Ownership structure and firm performance: Evidence manufacturing companies listed in Dhaka Stock Exchange

Purpose: This study aims at examining the relationship between ownership structure and firm performance about manufacturing companies listed in Dhaka Stock Exchange (DSE). Research Methodology: The analysis empirically uses dynamic panel data from 15 pharmaceutical and chemical companies enlisted in Dhaka Stock Exchange (DSE). The study period was 2011-2020. The study used panel data regression analysis. Results: The study confirms that sampled companies' financial performance represented by ROA and ROE is significantly influenced by institutional ownership, ownership concentration, and foreign ownership whereas, negatively influenced by managerial ownership and insider ownership. The study didn't find any noteworthy association between block holders' ownership with firms' performance. Contribution: This study keeps a significant role in understanding the ownership structures’ influence on firms’ performance. More specifically the policymakers may consider the study for implementing the relevant issues. Limitations: The study's results were restricted to 15 Bangladeshi pharmaceutical and chemical companies enlisted in DSE and could not be applied to other companies doing business in Bangladesh

Human Capital and Economic Growth: The Quest for the Most Relevant Level of Education in Pakistan

The study examines the role of human capital in the economic growth of Pakistan by using primary, secondary and higher education enrolments as proxies for human capital in three different specifications. The idea behind these models is to find out the most relevant level of education in terms of its contribution in economic growth. The order of integration of the variables is checked through Augmented Dickey Fuller and Phillips Perron test. In order to find out the evidences of the long run relationship, Engle-Granger two step procedure is used. The findings are re-examined through Johansen and Juselius method. The study found that human capital is positively related with the long run economic growth with primary education as the most relevant level of education. It is therefore recommended that special attention should be given to the primary level of education

Social Support, Inflammation, and Depressive Symptoms among Cancer Survivors and Older Adults: Testing Direct and Mediation Effects

Background: There are two leading hypotheses that explain how social networks influence chronic diseases, such as depression. The “main effects hypothesis” describes a direct relationship between social support and depressive symptoms. The “stress- buffering hypothesis” posits that inadequate social support and life events increase the risk of disease outcomes. Insufficient social support is believed to be expressed through physiological changes (e.g., inflammation) that lead to the development of depression and other chronic conditions. The objective of this study was to empirically test these two leading hypotheses among cancer survivors and older adults without cancer and to explore the intermediate pathways between social support, chronic inflammation, and depressive symptoms.Method: A secondary analysis of two waves of data (2005-2011) from the National Social Life, Health, and Aging Project (NSHAP) was used to test the hypotheses of interest (n=698). Depressive symptoms were measured with the 11-item Iowa version of the CES-D. Inflammation was measured by C-reactive protein (CRP), Tumor necrosis factor-alpha (TNF-α), and Vascular Endothelial Growth Factor (VEGF). Social support was assessed with six items measuring emotional and tangible support. Structural equation models were used to assess direct and indirect paths between social support, inflammation, and depressive symptoms.Results: Cancer survivors and older adults without a history of cancer were similar in terms of their depressive symptoms, inflammatory levels and social support over time. A significant negative direct effect was observed between the total amount of social support in Wave 2 (W2) and depressive symptoms in W2 (p=0.01). No differences between cancer survivors and older adults without cancer were observed in path models and no indirect paths between social support, inflammation, and depressive symptoms were statistically significant in either group.Discussion: The results support the main effects hypothesis, whereby social networks directly influence depressive symptoms. Clinicians should consider screening for social support to prevent or reduce depressive symptomatology

Association of vitamin D level with systemic lupus erythematosus: A case-control study

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune connective tissue disease. SLE patients are more prone to vitamin D deficiency because of their light sensitivity, renal involvement, and prolonged steroid use. This current study aimed to determine the relationship between vitamin D deficiency and SLE. Methods: In this case-control study, 50 SLE patients (cases) and 50 healthy people (controls) were recruited. The reference value of vitamin D is as follows: normal ≥ 30 ng/ml, deficient ≤ 20 ng/ml, and insufficient 21–29 ng/ml. Results: The mean (standard deviation) of vitamin D levels in SLE patients was 19.5 (5.3) ng/mL, which was significantly lower than those in healthy controls, 27.3 (10.0) ng/mL (P<0.001). The healthy controls had a higher proportion of people with normal vitamin D levels (≥ 30 ng/ml). We noticed a moderate negative correlation between vitamin D levels and fatigability, while a moderate positive correlation was seen with hemoglobin levels and the duration of sun exposure. However, in multiple logistic regression analysis, all the associations mentioned above disappeared. Conclusion: Although vitamin deficiency was found to be associated with SLE, its relationship disappears when the confounding by other variables is considered in the analysis.

Prevalence of Muscle Dysmorphia and Associated Health Activities in Male Medical Students in Karachi, Pakistan

Background: Muscle Dysmorphia (MD) is a subtype of body dysmorphic disorder (BDD) and is currently classified under anxiety disorders (subheading: Obsessive-compulsive disorder) in DSM 5. MD is hypothesized to affect the self-esteem and social outlook of the younger generation. MD shows a higher rate in males and may influence their self-confidence rendering them more prone towards using steroids, supplementary proteins and other drugs to alter their physical outlooks as shown in previous studies. This problem has been on the rise lately due to revolutionary advancement in the media and film industry and the abrupt changes about the standards of physical good looks and body shapes. With the lack of studies done in our population, our study will be helpful to consider the prevalence of the disease in our setting and increase awareness in the general public and clinicians. We hope to help clinicians/ therapists find better options in managing the disease. Materials: We performed a cross-sectional study with a sample size of 246 medical school students in Karachi to collect data through self-administered questionnaires. We used the DSM 5 criteria for the diagnosis of BDD and additional questions on the presence of MD. Nutritional habits, exercise routines, use of supplements and drugs were also obtained for exploratory analysis. Results: Our study predicted the prevalence of MD to be 25%. Other main findings included statistical significant associations between MD and the thoughts and practice of steroid use for muscularity. Conclusion: MD is an underdiagnosed and often unrecognized disease that we believe has significant consequences for the young male population. Further work is needed on this in our part of the world. Our research, we believe, can be a stepping stone for further studies that would incorporate wider populations

