Tind Technologies (Norway)
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15764 research outputs found
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Ruut Veenhoven (1942-2024) ::the secret of a happy and productive life
No full textHeaven must be a happy place. After recently adding the eminent happiness scholar Ed Diener to its ranks in 2021, fellow giant in the field, Ruut Veenhoven joined him December 9, 2024. When our own time has come to knock on heaven’s door, we imagine Ruut will be waiting for us showing data on happy afterlife years. Diener will likely lecture on the psychological processes that enable us to enjoy heavenly peace
The impact of big data on decision‐making, processes and organizational change ::an essay of synthesis
No full textThis special issue looks at how big data affects business decisions, processes, and organizational change within organizations. The issue starts with a review of the latest research in the field, including key developments and ongoing debates. The literature review shows how Big Data is affecting how organizations work, including ethical issues, internal rules, and using new technology. Next, the issue presents three key papers on how Big Data affects modern organizations. The first paper looks at how Big Data how Big Data is helping to make cities smarter. The third paper looks at how new technologies like artificial intelligence, blockchain, and quantum computing affect financial organizations. Together, these contributions show the need to balance innovation with risk management. They advocate for ethical considerations and policy frameworks as organizations navigate the complexities of the Big Data era. This essay of synthesis, from literature review to focused studies on decision-making, operations, and organizational change, provides a holistic understanding of the role of Big Data in shaping the future of business.Ce numéro spécial examine la manière dont les données massives (big data) influencent les décisions, les processus et les changements organisationnels au sein des organisations. Le numéro commence par un examen des dernières recherches dans le domaine, y compris les développements clés et les débats en cours. La revue de la littérature montre comment le Big Data affecte la façon dont les organisations travaillent, y compris au niveau des questions éthiques, des règles internes de gestion et de l'utilisation des nouvelles technologies. Ensuite, le numéro présente trois articles clés sur la façon dont le Big Data affecte les organisations modernes. Le premier article examine comment le Big Data aide à prendre de meilleures décisions à tous les niveaux de l’entreprise. Le deuxième article examine comment le Big Data contribuent à rendre les villes plus intelligentes. Le troisième article examine comment les nouvelles technologies telles que l'intelligence artificielle, la blockchain et l'informatique quantique affectent les organisations financières. Ensemble, ces contributions montrent la nécessité d'équilibrer l'innovation et la gestion des risques. Elles plaident en faveur de considérations éthiques et de cadres politiques alors que les organisations naviguent dans les complexités de l'ère du Big Data. Cet essai de synthèse, qui va de l'analyse documentaire à des études ciblées sur la prise de décision, les opérations et le changement organisationnel, offre une compréhension holistique du rôle du Big Data dans le façonnement de l'avenir des entreprises
A review on machine learning approaches for diagnosis of Alzheimer’s disease and mild cognitive impairment based on brain MRI
No full textAlzheimer’s disease is a progressive disease for which researchers have yet to discover the main cause, but believe it probably involves a combination of age-related changes in the brain, genetic, environmental and lifestyle factors. Alzheimer’s is an irreversible disease that still has no cure. Therefore, its early diagnosis is very important to prevent its progression. Developing Machine Learning algorithms in healthcare, especially in brain disorders such as Alzheimer’s disease, provides new opportunities for early diagnosis and recognition of important biomarkers. This paper presents an overview of advanced studies based on Machine Learning techniques for diagnosing Alzheimer’s disease and different stages of mild cognitive impairment based on magnetic resonance imaging (MRI) images in the last 10 years. Also, this paper comprehensively describes the commonly efficient Machine Learning algorithms in each stage of magnetic resonance imaging processing used in the papers, which can facilitate the comparison of algorithms with each other and provide insight into the impact of each technique on classification performance. This review can be a valuable resource to gain a new perspective on the various research methods used in recent studies on Alzheimer’s disease
Fast refacing of MR images with a generative neural network lowers re‐identification risk and preserves volumetric consistency
No full textWith the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have been developed to de-identify imaging data using blurring, defacing or refacing. Completely removing facial structures provides the best re-identification protection but can significantly impact post-processing steps, like brain morphometry. As an alternative, refacing methods that replace individual facial structures with generic templates have a lower effect on the geometry and intensity distribution of original scans, and are able to provide more consistent post-processing results by the price of higher re-identification risk and computational complexity. In the current study, we propose a novel method for anonymized face generation for defaced 3D T1-weighted scans based on a 3D conditional generative adversarial network. To evaluate the performance of the proposed de-identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox). The aim was to evaluate (i) impact on brain morphometry reproducibility, (ii) re-identification risk, (iii) balance between (i) and (ii), and (iv) the processing time. The proposed method takes 9 s for face generation and is suitable for recovering consistent post-processing results after defacing
Interdisciplinary strategies to reduce surgical infectious risk in the operating theater ::protocol for scoping review
