Scrapers are useful construction equipment when hauling distances range between 500 to 3000 feet. When preparing an estimate for an earthmoving project utilizing scrapers, the capacity of the scraper and the cycle time for the given project conditions must be calculated. Since travel time varies widely based on the conditions of the haul road and the performance of the equipment, determining the most economical selection (size and model) and the correct number of scrapers and pushers is a rather tedious process. The calculation of travel time between the cut and fill zone involves repetitive calculations. A spreadsheet-based interactive advisory system was created in order to facilitate these calculations and generate a list of recommended equipment. The system contains a scrapers database, performance charts, soil properties, and a user interface to solicit data that is specific to the project such as haul road surface conditions and characteristics. Data such as efficiency (minutes worked per hour) and hourly rates for operators and other workers can also be specified in the user interface. Once the user enters the quantity to be moved the application calculates the production rate, time required for the job, and the estimated unit cost for each scraper in the database. The system then produces a list of all scrapers, sorted in the order of shortest time or lowest unit price.

Scrapers are useful construction equipment when hauling distances range between 500 to 3000 feet. When preparing an estimate for an earthmoving project utilizing scrapers, the capacity of the scraper and the cycle time for the given project conditions must be calculated. Since travel time varies widely based on the conditions of the haul road and the performance of the equipment, determining the most economical selection (size and model) and the correct number of scrapers and pushers is a rather tedious process. The calculation of travel time between the cut and fill zone involves repetitive calculations. A spreadsheet-based interactive advisory system was created in order to facilitate these calculations and generate a list of recommended equipment. The system contains a scrapers database, performance charts, soil properties, and a user interface to solicit data that is specific to the project such as haul road surface conditions and characteristics. Data such as efficiency (minutes worked per hour) and hourly rates for operators and other workers can also be specified in the user interface. Once the user enters the quantity to be moved the application calculates the production rate, time required for the job, and the estimated unit cost for each scraper in the database. The system then produces a list of all scrapers, sorted in the order of shortest time or lowest unit price.

Physical inactivity, a phenomenon on the rise in numerous countries, has gained global attention because of its negative effects on humans' physical wellness. It represents a stumbling block in the way of living a healthy lifestyle. Recent statistics of World Health Organization (WHO) ranked physical inactivity as the fourth leading risk factors for adults' mortality all over the world [1]. Also, physical inactivity is considered as one of the most prominent contributing factors in several severe diseases such as breast and colon cancer, diabetes and many heart- related diseases [1]. Therefore, improving daily physical activity levels is an urgent societal goal in order to tackle the physical inactivity problem. Achieving such challenging goal requires addressing the factors that affect adults’ physical activity. In fact, there are many factors that lead to physical inactivity such as the busy lifestyle, lack of awareness regarding required physical activity levels and other environmental factors. Physical activity advisory systems can be seen as a promising solution for the inactivity problem. In order to enhance their effectiveness, these systems must take into account most of the factors previously mentioned. In this thesis, we aim to provide a method to promote the increase of daily physical activity levels by leveraging biofeedback and context awareness features. In order to achieve this purpose, we design and develop an algorithm that provides a user with personalized physical activity advice. This advice increases the user's awareness through the use of calories expenditure. To add a context awareness component to our algorithm, we propose an extension of the Ubiquitous Biofeedback (UB) Model [2]. We believe that combining the biofeedback feature with context awareness component would make the system sensitive to the user’s status and thus increase the chances of her or him following it. This advice represents the daily-recommended amount of physical activity for maintaining healthy lifestyle according to [3, 4]and other international organizations' recommendations. In order to prove the concept of the proposed algorithm and extended UB Model, we design and develop a system called "CAB". It is a context aware biofeedback system that tracks user's physical movement and estimates the amount of calories burnt to provide the user with a personalized physical activity advice that considers user's current status, preferences and surrounding environmental context. The system utilizes a biofeedback sensor and a smart phone in order to provide the personalized advice that is delivered to the user in a form of multiple-mode feedback/notification (text, audio and haptic). In this thesis, we provide detailed information about the design requirements, the design model, the proposed system and its related hardware components and software modules. The qualitative and quantitative evaluation of the developed system CAB shows a positive impact on the experiment sample group by motivating the participants to reach or exceed the recommended number of calories to be burned daily for most of the evaluation days.

