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Purpose: The study looked at the effect of corporate governance on the cost of capital of firms in Pakistan's non-financial sector. Design/Methodology/Approach: The study sample is comprised of balanced data set of 175 non-financial companies listed on the Pakistan Stock Exchange between 2008 and 2018. The study used the dynamic panel GMM estimator technique. Findings: The findings revealed that an increase in the number of directors, board independence, CEO duality, and inflation negatively influence the cost of capital. On the other hand, the increase in institutional holdings increased the cost of capital. In addition, it is discovered that board committees, political connections, and economic growth do not affect the cost of capital Implications/Originality/Value: When board size, CEO duality, board independence, and inflation increased, the cost of capital decreased in Pakistan's non-financial sector. Furthermore, board committees, political connections, company leverage, and economic growth do not affect the cost of capital in Pakistan's non-financial sector. In comparison, an increase in institutional shareholding increased the cost of capital in Pakistan's non-financial sector.

Cryptocurrencies are quickly becoming a key tool in investment decisions. The volatile nature of bitcoin prices has spurred the demand for robust predictive models. The primary objective of this study is to evaluate and compare the effectiveness of different machine learning models with the combination of technical indicators in predicting bitcoin prices. The study used 27 critical technical indicators to evaluate four machine learning techniques, namely Artificial Neural Network (ANN), a Hybrid Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM), Support Vector Machine (SVM), and Random Forest. The results showed that ANN and SVM achieve a significant prediction accuracy of 81% and 82%, respectively, which is higher than the results of traditional models such as standard ARIMA. In practical applications, these methods often improve prediction accuracy by 20-30% over traditional models. The novelty of the analysis lies in the use of temporal and spatial trends via momentum, ROC, and %K features, making for a holistic approach to cryptocurrency market forecasting. This study underscores the critical importance of specific technical indicators and the imperative role of data mining in revolutionizing cryptocurrency market navigation. The research results highlight opportunities to improve investment strategies and risk management policies in the bitcoin market using machine learning models, making the latter valuable to investors and financial experts.

The study was conducted to investigate the maternal demographic determinants of low birth weight babies in district Jhang, Pakistan. In the study demographic characteristics of mothers of low birth weight babies were observed. These characteristics were residential area, education level, socio-economic status, age of mothers at the time of birth and pregnancy interval. These all mentioned characteristics were taken as independent variables against the dependent variable, low birth weight of infants. A sample size of 220 mothers who gave birth to a low weight baby was surveyed through purposive sampling technique in government hospitals of District Jhang and its Tehsils. Data was entered and analyzed by using SPSS. One way analysis of variance with mean plots was used to find out the mean differences between maternal demographic characteristics and low birth weight of babies. Rural women, comparatively aged women, early married women and low educated women were found to have extremely low birth weight babies. DOI:10.5901/mjss.2015.v6n4s1p498

Non-Government Organizations (NGOs) have been considered as the key player in the provision of services to address the economic, environmental and socio-cultural developmental issues. United Nations global sustainable development goals (SDGs) focused the role of partnerships between different sectors in addressing sustainable development issues. The study focuses on the role of NGOs especially the local organization called as Balochistan Rural Development & Research Society (BRDRS) in empowering community in Balochistan. We conducted a cross-sectional survey in two districts of Balochistan province to investigate role of NGOs among beneficiaries of BRDRS education projects and programs in focused communities by following the quantitative research approach. By using proportionate random sampling technique, 400 respondents were selected out of the total beneficiaries. The results indicated significant associations between BRDRS educational projects (i.e. Arranging Students Exposure Visits, Conducted Speech Competitions, Arranging Enrollment Campaigns, Renovation of the Schools, and Follow-up Mechanism to Schools) and level of satisfaction among the beneficiaries. The study recommends that government should also be involved to improve the policy and make a supportive and conducive environment through partnerships in the education sector with NGOs for the sustainable community development.