The COVID-19 pandemic that occurred in early 2020 around the world has implications for Indonesia’s education sector. This pandemic led to the Indonesian government policy to study from home at all academic levels using a distance learning approach. Studies on e-learning preparedness in Indonesia involving more comprehensive samples of universities during the pandemic are still limited. This study extended samples from several public and private universities in Indonesia to get a broader picture of e-learning readiness in various faculties with diverse university online learning cultures. This study used Rasch analysis to determine the validity and reliability of the instrument and differential item functioning (DIF) analysis to identify responses based on students’ demographic profiles. The results show that most students were ready to study online, but a few were not ready. Moreover, the results show significant differences in students’ e-learning readiness based on the academic year at university, the field of study, the level of organizational e-learning culture of the university, gender, and region. This work provides an insight into student readiness to study online, especially in higher education in Indonesia. The article presents the implications of online learning practices in universities and recommendations for future e-learning research.

Breast cancer is diagnosed using histopathological imaging. This task is extremely time-consuming due to high image complexity and volume. However, it is important to facilitate the early detection of breast cancer for medical intervention. Deep learning (DL) has become popular in medical imaging solutions and has demonstrated various levels of performance in diagnosing cancerous images. Nonetheless, achieving high precision while minimizing overfitting remains a significant challenge for classification solutions. The handling of imbalanced data and incorrect labeling is a further concern. Additional methods, such as pre-processing, ensemble, and normalization techniques, have been established to enhance image characteristics. These methods could influence classification solutions and be used to overcome overfitting and data balancing issues. Hence, developing a more sophisticated DL variant could improve classification accuracy while reducing overfitting. Technological advancements in DL have fueled automated breast cancer diagnosis growth in recent years. This paper reviewed studies on the capability of DL to classify histopathological breast cancer images, as the objective of this study was to systematically review and analyze current research on the classification of histopathological images. Additionally, literature from the Scopus and Web of Science (WOS) indexes was reviewed. This study assessed recent approaches for histopathological breast cancer image classification in DL applications for papers published up until November 2022. The findings of this study suggest that DL methods, especially convolution neural networks and their hybrids, are the most cutting-edge approaches currently in use. To find a new technique, it is necessary first to survey the landscape of existing DL approaches and their hybrid methods to conduct comparisons and case studies.

Background/Objective: Early periodontitis diagnosis is challenging due to varying staging and grading systems. While clinical parameters like bleeding on probing (BoP) and pocket depth (PD) are commonly used, periapical radiographs provide valuable information about bone loss and periodontal ligament changes. However, a clear definition of early periodontitis, particularly regarding alveolar bone crest changes, remains elusive. Methods: This cross-sectional study involved 21 participants aged 20–30 with clinical signs of periodontitis and radiographic evidence of alveolar bone changes and periodontal ligament widening. Four dentists assessed 40 cases for BoP, 40 for PD, and 40 for periapical radiographs. Results: Statistical analysis revealed that the pocket depth measurement was the most significant factor in diagnosing early periodontitis (Fisher’s exact test, p-value = 0.000). Additionally, the irregularity of the alveolar crest proved to be a significant marker compared to periodontal ligament width (Fisher’s exact test, p-value = 0.000). A Kendall Tau_b test (p-value = 0.000, r = 1.000) confirmed pocket depth to be the most influential parameter among the assessed factors. Conclusions: While this study highlights the importance of clinical and radiographic assessments in early periodontitis detection, integrating these findings into a definitive diagnosis can be complex. The standardization of diagnostic techniques and the development of advanced radiographic interpretation methods are crucial to improve accuracy. Further research is needed to refine diagnostic criteria and explore additional tests for early periodontitis detection.

The invention of graphical processing units (GPUs) has significantly improved the speed of long processes used in molecular dynamics (MD) to search for drug candidates to treat diseases, such as malaria. Previous work using a single GTX GPU showed considerable improvement compared to GPUs run in a cluster environment. In the current work, AMBER and dual GTX 780 and 970 GPUs were used to run an MD simulation on the Plasmodium falciparum enoyl-acyl carrier protein reductase enzyme; the results showed that performance was improved, particularly for molecules with a large number of atoms using single GPU.

Predicting bankruptcy is crucial to avert company failures, which could lead to a systemic collapse of the economy. This study examines the network of executives, directors, and shareholders to identify conglomerates, which are often characterized by a lack of explicit connections between these individuals or institutions. Understanding these networks is crucial for mitigating the risk of bankruptcy and its potential systemic effects. We proposed a technique that uses non-financial factors that could serve as predictors of bankruptcy, as well as the link among the ultimate owners. A regression analysis is employed to evaluate the network’s effect on bankruptcy prediction. The findings indicate a significant impact of the directors’ degree of centrality and the direct bankruptcy rate of director and executive networks on the likelihood of bankruptcy. Additionally, the predictions for one and two years ahead are significantly influenced by the strength or weighted degree of centrality and betweenness centrality of directors. Notably, the influence of executive and shareholder indirect bankruptcy rates becomes increasingly prominent in predicting distress. These results offer a novel perspective on incorporating network variables into bankruptcy prediction models, with an accuracy of 86% using random forest and XGBoost models. The findings indicate that bankruptcy prediction techniques can employ network variables, as alternative data to financial indicators.