Fostering Fintech adoption in growing economies: Opportunities, challenges, and strategies

Fintech adoption has become increasingly popular in many developing countries due to its potential to increase financial inclusion and promote economic growth. However, fostering fintech adoption in growing economies presents challenges, opportunities, and strategies that differ from those in developed countries. This systematic literature review aims to identify the current state of fintech adoption in growing economies, including the challenges and opportunities of fintech adoption and the strategies used to foster it. A comprehensive search of electronic databases, including Scopus and Web of Science, was conducted for relevant articles published between 2018 and 2023. The screening process identified 12 articles that were critically appraised to ensure the quality of their content. The findings suggest that although fintech adoption in growing economies presents unique challenges, such as lack of infrastructure and limited digital literacy, it also provides various opportunities to increase financial inclusion, promote economic growth, and improve overall financial services. The strategies for fostering fintech adoption in growing economies include developing supportive regulatory frameworks, building digital infrastructure, and improving digital literacy through education and awareness programs. This study contributes to a better understanding of the current development of fintech adoption in growing economies, provides insights for policymakers and practitioners, and highlights the need for further research in this area

Bacillus thuringiensis and its application in agriculture

Presently, a number of approaches to pest control via genetic engineering have been developed and genetically engineered crops expressing insecticidal characteristics are under cultivation for the last 15 years. Use of Bacillus thuringiensis genes encoding o̅ endotoxins with insecticidal characteristics is the major approach and a number of such B. thuringiensis genes have been expressed in crops with variable level of efficiency. It is very crucial to achieve adequate level of B. thuringiensis gene expression to have durable resistance against target insect pests. As with many aspects of genetic engineering, politics can impact on the success of a project involving the development of B. thuringiensis transgenic crops, irrespective of its apparent social, economic or environmental benefits. Public education will be essential to ensure the widespread adoption of genetic adoption technologies in agriculture, and scientists will have to play an active role in this process

Correlation of Atrial Fibrillation with Left Atrial Volume in Patients with Mitral Stenosis. a Single Centre Study From Pakistan

Background: Rheumatic heart disease has a strong association with mitral valve stenosis. Atrial fibrillation is one of the most common complications of this condition and is a poor prognostic factor. Early detection and prompt management of atrial fibrillation can help to improve the quality of life and increase the life expectancy of the patients. We carried out this study to investigate the significance of left atrial volumetric changes in mitral stenosis and its correlation with atrial fibrillation. Methodology: We audited the data of 60 patients of rheumatic heart disease who had mitral valve stenosis. The patients were randomized into atrial fibrillation (Group A) and normal sinus rhythm (Group B). We conducted this cross-sectional analytical study at Cardiology Department, Mayo Hospital, Lahore, from 1st February 2017 to 31st January 2018. We only included those patients who consented to be a part of this study and fulfilled our predefined inclusion criteria. Left atrial volume was measured by prolate ellipse method and biplane methods on echocardiography. The Data was analyzed on SPSS v20. Results: Sixty patients were included in the study. Among the subjects, thirty-six (60%) were males, and twenty-four (40%) were females. Atrial fibrillation was noted in 43.33% of the patients of mitral valve stenosis. There was a marked difference in the mean volume of the left atrium among the two groups. We observed that the mean area of the mitral valve for Group A patients was larger than that of patients in Group B. Our study showed an inverse correlation between left atrial volume and mitral valve area among Group A patients. Conclusion: Patients of mitral stenosis are at an increased risk of developing atrial fibrillation if the left atrial volume is increasing. All patients with mitral stenosis should have routine echocardiography & measurement of left atrial volumes, so that proper treatment can be started if the left atrial volume is increasing, to prevent atrial fibrillation

Does the interaction between the knowledge management process and sustainable development practices boost corporate green innovation?

Green innovations are being deployed in manufacturing industries to promote organisational sustainability by embracing sustainable development practices (SDPs). However, little is known about how corporate green innovation (CGI) is influenced by the knowledge management process (KMP). To fill this gap, we have developed a multidimensional framework based on the resource-based view (RBV) theory that provides a foundation for sculpturing the process by which KMP was observed to capture and sustain CGI through SDPs. Data were collected from 393 respondents of large- and medium-sized manufacturing corporations in Pakistan and analysed using partial least squares structural equation modelling (SEM) and fuzzy set qualitative comparative analysis (fsQCA). This study provides several key findings. First, KMP dimensions (acquisition, dissemination and application) significantly improve the SDPs' dimensions (environment, economic and social). Second, SDP dimensions play a significant role in achieving CGI. Third, the implementation of SDPs partially mediates the relationship between the KMP and CGI. Furthermore, the fsQCA results signify the robustness of all integrated constructs. Our results demonstrate that investing in and adopting the latest technologies and sustainable practices are not only valuable for long-term success but the soft concerns such as managing organisational knowledge are also vital in the current knowledge-based economy. Finally, in light of our findings, theoretical and managerial implications, with propositions for future studies, have been provided at the end of the paper

Fishermen's ingenuity in utilizing thermocole for making fishing crafts at Satpati (Maharashtra)

The ingenious traditional fishers of Satpati(Maharashtra) designed and fabricated fishing crafts uusing cheap thermocole(polyurethene)

Enhancing heart disease prediction using a self-attention-based transformer model

Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction