The subject of this Master thesis is to identify the current situation and find an optimal solutions for communication; especially for e-communication between Municipal District Prague 9 and citizens, based on outcomes from surveys conducted with the citizens, employees and observe other Municipal offices. The theoretical part is mainly focused on the interpretation of terms, some of the relevant types of e-communication, methodology and process of market research. In the practical part is depict the current situation, further analyzed the outcome from the conducted survey research and the resulted suggestions and recommendations. I strongly believe that the outcomes from this Master thesis will contribute to achieve a desired objective of Municipal District Prague 9, which is optimal satisfaction of their citizens.

This dissertation entitled Crystals Collector is concerned with the phenomenon of collecting, and more specifically with crystals as objectes in an imaginary collection. The objects are selected based on a personal fascination of the author. However, the collection always has, similarly as a crystal, a predetermined internal structure. The meaning of individual parts only become apparent when they are viewed as a whole. The main artefacts of the collection are paintings on canvas - with the painting becoming a similar 'fetish' as a collected crystal. Mineral it the key artefact and as such it is subjected to deeper examination (experiments with growth of crystals, studies of crystals, artefacts having a close relationship to crystals...).

This work deal with developing dedicated autonomous device for data collection. The work describes the approaches how to design that. Solve choosing of the sensor taking into his properties for this device. And regard on the comparison with similar devices on market according behavior and function. General concept of the suggested datalogger. The thesis contains hardware and software equipment of this device.

This study is based on the catalogue/checklist of Pierneef works in the Pretoria Art Museum collection. The artist’s life, social, political and artistic influences of the period, both local and international, may be deduced from works analysed and discussed. The Arts and Crafts movement was a powerful influence affecting ideas on national identity, folk art and the vernacular from the middle of the nineteenth century onwards. A world-wide romantic nationalism stimulated a search for identity and exploitation of the indigenous. It is argued that these trends may be identified in the artistic development of Pierneef who, through friends, wide reading and intensive study was alive to European developments but focused on the indigenous arts of Southern Africa. He was the first South African artist to recognize Busman art and that of the black peoples. They had a profound influence on his own development and the motifs of his art. / Dissertation (MA)--University of Pretoria, 1997. / Historical and Heritage Studies / unrestricted

The notorious fuel consumption and environmental impact of conventional diesel refuse collection trucks (D-RCTs) encourage collection fleets to adopt alternative technologies with higher efficiency and lower emissions/noise impacts into their fleets. Due to the nature of refuse trucks’ duty cycles with low driving speeds, frequent braking and high idling time, a battery-electric refuse collection truck (BE-RCT) seems a promising alternative, taking advantage of energy-saving potentials along with zero tailpipe emissions. However, whether or not this newly-introduced technology can be commercially feasible for a collection fleet and/or additionally mitigate GHG emissions should be examined over its lifetime explicitly for the specific fleet. This study evaluates the performance of a D-RCT and BE-RCT in a collection fleet to assess the potential of BE-RCT in reducing diesel fuel consumption and the total GHG emissions. A refuse truck duty cycle (RTDC) was generated representing the driving nature and vocational operation of the refuse truck, including the speed, mass, and hydraulic cycles along with the extracted route grade profile. As a case study, the in-use data of a collection fleet, operating in the municipality of Saanich, British Columbia (BC), Canada, are applied to develop the representative duty cycle. Using the ADVISOR simulator, the D-RCT and BE-RCT are modeled and energy consumption of the trucks are estimated over the representative duty cycle. Fuel-based Well-to-Wheel (WTW) GHG emissions of the trucks are estimated considering the fuel (diesel/electricity) upstream and downstream GHG emissions over the 100-year horizon impact factor for greenhouse gases. The results showed that the BE-RCT reduces energy use by 77.7% and WTW GHG emissions by 98% compared to the D-RCT, taking advantage of the clean grid power in BC. Also, it was indicated that minimum battery capacity of 220 kWh is required for the BE-RCT to meet the duty cycle requirements for the examined fleet. A sensitivity analysis has been done to investigate the impact of key parameters on energy use and corresponding GHG emissions of the trucks. Further, the lifetime total cost of ownership (TCO) for both trucks was estimated to assess the financial competitiveness of the BE-RCT over the D-RCT. The TCO indicated that the BE-RCT deployment is not financially viable for the examined fleet unless there are considerable incentives towards the purchase cost of the BE-RCT and/or sufficient increase in carbon tax/diesel fuel price. From the energy useevaluation, this study estimates the required battery capacity of the BE-RCT for the studied fleet, and the TCO outputs can assist them in future planning for the adoption of battery-electric refuse trucks into their collection fleet where the cost parameters evolve. / Graduate