Physical inactivity, a phenomenon on the rise in numerous countries, has gained global attention because of its negative effects on humans' physical wellness. It represents a stumbling block in the way of living a healthy lifestyle. Recent statistics of World Health Organization (WHO) ranked physical inactivity as the fourth leading risk factors for adults' mortality all over the world [1]. Also, physical inactivity is considered as one of the most prominent contributing factors in several severe diseases such as breast and colon cancer, diabetes and many heart- related diseases [1]. Therefore, improving daily physical activity levels is an urgent societal goal in order to tackle the physical inactivity problem. Achieving such challenging goal requires addressing the factors that affect adults’ physical activity. In fact, there are many factors that lead to physical inactivity such as the busy lifestyle, lack of awareness regarding required physical activity levels and other environmental factors. Physical activity advisory systems can be seen as a promising solution for the inactivity problem. In order to enhance their effectiveness, these systems must take into account most of the factors previously mentioned. In this thesis, we aim to provide a method to promote the increase of daily physical activity levels by leveraging biofeedback and context awareness features. In order to achieve this purpose, we design and develop an algorithm that provides a user with personalized physical activity advice. This advice increases the user's awareness through the use of calories expenditure. To add a context awareness component to our algorithm, we propose an extension of the Ubiquitous Biofeedback (UB) Model [2]. We believe that combining the biofeedback feature with context awareness component would make the system sensitive to the user’s status and thus increase the chances of her or him following it. This advice represents the daily-recommended amount of physical activity for maintaining healthy lifestyle according to [3, 4]and other international organizations' recommendations. In order to prove the concept of the proposed algorithm and extended UB Model, we design and develop a system called "CAB". It is a context aware biofeedback system that tracks user's physical movement and estimates the amount of calories burnt to provide the user with a personalized physical activity advice that considers user's current status, preferences and surrounding environmental context. The system utilizes a biofeedback sensor and a smart phone in order to provide the personalized advice that is delivered to the user in a form of multiple-mode feedback/notification (text, audio and haptic). In this thesis, we provide detailed information about the design requirements, the design model, the proposed system and its related hardware components and software modules. The qualitative and quantitative evaluation of the developed system CAB shows a positive impact on the experiment sample group by motivating the participants to reach or exceed the recommended number of calories to be burned daily for most of the evaluation days.

With the rapidly increasing demand for energy and the increasing prices for oil and gas, the role of unconventional gas reservoirs (UGRs) as energy sources is becoming more important throughout the world. Because of high risks and uncertainties associated with UGRs, their profitable development requires experts to be involved in the most critical development stages, such as drilling, completion, stimulation, and production. However, many companies operating UGRs lack this expertise. The advisory system we developed will help them make efficient decisions by providing insight from analogous basins that can be applied to the wells drilled in target basins. In North America, UGRs have been in development for more than 50 years. The petroleum literature has thousands of papers describing best practices in management of these resources. If we can define the characteristics of the target basin anywhere in the world and find an analogous basin in North America, we should be able to study the best practices in the analogous basin or formation and provide the best practices to the operators. In this research, we have built an advisory system that we call the Unconventional Gas Reservoir (UGR) Advisor. UGR Advisor incorporates three major modules: BASIN, PRISE and Drilling & Completion (D&C) Advisor. BASIN is used to identify the reference basin and formations in North America that are the best analogs to the target basin or formation. With these data, PRISE is used to estimate the technically recoverable gas volume in the target basin. Finally, by analogy with data from the reference formation, we use D&C Advisor to find the best practice for drilling and producing the target reservoir. To create this module, we reviewed the literature and interviewed experts to gather the information required to determine best completion and stimulation practices as a function of reservoir properties. We used these best practices to build decision trees that allow the user to take an elementary data set and end up with a decision that honors the best practices. From the decision trees, we developed simple computer algorithms that streamline the process.

