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Fifty years ago, Junko Tabei became the first woman to summit Everest – why do so few people know her story?
No full textThe moderating role of education on the financial inclusion-food poverty nexus: the case of Uganda
Get PDFPurpose
This paper aims to investigate the moderating role of education on the financial inclusion-food poverty nexus: the case of Uganda.
Design/methodology/approach
Using data from the Uganda National Household Survey 2019 / 2020, this study uses a binary Logit model to examine the impact of three dimensions of financial inclusion, namely, ownership of a savings account, access to credit and a financial inclusion index on food poverty, with emphasis on the interaction between education and financial inclusion.
Findings
The study finds that both financial inclusion and education significantly reduce food poverty, with education enhancing the effectiveness of financial inclusion in this regard. The interaction between financial inclusion measures and education is statistically significant, highlighting education’s role in improving the utilisation of financial services to alleviate food poverty.
Originality/value
While financial inclusion’s role in reducing poverty and improving economic well-being has been studied, the moderating role of education remains underexplored. This paper addresses this gap by analysing how education interacts with financial inclusion to jointly influence food poverty, focusing on education as a moderator in the financial inclusion – food poverty relationship
Reply to letter to the editor: Comparative analysis of machine learning models for coronary artery disease prediction with optimized feature selection- ethical and transparency considerations in AI-based CAD prediction.
No full textRethinking Business Practices: Harnessing Indigenous Knowledge Systems and Sustainable Strategies for Resilient Entrepreneurial Success
Get PDFEntrepreneurship is increasingly reframed beyond profit maximisation toward models that integrate cultural resilience, sustainability and socio‐ecological responsibility. This study examines how Indigenous Knowledge Systems (IKS) and Sustainable Strategies (SS) jointly influence entrepreneurial success (ES), addressing a gap where culturally embedded and ecologically responsive practices are rarely considered together. Drawing on the Sustainable Indigenous Entrepreneurship Model (SIEM), ES is conceptualised as a multidimensional construct encompassing growth, innovation, resilience, efficiency and competitiveness. A cross‐sectional survey of 124 entrepreneurs across Africa, Asia, Europe and the Americas was analysed using correlation and regression techniques. Results demonstrate that both IKS and SS significantly predict entrepreneurial success, with IKS exerting the stronger influence. Practices such as oral knowledge transmission, traditional work ethics and environmental adaptability emerged as particularly impactful in shaping innovation, efficiency and resilience. These findings affirm the value of hybrid entrepreneurial logics that blend ancestral knowledge with sustainability‐oriented strategies. The study contributes theoretically by advancing the indigenisation of entrepreneurship scholarship and empirically validating the integration of cultural and ecological practices. Practically, it offers guidance for policymakers, educators and development actors seeking to promote inclusive, sustainable entrepreneurship. Embedding Indigenous knowledge within sustainability frameworks can enhance resilience and competitiveness while aligning business practices with ecological and cultural integrity
Enhancing home rehabilitation through AI-driven virtual assistants: a narrative review.
Get PDFBackground and objectiveArtificial intelligence (AI)-driven virtual physiotherapy assistants (VPAs) are increasingly adopted in home-based rehabilitation, offering real-time feedback and personalised guidance through wearable sensors. These systems enhance treatment adherence, minimise clinic visits, and improve rehabilitation outcomes. However, challenges such as sensor accuracy, patient engagement, and affordability hinder widespread implementation. This review explores current applications, benefits, and limitations of AI-driven VPAs.MethodsA comprehensive narrative review was conducted across PubMed, IEEE Xplore, Scopus, Google Scholar, and Web of Science databases. Search terms such as: "artificial intelligence", "virtual physiotherapy assistants", "home rehabilitation", and "wearable sensors". From 847 initially identified articles, 31 peer-reviewed publications (2018-2024) met inclusion criteria. Exclusion criteria eliminated non-English publications, conference abstracts, and studies without AI components. The review synthesised literature on sensor accuracy, AI-based monitoring algorithms, and patient engagement strategies.Key content and findingsAnalysis of 31 studies revealed that AI-driven VPAs enhance adherence and reduce in-person visits. Integrating wearable sensors and AI facilitates real-time feedback and personalised support, improving exercise accuracy. Critical limitations include inertial measurement unit drift, electromyography sensor placement variability, and optical system environmental dependencies. Challenges remain in sensor precision, user motivation, cost barriers, and technology accessibility. Novel findings highlight potential for predictive analytics, gamification strategies, and telehealth integration.ConclusionsAI-driven VPAs offer a promising accessible, personalised home-based rehabilitation solution. Evidence demonstrates therapeutic potential, though systematic addressing of sensor accuracy, engagement strategies, and accessibility barriers is essential for implementation. Technological improvements and increased affordability are crucial for broader adoption and long-term impact on rehabilitation delivery
Finding identical sequence repeats in multiple protein sequences: An algorithm
Get PDFIn recent years, several experimental evidences suggest that amino acid repeats are closely linked to many disease conditions, as they have a significant role in evolution of disordered regions of the polypeptide segments. Even though many algorithms and databases were developed for such analysis, each algorithm has some caveats, like limitation on the number of amino acids within the repeat patterns and number of query protein sequences. To this end, in the present work, a new method called the internal sequence repeats across multiple protein sequences (ISRMPS) is proposed for the first time to identify identical repeats across multiple protein sequences. It also identifies distantly located repeat patterns in various protein sequences. Our method can be applied to study evolutionary relationships, epitope mapping, CRISPR-Cas sequencing methods, and other comparative analytical assessments of protein sequences
Sentiment Analysis and Predictive Analytics of COVID-19 Tweets Using Machine Learning Techniques.
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