Presently, there exists a burgeoning trend of female entrepreneurs worldwide, notably within the realm of small and medium-sized enterprises (SMEs), many of which manifest as family-run enterprises. The systematic literature review endeavors to construct an integrative framework concerning the practical ramifications of female involvement in family businesses by amalgamating extant global studies. The findings elucidate the practical implications inherent in female participation across global family businesses, concurrently furnishing a reservoir of prospects for prospective investigations. The deduction posits the imperative eradication of gender disparities, cognizant that gender parity underpins economic and financial advancement and is contingent upon female involvement. Furthermore, familial enterprises are urged to acknowledge and integrate women’s contributions in entrepreneurial decision-making processes.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.

Conspiracy theories during Covid-19 pandemic spread worldwide, including in Indonesia. What political and religious factors explain their spread in Indonesia with particular reference to the DKI Jakarta province, its surrounding municipalities, and West Sumatera province? This study aimed to answer the questions. It employed a qualitative approach with multi-data collection methods, including those from media, documents, and interviews. The spread of Conspiracy theories benefited from the democratic system that promotes the freedom of information in using social media. First, the government officials initially spread conspiracy theories to satisfy people’s anxiety about the obscured Pandemic. However, they resulted in the government’s ambiguous, controversial, and reckless policies leading to people’s distrust of the government. Jokowi-Makruf Amien, political opponents capitalized on the government’s poor policies to spread conspiracy theories which partly discredited the Jokowi-Amien administration. Both government officials and the opposition capitalized on politics and religious teaching or supra-natural pretexts to posit their conspiracy theories.

This study conducts a systematic review to explore the applications of Artificial Intelligence (AI) in mobile learning to support indigenous communities in Malaysia. It also examines the AI techniques used more broadly in education. The main objectives of this research are to investigate the role of Artificial Intelligence (AI) in support the mobile learning and education and provide a taxonomy that shows the stages of process that used in this research and presents the main AI applications that used in mobile learning and education. To identify relevant studies, four reputable databases—ScienceDirect, Web of Science, IEEE Xplore, and Scopus—were systematically searched using predetermined inclusion/exclusion criteria. This screening process resulted in 50 studies which were further classified into groups: AI Technologies (19 studies), Machine Learning (11), Deep Learning (8), Chatbots/ChatGPT/WeChat (4), and Other (8). The results were analyzed taxonomically to provide a structured framework for understanding the diverse applications of AI in mobile learning and education. This review summarizes current research and organizes it into a taxonomy that reveals trends and techniques in using AI to support mobile learning, particularly for indigenous groups in Malaysia.

This paper discusses the concept of creating a new reality using the approaches of smart cities to develop eco-cities, in which the necessary balance between nature and progress can be maintained. The authors propose that the concept of smart cities should be used as a tool for the creation of eco-cities, and argue that the positive synergies between the two will be strongest if the smart concept acts as a tool for the creation of eco. The core elements of a smart eco-city are identified as smart sustainable use of resources, a smart sustainable healthy community, and a smart sustainable economy. The results of the article were the foundation for the development concept for Vision Bratislava 2050—the vision and strategy for the development of the capital of the Slovak Republic. The authors also discuss the challenges of transforming cities into smart eco-formats, including the need for digital resilience in the face of potential cataclysms. They suggest that this is a promising area for further research into the concept of smart eco-cities.

City planning is becoming more and more crucial as modernization and urbanization progress quickly. Making maps is an essential and helpful way in the city planning process for gathering data about the layout of a city and its elements, including the roads, traffic, buildings, and environment. Thanks to advancements in technology, computer software is now used to create maps, yielding more accurate and varied results. As a result, cartography is now closely related to and plays a crucial part in city planning. This brief essay will discuss the value of cartography in urban development and planning, as well as the connection between the two.