This research aims to analyze the strategic role of the Islamic organizations Muhammadiyah and Al-Washliyah in the electoral dynamics of North Sumatra. The background for this study stems from the significant influence these organizations hold in the social, educational, and political spheres of the region, leveraging their extensive membership base and organizational structure. The urgency of this research arises from the need to understand how religious organizations shape political outcomes, which is crucial for developing more inclusive governance strategies. Employing a qualitative descriptive methodology, this study explores how these organizations mobilize support during elections and influence policies through their educational and social programs. Findings reveal that Muhammadiyah and Al-Washliyah effectively utilize mass mobilization and social movement theories to maintain their influence in the political landscape of North Sumatra, subtly navigating and shaping local politics through strategic engagement and advocacy.

This research aims to assess the impact of bargaining power on budget implementation while also considering the deviation in capital expenditure as a moderating factor. The research sample included 34 provincial governments in Indonesia between 2019 and 2022. The sample determination method used purposive sampling, so the final sample size was 134 observations. The research employed panel data regression to test the hypotheses and continued with the Chow, Lagrange multiplier, and Hausman tests. The study results indicate that bargaining power has a positive and significant effect on budget implementation, with the deviation in capital expenditure not diminishing its impact. The research’s practical implication is that regional governments must effectively manage their revenues to finance regional spending needs through regional tax intensification and extensification policies. The study contributes to signaling theory by highlighting that regional governments can finance regional spending needs through fiscal independence and society’s involvement. It also contributes to agency theory by demonstrating that capital expenditure deviation in the form of information asymmetry in regional governments does not reduce their ability to finance regional expenditure needs. Nonetheless, the study suggests that the proxies used in this research are limited, and further exploration of other proxies to measure tested variables. This research provides new knowledge for stakeholders regarding the dynamics of regional budgeting, especially regarding assessing the impact of bargaining power on budget implementation and considering deviations in capital expenditure as a moderating factor.

Purpose: Kindergartens are an important educational environment for the development of children at an early age, and they also play a crucial role in developing the values of sustainable development. The purpose of this study is to investigate kindergarten teachers’ perceptions of observable and sustainable development practices. Design, methodology, approach: Semi-structured interviews were conducted with 302 Saudi kindergarten teachers. Additionally, observation cards were utilized to collect data on actual practices of sustainable development in kindergartens. Data were analyzed using Nvivo12, a qualitative data analysis software, and descriptive analysis methods. The main themes were produced first, and then the perspectives were organized around them. Finding: The impact of social and cultural factors on the development of values, the lack of resources available to implement educational activities, and teacher awareness and training gaps were found to be the main barriers to the development of sustainable development values in kindergartens. Originality, value: To the best of the author’s knowledge, this is the first study in Saudi Arabia that has looked into the environmental and social perceptions of early childhood teachers about sustainable development practices, so the study’s findings can highlight the importance of reorienting teacher education programs toward sustainability in order to bridge knowledge and practice gaps.

QR code transforms the way retailers offer their shopping experiences in the current context. In response, various retailers adopted innovative approaches such as QR code-based applications to attract their consumers. A QR code-based virtual supermarket refers to a space where goods or services are traded in a virtual space using a smart app-based QR code. To fully understand the opportunities of this type of supermarket applying QR-code technology, initial research is required to assess consumers’ use intention. This study has examined the antecedents of the adoption of QR code-based virtual supermarket among Vietnam consumers using the expanded Technology Acceptance Model (TAM) and explored the moderating effect of perceived risk on the relationship between attitude and consumers’ intention to use QR code-based virtual supermarket. A questionnaire was used to collect data from a sample of 335 consumers in Vietnam. The findings revealed that the antecedents are effective in predicting consumers’ attitudes and intentions toward QR code-based virtual supermarket adoption. The results showed the negative moderation effects of perceived risk for the effect of attitude on consumers intention. In addition, practical implications are supported for the application of new shopping technology and are likely to stimulate further research in the area of virtual supermarket shopping.

Inflammation of the lungs, called pneumonia, is a disease characterized by inflammation of the air sacs that interfere with the exchange of oxygen and carbon dioxide. It is caused by a variety of infectious organisms, including viruses, bacteria, fungus, and parasites. Pneumonia is more common in people who have pre-existing lung diseases or compromised immune systems, and it primarily affects small children and the elderly. Diagnosis of pneumonia can be difficult, especially when relying on medical imaging, because symptoms may not be immediately apparent. Convolutional neural networks (CNNs) have recently shown potential in medical imaging applications. A CNN-based deep learning model is being built as part of ongoing research to aid in the detection of pneumonia using chest X-ray images. The dataset used for training and evaluation includes images of people with normal lung conditions as well as photos of people with pneumonia. Various preprocessing procedures, such as data augmentation, normalization, and scaling, were used to improve the accuracy of pneumonia diagnosis and extract significant features. In this study, a framework for deep learning with four pre-trained CNN models—InceptionNet, ResNet, VGG16, and DenseNet—was used. To take use of its key advantages, transfer learning utilizing DenseNet was used. During training, the loss function was minimized using the Adam optimizer. The suggested approach seeks to improve early diagnosis and enable fast intervention for pneumonia cases by leveraging the advantages of several CNN models. The outcomes show that CNN-based deep learning models may successfully diagnose pneumonia in chest X-ray pictures.