No full textBackground: Surgical site infections (SSIs) represent one of the most prevalent and significant complications associated with surgical procedures, often leading to prolonged hospitalization and delayed patient recovery. While recent international consensus guidelines have proposed evidence-based strategies to mitigate SSIs, they fall short in addressing the efficient and interdisciplinary implementation of these measures within the operating theater. Consequently, further research is required to identify and evaluate optimal interdisciplinary organizational approaches for the prevention of SSIs. Objective: This study aims to map the scope, diversity, and nature of research on interdisciplinary strategies aimed at reducing SSIs and to analyze the impact of interdisciplinary on the effectiveness of preventive interventions. Methods: Using the Joanna Briggs Institute (JBI) methodology for scoping reviews, a comprehensive search will be conducted across databases including Embase (encompassing MEDLINE and PubMed-not-MEDLINE), CINAHL, and the Cochrane Library, supplemented by manual searches of reference lists from included papers. This review targets studies published between 2016 and 2024, aligning with the World Health Organization’s 2016 SSI prevention guidelines, which introduced significant advancements in practice and remain the global benchmark. Only studies published in English or French will be considered. Around 5 reviewers independently distributed the included papers for detailed reading and data extraction, while the lead author concurrently and independently reviewed all papers. Inclusion criteria follow the Participants, Concept, and Context (PCC) framework, specifying that the eligible population comprises surgical teams. The primary concept of interest is interdisciplinary strategies aimed at preventing infection risk. The context focuses on adult surgical procedures within the operating room during turnover periods. Studies using experimental, quasi-experimental, preexperimental, observational, case-control, or cross-sectional designs will be included. Results: From the 1679 papers initially identified, 45 were selected for detailed analysis by 5 reviewers, with the selection process completed by November 2024. Conclusions: Emerging interdisciplinary strategies demonstrate significant potential in reducing the incidence of SSIs. This initiative forms part of a broader global project focused on codeveloping standardized protocols for preoperative preparation within the operating room to mitigate SSI risks. The findings of this scoping review will serve as the foundation for a subsequent qualitative survey and a pre-post quasi-experimental quantitative study to evaluate the integration and effectiveness of these strategies in clinical practice. The review protocol will be formally registered in the Open Science Framework (OSF) in 2024
Diversity and inclusion as a strategic focus ::building sustainable and resilient organizations in the Swiss business context
No full textIn today’s globalized and diverse business context, effective leadership requires a deep understanding of the importance of diversity and inclusion. By actively embracing differences in backgrounds, perspectives, and experiences, leaders can foster a more inclusive culture that encourages innovation and creativity. This article explores how companies can use diversity and inclusion as strategic pillars in their leadership approach, considering the benefits they bring to organizational performance and employee satisfaction. Through a case study analysis, this research will examine successful strategies and best practices implemented by companies that prioritize diversity and inclusion in their leadership models. By exploring real-life examples and their outcomes, this study aims to provide insights to organizations that wish to improve their leadership practices and create a more inclusive and sustainable work environment based on diversity
R-AI-diographers ::a European survey on perceived impact of AI on professional identity, careers, and radiographers’ roles
No full textObjectives: Radiographers use advanced medical imaging and radiotherapy (MIRT) equipment. They are also a digitally mature and digitally resilient workforce in healthcare. Artificial intelligence is already changing their clinical practice and roles in data acquisition, post-processing, and workflow management. It is therefore vital to understand the impact of AI on the careers, roles and professional identity of radiographers, as key stakeholders of the digital transformation of healthcare within the medical imaging ecosystem. Methods: A European radiographer survey, endorsed by the European Federation of Radiographer Societies (EFRS), was distributed online. It was piloted with twelve radiographers and translated into eight languages. Although this study included both qualitative and quantitative results, this paper emphasises the quantitative aspect. Results: A total of 2206 European radiographers have responded from 37 different countries. Despite some concerns around workforce deskilling, future professional identity, and job prospects, participants showed overall optimistic views about the use of AI in healthcare. This was particularly strong for those with prior AI education (mean: 2.15 vs. 1.89; p-value: < 0.001), hands-on experience with AI (correlation: 0.047; p-value: 0.038), from countries with higher digital literacy (mean: 2.00 vs.1.93; p-value: 0.027) and a higher academic level of radiography education (mean: 3.28 vs. 3.15; p-value: 0.002). Men appeared slightly more enthused about the development of technological skills and women about the honing of patient-centred care skills. Finally, interprofessional collaboration was seen as essential not only for the seamless clinical integration of AI but also for supporting patient benefit. Conclusion: While AI implementation advances, AI education needs to keep at pace to ensure acceptability, trust, and safe use of this technology by healthcare professionals, minimising their concerns around professional role changes and enabling them to see the opportunities of service transformation. Critical relevance statement: This paper aims to map out the perceived impact of AI on the professional identity and careers of European radiographers