Software evolution and maintenance have received great attention with the steadily increasing complexity of software systems. One recent approach in this field is adaptive programming, which focuses on the evolution of large class hierarchies. Its main objectives are to manage the change in evolutionary systems and to keep costs for adaptive maintenance low. In this thesis we present our experiences with the application of adaptive programming for modeling and implementing the legal advisory system ADONIS. Unlike most other information systems, ADONIS does not simply process data but regulations. Since regulations and laws are frequently a subject to change, we have chosen this domain as the basis for our practical research on adaptive programming. / Die Evolution und Wartung von Software haben wegen der stetig wachsenden Komplexität von Softwaresystemen deutlich an Beachtung gewonnen. Ein aktueller Ansatz auf diesem Gebiet ist die adaptive Programmierung, welche sich auf die Evolution von großen Klassenhierarchien konzentriert. Ihre Hauptziele liegen darin, die wechselnden Anforderungen in evolutionären Systemen zu bewältigen und die Kosten für adaptive Wartung gering zu halten. In dieser Arbeit präsentieren wir unsere Erfahrungen mit der Anwendung der adaptiven Programmierung zur Modellierung und Implementierung des rechtlichen Beratungssystemes ADONIS. Im Gegensatz zu vielen anderen Informationssystemen verarbeitet ADONIS nicht einfach Daten, sondern Vorschriften. Aufgrunddessen, daß Vorschriften und Gesetze häufig geändert werden, haben wir diese Domäne als Grundlage für unsere praktischen Nachforschungen zum Thema adaptive Programmierung ausgewählt.

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<p>This master thesis has been written at the School of Mathematics and System Engineering (MSI) at Växjö University in the field of computer science. IT security has become one of the main topics of every enterprise since they all use information technology in their business. Investments have to be done in order to achieve a high protection status of the IT environments. Specially small and medium-sized enterprises need more knowledge and advisory how to handle their IT security. This thesis analyses management tools that have the goal to improve the IT security. The second and main part of the thesis is the design of a tool that should helps to solve the described problems.</p>

Software evolution and maintenance have received great attention with the steadily increasing complexity of software systems. One recent approach in this field is adaptive programming, which focuses on the evolution of large class hierarchies. Its main objectives are to manage the change in evolutionary systems and to keep costs for adaptive maintenance low. In this thesis we present our experiences with the application of adaptive programming for modeling and implementing the legal advisory system ADONIS. Unlike most other information systems, ADONIS does not simply process data but regulations. Since regulations and laws are frequently a subject to change, we have chosen this domain as the basis for our practical research on adaptive programming. / Die Evolution und Wartung von Software haben wegen der stetig wachsenden Komplexität von Softwaresystemen deutlich an Beachtung gewonnen. Ein aktueller Ansatz auf diesem Gebiet ist die adaptive Programmierung, welche sich auf die Evolution von großen Klassenhierarchien konzentriert. Ihre Hauptziele liegen darin, die wechselnden Anforderungen in evolutionären Systemen zu bewältigen und die Kosten für adaptive Wartung gering zu halten. In dieser Arbeit präsentieren wir unsere Erfahrungen mit der Anwendung der adaptiven Programmierung zur Modellierung und Implementierung des rechtlichen Beratungssystemes ADONIS. Im Gegensatz zu vielen anderen Informationssystemen verarbeitet ADONIS nicht einfach Daten, sondern Vorschriften. Aufgrunddessen, daß Vorschriften und Gesetze häufig geändert werden, haben wir diese Domäne als Grundlage für unsere praktischen Nachforschungen zum Thema adaptive Programmierung ausgewählt.

No description available.