At the Crossroads: Exploring the Relationship Between Financial Decisions and Firm Performance of Distressed Firms in the Pakistan Stock Exchange

The primary aim of this study is to assess the influence of financial decisions on the financial performance of distressed firms in Pakistan. Specifically, the research delves into three pivotal financial decisions: dividends, capital structure, and investment choices. Utilising balance sheet analyses published by the State Bank of Pakistan, relevant data were gathered for companies listed on the Pakistan Stock Exchange. The sample for this investigation comprises 185 non-financial firms listed on the PSX over the past eight years (2010-2017), utilising panel data. Regression analysis using the Ordinary Least Squares (OLS) technique was employed to examine the relationships. The results reveal that the dividend policy significantly and positively impacts return on assets, while its effect on return on equity is found to be insignificant. The study also explores the impact of capital structure on the performance of distressed firms, indicating an insignificant and negative effect on return on assets. Additionally, a highly significant positive relationship is identified between taxes and both returns on assets. Concerning return on equity, a negative and highly significant association is observed between long-term debt and ROE. Furthermore, the investigation examines the effects of over- and under-investment on firm performance in distressed firms. The findings suggest that over-investment has a significant and positive impact on return, while under-investment demonstrates a significant and positive effect on return on assets and an insignificant positive impact on return on equity. This study contributes to the understanding of the impact of over and under-investment on firm performance, an aspect not previously explored in distressed firms

Impact of Online Trade and Covid-19 on Consumer Behaviour in Covid-19: Evidence from Bank Employees

This research documented the influence of Covid-19 and Online Trade on consumer behaviour in Pakistan. COVID-19 significantly impacts communal health and creates rapid variations in lifestyle, social isolation, and household segregation, with societal and economic effects. This specifies that the country's economic growth in the country has declined. Data is collected through a questionnaire using Google forms from bank employees in Pakistan. SPSS is employed to analyse the data. Results showed a significant and positive relation between online trade and consumer behaviour. It is also revealed that Covid-19 has a negative but significant impact on consumer behaviour. The outcome displays that China is a major producer, and substantial industrial numbers are closed

Impact of mobility models on clustering based routing protocols in mobile WSNs

This paper presents comparison of different hierarchical (position and non-position based) protocols with respect to different mobility models. Previous work mainly focuses on static networks or at most a single mobility model. Using only one mobility model may not predict the behavior of routing protocol accurately. Simulation results show that mobility has large impact on the behavior of WSN routing protocols. Also, position based routing protocols performs better in terms of packet delivery compared to non position based routing protocols

NON-SYMMETRIC STOLARSKY MEANS

Abstract. In this paper we construct n -exponentially convex functions and exponentially convex functions using the functional defined as the difference of the right parts of the HermiteHadamard inequality, for different classes of functions. Applying these results on some starshaped functions, we derive non-symmetric means of Stolarsky type

Prediction of Soil Macronutrients Using Machine Learning Algorithm

In this research work, machine learning algorithms were applied to find the relationship between independent variables and dependent variables for soil data analysis. The independent variables include moisture, temperature, soil pH, Cation Exchange Capacity(CEC) whereas, the dependent variables include Nitrogen, Phosphorus and Potassium (NPK). This research concludes relationships between Phosphorus, Potassium,  soil pH and CEC; Nitrogen and soil moisture and temperature using machine learning(ML) algorithms so as to deduce NPK content of soil. A comparative analysis with obtained results from each ML method is also presented. Machine learning algorithms are best performed on data with multiple independent variables. The values computed for nitrogen relationship were more accurate than PK relationship values. The accuracy of data set I was less than data set II. A large data set would produce more accurate results for both data sets

Incidence of Depression and Anxiety among Patients with Acute Myocardial Infarction during COVID-19 Pandemic

Objective:  This study aimed to assess the incidence of anxiety and depression among AMI patients during COVID-19 pandemic in Pakistan. Material & Method:  This cross-sectional study was conducted from February 27 to June 2, 2020, at the Cardiology ward of Dera Ghazi Khan Teaching Hospital.  Total 611 patients selected through convenience sampling. The patients were requested to respond to Hospital Anxiety and Depression Scale (HADS) and demographic information. An independent samples t test was applied for comparisons. Results:  Patients’ age was ranging from 41 to 79 years, with a mean age of 52.35 ± 5.12. There was a high frequency of anxiety (51.72%) and depression (34.86%) among AMI patients. A significant difference was observed in the level of anxiety (p = 0.001) and depression (p = 0.000) among male and female patients. Conclusion:  The present study findings affirmed that increased level of anxiety and depression are prevalent in AMI patients. Additionally, both anxiety and depressive symptoms were more common in female patients. Keywords:  COVID-19, Anxiety, Depression, Acute Myocardial Infarction (AMI)

