Sågverken i Sverige letar kontinuerligt efter lösningar för att kunna effektivisera produktionen. I denna studie analyseras specifikt Derome Kinnareds sågens timmerplan. Genom en stopptidsanalys, undersökningar av nya tekniska lösningar, muntliga kommunikationer med personal samt arbetsledare och andra företag införskaffades underlag till analys. Åtgärder som kan utföras för att minimera produktionskostnaderna eller öka produktionen på timmerplan är att implementera ny teknik t.ex. fjärrmätning, digitalisera mätbesked med hjälp av SDC:s arkiv, omorganisera personalstyrkan samt införskaffa bemyndigad mätare. Förslagen indikerar att implementering av dem kan förbättra Kinnaredsågens ekonomi. Beroende på hur mycket företaget är villigt att investera kan det effektiviseras i olika grad. I samband med kommunikation med företagsrepresentanter kom man dock fram till att ny teknik kan vara bättre att implementera vid en nybyggnation.

Uncontrolled open dumping and burning of municipal solid waste (MSW) has resulted in soil, water, and air pollution in many urban cities in India. Landfills are the most common cost-effective solution for MSW management in many developing countries like India. However, the identification of suitable landfill sites always remains a challenging task as it involves the selection of several environmental criteria set by the local authorities. The objective of this study is to identify the most potential landfill sites proposed by the Government in Bengaluru Metropolitan Region, Karnataka state, India using Geographic Information System enabled Analytical Hierarchy Process based multi-criteria evaluation technique. Several criteria and constraints as recommended by the local authorities along with the proximity to the solid waste processing plants are used to identify the potential landfill sites in the study region. The study identified three highly suitable sites (Neraluru, Gudhatti, Madivala) for landfills which are not only environmentally sustainable but also economically attractive as they are closer to the solid waste processing plants minimizing the transportation cost involved in the disposal of solid waste from the source to the final disposal sites in the study region.

Within any ecosystem, information needs are shared to be fulfilled and to support the ecosystem in a way or another. They are vital for designing appropriate information systems that will provide those needs. In this thesis, the gravel road ecosystem is studied, where efficient information sharing is not enabled due to the lack of appropriate information systems. Thus,when focusing on the gravel roads ecosystem, information plays a crucial role regarding the maintenance acts upon these roads. Moreover, not enough literature investigates theinformation needs of gravel roads stakeholders with the aim to improve the maintenance ofthese roads.

Therefore, this thesis intends to elicit information needs of gravel road stakeholders by performing an exploratory case study. The case study forms a part of an ongoing project with the goal to build a cloud-based information system for a sustainable gravel road maintenance. Data were collected through telephone- and online-based interviews with several key stakeholders in Sweden and were thereafter structured through template analysis. The major findings were a set of information needs and several needed sensors within the pre-mentioned information system. This thesis concluded that the future cloud-based information system in the ongoing project is a useful system for sharing vital information among future gravel roads stakeholders, considering the variety of needed information that affects the maintenance of the gravel roads.

Within any ecosystem, information sharing is essential. In this paper, the Swedish gravel road ecosystem is studied, where information plays a crucial role for the effective management of operations and maintenance. However, efficient information sharing is not enabled due to the lack of appropriate information systems. For addressing this issue, this paper intends to elicit information needs of gravel road stakeholders to support the design of a cloud-based information system. The main purpose is to explore the information needs of stakeholders within the Swedish gravel road ecosystem. Data were collected through in-depth semi-structured interviews with 11 participants representing key stakeholders in the ecosystem. Template analysis was used for analyzing the interview results. The major findings were a set of information needs covering road identification and condition, weather conditions, accessibility and traffic, maintenance policy, and sensor data. The results form a comprehensive information model for the further development of a cloud-based gravel road management system that would contribute to increased traffic safety and comfort, lower maintenance and management costs, and better decision-making abilities.

Modern software systems are increasingly more connected, pervasive, and dynamic, as such, they are subject to more runtime variations than legacy systems. Runtime variations affect system properties, such as performance and availability. The variations are difficult to anticipate and thus mitigate in the system design.

Self-adaptive software systems were proposed as a solution to monitor and adapt systems in response to runtime variations. Research has established a vast body of knowledge on engineering self-adaptive systems. However, there is a lack of systematic process support that leverages such engineering knowledge and provides for systematic reuse for self-adaptive systems development. 

This thesis proposes the Autonomic Software Product Lines (ASPL), which is a strategy for developing self-adaptive software systems with systematic reuse. The strategy exploits the separation of a managed and a managing subsystem and describes three steps that transform and integrate a domain-independent managing system platform into a domain-specific software product line for self-adaptive software systems.

Applying the ASPL strategy is however not straightforward as it involves challenges related to variability and uncertainty. We analyzed variability and uncertainty to understand their causes and effects. Based on the results, we developed the Autonomic Software Product Lines engineering (ASPLe) methodology, which provides process support for the ASPL strategy. The ASPLe has three processes, 1) ASPL Domain Engineering, 2) Specialization and 3) Integration. Each process maps to one of the steps in the ASPL strategy and defines roles, work-products, activities, and workflows for requirements, design, implementation, and testing. The focus of this thesis is on requirements and design.

