Money laundering has become a vital issue all over the world especially in the emerging economy over the last two decades. Till now, the developing and emerging countries face challenges about the remedies and inceptions of anti-money laundering issues. The objective of the study is to provide a thorough picture of the diversified movements of academic research on money laundering and anti-money laundering activities all over the world. This study aims at exploring the contemporary issues in Anti-money laundering based on the academic points of view. Further, the study is explored to render a portrayal of anti-money laundering activities from an emergency country context. A review of publicly available reports, published documents, daily newspapers, case studies, and previous academic research comprised the main sources of data for the study. It is found that the contemporary money laundering and anti-money laundering academic research might be classified into four broad categories. An emerging country like Bangladesh has taken little initiative to inductee anti-money laundering initiatives. It implies that for the successful implementation of anti-money laundering activities, good governance along with a congenial regulatory framework is a prerequisite in an emerging country context. In addition, the machine learning may enhance the quality of money laundering detections in Bangladesh.

Entrepreneurial Orientation (EO) emphasizes the identification and exploitation of business opportunities, while entrepreneurial action learning (EAL) underscores the acquisition of knowledge through practical experience and continuous improvement. Breakthroughs in both aspects contribute to maintaining flexibility, adapting to changes, and enabling success in competitive markets. The key to the development of small and medium-sized enterprises (SMEs) lies in a clear Entrepreneurial Orientation, a focus on Entrepreneurial Action Learning, and the cultivation of innovation spirit through continuous practice and experience accumulation, thereby enhancing entrepreneurial performance (EP). This study aims to explore the impact of Entrepreneurial Orientation on the Entrepreneurial Performance of SMEs, clarify the mediating role of Entrepreneurial Action Learning between Entrepreneurial Orientation and Entrepreneurial Performance, and investigate the variability of Entrepreneurial Performance among different industries. By means of data collection from 598 SMEs, data analysis was conducted using Structural Equation Modeling (SEM) and Analysis of Variance (ANOVA). The analysis results indicate that entrepreneurial orientation has a positive impact on entrepreneurial action learning and entrepreneurial performance, and entrepreneurial action learning has a positive impact on entrepreneurial performance. The study also found that entrepreneurial action learning partially mediates the relationship between entrepreneurial orientation and entrepreneurial performance. There are certain differences in entrepreneurial performance among different industries. This study enriches the relevant literature in the field of entrepreneurship. Additionally, research on entrepreneurial orientation, entrepreneurial action learning, and entrepreneurial performance in specific regional contexts is very limited, making this study valuable for subsequent research in related areas.

Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.

In the present and future of education, fostering complex thinking, especially in the context of the Sustainable Development Goals (SDGs), is critical to lifelong learning. This study aimed to analyze learning scenarios within the framework of a model that promotes complex thinking and integrated design analysis, to identify the contributions of linking design models to the SDGs. The research question was: How does the open educational model of complex thinking link to the SDGs and scenario design? The analysis examined a pedagogical approach that introduced 33 participants to the instructional design of real-life or simulated situations to develop complex thinking skills. The categories of analysis were the model components, the SDGs, and scenario designs. The findings considered (a) innovative design capacity linked to SDG challenges, (b) linking theory and practice to foster complex thinking, and (c) the critical supporting tools for scenario design. The study intends to be of value to academic, social, and business communities interested in mobilizing complex thinking to support lifelong learning.

A method for studying the resilience of energy and socio-ecological systems is considered; it integrates approaches developed at the International Institute of Applied Systems Analysis and the Melentyev Institute of Energy Systems (MESI) of the Siberian Branch of the Russian Academy of Sciences. The article discusses in detail the methods of using intelligent information technologies, in particular semantic technologies and knowledge engineering (cognitive probabilistic modeling), which the authors propose to use in assessing the risks of natural and man-made threats to the resilience of the energy sector and social and ecological systems. More attention is paid to the study and adaptation of the integral indicator of quality of life, which makes it possible to combine these interdisciplinary studies.

This study investigates the impact of the metaverse on English language teaching, focusing on the perspectives of students from the University of Boyacá. The use of the metaverse was compared with the Moodle platform in a virtual educational environment. A mixed-method approach combining quantitative and qualitative methods was employed. The sample consisted of 30 university students enrolled in English courses, randomly assigned to two groups: one using the metaverse and the other using Moodle. Students’ grades on different activities and assessments throughout the course were collected, and semi-structured interviews were conducted to explore students’ perceptions of the educational platforms. Results revealed that while students recognize the potential of the metaverse to enhance interactivity and learning experience, they also identified technical and accessibility challenges. Although no significant differences in grades were found between the groups, less variability in grades was observed in the metaverse group. The mixed design allowed for a more comprehensive understanding of the impact of the metaverse on English language teaching, while providing a variety of student perspectives on their experience with educational technology. This research contributes to understanding the role of the metaverse in English language teaching and highlights key areas for future research and developments in the field of virtual education.