Habib University

The rapid rise of fintech, driven primarily through technological innovation, has changed finance (Financial Services) through increased efficiency, new products, and more access to finance. The success of fintech is greatly affected by the quality of their entrepreneurial ecosystem (entrepreneurial culture, access to finance, human capital, market conditions, regulations, support services). The purpose of this study is to assess the impact of these six key components on the success of fintech startups. The study takes an Ecosystem Approach to build a model that explains how these components affect fintech startup success based on data collected from fintech professionals and ecosystem participants via surveys and interviews. The model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The analysis showed that all six components had a statistically significant and positive effect on the success of fintech startups. The regulatory component, followed by access to finance, showed the greatest positive effect on success. This study builds on prior literature and adds to the growing body of research within the fintech ecosystems by presenting new empirical evidence from an emerging economy, as well as having practical policy implications for developing more successful fintech ecosystems. The findings offer valuable recommendations for policymakers, investors, and entrepreneurs pursuing to promote sustainable fintech development and startup success.

Background Colorism, a discrimination against individuals based on their skin color. It remains an under-researched social problem in the South Asian region, particularly in countries like Pakistan, India, and Bangladesh. This silent bias shape one’s social acceptance and identity, education, and career opportunities. Despite its significance, this topic has received limited attention in Pakistani research. Methodology In order to analyze the impact of colorism on self-perception and confidence along social dimensions, a cross-sectional online survey was conducted with over 385 residents from Karachi, Pakistan. The questionnaire is designed in a way to cover the insights across four key domains: socio-demographic information, including age and grade level, as well as socioeconomic status, like income or occupation; self-esteem; and self-efficacy. Assessed through carefully constructed sets of assertive/ non-assertive items, participants’ perceptions about their skin tone worth together with capabilities were evaluated for endorsement at varying levels of confidence. Results The findings highlight the widespread yet unspoken influence of colorism on societal views and personal confidence within Pakistani society. Although many participants reported denying the role of skin tone they possess, deep-rooted skin tone biases do exist. Of the 385 respondents, 58.4% were female and 41.6% male, with 34% identifying as having a brown complexion. A majority (74.1%) of being under the age of 30 were socially active as well as vulnerable, as this age group is more exposed to societal standards of beauty and success, making them particularly susceptible to the psychological impacts of colorism. While 53.8% of participants reported that skin tone does not affect social relations, a significant proportion of 23.6% recognized its effect, while another 22.6% remained neutral, demonstrating some degree of internalized colorism. Regarding employment, only 19.2% believed their skin color had a negative impact on job prospects compared to 62.6% who disagreed, and the remainder (18.2%) were uncertain—regardless, these attitudes reveal persistent undertones of colorism in society. Further analysis using the Chi-square test showed that beliefs about career-related colorism significantly correlated with efforts to reduce or alter one’s skin tone, illustrating the psychological and behavioral impacts of such discrimination. Conclusion: The study highlights that perceptions of skin tone can influence social and interpersonal lives. It underscores the need for further research on colorism in the region to understand its implications better and promote discussion on this critical issue. BJMS, Vol. 25 No. 01 January’26 Page : 169-175

Comprehensive biochemical analysis is an essential part of clinical diagnostics, pharmaceutical development, and biomedical research. The gold-standard technique for such analysis is spectrophotometry, which has demonstrated excellent reliability, quantitative accuracy, and broad biomolecular sensitivity. Commercial micro-volume spectrophotometers, which are instrumental to applications with limited sample volumes, are often restricted to well-equipped laboratories due to the high cost and portability limitations especially in resource-limited settings. To address this gap, we propose a proof-of-concept, 3D-printed micro-volume spectrophotometer that integrates precision optics, microfluidic sample handling (8–15 µL), combined with spectral reconstruction and machine learning-based concentration estimation algorithm in a compact and affordable format. Preliminary validation of the proposed system based on bovine serum albumin (BSA) protein quantification via Biuret assay demonstrates strong agreement with commercial spectrophotometers (R2 > 0.91). With a production cost of approximately $36 and rapid measurement, the architecture provides a framework that can be extended to other absorbance-based biochemical assays ensuring accessible spectrophotometric systems for point-of-care and resource-limited applications, overcoming traditional limits of cost, sample volume, and portability.