We validate the ASPLe through demonstration and evaluation. We developed three demonstrator product lines using the ASPLe. We also conducted an extensive case study to evaluate key design activities in the ASPLe with experiments, questionnaires, and interviews. The results show a statistically significant increase in quality and reuse levels for self-adaptive software systems designed using the ASPLe compared to current engineering practices.

Software quality is critical in today's software systems. A challenge is the trade-off situation architects face in the design process. Designers often have two or more alternatives, which must be compared and put into context before a decision is made. The challenge becomes even more complex for dynamic software product lines, where domain designers have to take runtime variations into consideration as well. To address the problem we propose extensions to an architectural reasoning framework with constructs/artifacts to define and model a domain's scope and dynamic variability. The extended reasoning framework encapsulates knowledge to understand and reason about domain quality behavior and self-adaptation as a primary variability mechanism. The framework is demonstrated for a self-configuration property, self-upgradability on an educational product-line.

Software architecture serves as a foundation for the design and development of software systems. Designing an architecture requires extensive analysis and reasoning. The study presented herein focuses on the architectural analysis and reasoning in support of engineering self-adaptive software systems with systematic reuse. Designing self-adaptive software systems with systematic reuse introduces variability along three dimensions; adding more complexity to the architectural analysis and reasoning process. To this end, the study presents an extended Architectural Reasoning Framework with dedicated reasoning support for self-adaptive systems and reuse. To evaluate the proposed framework, we conducted an initial feasibility case study, which concludes that the proposed framework assists the domain architects to increase reusability, reduce fault density, and eliminate differences in skills and experiences among architects, which were our research goals and are decisive factors for a system's overall quality.

Advances in computing technologies are pushing software systems and their operating environments to become more dynamic and complex. The growing complexity of software systems coupled with uncertainties induced by runtime variations leads to challenges in software analysis and design. Self-Adaptive Software Systems (SASS) have been proposed as a solution to address design time complexity and uncertainty by adapting software systems at runtime. A vast body of knowledge on engineering self-adaptive software systems has been established. However, to the best of our knowledge, no or little work has considered systematic reuse of this knowledge. To that end, this study contributes an Autonomic Software Product Lines engineering (ASPLe) methodology. The ASPLe is based on a multi-product lines strategy which leverages systematic reuse through separation of application and adaptation logic. It provides developers with repeatable process support to design and develop self-adaptive software systems with reuse across several application domains. The methodology is composed of three core processes, and each process is organized for requirements, design, implementation, and testing activities. To exemplify and demonstrate the use of the ASPLe methodology, three application domains are used as running examples throughout the report.

This work studies systematic reuse in the context of self-adaptive software systems. In our work, we realized that managing variability for such platforms is different compared to traditional platforms, primarily due to the run-time variability and system uncertainties. Motivated by the fact that recent trends show that self-adaptation will be used more often in future system generation and that software reuse state-of-practice or research do not provide sufficient support, we have investigated the problems and possibly resolutions in this context. We have analyzed variability for these systems, using a systematic reuse prism, and identified a research gap in variability management. The analysis divides variability handling into four activities: (1) identify variability, (2) constrain variability, (3) implement variability, and (4) manage variability. Based on the findings we envision a reuse framework for the specific domain and present an example framework that addresses some of the identified challenges. We argue that it provides basic support for engineering self-adaptive software systems with systematic reuse. We discuss some important avenues of research for achieving the vision.

Designing a software architecture requires architectural reasoning, i.e., activities that translate requirements to an architecture solution. Architectural reasoning is particularly challenging in the design of product-lines of self-adaptive systems, which involve variability both at development time and runtime. In previous work we developed an extended Architectural Reasoning Framework (eARF) to address this challenge. However, evaluation of the eARF showed that the framework lacked support for rigorous reasoning, ensuring that the design complies to the requirements. In this paper, we introduce an analytical framework that enhances eARF with such support. The framework defines a set of artifacts and a series of activities. Artifacts include templates to specify domain quality attribute scenarios, concrete models, and properties. The activities support architects with transforming requirement scenarios to architecture models that comply to required properties. Our focus in this paper is on architectural reasoning support for a single product instance. We illustrate the benefits of the approach by applying it to an example client-server system, and outline challenges for future work. © 2016 IEEE.

More than two decades of research have demonstrated an increasing need for software systems to be self-adaptive. Self-adaptation is required to deal with runtime dynamics which are difficult to predict before deployment. A vast body of knowledge to develop Self-Adaptive Software Systems (SASS) has been established. We, however, discovered a lack of process support to develop self-adaptive systems with reuse. To that end, we propose a domain-engineering based methodology, Autonomic Software Product Lines engineering (ASPLe), which provides step-by-step guidelines for developing families of SASS with systematic reuse. The evaluation results from a case study show positive effects on quality and reuse for self-adaptive systems designed using the ASPLe compared to state-of-the-art engineering practices.