Les réseaux de capteurs sont utilisés dans l’IoT pour collecter des données. Cependant, une expertise envers les réseaux de capteurs est requise pour interagir avec ces infrastructures. Pour un ingénieur logiciel, cibler de tels systèmes est difficile. Les spécifications des plateformes composant l'infrastructure de capteurs les obligent à travailler à un bas niveau d'abstraction et à utiliser des plateformes hétérogènes. Cette fastidieuse activité peut conduire à un code exploitant de manière non optimisée l’infrastructure. En étant spécifiques à une infrastructure, ces applications ne peuvent également pas être réutilisées facilement vers d’autres infrastructures. De plus, le déploiement de ces applications est hors du champ de compétences d’un ingénieur logiciel car il doit identifier la ou les plateforme(s) requise(s) pour supporter l’application. Enfin, l’architecture peut ne pas être conçue pour supporter l’exécution simultanée d’application, engendrant des déploiements redondants lorsqu’une nouvelle application est identifiée. Dans cette thèse, nous présentons une approche qui supporte (i) la définition de politiques de collecte de données à haut niveau d’abstraction et réutilisables, (ii) leur déploiement sur une infrastructure hétérogène dirigée par des modèles apportés par des experts réseau et (iii) la composition automatique de politiques sur des infrastructures hétérogènes. De ces contributions, un ingénieur peut dès lors manipuler un réseau de capteurs sans en connaitre les détails, en réutilisant des abstractions architecturales disponibles lors de l'expression des politiques, des politiques qui pourront également coexister au sein d'un même réseau. / Sensing infrastructures are classically used in the IoT to collect data. However, a deep knowledge of sensing infrastructures is needed to properly interact with the deployed systems. For software engineers, targeting these systems is tedious. First, the specifies of the platforms composing the infrastructure compel them to work with little abstractions and heterogeneous devices. This can lead to code that badly exploit the network infrastructure. Moreover, by being infrastructure specific, these applications cannot be easily reused across different systems. Secondly, the deployment of an application is outside the domain expertise of a software engineer as she needs to identify the required platform(s) to support her application. Lastly, the sensing infrastructure might not be designed to support the concurrent execution of various applications leading to redundant deployments when a new application is contemplated. In this thesis we present an approach that supports (i) the definition of data collection policies at high level of abstraction with a focus on their reuse, (ii) their deployment over a heterogeneous infrastructure driven by models designed by a network export and (iii) the automatic composition of the policy on top of the heterogeneous sensing infrastructures. Based on these contributions, a software engineer can exploit sensor networks without knowing the associated details, while reusing architectural abstractions available off-the-shelf in their policy. The network will also be shared automatically between the policies.

One of the greatest challenges facing school staff is problem behavior in the classroom (Public Agenda, 2004). Children who engage in problem behavior in the classroom setting greatly challenge teachers and diminish the ability to learn. This study evaluated the effects of self-monitoring using the Individualized Behavior Rating Scale Tool (IBRST) on problem behavior and on-task behavior in a classroom setting using a multiple-baseline across participants design. This study also evaluated the extent to which students self-rating on the IBRST correlated with direct observation data. Results indicate that self-monitoring using the IBRST was an effective strategy for increasing on-task behavior and decreasing problem behavior for all three students. Results also indicate that the IBRST may be an accurate and reliable means of measuring data when direct observation data may not be feasible or possible. There were 56/60 perfect agreements, with the other four opportunities being only 1-pt value apart. Limitations and future research are discussed.