Indigenous Design for Automatic Testing of Tensile Strength Using Graphical User Interface

Tensile Testing is a fundamental material test to measure the tenacity and tensile strength. Tensile strength means ability to take tensile stress. This Universal Testing Machine is designed using Dual Cylinder Technique in order to comply with the maximun load (tensile force) with the reduction of minimum physical effort and minimized losses.It is to provide material testing opportunity to the students of different institutions, locally and globally, at lowest price; so that they can have a comprehensive understanding of the testing procedures and examining material properties on practical grounds with a minimum expenditure. The testing mechanism is automated along with the inclusion of all necessary parameters and the movement of the members. Specimens of different engineering materials were used to carry out the test and their mechanical strength was tested and compared with the actual value

Indigenous Design for Automatic Testing of Tensile Strength Using Graphical User Interface

Tensile Testing is a fundamental material test to measure the tenacity and tensile strength. Tensile strength means ability to take tensile stress. This Universal Testing Machine is designed using Dual Cylinder Technique in order to comply with the maximun load (tensile force) with the reduction of minimum physical effort and minimized losses.It is to provide material testing opportunity to the students of different institutions, locally and globally, at lowest price; so that they can have a comprehensive understanding of the testing procedures and examining material properties on practical grounds with a minimum expenditure. The testing mechanism is automated along with the inclusion of all necessary parameters and the movement of the members. Specimens of different engineering materials were used to carry out the test and their mechanical strength was tested and compared with the actual value

Leveraging machine learning to evaluate factors influencing vitamin D insufficiency in SLE patients: A case study from southern Bangladesh.

Vitamin D insufficiency appears to be prevalent in SLE patients. Multiple factors potentially contribute to lower vitamin D levels, including limited sun exposure, the use of sunscreen, darker skin complexion, aging, obesity, specific medical conditions, and certain medications. The study aims to assess the risk factors associated with low vitamin D levels in SLE patients in the southern part of Bangladesh, a region noted for a high prevalence of SLE. The research additionally investigates the possible correlation between vitamin D and the SLEDAI score, seeking to understand the potential benefits of vitamin D in enhancing disease outcomes for SLE patients. The study incorporates a dataset consisting of 50 patients from the southern part of Bangladesh and evaluates their clinical and demographic data. An initial exploratory data analysis is conducted to gain insights into the data, which includes calculating means and standard deviations, performing correlation analysis, and generating heat maps. Relevant inferential statistical tests, such as the Student's t-test, are also employed. In the machine learning part of the analysis, this study utilizes supervised learning algorithms, specifically Linear Regression (LR) and Random Forest (RF). To optimize the hyperparameters of the RF model and mitigate the risk of overfitting given the small dataset, a 3-Fold cross-validation strategy is implemented. The study also calculates bootstrapped confidence intervals to provide robust uncertainty estimates and further validate the approach. A comprehensive feature importance analysis is carried out using RF feature importance, permutation-based feature importance, and SHAP values. The LR model yields an RMSE of 4.83 (CI: 2.70, 6.76) and MAE of 3.86 (CI: 2.06, 5.86), whereas the RF model achieves better results, with an RMSE of 2.98 (CI: 2.16, 3.76) and MAE of 2.68 (CI: 1.83,3.52). Both models identify Hb, CRP, ESR, and age as significant contributors to vitamin D level predictions. Despite the lack of a significant association between SLEDAI and vitamin D in the statistical analysis, the machine learning models suggest a potential nonlinear dependency of vitamin D on SLEDAI. These findings highlight the importance of these factors in managing vitamin D levels in SLE patients. The study concludes that there is a high prevalence of vitamin D insufficiency in SLE patients. Although a direct linear correlation between the SLEDAI score and vitamin D levels is not observed, machine learning models suggest the possibility of a nonlinear relationship. Furthermore, factors such as Hb, CRP, ESR, and age are identified as more significant in predicting vitamin D levels. Thus, the study suggests that monitoring these factors may be advantageous in managing vitamin D levels in SLE patients. Given the immunological nature of SLE, the potential role of vitamin D in SLE disease activity could be substantial. Therefore, it underscores the need for further large-scale studies to corroborate this hypothesis

Adsorptive desulfurization of model oil using untreated, acid activated and magnetite nanoparticle loaded bentonite as adsorbent

The present research work focuses on a novel ultraclean desulfurization process of model oil by the adsorption method using untreated, acid activated and magnetite nanoparticle loaded bentonite as adsorbent. The parameters investigated are effect of contact time, adsorbent dose, initial dibenzothiophene (DBT) concentration and temperature. Experimental tests were conducted in batch process. Pseudo first and second order kinetic equations were used to examine the experimental data. It was found that pseudo second order kinetic equation described the data of the DBT adsorption onto all types of adsorbents very well. The isotherm data were analyzed using Langmuir and Freundlich isotherm models. The Langmuir isotherm model fits the data very well for the adsorption of DBT onto all three forms of adsorbents. The adsorption of DBT was also investigated at different adsorbent doses and was found that the percentage adsorption of DBT was increased with increasing the adsorbent dose, while the adsorption in mg/g was decreased with increasing the adsorbent dose. The prepared adsorbents were analyzed by scanning electron microscopy (SEM), energy dispersive X-ray spectrometry (EDX) and X-ray diffraction (XRD)