Forests are essential for life, providing various ecological, social, and economic benefits worldwide. However, one of the main challenges faced by the world is the forest damage caused by biotic and abiotic factors. In any case, the forest damages threaten the environment, biodiversity, and ecosystem. Climate change and anthropogenic activities, such as illegal logging and industrial waste, are among the principal elements contributing to forest damage. To achieve the United Nations' Sustainable Development Goals (SDGs) related to forests and climate change, detecting and analyzing forest damages, and taking appropriate measures to prevent or reduce the damages are essential. To that end, we envision establishing a Smart Forest Observatories (SFOs) network, as shown below, which can be either a local area or a wide area network involving remote forests. The basic idea is to use Monitor, Analyze, Plan, Execute, and Knowledge (MAPE-K) architecture from autonomic computing and self-adaptive software systems domain to design and develop the SFOs network. The SFOs are planned to collect, analyze, and share the collected data and analysis results using state-of-the-art methods. The principal objective of the SFOs network is to provide accurate and real-time data to policymakers and forest managers, enabling them to develop effective policies and management strategies for global forest conservation that help to achieve SDGs related to forests and climate change.

Facial recognition technology has become a ubiquitous tool in security and personal identification. However, the rise of this technology has been accompanied by concerns over inherent biases, particularly regarding ethnic and gender. This thesis examines the extent of these biases by focusing on the influence of dataset imbalances in facial recognition algorithms. We employ a structured methodological approach that integrates AI-generated images to enhance dataset diversity, with the intent to balance representation across ethnics and genders. Using the ResNet and Vgg model, we conducted a series of controlled experiments that compare the performance impacts of balanced versus imbalanced datasets. Our analysis includes the use of confusion matrices and accuracy, precision, recall and F1-score metrics to critically assess the model’s performance. The results demonstrate how tailored augmentation of training datasets can mitigate bias, leading to more equitable outcomes in facial recognition technology. We present our findings with the aim of contributing to the ongoing dialogue regarding AI fairness and propose a framework for future research in the field.

Asynchronous machines are considered nowadays the most commonly used electrical machines, which are mainly used as electrical induction motors. Starting the induction motor is the most important and dangerous step. The theory behind this project is based on representing the real motor by a set of equations and values in Matlab using the subsystem feature, forming a corresponding idealistic motor in a way where all the physical effects are similar. The motor is started under different loads in two methods: Direct and Soft starting. Each method is studied and discussed using supporting simulation of currents, torque, speed, efficiency and power factor curves.

The Cherenkov Telescope Array (CTA), the new-generation ground-based observatory for gamma-ray astronomy, provides unique capabilities to address significant open questions in astrophysics, cosmology, and fundamental physics. We study some of the salient areas of gamma-ray cosmology that can be explored as part of the Key Science Projects of CTA, through simulated observations of active galactic nuclei (AGN) and of their relativistic jets. Observations of AGN with CTA will enable a measurement of gamma-ray absorption on the extragalactic background light with a statistical uncertainty below 15% up to a redshift z = 2 and to constrain or detect gamma-ray halos up to intergalactic-magnetic-field strengths of at least 0.3 pG. Extragalactic observations with CTA also show promising potential to probe physics beyond the Standard Model. The best limits on Lorentz invariance violation from gamma-ray astronomy will be improved by a factor of at least two to three. CTA will also probe the parameter space in which axion-like particles could constitute a significant fraction, if not all, of dark matter. We conclude on the synergies between CTA and other upcoming facilities that will foster the growth of gamma-ray cosmology.

A search for dark matter linelike signals iss performed in the vicinity of the Galactic Center by the H.E.S.S. experiment on observational data taken in 2014. An unbinned likelihood analysis iss developed to improve the sensitivity to linelike signals. The upgraded analysis along with newer data extend the energy coverage of the previous measurement down to 100 GeV. The 18 h of data collected with the H.E.S.S. array allow one to rule out at 95% C.L. the presence of a 130 GeV line (at l = -1.5 degrees, b = 0 degrees and for a dark matter profile centered at this location) previously reported in Fermi-LAT data. This new analysis overlaps significantly in energy with previous Fermi-LAT and H.E.S.S. results. No significant excess associated with dark matter annihilations was found in the energy range of 100 GeV to 2 TeV and upper limits on the gamma-ray flux and the velocity weighted annihilation cross section are derived adopting an Einasto dark matter halo profile. Expected limits for present and future large statistics H.E.S.S. observations are also given.

A search for new supernova remnants (SNRs) has been conducted using TeV gamma-ray data from the H.E.S.S. Galactic plane survey. As an identification criterion, shell morphologies that are characteristic for known resolved TeV SNRs have been used. Three new SNR candidates were identified in the H.E.S.S. data set with this method. Extensive multiwavelength searches for counterparts were conducted. A radio SNR candidate has been identified to be a counterpart to HESS J1534-571. The TeV source is therefore classified as a SNR. For the other two sources, HESS J1614-518 and HESS J1912 + 101, no identifying counterparts have been found, thus they remain SNR candidates for the time being. TeV-emitting SNRs are key objects in the context of identifying the accelerators of Galactic cosmic rays. The TeV emission of the relativistic particles in the new sources is examined in view of possible leptonic and hadronic emission scenarios, taking the current multiwavelength knowledge into account.