Alice Villar

This study investigated the relationship between periodontal disease (PD) and Alzheimer’s Disease (AD) through a Systematic Literature Network Analys

This paper introduces the Open Knowledge Resilience Framework (OKRF), a set of seven cooperative data standards, and demonstrates its alignment with the Supply Chain Interoperability Specification (SCIS) Platform. Building upon the Internet of Production Alliance (IoPA) work, this article explores how integrating OKRF and SCIS can enhance production flexibility and efficiency through substitutability and resource repurposing strategies. The OKRF-SCIS integration exhibits the potential to enhance Supply Chain Resilience (SCR) by empowering organizations to adapt to fluctuating demand and availability through adaptability strategies such as supplier/product substitution and resource repurposing. While this study primarily focuses on the conceptual design of OKRF and SCIS, further empirical research is needed to validate its effectiveness. Additionally, investigating the framework’s applicability across various industries and contexts would provide valuable insights. The integration of OKRF and SCIS can enable organizations to optimize supply chain operations, enhancing resilience and facilitating adaptation to changing demands through seamless substitutability and resource repurposing. This integration can also reduce transportation costs and increase responsiveness to local demand. Furthermore, it can enhance SCR and promote sustainable, resilient supply chain ecosystems by improving data transparency and resource utilization efficiency. This paper illustrates the potential of OKRF and SCIS in healthcare supply chains.

Utilizing a dataset sourced from a higher education institution, this study aims to assess the efficacy of diverse machine learning algorithms in predicting student dropout and academic success. Our focus was on algorithms capable of effectively handling imbalanced data. To tackle class imbalance, we employed the SMOTE resampling technique. We applied a range of algorithms, including Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), as well as boosting algorithms such as Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), CatBoost (CB), and Light Gradient Boosting Machine (LB). To enhance the models' performance, we conducted hyperparameter tuning using Optuna. Additionally, we employed the Isolation Forest (IF) method to identify outliers or anomalies within the dataset. Notably, our findings indicate that boosting algorithms, particularly LightGBM and CatBoost with Optuna, outperformed traditional classification methods. Our study's generalizability to other contexts is constrained due to its reliance on a single dataset, with inherent limitations. Nevertheless, this research provides valuable insights into the effectiveness of various machine learning algorithms for predicting student dropout and academic success. By benchmarking these algorithms, our project offers guidance to both researchers and practitioners in their choice of suitable approaches for similar predictive tasks.

The purpose was to investigate the impact of the Industry 5.0 paradigm on the supply chain research field. Our study contributes to the conceptualization of supply chain 5.0, a term that has been receiving increased attention as supply chains adapt to the fifth industrial revolution. We conducted a systematic literature network analysis (SLNA) to examine the research landscape of Industry 5.0 supply chains. We used VOSViewer software and Bibliometrix R-package for multiple bibliometric analyses using 682 documents published between 2016 and 2022. We present a comprehensive framework of supply chain 5.0, including its key concepts, technologies, and trends. Additionally, this research offers a future research agenda to inspire and support further development in this field. We utilized three academic databases for bibliometric analyses: Dimension, Scopus and Lens. Additional databases could provide a wider research landscape and better field representation. We demonstrate how Industry 5.0 enables supply chain evaluation and optimization to assist companies in navigating disruptions without compromising competitiveness and profitability and provide a unique contribution to the field of supply chain 5.0 by exploring promising research areas and guiding the transition to this new paradigm for practitioners and scholars.