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This research builds on the assertion that spirituality can be beneficial to human-wellbeing and in creating nature-connection. Applying that notion within the historical context of Christianity and its ties to the work-ethics of capitalism, the paper presents a case-study of the Threeclover Church, a Protestant Green Church in Denmark’s capital region. The aim is to gain further understanding of

Operational systems, such as industrial automation, autonomous vehicles, and larger air/sea/landcrafts, often contain a large number of heavily connected systems with real-time requirements for their functionality. For such systems, detecting and responding to anomalies is both challenging and crucial. Until recently, such anomalies were monitored using heuristic methods, or even humans monitoring

The wide adoption of machine learning (ML) models in resource-constrained devices for complex vision tasks is contingent on improved computational efficiency. Quantization is a tool for model compression in which weights and activations are approximated with low-precision datatypes, to achieve a smaller model with reduced latency and memory usage. In this thesis, we seek to apply post-training mix

In the field of automatic control, embedded Model Predictive Control (MPC) presents significant advantages, including efficiency, constraint incorporation, and non-causal setpoint handling. Despite its potential, the implementation of MPC in resource-constrained environments remains challenging due to its computational demands. This thesis explores the design and implementation of MPC within ABB’s

Buildings worldwide account for roughly 40% of the total energy consumption which boils down to generating 30% of the total carbon dioxide emissions. Heating, Ventilation, and Air Conditioning (HVAC) systems are the largest consumers of energy within these structures. The complexity of building cooling systems poses significant challenges for achieving optimal control. Conventional approaches to i

As the global environment gradually deteriorates, developing green technology innovations for enterprises has become an important goal. The ESG concept of advocating sustainable development has steadily received attention. Therefore, it is essential to understand the relationship between ESG and green technology innovations. This thesis aims to explore whether the environmental, social, and govern

The following thesis is an attempt to analyse the concept of narrative in and around retellings of the events surrounding Czechoslovakia during the First World War and how they are subsequently depicted. This is done by primarily looking at three primary source materials: in English Czech historiography from the past 20 years, an autobiography from WWI and a modern video game on the same subject.

This thesis explores the discursive depiction of life in Portugual from the perspective of lifestyle migrants and digital nomads on TikTok. The aim of this thesis is to uncover the motivations of foreigners to live in Portugal, lifestyle migrants’ integration into Portuguese society, and their acknowledgement of the growing gentrification issues in Portugal. The method used in this study is Norman

This paper examines what effect recent minimum wage increases have had on poverty rates in U.S. states. In the past fifteen years, some states have experienced substantial minimum wage increases while others have seen smaller increases or no changes at all. First, we review some theoretical arguments for and against increases in the minimum wage, discussing its potential effects on unemployment an

This small qualitative case study aimed to examine how professions in interprofessional teams define the role and specific competence of a healthcare social worker at a child habilitation center. I interviewed seven professional team members at one child habilitation center. Three are healthcare social workers, one is an occupational therapist, two are psychologists and one is a special needs educ

Digitalization is part of the ongoing transformation for industries that are going through the fourth industrial revolution, Industry 4.0. The Fourth Industrial Revolution introduces significant advancements in digital technology, fundamentally transforming manufacturing environments through interconnected and intelligent systems. Organizations’ willingness to adopt and integrate various AI soluti

This thesis employs deep reinforcement learning, a branch of machine learning, to carry out robotic tasks. The objective centers around teaching an agent controlling a seven-axis robot arm with a gripper tool, to complete an object isolation task. For this task, a robot manipulates a cluttered environment in such a way that a predetermined target object becomes isolated. Sub-tasks were developed t

Automation with the use of robotics is a crucial trend across industries, enhancing efficiency and quality while improving health and safety conditions by minimizing human exposure to hazardous tasks. Alfa Laval, a global provider of products within heat transfer, separation, and fluid handling, is embracing this trend by exploring how to solve labor-intensive processes like the production of rubb

This research examined how ESG factors affected organizational performance in the post-pandemic year of 2022 for 271 large-cap firms in the Nordic Stock Exchange market. The study adopted a deductive research approach to establish how ESG practices impacted corporate resilience and financial performance during this period. Only secondary data sources were used for data gathering, and the analysis

This study examines price elasticity of demand for train travel along the Southern Main Line in Sweden, focusing on four specific routes: Malmö-Stockholm, Lund-Stockholm, Malmö-Linköping, and Linköping-Stockholm. Using sales data provided by Snälltåget for a period of 28 days in 2024, the research analyzes how ticket prices influence passenger numbers, applying econometric models such as log-log r

Alzheimer's Disease (AD) is a prevalent neurodegenerative disorder characterized by progressive cognitive decline. Early and precise diagnosis is crucial for effective prophylaxis and treatment. However, the scarcity of annotated medical imaging data, compounded by privacy restrictions and the high cost of acquiring detailed scans like brain Magnetic resonance imaging (MRI), which have proven

This thesis considers the problem of finding an optimal control policy for control problems in continuous time and space, utilising reinforcement learning techniques. Finding analytical solutions to optimal control problems is often intractable due to involved constraints, cost-function objectives, and uncertainty. The focal point will be on reinforcement learning RL techniques to find approximate