Revisionsbranschen har genomgått betydande förändringar och anpassningar över tid, med digitalisering som en av de mest inflytelserika faktorerna. Den svenska regeringen strävar efter att Sverige ska vara ledande globalt när det gäller att utnyttja de möjligheter som digitaliseringen erbjuder. I takt med denna utveckling har artificiell intelligens (AI) blivit en viktig del av digitaliseringen, vilket har påverkat hur revision genomförs. Många arbetsuppgifter inom revision är strukturerade och repetitiva, vilket skapar en stor potential för automatisering av specifika moment. AI och andra digitala verktyg bidrar till ökad effektivitet och noggrannhet inom revision, vilket gör det möjligt för revisorer att hantera större mängder data och identifiera risker snabbare och mer exakt än tidigare. Denna studie undersöker hur dessa teknologiska framsteg påverkar revisionsbranschen och revisionsprocessen som revisorer arbetar med. Studien analyserar de utmaningar som AI-implementering medför, utifrån en reviderad version av TOE-modellen. En kvantitativ metod har använts för datainsamling, inkluderande litteraturstudier och distribution av enkätformulär till erfarna revisorer. Respondenternas svar indikerar en allmän uppfattning om låg teknologisk risk vid användning av AI. Samtidigt framhålls att kritiskt tänkande förblir viktiga aspekter i revisionsarbetet. Etiska frågor, såsom dataintegritet och ansvarsfördelning, fortsätter att vara betydande. Dessutom varierar revisorernas förståelse för AI-teknologi, vilket kan påverka deras förtroende för AI-system. / The audit industry has undergone significant changes and adaptations over time, with digitization being one of the most influential factors. The Swedish government strives for Sweden to be a global leader when it comes to making use of the opportunities that digitization offers. In line with this development, artificial intelligence (AI) has become an important part of digitization, which has affected how auditing is carried out. Many tasks in auditing are structured and repetitive, which creates a great potential for automation of specific elements. AI and other digital tools are contributing to increased efficiency and accuracy in auditing, enabling auditors to handle larger amounts of data and identify risks faster and more accurately than before. This study examines how these technological advances affect the audit industry and the audit process with which auditors work. The study analyzes the challenges that AI implementation entails, based on a revised version of the TOE model. A quantitative method has been used for data collection, including literature studies and distribution of questionnaires to experienced accountants. Respondents' responses indicate a general perception of low technological risk when using AI. At the same time, it is emphasized that critical thinking remains important aspects in audit work. Ethical issues, such as data integrity and accountability, continue to be significant. In addition, auditors' understanding of AI technology varies, which may affect their trust in AI systems.

Artificial intelligence (AI) in the public sector is a debated topic that has the potential to enhance performance. The public sector is complex and characterized by bureaucratic challenges, posing significant procedural and financial difficulties. Using AI systems can substantially enhance the efficiency of administrative processes and the quality of services provided to citizens. However, the use of AI, in general and in the public sector, has mainly been focused on ethical issues such as trust, bias, or surveillance. One crucial factor directly affecting the efficient and ethical use of AI systems is employee competence. Employees' competence is, however, one of the least explored areas when it comes to working with AI systems, especially in the public sector. This study addresses this gap by examining the perceived necessary competences of employees within the Swedish public sector utilizing a qualitative research method and semi-structured interviews. The findings reveal three overarching areas of competences: 1) systemic competence, encompassing AI literacy and general knowledge; 2) Particular competence relating to expertise where the AI is being used; 3) Contextual competence involving data governance, legal implications, and personal data protection.

Planning is essential for an action-oriented, goal-driven software agent. In order to achieve a specific goal, an agent must first generate a plan. However, as the poet Robert Burns once noted, the best laid plans can often go awry. Each step of the plan is subject to the possibility of failure, a truth particularly relevant in the realworld or a realistic simulated environment. External influences not originally considered can often cause sudden, unanticipated consequences during the execution of the plan. When this happens, an intelligent software agent needs to answer the following important questions: What are the consequences of this event on its plan? How will the plan be affected? Can the plan be adjusted to accommodate the unanticipated effects? The research described in this thesis develops a model whereby intelligent agents can automatically determine consequences of unplanned events. Such a model provides agents with the ability to detect if and how events will affect the plan. This allows agents to subsequently modify the plan to mitigate unfavorable consequences or take advantage of favorable consequences.

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Yes / The technological choices facing the manufacturing industry are vast and complex as the industry contemplates the increasing levels of digitization and automation in readiness for the modern competitive age. These changes broadly categorized as Industry 4.0, offer significant transformation challenges and opportunities, impacting a multitude of operational aspects of manufacturing organizations. As manufacturers seek to deliver increased levels of productivity and adaptation by innovating many aspects of their business and operational processes, significant challenges and barriers remain. The roadmap toward Industry 4.0 is complex and multifaceted, as manufacturers seek to transition toward new and emerging technologies, whilst retaining operational effectiveness and a sustainability focus. This study approaches many of these significant themes by presenting a critical evaluation of the core topics impacting the next generation of manufacturers, challenges and key barriers to implementation. These factors are further evaluated via the presentation of a new Industry 4.0 framework and alignment of I4.0 themes with the UN Sustainability Goals.