Mentoring and its Effects on Turnover Intensions in Perspective of Pakistan’s Telecom Sector

In today’s era of towering competition the retention of workforce is highly desirable for long term success of any organization. The employees serve as backbone for any organization and are responsible for attaining the laid down objectives of the organization. The ongoing study investigates the effects of mentoring on turnover intensions of employees working in telecom sector of Pakistan. Researchers retrieved data with the help of questionnaires based on five point likert scale from almost 300 employees working in telecom organizations of Pakistan. Multiple regressions were used to analyze the collected data. Result shows that mentoring mostly is negatively associated with turnover intension because employee was not satisfied with mentoring and commitment in employees is only due to experienced learning which helps to develop additional skills in employee

Obstructive sleep apnea in middle age male smokers

OBJECTIVE: Obstructive sleep apnea is the most common type of sleep apnea. This was designed to establish the relationship between smoking and obstructive sleep apnea. Methods:This cross-sectional study was conducted at the outpatient department of Pakistan Institute of Medical Sciences, Islamabad, Pakistan, from August 2018 to November 2018 (dates mentioned in methods are different) and comprised of males aged 30-50 years. Participants were divided into two groups of smokers and non-smokers and Berlin questionnaire was used to collect data employing systemic random sampling. SPSS 22 was used for analysis. Results:There were 768 male participants in the study equally split between smokers and non-smokers. Mean age was 39±5.76 years. Percentage of high-risk group among smokers was 36.5% and among non-smokers was 16.7%. Percentage of low risk group among smokers was 63.5% and among non-smokers was 83.3%. The odds ratio of obstructive sleep apnea was 2.87 times more in smokers than non-smokers. Conclusion:Long term smoking had a strong association with obstructive sleep apnea

Agro-morphological and genetic diversity studies in Rice (Oryza sativa L.) germplasm using microsatellite markers

Background Knowledge of the genetic diversity and population structure of germplasm collections is an important foundation for crop improvement. Rice production across a broad range of rice-growing environments results in a diverse array of local rice varieties. Many rice varieties have been lost as a result of biodiversity loss and are now grown in Pakistan. Methods and results To protect the biodiversity of rice varieties, an experiment was carried out to check the genetic and morphological variations between 8 exotic and 7 local rice genotypes, using 5 different SSR markers, i.e., RM3, RM259, RM341, RM520, and RM11943. The analysis of morphological and quality traits of rice observed significant variation across genotypes. The results revealed that genotype Irri-Pak attained the highest plant height and primary branch plant-1, while genotype Mushkan produced a higher number of productive tillers and obtained a higher fertility factor (%). Similarly, the highest value for panicle length was observed for genotype Faker-e-Malakand, 1000-grains weight in genotype Calmochi, and maximum days to maturity was noticed in genotype Swati-2014. Moreover, the genotype Brio attained the highest value of stem diameter, while maximum seed length was noted in the genotype Sug Dasi. The highest number of primary branches plant(-1) in genotype Ibge-I and secondary branches plant(-1) in genotype Calmochi were noticed. A higher concentration of sodium and potassium was observed for the genotype Marte, while the genotype Muskan attained the maximum content of copper. Moreover, the highest concentration of iron in genotype Originario, zinc in genotype JP-5, and cadmium content were noticed in genotype Ibge. Similarly, the dendrogram analysis for quantitative parameters showed three clusters at 74.13% similarities. Whereas all the genotypes of European origin formed a separate cluster. A set of 5 simple sequence repeat primers, covering four chromosomes, amplified a total of 14 alleles and showed 100% polymorphism with an average PIC value ranging from 0.39 to 0.91. The UPGMA cluster analysis separated the 15 rice genotypes into 3 main groups based on 32.5% similarities and the highest genetic distance (45.1%) was observed between two genotypes (Fakher-e-malakand and Musa), having different geographical origins. There was no genetic distance between the genotypes Marte and Brio, irrespective of having the same origin. Conclusions The maximum genetic distances were noted for genotype, Fakhre-e-Malakand and Musa having a different origin, while the minimum genetic distance was shown by genotypes, Marte and Onice, from the same origin.Peer reviewe