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The purpose of this study is to examine various data sources to identify trends and themes in humanitarian supply chain management (HSCM) in the digital age.

In this study, various data sources such as published literature and social media content from Twitter, LinkedIn, blogs and forums are used to identify trending topics and themes on HSCM using topic modelling.

The study examined 33 published literature and more than 94,000 documents, including tweets and expert opinions, and identified eight themes related to HSCM in the digital age namely “Digital technology enabled global partnerships”, “Digital tech enabled sustainability”, “Digital tech enabled risk reduction for climate changes and uncertainties”, “Digital tech enabled preparedness, response and resilience”, “Digital tech enabled health system enhancement”, “Digital tech enabled food system enhancement”, “Digital tech enabled ethical process and systems” and “Digital tech enabled humanitarian logistics”. The study also proposed a framework of drivers, processes and impacts for each theme and directions for future research.

Previous research has predominantly relied on published literature to identify emerging themes and trends on a particular topic. This study is unique because it examines the ability of social media sources such as blogs, websites, forums and published literature to reveal evolving patterns and trends in HSCM in the digital age.

This study compared approaches to school closures in four Latin American countries (Bolivia, Colombia, Ecuador, Peru), describing the impact on the health and educational wellbeing of school-age children and youth, and evaluating their approaches in regard to continuing education through the pandemic.

We collected 75 publicly available documents including scientific and gray literature (government documents and news releases), that referred to school closures and their impact on children’s health and wellbeing. We did thematic analyses using open, axial, and selective coding and applied the latest Health Promoting Schools standards and indicators to the findings.

Results showed that countries followed epidemiological reasons for prioritizing school closures while adopting some policies that abide by Health Promoting School principles. While they emphasized the need to reopen schools so that instruction could continue, school closures were among the longest in the world. The most significant impacts on wellbeing identified in the four countries were related to food security and mental health.

This study focused on a particular set of documents, and it may not capture the full spectrum of relevant information in different contexts or regions.

By comparing school closures approaches among four Latin American countries, this study highlights the importance of context-specific interventions. In a post-pandemic era, lessons learned from these experiences should help foster more resilient and inclusive educational systems and explore the paths forward for following the new Health Promoting Schools framework in the region.

Cross-country qualitative analyses on this topic are rare. This study adds to the knowledge base by eliciting lessons for future health education research and policy efforts.

This study aims to introduce an innovative approach to predictive maintenance by integrating time-series sensor data with event logs, leveraging the synergistic potential of deep learning models. The primary goal is to enhance the accuracy of equipment failure predictions, thereby minimizing operational downtime.

The methodology uses a dual-model architecture, combining the patch time series transformer (PatchTST) model for analyzing time-series sensor data and bidirectional encoder representations from transformers for processing textual event log data. Two distinct fusion strategies, namely, early and late fusion, are explored to integrate these data sources effectively. The early fusion approach merges data at the initial stages of processing, while late fusion combines model outputs toward the end. This research conducts thorough experiments using real-world data from wind turbines to validate the approach.

The results demonstrate a significant improvement in fault prediction accuracy, with early fusion strategies outperforming traditional methods by 2.6% to 16.9%. Late fusion strategies, while more stable, underscore the benefit of integrating diverse data types for predictive maintenance. The study provides empirical evidence of the superiority of the fusion-based methodology over singular data source approaches.

This research is distinguished by its novel fusion-based approach to predictive maintenance, marking a departure from conventional single-source data analysis methods. By incorporating both time-series sensor data and textual event logs, the study unveils a comprehensive and effective strategy for fault prediction, paving the way for future advancements in the field.