Articles

High-quality collaborative discourse is essential for deep learning in Problem-Based Learning (PBL). However, novice facilitators frequently struggle with topic drift, which occurs when discussions deviate from core learning objectives. Grounded in Cognitive Load Theory and instructional scaffolding, this paper analyzes the cognitive mechanisms behind topic drift in PBL. It identifies three typical deviation patterns: "Premature Resolution" (bypassing mechanistic deduction), "Detail Fixation" (over-engagement with peripheral details), and "Social Avoidance" (stemming from dysfunctional group dynamics). To address these patterns, this paper presents a literature-based facilitation guide offering specific intervention strategies and discursive prompts. These include mechanistic backtracking, metacognitive monitoring, and cognitive visualization. The study argues that effective pedagogical intervention relies on the strategic timing of facilitation rather than the immediate correction of factual errors. Ultimately, built on a foundation of relational trust, this process requires educators to shift their role from "epistemic authorities" to "cognitive coaches."

<b>Objective: </b>Previous research has underscored the link between allostatic load—a comprehensive indicator of the cumulative physiological burden of chronic stress—and depression. However, there remains a significant gap in understanding how this relationship may differ across race and sex intersectional groups. This study aimed to investigate variations in the association between elevated allostatic load (AL>4) and depression among different race-sex intersectional groups within the general population. <b>Methods: </b>This cross-sectional secondary analysis utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning 2005-2018. The analysis included variables such as race, sex, age, socioeconomic status, depression (measured via the Patient Health Questionnaire - PHQ), and allostatic load. Linear regression analyses were conducted to examine the interactions between race and sex with allostatic load, focusing on the likelihood of high depression as the outcome. <b>Results: </b>Across the pooled sample, an allostatic load greater than 4 was significantly associated with increased depression. Notably, an interaction effect was observed between race and AL>4 on depression among women, indicating that non-Hispanic Black women with a high allostatic load exhibited more pronounced depressive symptoms (Beta: 1.09, CI: 0.02-2.61). Conversely, among men, allostatic load greater than 4 neither correlated with nor interacted with race to influence depression levels. <b>Conclusion: </b>The study highlights the critical need to consider allostatic load as a key target for interventions that aim to reduce depression among Black women. These findings underscore the necessity for customized intervention strategies that address the nuanced race-sex disparities in the impact of allostatic load on mental health across populations.

Modern organizations leverage highly distributed, global deployments to provide high availability and resiliency for cloud-first applications. By hosting these applications across multiple geographic locations and relying on highly available services, organizations can prevent disruption to their business and reduce complexity by employing the scale of infrastructure offered by major cloud providers. Global deployments in the cloud are built on well-known models such as failover, load balancing, and scalability. However, traditional methods used to recover from regional failure—while effective—can be complex. Typical multi-region recovery and high availability system architectures have latency and cost risks that should be considered when facing other limitations such as deployment models in the cloud. This document describes the different traffic management techniques that can be applied to multi-region strategies, focusing on trade-offs and costs. The introduction of new traffic management techniques being applied to the traditional global architectures now allows organizations to adopt cloud services more efficiently. Traffic management is much more straightforward in some environments, while others have started to leverage their traffic management platform via routing. In multi-region deployments, active-active and active-passive are the most common architectural models, allowing organizations to seamlessly handle failover, scalability, and global distribution based on business goals and requirements. However, traffic management for these infrastructures is critical to ensure just data distribution and efficiency, maintaining costs under control and workloads rerouted when necessary. Using the new traffic management techniques will allow organizations to evolve system architectures easily based on business requirements, taking advantage of cost benefits from multiple infrastructures. In these scenarios, traffic management becomes a crucial backbone of success to ensure that traffic is being efficiently and intelligently distributed [1].

Machine Learning (ML) and Artificial Intelligence (AI) are having an increasingly transformative impact on all industries and are already used in many mission-critical use cases in production, bringing considerable value. Data engineering, which combines ETL pipelines with other workflows managing data and machine learning operations, is also significantly impacted. The Intelligent Data Engineering and Automation framework offers the groundwork for intelligent automation processes. However, ML/AI are not the only disruptive forces; new Big Data technologies inspired by Web2.0 companies are also reshaping the Internet. Companies having the largest Big Data footprints not only provide applications with a Big Data operational model but also source their competitive advantage from data in the form of AI services and, consequently, impact the cost/performance equilibrium of ETL pipelines. All these technologies and reasons help explain why the traditional ETL pipeline design should adapt to current and emerging technologies and may be enhanced through artificial intelligence.

Knowledge about the glycaemic load of a food is very important in minimizing the prevalence of diabetes and other Non-Communicable Diseases. The purpose of this study was to determine the glycaemic load of the different varieties of fufu in the Wenchi municipality in Ghana. Quantitatively, the study adopted a crossover experimental research design. The research was carried out in Wenchi, the capital of the Wenchi Municipal Assembly, in the Bono Region. Convenience and purposive sampling techniques were used to select ten (10) healthy adults for blood glucose tests in this study.<b> </b>Materials used for the study were<b> </b>Fresh cassava, plantain, yam, and cocoyam. Descriptive analysis was used in analysing and interpretation of the data. Values were analyzed by one-way analysis of variance (ANOVA). Statistical significance was set at p<0.05. Statistical analysis was performed using Statistical Package for Social Science (SPSS) 20.0. Proximate analysis of the study concluded that, plantain fufu contained the least carbohydrate content among the three fufu mixtures. The study also revealed that all fufu combinations had a high glycaemic load and this is as a result of the large portion size of fufu that is eaten at a serving. The glycaemic load of fufu combinations showed no significant difference, however, looking at the actual values, there are differences in them which should not be overlooked. It is recommended that consumers of fufu can eat any of the three mixtures of fufu, but there will be the need to take a smaller portion size of the fufu since a larger size can have adverse effects on their blood glucose level. It is also recommended that in other for fufu to be digested well and glucose to be absorbed easily, especially yam fufu, consumers should make sure to eat fufu at least about five hours before going to bed. It is recommended that nutritionists, dieticians, and diet therapists can as well recommend yam fufu and cocoyam fufu for diabetics and prediabetics, to bring about varieties in their diet.

Data warehousing is a technique for collecting, managing, and presenting data to help people analyze and use that data effectively. It involves a large database designed to support management-level staff by providing all the relevant historical data for analysis. This chapter begins with a definition of data warehousing, followed by an overview of large-scale policy management to highlight the need for data warehousing. Next, an overview of an ETL framework is presented, along with a discussion of advanced ETL techniques. The chapter concludes with an outline of performance optimization techniques for data warehousing. Data warehousing is considered a key enabler for efficient reporting and analysis, with implementation choices ranging from cost-effective desktop systems to large-scale, mission-critical data marts and warehouses containing petabytes of data. Extract, transform, and load (ETL) systems remain one of the largest cost and effort areas within data warehouse development projects, requiring significant planning and resources to build, manage, and monitor the flow of data from source systems into the data warehouse. The technology and techniques used for ETL can greatly influence the success or failure of a data warehouse. Complex business requirements for data cleansing, loading, transformation, and integration have intensified, while operational plans for real-time and near-real-time reporting add additional challenges. Parallel loading mechanisms, incremental data loading, and runtime update and insert strategies not only improve ETL performance but also optimize data warehousing performance, particularly for large-scale policy management.