Le processus de sélection de photos dans des albums peut être considérablement amélioré à l’aide d’un critère d’évaluation automatique des qualités d’une photo. Cependant, les méthodes existantes abordent ce problème de manière indépendante, c’est à dire en évaluant chaque image séparément des autres images d'un album. Dans cette thèse, nous explorons la modélisation du contexte d’une photo via une approche de clustering de collections de photos et la possibilité d'appliquer l’information de contexte à l'évaluation d’une photo. Nous avons effectué des études subjectives permettant d’étudier la manière dont les utilisateurs regroupent et sélectionnent des photos dans un album. Ces études ont permis une estimation du niveau de l’accord entre les différents utilisateurs. Nous avons aussi étudié la manière dont le contexte influence leurs décisions. Après avoir étudié la nature des décisions des utilisateurs, nous proposons une approche informatique pour modéliser leur comportement. Tout d'abord, nous introduisons une méthode de clustering hiérarchique, qui permet de regrouper des photos similaires selon une structure de similarité à plusieurs niveaux, basée sur des descripteurs visuels. Ensuite, les informations de contexte de la photo sont utilisées pour adapter le score de la photo pré-calculé indépendamment, en utilisant les données basées sur des statistiques et une approche d'apprentissage automatique. De plus, comme la majorité des méthodes récentes d'évaluation de la photo sont basées sur des réseaux de neurones convolutionnels, nous avons exploré et visualisé les caractéristiques esthétiques apprises par ces méthodes. / An automatic photo assessment can significantly aid the process of photo selection within photo collections. However, existing computational methods approach this problem in an independent manner, by evaluating each image apart from other images in a photo album. In this thesis, we explore the modeling of photo context via a clustering approach for photo collections and the possibility of applying such context information in photo assessment. To better understand user actions within photo albums, we conduct experimental user studies, where we study how users cluster and select photos in photo collections. We estimate the level of agreement between users and investigate how the context, defined by similar photos in corresponding clusters, influences their decisions. After studying the nature of user decisions, we propose a computational approach to model user behavior. First, we introduce a hierarchical clustering method, which allows to group similar photos according to a multi-level similarity structure, based on visual descriptors. Then, the photo context information is extracted from the obtained cluster data and used to adapt a pre-computed independent photo score, using the statistics-based data and a machine learning approach. In addition, as the majority of recent methods for photo assessment are based on convolutional neural networks, we explore and visualize the aesthetic characteristics learned by such methods.

Data collection and online personalization has become essential part of modern marketing, and thus, embedded into consumer’s everyday life. This has emerged a lot of negative attention in the media and privacy concerns among consumers – however, their attitudes towards privacy seems to be controversial with lack of privacy enhancing behavior. The purpose of this study was to find out what is consumers take on online personalization, data collection and GDPR. In order to the tackle the causing reasons of such perceptions, focus group discussions were performed. The emerging thoughts were analyzed with the concepts of privacy paradox and information asymmetry – how structural imbalance between the advertisement network, companies and consumers impacted to their thinking about personalization and which factors caused the unwillingness to enhance one’s privacy, despite the attitudes that would predict different behavior. The results showed, that many respondents do not mind personalization if they perceive it relevant. However, the intrusive nature of its practices made the participants, directly or indirectly, reluctant towards it, as it was highlighted that it is not personalization per se that made the respondents uncomfortable, but how it was done. Due to the advertisement networks’ opaque nature, the participants founded challenging to comprehend how personalization was performed. Thus, conspiracy theories about surveillance, such as tapping via smartphone, were broad up to explain companies’ ability to know and target them so well. The main channel for companies to inform consumers about their privacy policy is terms and conditions. However, due to several reasons, the decision making for one’s privacy face many hinders, that may influence in how consumers perceive their privacy and how their personal data is collected and used. A controversiality between GDPR’s, companies’ and consumers’ view on privacy self-management is evident, as the regulation and companies rely too much on consumer’s own responsibility.

The main focus of this thesis is the characterization of the charge transport in CdZnTe radiation detectors and the study of the effect of the detector illumination on charge transport. The transport properties are evaluated using Laser-induced Transient Current Technique and the Monte Carlo simulation is used for fitting the measured current waveforms. The properties of the detector prepared from semi-insulating CdZnTe single crystal with a platinum Schottky contacts were measured in the dark in the unpolarized and polarized state and under the anode and cathode continuous LED above-bandgap illumination.