Association rule learning is a machine learning approach aiming to find substantial relations among attributes within one or more datasets. We address the main problem of this technology, which is the excessive computation time and the memory requirements needed for the processing of discovering the association rules. Most of the literature pertaining to the association rules deals extensively with these issues as major obstacles, especially for very large databases. In this paper, we introduce a method that requires substantially lowers the run time and memory requirements in comparison to the methods presently in use (reduction from ${O(2}^m)$ to $O\left(2^{\frac{m}{2}}\right)$ in the worst case).

Objective: The objective of this pilot study is to evaluate the feasibility of using an automatic weight management system to follow patients’ response to weight reduction medications and to identify early deviations from weight trajectories. Methods: The pilot study involved 11 participants using Semaglutide for weight management, monitored over a 12-month period. A cloud-based, Wi-Fi-enabled remote weight management system collected and analyzed daily weight data from smart scales. The system's performance was evaluated during a period marked by a Semaglutide supply shortage. Results: Participants achieved a cumulative weight loss of 85 kg until a supply shortage-induced trough in October 2022. This was followed by a 6–8 week plateau and a subsequent 13 kg cumulative weight gain. The study demonstrated the feasibility of digitally monitoring weight without attrition over 12 months and highlighted the impact of anti-obesity drug (AOD) supply constraints on weight trajectories. Conclusions: The remote weight management system proved important for improving clinic efficacy and identifying trends impacting obesity outcomes through electronic data monitoring. The system's potential in increasing medication compliance and enhancing overall clinical outcomes warrants further research, particularly in light of the challenges posed by AOD supply fluctuations.

This study examines the motivational changes of undergraduate students participating in a practical course designed to address the challenge of aligning academic training with industry needs. The study spanned two iterations of the course. During the first iteration, qualitative interviews were conducted to assess changes in student motivation throughout the practical course. In the second iteration, a motivation questionnaire was administered at three points: before, during, and after course completion. The results indicate a general decrease in student motivation during the course, followed by a partial recovery towards the end. This fluctuation highlights the need for continuous support and realistic feedback to maintain student engagement and motivation. This study contributes to the expanding body of research on how practical, industry-focused courses influence student motivation. By incorporating real-world contexts and challenges, this type of course can greatly enrich learning experiences and better prepare students for professional settings. However, one should carefully design these courses and implement effective mechanisms to maintain and foster student motivation throughout the course duration.

Objective: The COVID-19 outbreak compelled countries to take swift actions across various domains amidst substantial uncertainties. In Israel, significant COVID-19-related efforts were assigned to the Israeli Home Front Command (HFC). HFC faced the challenge of anticipating adequate resources to efficiently and timely manage its numerous assignments despite the absence of a COVID-19 spread forecast. This paper describes the initiative of a group of motivated, though nonexpert, people to provide the needed COVID-19 rate of spread of the epidemic forecasts. Methods: To address this challenge, the Planning Chamber, reporting to the HFC Medical Commander, undertook the task of mapping HFC healthcare challenges and resource requirements. The nonexpert team continuously collected public COVID-19-related data published by the Israeli Ministry of Health (MoH) of verified cases, light cases, mild cases, serious condition cases, life-support cases, and deaths, and despite lacking expertise in statistics and healthcare and having no sophisticated statistical packages, generated forecasts using Microsoft^® Excel. Results: The analysis methods and applications successfully demonstrated the desired outcome of the lockdown by showing a transition from exponential to polynomial growth in the spread of the virus. These forecasting activities enabled decision-makers to manage resources effectively, supporting the HFC's operations during the pandemic. Conclusions: Nonexpert forecasting may become a necessity and be beneficial, and similar analysis efforts can be easily replicated in future events. However, they are inherently short-lived and should persist only until knowledge centers can bridge the expertise gap. It is crucial to identify major events, such as lockdowns, during forecasting due to their potential impact on spread rates. Despite the expertise gap, the Planning Chamber's approach provided valuable resource management insights for HFC's COVID-19 response.