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This paper examines the impact of corporate governance on the financial performance of A and B class banks in Nepal. The study utilizes panel data from 26 commercial banks and 8 national-level development banks over a 10-year period from 2011 to 2020. Various corporate governance indicators, including Board Size, Board Independence, Frequency of Board Meetings, Audit Committee Size, and the presence of Female Directors, are used as explanatory variables. The performance indicators, Return on Assets (ROA) and Return on Equity (ROE), serve as dependent variables. Control variables such as Leverage and Firm Size, along with macroeconomic variables like GDP and Inflation, are also included. The research employs both Fixed and Random Effect Models, chosen based on the results of the Hausman test, to analyze the data. These models allow the researchers to assess variations among different banks and within individual banks over time.The findings reveal that all corporate governance indicators, namely Board Size, Board Independence, Frequency of Board Meetings, Audit Committee Size, and Female Directors, are insignificant in explaining firm performance as measured by ROA using the Random Effect Model. However, the control variable: Firm Size, shows a significant positive relationship with ROA, while Leverage and macroeconomic variables (GDP and Inflation) do not significantly affect ROA. Similarly, when measuring firm performance using ROE with the Fixed Effect Model, all corporate governance indicators remain insignificant. Firm Size and GDP show a significant positive relationship with ROE, whereas Leverage and Inflation are insignificant.Overall, the use of both Fixed and Random Effect Models indicates that corporate governance has an insignificant impact on firm performance. This suggests that, in the context of A and B class banks in Nepal, corporate governance does not play a significant role in influencing financial performance.

Breast cancer cells continues to be a considerable worldwide health and wellness worry needing precise along with prompt discovery techniques for boosted individual results. This research study recommends an integrative artificial intelligence structure for bust cancer cells discovery leveraging the Random Forest formula, Decision Tree, Logistic Regression version, together with OpenCV for photo handling. The technique entails several phases. To start with electronic mammography pictures are preprocessed making use of OpenCV to improve function removal and also alleviate sound artifacts. Following a function choice procedure is used to recognize pertinent photo includes vital for category. Ultimately 3 distinct artificial intelligence formulas are used: Random Forest, Decision Tree as well as Logistic Regression. These formulas are educated together with confirmed making use of a detailed dataset consisting of mammography photos with connected ground fact tags showing malignant or non-cancerous areas. The Random Forest formula harnesses the power of set finding out, accumulating the outcomes of numerous choice trees to boost category precision plus strength. Decision Tree designs are utilized for their interpretability along with simpleness supplying understandings right into the underlying decision-making procedure. Logistic Regression an extensively made use of straight classifier, supplies a probabilistic analysis of the chance of boob cancer cells event based upon input functions. The efficiency of each formula is carefully examined utilizing metrics such as precision, level of sensitivity, specificity as well as location under the receiver operating attribute contour (AUC-ROC) to analyze category efficiency and also generalization capability. These algorithms are trained on the extracted feature set to learn the patterns indicative of malignant and benign of breast tissues. Random Forest Algorithm provides high accuracy due to its ensemble nature. The performan

This comprehensive review aims to explore the future potential of nasopulmonary drug delivery systems for the administration of drugs through the nasal route. Nasal drug delivery has gained significant attention due to its numerous advantages, including rapid absorption, avoidance of first-pass metabolism, and non-invasive administration. This review provides an overview of the nasal anatomy and physiology, highlighting the factors influencing drug absorption and bioavailability. Additionally, it discusses the various types of nasopulmonary drug delivery systems, such as nasal sprays, powders, and gels, along with their advantages and limitations. Furthermore, the review delves into the challenges associated with nasal drug delivery, including mucociliary clearance, nasal irritation, and formulation issues. The potential applications of nasopulmonary drug delivery systems in the treatment of various diseases, such as allergies, respiratory disorders, and systemic conditions, are also discussed. Finally, the review concludes with an outlook on the future prospects of nasopulmonary drug delivery systems, emphasising the need for further research and development to optimise their efficacy and safety. Keywords: nasal pulmonary; nasal spray; nasal mucosa; gels; drops; nasal approaches; nasal route; nasal delivery.