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Is Covid booster mandated for healthcare workers? 

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COVID-19 negatively impacts healthcare workers’ well-being.
Healthcare workers (HCWs) have an increased risk of contracting COVID-19, but a recent study has reported that other workers may also be exposed to the Coronavirus, including staff in the tourism, retail and hospitality industries, transport and security workers, and construction site workers [4].
Health care workers who are COVID-19 positive constituted a significant proportion of all COVID-19 patients; but the severity and mortality were lower among them.
COVID-19 has been associated with an increased mortality in doctors and health care workers.
Guidelines allow a health-care provider the time and availability necessary care for COVID-19 patients, andminimize potential exposure to COVID-19 for patients and health-care workers.

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Can AI-driven digital transformation strategies in healthcare contribute to sustainable development goals and improve healthcare outcomes?
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AI-driven digital transformation strategies in healthcare, such as omnichannel blood supply chain models, telehealth services, and AI-supported predictive models, play a crucial role in contributing to sustainable development goals and enhancing healthcare outcomes. These strategies leverage technologies like artificial intelligence, mobile health, and electronic health records to address supply-demand imbalances, improve patient access to care, enhance patient engagement, and optimize treatment planning. The collaborative approach of human-AI interaction ensures that AI complements rather than replaces healthcare providers, leading to improved service quality and patient outcomes. By prioritizing digital transformation in healthcare, stakeholders can achieve sustainable approaches, efficient resource utilization, and enhanced patient outcomes, aligning with the United Nations' Sustainable Development Goals, particularly goal 3 of ensuring healthy lives and well-being for all.
What are the historical roots of artificial intelligence research?
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The historical roots of artificial intelligence (AI) research are deeply intertwined with various scientific, philosophical, and technological developments spanning several decades. The inception of AI as a formal field is often attributed to the 1956 Dartmouth conference, which is considered the 'Big Bang' moment for AI, marking the beginning of AI as a distinct field of study within the grand European philosophical tradition of reason, logic, intelligence, and cognition, as highlighted by Kombarov. This period coincided with significant global socio-political changes, including the decolonization of Asia and Africa, which influenced diverse perspectives on intelligence and cognition, including those from post-colonial contexts. The philosophical underpinnings of AI research have been shaped by debates between symbolism and connectionism, representing two main paradigms. Symbolism posits that the world can be explained through symbols and logical processes, while connectionism advocates for understanding intelligence through artificial neural networks. These paradigms reflect the broader historical engagement with the formalization of reasoning and learning mechanisms, as well as the design of autonomous machines. Moreover, the development of AI has been influenced by the military and civilian electronics advancements in the USSR, highlighting the global contributions to the field beyond the Western-centric narrative. Early challenges in AI research included the limitations of computing technology, such as the inability of early computers to store commands and their high costs. The field has also faced epistemological and methodological crises, inherited from psychology, rooted in the dualist ontology of rationalism and objectivism. These crises have been addressed through various approaches, including the cultural-historical activity theory (CHAT) and dialectical logic, to confront the challenges of meaning emergence, abstraction, and concept formation. In summary, the historical roots of AI research are multifaceted, drawing from ancient philosophical traditions, the evolution of computing technology, and the interplay between psychological theory and technological development. This rich history underscores the complexity and interdisciplinary nature of AI as a field.
How attention is important for object detection?
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What are the uses of evolutionary algorithms in robotics ?
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