Background: AI in Purchasing and Supply Management (PSM) enhances business operations but faces challenges in adoption due to limited research and AI readiness assessment. Although existing research explores AI's potential, the issue of assessing and achieving AI readiness in PSM remains underexamined. Exploring this gap is crucial to understanding how AI can effectively transform procurement processes and improve strategic operations.  Purpose: This study aims to identify and evaluate the essential capabilities that PSM organizations need to develop for AI readiness, using a dynamic capabilities framework to provide insights for both academia and practitioners.  Method: Grounded theory is applied for its flexibility and constructivist principles, allowing theories to emerge from the data collected through semi- structured interviews, providing a comprehensive understanding of AI readiness in PSM. The primary data consisted of 13 interviews with AI users, implementers, and developers.  Conclusion: Identified capabilities needed for successful AI implementation in PSM, include robust technological infrastructure, effective AI governance, and the importance of communication and continuous learning. The study concludes that AI readiness in PSM requires a holistic strategy and dedicated leadership to align technology, strategic goals, and people.

This study investigates the rhetorical strategies used in Jimmie Åkesson's first AI-translated speech on immigration and integration, targeting Arabic-speaking communities in Sweden. It examines how Åkesson frames issues concerning Arabic immigrant minorities through various framing techniques and persuasive appeals and assesses the impact of these strategies on social identity dynamics. The theoretical framework encompasses framing theory and social identity theory, offering insights into rhetorical strategies, and social identity construction. Through qualitative rhetorical analysis, Åkesson's speeches reveal the strategic use of ethos, logos, pathos, and doxa, emphasizing emotional appeals and ingroup-outgroup dynamics. The findings suggest a narrative that reinforces Swedish national identity while potentially marginalizing immigrant communities. However, further examination is needed to assess the nuanced effects of AI-translated speeches on rhetorical choices and audience perceptions. These insights have implications for strategic communication in politics and raise ethical considerations regarding the use of AI in shaping public opinion on immigration.

<p dir="ltr">Cardiovascular diseases remain the leading cause of death globally, with a life lost every three seconds in the U.S. Early detection and effective hypertension management are crucial to reducing its impact. However, traditional cuff-based blood pressure (BP) monitors, while accurate, pose usability challenges due to their size, cost, and complexity, particularly for in-home monitoring. Although cuffless BP monitors offer simplicity, their need for frequent calibration against traditional devices limits widespread adoption. This study aimed to develop artificial intelligence (AI) models capable of accurately estimating cuffless BP without the need for periodic calibration.</p><p dir="ltr">The research utilized data from 147 participants using the Avidhrt Sense device, incorporating demographic variables such as BMI, age, and gender, alongside physiological signals like ECG and PPG captured from the fingertips to enhance predictive accuracy. Additionally, novel features—including SpO₂ filtering, skin temperature, environmental temperature, core body temperature, and ECG classification—were integrated to further enhance model performance, particularly for diastolic BP estimation. The study employed Multiple Linear Regression, XGBoost, Feedforward Neural Networks, and a Hybrid model combining Convolutional Neural Networks with Recurrent Neural Networks. Among these models, XGBoost achieved the highest accuracy, with a Mean Squared Error of 6.15 and a Mean Error of -0.67 ± 2.39 for systolic BP, and a Mean Squared Error of 10.03 with a Mean Error of 0.44 ± 3.14 for diastolic BP. These results represent one of the best performances reported in cuffless BP measurement research.</p><p dir="ltr">The findings indicate that AI-enhanced cuffless BP monitoring, when augmented with additional physiological features, can achieve accuracies meeting ANSI/AAMI standards, making it a viable alternative to traditional BP monitors. Furthermore, excluding socioeconomic factors and race from model inputs reduced potential biases, thereby enhancing the model’s generalizability across diverse populations. Future research should focus on expanding the dataset, exploring continuous monitoring, and integrating real-time feedback systems to further enhance clinical applicability.</p>

Convergence of novel technologies with smart cities is evolving now. Specifically Artificial Intelligence (AI) by means of sensors and cameras is used to make sense of city data for multiple purposes. Recent COVID-19 pandemic has shown that cities worldwide try to use AI technology for assisting in decision making and see the impact of certain socio-economic measures of city authorities in connection to pandemic. This thesis is a Qualitative study of a Helsingborg city AI application project for temperature detection by means of thermal sensors located on Helsingborg, Sweden central station for anonymous measuring of people temperature. Specifically, this study aims to understand how aware are project team members of the ethical considerations in connection to AI application for health detection in smart city of Helsingborg, related decision making and how they perceive it.   Data was gathered by means of Qualitative interviews which were hold online. This study has shown that convergence of AI and a smart city raises important ethical questions and perceived by some respondents as possibility to change the attitude towards privacy if connected to crisis events such as pandemics. The case study shows that in the researched AI project project team members considered ethics in connection to AI in terms of technology, legal issues, open collaboration and open data sharing with citizens. At the same time this project is willing to challenge existing norms and drive forward the development of ethics in connection to AI usage in a smart city.

International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / The science payload for NASA's Extreme Ultraviolet Explorer (EUVE) satellite is controlled from the EUVE Science Operations Center (ESOC) at the Center for EUV Astrophysics (CEA), University of California, Berkeley (UCB). The ESOC is in the process of a transition from a single staffed shift to an autonomous, zero-shift, "lights out" science payload operations scenario (a.k.a., 1:0). The purpose of the 1:0 transition is to automate all of the remaining routine, daily, controller telemetry monitoring and associated "shift" work. Building on the ESOC's recent success moving from three-shift to one-shift operations (completed in Feb 1995), the 1:0 transition will further reduce payload operations costs and will be a "proof of concept" for future missions; it is also in line with NASA's goals of "cheaper, faster, better" operations and with its desire to out-source missions like EUVE to academe and industry. This paper describes the 1:0 transition for the EUVE science payload: the purpose, goals, and benefits; the relevant science payload instrument health and safety considerations; the requirements for, and implementation of, the multi-phased approach; a cost/benefit analysis; and the various lessons learned along the way.