This study was conducted to find the impact of capital structure and liquidity condition on the profitability of pharmaceutical firms listed with Pakistan Stock Exchange (PSX). The dataset was comprised of eleven years 2010 to 2021. To assess profitability level, to dimensions return on assets (ROA) and gross profit margin (GPM) were used. The capital structure was measured through debt to equity ratio (DER) and debt to total funds (DTF). The liquidity level was measured through current ratio (CR) and acid test ratio (ATR). The OLS regression, fixed and random effect models were used for analysis. The findings proved that high debt to equity ratio significantly and negatively affect the profitability. The liquidity conditions have positive association with profitability of firms. The study suggested that owners and company managers should use optimal value of debt and liquidity conditions for profit maximization and to reduce the cost associated with debt capital.

This study gives details of the interrelationship among important socioeconomic variables and food security status in Pakistan. The major socio-economic explanatory variables; education, livestock, poverty, receipts of foreign remittances, and female house-head status were analyzed against food security. The data was derived from Pakistan Social and Living Standard Measurement survey dataset 2019-20. A binary logistic model has been applied for the estimation of poverty and food security indices. The study results showed that education, livestock, foreign remittances, and female house-head have a positive significant impact on food security while poverty has a negative and significant impact on food security. The study recommendations are that government must focus to increase agriculture growth, increased dependence on livestock, foreign remittances, and education.

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This study used a modified Cobb-Douglas production model to estimate and test production input co-efficient for Group 28 (including the U.K), Group 27 (excluding the U.K) and individual European Union member countries by using the data of 31 years from 1990 to 2020. Results indicate that the log-linear C-D production model fits the data very well in terms of capital, male and female labour force elasticities, measuring the return to scale, standard errors and economies of scale for Group as well as for individual member countries. Results showed EU 28, EU 27 and from the list of member countries only United Kingdom, Slovak Republic, Slovenia, Czech Republic, Malta, Cyprus, Poland, Hungary, Estonia, Finland, Germany and Netherland are on increasing return to scale, only France is a constant return to scale (as value 0.99, close to 1) and remaining countries are on decreasing return to scale. The study also finds that the United Kingdom as an individual performing increasing return to scale so U.K separation (Brexit) from EU will not harm the U.K and even EU itself, as EU is on increasing return to scale after including/excluding U.K. Study also finds that EU as a group of 27 member countries exhibits increasing return to scale, which is a symbol for overall EU growth and development and suggestion for East Asian and South Asian countries to make a trading bloc or union like European Union.

This paper attempts to explore many signs of progress enabled by Artificial Intelligence (AI) in financial and corporate business management. It also amid to identify the benefits and cons of AI applications in social life. A systematic content analysis approach has been used to demonstrate the developmental phases of AI. Four distinct organizational maturity clusters i.e. Pioneers, Investigators, Experimenters, and Passives have been developed on basis of dataset. Data collections was carried through emails, customizable chatbots, live chat softwares and automated helpers of top ten online companies and various banking and financial institutions located in Lahore and Karachi cities for making behavioral analysis. The data results revealed that all aspects of financial managements and corporate business activities have been highly influenced by the application of AI. The study demonstrated that 80% senior business executives were of view that AI boost productivity and creates new business avenues. The results also demonstrated that 88% Pioneer organizations have understand and adopted AI techniques according to organization requirements, 82% Investigator organizations are not using it beyond the pilot stage whereas 24% Experimental organizations were adopting AI without understanding it. These results seem to reflect that AI has profound effects on financial industry to streamline its credit decisions from quantitative trading to financial risk management and fraud detection. This study also discovered that the widespread use of AI have raised a number of ethical, moral and legal challenges that are yet to be addressed. Although AI is gaining popularity day by day and it is believed that AI will improve work performance beyond human standards but it could not replace human resources fully.