Research background: Digitalization and the associated digital divide are crucial issues impacting socio-economic development globally. Extensive research has examined digitalization and the digital divide in EU countries, but there is a lack of understanding regarding comparisons with studies conducted in Western Balkan countries. This study investigates digitalization trends in research from the past five years in both regions, focusing on efforts and factors contributing to the digital gap. Purpose of the article: The study analyzes research on digitalization from 2018 to 2023 in the EU and Western Balkans. It explores factors causing the digital divide and efforts in digitalization, aiming to guide future research and policy for digital inclusion and sustainable development. Methods: The study employs a meticulous data selection process, choosing Scopus as the database for its extensive coverage of diverse journals. A total of 1119 articles from EU countries and 277 from Western Balkan countries are selected for bibliometric analysis, adhering to PRISMA guidelines. Findings & value added: The research reveals a growing interest in digitalization-related issues, demonstrating the multidisciplinary nature of ongoing research. It points out the distribution of publications on digitalization in the EU and Western Balkans countries. The EU focuses on digital technologies, economic growth, and sustainability, while Western Balkan countries focus on COVID-19 impact and digitalization in education and business. The research compares digitalization efforts in the EU and Western Balkan countries presented in the literature, pointing to new dimensions of the digital divide studies. It discusses how socio-economic contexts affect digital transformation and stresses the need for tailored policy approaches for digital inclusivity. These insights are of great importance for policymakers, researchers, and practitioners working towards global digital development and bridging the digital divide. The study lays the groundwork for future research and policy considerations, considering limitations like potential bias in databases and search criteria.

The digital revolution is reshaping global economies and societies, and Southeast Asia is no exception. This paper presents a bibliometric analysis of the state of digitalization in Southeast Asia, focusing on the period from 2018 to 2023. By leveraging the Scopus database, we conducted a comprehensive review of literature in the digital domain, identifying key trends, regional disparities, and thematic focuses within the Association of Southeast Asian Nations (ASEAN) nations. Our analysis reveals significant strides in digitalization, marked by increased scholarly attention and publication output, particularly from countries like Indonesia, Malaysia, Singapore, and Thailand. The study also highlights a stark digital divide within the region, with countries like Laos, Cambodia, and Myanmar lagging in digital infrastructure and literacy. We analyzed 5058 articles from the last five years using a bibliometric approach to assess publication patterns, author affiliations, and emerging research themes, revealing a diversified research landscape. Significant contributions were found in areas including digital governance, economy, infrastructure, and societal impacts. The paper also discusses the role of digitalization in economic growth, social inclusion, and sustainable development within the region. Our findings underscore the importance of regional collaborations and targeted policies to bridge the digital divide. We emphasize the necessity of inclusive digital strategies that consider the unique socio-economic and cultural contexts of each ASEAN country. The study provides valuable insights for policymakers, academicians, and industry stakeholders, guiding future research agendas and digital initiatives in Southeast Asia. The bibliometric analysis not only maps the scholarly terrain but also offers actionable intelligence for decision-makers in navigating the digitalization landscape across this dynamic and diverse region.

Chronic pain is a widespread condition that profoundly affects the daily functioning of many people worldwide, characterized by persistent sensory and emotional discomfort associated with real or perceived tissue injury. This study aims to predict chronic pain based on post-traumatic stress disorder (PTSD), alexithymia, anxiety, pain catastrophizing, stress, depression, and demographic variables as correlates. The analysis included data from 234 males and 307 females experiencing chronic pain in Tehran province between 2022 and 2023. The classification results suggested that PTSD and alexithymia were the most significant predictors, followed by anxiety, depression, and pain catastrophizing. Six different machine learning (ML) classification techniques: Naive Bayes, Decision Tree, Gradient Boosting, Light Gradient Boosting Machine, Support Vector Machine, and Stochastic Gradient Boosting (SGB) were applied to examine a dataset detailing various dimensions associated with chronic pain. The findings in the study classified pain severity into three levels, low, medium, and high, based on quantiles and used six ML models to predict these classes. The SGB model outperformed the others, showing higher accuracy and F1 scores, particularly in predicting the medium pain class. SHAP analysis revealed that psychological factors such as alexithymia, anxiety, PTSD, depression, and stress were significant predictors of pain severity, while age and gender had less impact.

Air pollution is a critical environmental concern that poses significant health risks and affects multiple aspects of human life. ML algorithms provide promising results for air pollution prediction. In the existing scientific literature, Long Short-Term Memory (LSTM) predictive models, as well as their combination with other statistical and machine learning approaches, have been utilized for air pollution prediction. However, these combined algorithms may not always provide suitable results due to the stochastic nature of the factors that influence air pollution, improper hyperparameter configurations, or inadequate datasets and data characterized by great variability and extreme dispersion. The focus of this paper is applying and comparing the performance of Support Vector Machine and hybrid LSTM regression models for air pollution prediction. To identify optimal hyperparameters for the LSTM model, a hybridization with the Genetic Algorithm is proposed. To mitigate the risk of overfitting, the bagging technique is employed on the best LSTM model. The proposed predicitive model aims to determine the Common Air Quality Index level for the next hour in Niksic, Montenegro. With the hybridization of the LSTM algorithm and by applying the bagging technique, our approach aims to significantly enhance the accuracy and reliability of hourly air pollution prediction. The major contribution of this paper is in the application of advanced machine learning analysis and the combination of the LSTM, Genetic Algorithm, and bagging techniques, which have not been previously employed in the analysis of air pollution in Montenegro. The proposed model will be made available to interested management structures, local governments, national entities, or other relevant institutions, empowering them to make effective pollution level predictions and take appropriate measures.