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To enhance leadership acumen and intelligence among the managerial ranks, many healthcare organizations establish leadership development programs. Doing so makes perfect sense as the caliber of leadership within health and medical institutions profoundly influences all aspects of operation. Although leadership development programs are very capable mechanisms for advancing the state of leadership within healthcare organizations, a notable enhancement-evaluation-is needed to maximize their potential.

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An intelligent decision-making system for embryo transfer in reproductive technology: a machine learning-based approach.

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Department of Mathematics and Computer Science, Laboratory of Analysis, Modeling and Simulation, Faculty of Sciences Ben M'sik, Hassan II University of Casablanca, Casablanca, Morocco.

Infertility has emerged as a significant public health concern, with assisted reproductive technology (ART) is a last-resort treatment option. However, ART's efficacy is limited by significant financial cost and physical discomfort. The aim of this study is to build Machine learning (ML) decision-support models to predict the optimal range of embryo numbers to transfer, using data from infertile couples identified through literature reviews.

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Research suggests that the quality of care provided by family members may be influenced by the quality of relationship they have with the person living with dementia. The study investigated this in the context of assisting with daily activities. The quality of the relationship was assessed using the conceptual framework of relationship continuity/discontinuity which focuses on whether the carer experiences their relationship as continuous or discontinuous with the pre-dementia relationship.

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What makes β-NaYF:Er,Yb such a successful luminescent thermometer?

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Urban sensing in the era of large language models.

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Urban sensing has become increasingly important as cities evolve into the centers of human activities. Large language models (LLMs) offer new opportunities for urban sensing based on commonsense and worldview that emerged through their language-centric framework. This paper illustrates the transformative impact of LLMs, particularly in the potential of advancing next-generation urban sensing for exploring urban mechanisms.

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