Drug development in general and clinical trials in particular is expensive and time consuming processes. One mandatory procedure in clinical trials are data collection, about 15 years ago almost all data were collected with a paper based approach but with new digitalised technology for data collection the process were about to become more efficient in regard to time, cost and quality of data. However the adoption rate of these systems for data collection were much lower than anticipated and most previous research points toward poorly developed products as the main reason for the adoption failure. Nevertheless, these systems have become more user friendly and efficient and today almost all studies use Electronic Data Capture (EDC) as the primary method for data collection. This project aim to investigate if the reason for the slow diffusion was a result of poorly developed products or if there are external factors such as social or organisational aspects that caused this delay. Semi structured interviews were conducted with 15 informants who works with EDC systems daily and are professionals within this industry. The result indicates that the slow diffusion is partly caused by initially bad systems that in turn might have caused a resistance among the end users and partly caused by slow decision organisations such as multinational pharmaceutical companies. The advice given to the project owner who intends to acquire this market is to focus on electronic Patient Reported Outcome (ePRO), which is a tool used by individual patients for self-reporting of data in clinical trials. ePRO is an extension of the EDC systems and must be user friendly for the patients and easy to connect to other systems. The company should rather focus on small Contract Research Organisation (CRO) as main customers rather than Big Pharma. Big Pharma often conduct multinational studies and decisions regarding the protocol and how data is to be collected are centrally decided. Since the project owner is a newly started, small firm with limited experience of clinical trials my advice would be to target CROs that conduct smaller studies.

Large-scale earthmoving activities make up a costly and air-polluting aspect of many construction projects and mining operations, which depend entirely on the use of heavy construction equipment. The long-term jobsites and manufacturing nature of the mining sector has encouraged the application of automated controlling systems, more specifically GPS, to control the earthmoving fleet. Computer vision-based methods are another potential tool to provide real-time information at low-cost and to reduce human error in surface earthmoving sites as relatively clear views can be selected and the equipment offer recognizable targets. Vision-based methods have some advantages over positioning devices as they are not intrusive, provide detailed data about the behaviour of each piece of equipment, and offer reliable documentation for future reviews. This dissertation explains the development of a vision-based system, named server-customer interaction planner (SCIT), to recognize and estimate earth material loading cycles. The SCIT system consists of three main modules: object recognition, tracking, and action recognition. Different object recognition and tracking algorithms were evaluated and modified, and then the ideal methods were used to develop the object recognition and tracking modules. A novel hybrid tracking framework was developed for the SCIT system to track dump trucks in the challenging views found in the loading zones. The object recognition and tracking engines provide spatiotemporal data about the equipment which are then analyzed by the action recognition module to estimate loading cycles. The entire framework was evaluated using videos taken under varying conditions. The results highlight the promising performance of the SCIT system with the hybrid tracking engine, thereby validating the possibility of its practical application.

Large-scale earthmoving activities make up a costly and air-polluting aspect of many construction projects and mining operations, which depend entirely on the use of heavy construction equipment. The long-term jobsites and manufacturing nature of the mining sector has encouraged the application of automated controlling systems, more specifically GPS, to control the earthmoving fleet. Computer vision-based methods are another potential tool to provide real-time information at low-cost and to reduce human error in surface earthmoving sites as relatively clear views can be selected and the equipment offer recognizable targets. Vision-based methods have some advantages over positioning devices as they are not intrusive, provide detailed data about the behaviour of each piece of equipment, and offer reliable documentation for future reviews. This dissertation explains the development of a vision-based system, named server-customer interaction planner (SCIT), to recognize and estimate earth material loading cycles. The SCIT system consists of three main modules: object recognition, tracking, and action recognition. Different object recognition and tracking algorithms were evaluated and modified, and then the ideal methods were used to develop the object recognition and tracking modules. A novel hybrid tracking framework was developed for the SCIT system to track dump trucks in the challenging views found in the loading zones. The object recognition and tracking engines provide spatiotemporal data about the equipment which are then analyzed by the action recognition module to estimate loading cycles. The entire framework was evaluated using videos taken under varying conditions. The results highlight the promising performance of the SCIT system with the hybrid tracking engine, thereby validating the possibility of its practical application.

This degree project is a part of information and communication technology supported self-care system for the diabetes, mainly in diabetes data collection and visualization. The report is organized in four main sections: investigation and internet search, literature review, application design and implementation, system test and evaluation. Existed applications and research studies has been compared and, a responsive web application is developed aiming at providing relevant functionalities and services regarding diabetes self-management.

This thesis presents a collection disjointness analysis to find disjointness relations between collections in Java. We define the three types of disjointness relations between collections: must-shared, may-shared and not-may-shared. The collection- disjointness analysis is implemented following the way of a forward data-flow analysis using Soot Java bytecode analysis framework. For method calls, which are usually difficult to analyze in static analysis, our analysis provide a way of generating and reading annotations of a method to best approximate the behavior of the calling methods. Finally, this thesis presents the experimental results of the collection-disjointness analysis on several tests.

This thesis analyses the ratchet effect in the context of the performance scheme implemented by Brazilian tax collection in 1988 to reward tax officials for their effort in collecting taxes and uncovering tax violations, using panel data for 110 tax agencies from August 1989 to April 1993 and employing the GMM-system estimator. The estimates suggest the presence of ratchet effect, i.e., the more the tax officials do today, the more the tax officials are asked to do in the future. This result endangers the credibility of the Brazilian tax authority's incentive program as an incentive system.

This study develops a general procedure for forming a well-defined collection of documents on a research topic, which is suitable for bibliometric analysis. The procedure is applied to one research topic in Library and Information Science, Bradford's Law of Scattering. An analysis is made of the underlying concepts, viz. 'document', 'selection', 'collection comprehensiveness', 'topic', 'research topic', and 'on' and 'relevant to' a research topic. An important distinction is drawn between graphical and semantic attributes of documents, and between their modes of analysis. The central problem of the study is that, while 'topic' is a problematic semantic attribute of documents, a well-defined collection of documents requires a selection criterion formulated on unproblematic graphical attributes. The solution proposed is to let specialist A&I Services legitimate a research topic and to provide a sample of its documents; then to extract a diagnostic graphical pattern from the sample, and from this construct a criterion which can be mechanically applied to all documents. Modifications introduced into the general procedure include the iterative development of the criterion from the growing collection, and allowing a content analysis of documents to suggest diagnostic patterns in the text. The graphical selection criterion developed for the specific collection was composed of six alternative pairs of word-stems separated by at most two words in the body of the text of documents. It has an estimated precision of 96% against the background literature and retrieves 90% of all known documents which might be judged to be on the topic. The final collection consists of 1187 analytical-level and scholarly documents written in 19 languages; it is well-defined, accords with convention, and is judged to be near completion for documents more strongly on and influential in the topic. Inadequacies in the procedure and in the collection formed were examined and improvements have been suggested. For example, the mixing of semantic and graphical methods in the pattern extraction process is clarified, recall can be enhanced by the addition of several small sub-collections, and measures of topic aboutness and topic influence were installed in the collection.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (M.S.)--Ohio University, 2002. / Title from PDF t.p.

Examensarbete.