This research is a comprehensive work on analysis of musculoskeletal systems and integrating various machine learning algorithms for its classification. Conventionally EMG is specific for diagnosing or analyzing muscle function. It has rarely been used for pattern recognition or researching muscle adaptation. Our prime objective is to use the significant differences in the EMG of the gym and non-gym goers to distinguish between the two. The chosen muscle was biceps brachii which is a core muscle during elbow flexion. The acquired signals (5 gym and 5 non-gym subjects) while performing biceps curls were used to find 22 different features that would help us apply machine learning algorithms. The data was pre-processed to handle any empty values and normalized. 10 different machine learning algorithms with 5 different balancing techniques were applied and the best one was chosen based on the accuracy metrics and classification report. Great emphasis was laid on pinpointing overfitting by constantly comparing the cross-validation and test accuracies and final confirmation by Cohen’s Kappa and Mathew’s Correlation Coefficient (MCC) which were approximately 0.9867 and 0.9868 respectively. Thereby avoiding overfitting at all costs. After all the testing the best model of all was the random forest classifier with SMOTEENN sampling giving and accuracy of about 0.9375. The model was then separately implemented by grid searching the best parameters for our dataset. The final plots like the confusion matrices and the overall heatmaps gave us the confirmation that our trained model is working in a great manner thereby producing satisfactory results.

The study examines the economic costs and advantages of the Sehat Sahulat Program (SSP) in Pakistan based on the qualitative evidence collected in the urban and rural settings. SSP is a landmark health insurance program that would improve the proportionality of healthcare coverage and minimize the out-of-pocket costs. Nevertheless, the differences in implementation and use still define its efficacy. This paper relied upon in-depth interviews and thematic analysis to determine how households (particularly those in the lower-income bracket) perceive the accessibility, affordability, and long-term sustainability of the program. The results are sophisticated in terms of benefits and barriers. On the one hand, SSP saved a lot of direct treatment expenses on covered diseases and gave vulnerable groups a feeling of financial security. Structural and systematic factors, including low awareness, insufficient coverage of hospitals in rural areas, administrative inefficiency, and unfamiliar costs, restrained the potential of the program. Participants in the urban areas reported that they utilized it more due to the increased concentration of empanelled hospitals, whereas those in the rural areas complained about the cost of traveling, late reimbursements, and lack of equity. The study highlights the need to fill these gaps to ensure that SSP is more inclusive and influential. The policy implications indicate the necessity of better awareness campaigns, increased monitoring of the practices in the private hospitals, increased coverage in rural areas, and the transparency of the reimbursement system. This study adds to the literature on health economics and social protection in Pakistan by qualitatively informing the reader about lived experiences, providing lessons to other low- and middle-income countries that may be implementing similar schemes.

This paper addresses the limited existing work for emotion detection from Urdu audio, which has applications for 230 million Urdu speakers for various real-world use cases such as mental health services, AI chatbots, and automated systems such as robocalls. We used a dataset of 13,920 audio snippets in four emotions (anger, happiness, neutral, and sadness) to evaluate several models. The highest accuracy in emotion detection was achieved by the transformer-based, Self-Supervised AST (Audio Spectrogram Transformer) model at an accuracy of 98.71%, outperforming the other seven models we trained as well as those presented in other comparative studies.

Diabetes mellitus is a widespread medical and public health issue that can lead to significant health impairment. The objective of the study was to assess the antidiabetic effectiveness of various extracts of Atriplex crassifolia (A. crassifolia), namely n-hexane (ACNH), dichloromethane (ACDCM), ethyl acetate (ACEA), and n-butanol (ACNB). The initial qualitative analysis revealed the presence of bioactive components in all extracts, such as alkaloids, glycosides, phenols, flavonoids, terpenes, and saponins. ACEA and ACNB exhibited considerable radical scavenging activity at different doses, as determined by the DPPH free radical scavenging experiment used to assess in-vitro antioxidant activity. Male rats were administered 150 mg/kg of alloxan monohydrate to develop diabetes. A preliminary investigation was conducted to evaluate the potential of all extracts to lower blood glucose levels using an oral glucose tolerance test (OGTT) at three different doses (250, 500, and 1000 mg/kg/day). ACEA and ACNB exhibited promising results in the OGTT, thus prompting a 28-day hypoglycemic research to confirm their biological effectiveness in treating diabetes. When taken orally, ACEA at doses of 500 and 1000 mg/kg/day, and ACNB at doses of 250, 500, and 1000 mg/kg/day, effectively recovered the concentrations of urea, creatinine, glucose, cholesterol, triglycerides, HDL, and liver enzymes (AST, ALT, and ALP). The histopathological evidence corroborated the conclusions. HPLC and GC-MS were employed to identify the presence of medically important phytoconstituents in ACEA and ACNB. Myrecitin and Quercitin were found using High Performance Liquid Chromatography (HPLC) in ACEA, while sinapic acid was identified in ACNB. The GC-MS analyses of ACEA and ACNB revealed the presence of several compounds with hypolipidemic, antioxidant, and anti-diabetic properties. The present study suggests that A. crassifolia has potential as a natural remedy for managing hyperglycemia, diabetic hyperlipidemia, and other diabetes-related problems.