within a mothers’ daily routine is that of making meals for their children. This dissertation sought to gain a rich, in-depth description of the phenomenon of the mealtime experience for African American mothers of low socioeconomic status and young children living in an inner-city environment in the Midwest. This study used a phenomenological approach with modified photovoice and photo-elicitation interviews to capture the essence of mealtime for African American mothers raising young children. Six mothers were recruited for the study and consented to two in-depth interviews. Individual interviewing occurred along with a second photo elicitation interview with the participant’s photographs. Phenomenological analyses were used for textual data; the photographs were analyzed separately and then together with the textual data from the photo-elicitation interviews. Results of this study indicated the intricate complexities of the occupation of mealtime and mothering with African American mothers. From the photo-elicitation interviews, five themes and three subthemes emerged: (a) Sometimes it doesn’t happen smoothly, (b) We’re all together, (c) We sit there and we talk, (d) It’s an accomplishment, and (e) We’re in the kitchen together. Three subthemes also emerged: (a) Putting in the effort, (b) It was kind of a teaching moment, and (c) It’s like déjà vu. This research (a) promotes a greater understanding of mothers’ perceptions around mealtime with their young children, especially those mothers who have varied cultural and socioeconomic backgrounds; and (b) suggests a need for increased family-centered and culturally aware training. This study demonstrates how photo techniques can enhance the depth of phenomenological analysis to explicate meaning around mealtime occupations with a diverse group of mothers.

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This thesis examines two possible orders of combining multiple experts in elicitations with multiple de-composed events: Should experts be combined early or later in the decision process? This thesis is in conjunction with the paper (Baker & Olaleye, 2012) where we show that it is best to combine experts early as later combination leads to a systematic error. We conduct a simulation to more fully flesh out the theoretical model. We also conduct a theoretical analysis aimed at determining how significantly these two methods differ. We find that all results are in accordance with the theory but combining experts later might lead to less error in some cases due to randomness. We then conduct an empirical evaluation of the two methods using data from a previous study. We show that the experts exhibit some form of correlation. The impact of using the two methods of combining experts is then evaluated using an optimal R&D investment portfolio model. We find that the elicitation inputs have a significant effect on the outcome of the optimal portfolio and that there is an advantage from combining experts early.

This thesis was conducted by two students as part of their Strategic Information Systems Management degree program at Stockholm University. As presented in this study, the manual elicitation processes in Requirements Engineering are error-prone and time-consuming. Traditional approaches and techniques often produce requirements that are characterized by ambiguity, inadequacy, incompleteness, inconsistency, and obsolescence. The research problem is focused on the lack of clear understanding regarding AI's specific role in supporting the identification of precise and detailed requirements, and the need for summarizing findings of related work. The goal of this thesis is to investigate the impact of AI in Requirements Engineering, focusing primarily on Requirements Elicitation and Analysis. After presenting essential background knowledge of Requirements Engineering, traditional elicitation methods, and Artificial Intelligence, a systematic literature review was performed to unveil Artificial Intelligence methods, techniques, and tools used in Requirements Elicitation and Analysis. With the assistance of the PRISMA methodology, the key findings and results were summarized and presented. The majority of the online literature focused on various issues connected with traditional methods and presented how Artificial Intelligence Chatbots, Text Mining and Natural Language Processing techniques, Virtual Reality, Sentiment Analysis, Crowdsourcing, Deep Learning Techniques, Gamification, and Bayesian Networks are improving the quality and speed of Requirements Elicitation. One of the main challenges faced is that there is no extensive comparison with traditional methods and metrics on how Artificial Intelligence overall helps Requirements Elicitation – only metrics per case. Also, there is not explicit definition regarding which AI methodologies and tools are appropriate for each elicitation and analysis method.