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Objective: This study aimed to analyze the relationship between the consumption of selected food products and the risk of prevalence of selected metabolic and cardiovascular disorders among nurses. Methodology: This cross-sectional study was conducted among 405 nurses. To achieve the study objective, body composition analysis (Tanita MC-980), blood pressure measurement (Welch Allyn 4200B), anthropometric measurements, lipid profile, fasting blood glucose (CardioChek PA), and surveys regarding the consumption of specific food groups were conducted. Results: More than half of the respondents were overweight or/and obese, and almost 40% had elevated blood pressure levels. The results obtained from logistic regression models indicated that the consumption of specific food product groups may predispose to/increase the risk of hypertension, abdominal obesity, overweight, obesity, body fat accumulation, and the risk of cardiovascular events. Conclusions: These findings highlight the importance of targeted nutritional strategies to enhance the health and professional efficacy of nursing staff, paving the way for improved healthcare practices. [ABSTRACT FROM AUTHOR]

Churn is a serious challenge for the telecommunications industry because of the much higher costs of gaining new customers than maintaining existing ones. Therefore, efforts to increase loyalty and decrease customer churn are the focus of telecom's retention departments. In order to direct antichurn activities, profitable clients who have the highest probability of churning need to be identified. The data used to identify churners are often inaccurate and vague. In this paper, a fuzzy approach to modeling churn intent based on usage data in mobile telecommunications is presented. It appreciates the uncertainty of the data and provides insights into churn modeling. The goal of the study was to evaluate the applicability of the Mamdani and Sugeno models for building a churn model based on a limited but real-world dataset enriched with feature engineering. The additional goal was to find features most usable for churn modeling. Four metrics—accuracy, recall, precision, and F1-score—were used to estimate the performance of the models. The developed fuzzy rule-based systems show that to generalize possible churn identification factors with fuzzy rules, it is advisable to begin with features such as the change in the total amount of the invoice in the last period before the churning compared to the previous one, the total amount of the invoice in the period preceding the churning, the total amount of subscription in two months before the churning, the time of cooperation with the operator, and the number of calls out of the last quarter before leaving. [ABSTRACT FROM AUTHOR]