Educational Inequality in Rural and Urban Sindh

The key development objective of Pakistan, since its existence, has been to reduce poverty, inequality and to improve the condition of its people. While this goal seems very important in itself yet is also necessary for the eradication of other social, political and economic problems. The objective to eradicate poverty has remained same but methodology to analysing this has changed. It can be said that failure of most of the poverty strategies is due to lack of clear choice of poverty definition. A sound development policy including poverty alleviation hinges upon accurate and well-defined measurements of multidimensional socio-economic characteristics which reflect the ground realities confronting the poor and down trodden rather than using some abstract/income based criteria for poverty measurement. Conventionally welfare has generally been measured using income or expenditures criteria. Similarly, in Pakistan poverty has been measured mostly in uni-dimension, income or expenditures variables. However, recent literature on poverty has pointed out some drawbacks in measuring uni-dimensional poverty in terms of money. It is argued that uni-dimensional poverty measures are insufficient to understand the wellbeing of individuals. Poverty is a multidimensional concept rather than a unidimensional. Uni-dimensional poverty is unable to capture a true picture of poverty because poverty is more than income deprivatio

SPORTS AND SOCIAL WELL-BEING: PERCEPTION OF UNIVERSITIES’ PLAYERS

The purpose of the games and sports is to create a friendly and healthy environment for our up-to-the-minute generation and youth. It also helps the people to enhance their patience and control, and their mind-set toward other peoples of the society. Sports teach how to show tolerance and how to switch aggression. This study was quantitative in nature. In this research, population was students of the University of the Punjab. In this study researcher collected data through survey method and research instrument was rating scale. Statistical analysis showed that most of the players approved that sports promote the positive effects regarding social well-being

Value of elastography in differentiating benign from malignant breast lesions keeping histopathology as gold standard

Background: Breast cancer is the most common cancer in females, both in developed and developing countries. Pakistan has the highest breast cancer incidence rate in Asia. Guidelines recommend screening for detecting breast cancer with mammography and ultrasonography (US). Shear-wave elastography (SWE) is a newer technique that can aid additional characterization of breast lesions. Objective: The aim of this study was to determine the diagnostic accuracy of breast ultrasound elastography in differentiating benign from malignant breast lesions using histology diagnosis as the gold standard.Materials and methods: The study was conducted at the Abbasi Shaheed Hospital and Jinnah Post Graduate Medical Centre, Karachi. All consecutive patients undergoing breast biopsy and elastography of breast lesions were enlisted; 2 x 2 tables were used to measure the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of breast ultrasound elastography for differentiation of benign from malignant breast masses.Results: A total of 155 female patients were included with a mean age of 45.41 ± 14.24 years (range 20-70 years). On histological evaluation, 115 (74.2%) lesions were malignant and 40 (25.8%) were benign. The overall average mean elastography value was 108.45 kPa ± 52.75. The mean elastography (EMean) value for benign breast lesions was 48.96 kPa ± 42.32 and 132.78 kPa ± 42.32 for malignant lesions. The difference in mean elastography values of benign and malignant breast lesions was statistically significant (48.96 kPa ± 42.32 vs 32.78 kPa ± 42.32, P \u3c0.001). The area under the curve (AUC) was 0.952, optimal cutoff EMean value of 72 kPa and higher likelihood ratio was 9.41. A cutoff mean elastography (EMean) value of ≤ 72 kilopascal (kPa) for benign lesions had sensitivity 92.17%, specificity 90.4%, PPV 96.36%, NPV 80.0% and diagnostic accuracy 91.61%. Conclusion: Ultrasound elastography was found to have high sensitivity and specificity and diagnostic accuracy for differentiating benign from malignant breast lesions. Use of shear-wave elastography may increase malignancy detection rate by reducing the need for biopsy in benign breast lesions

Hospital-based ultra-sonographic prevalence and spectrum of thyroid incidentalomas in Pakistani population

Introduction: Thyroid incidentalomas (TIs) are clinically asymptomatic nodules found accidentally during imaging studies ordered for some other reasons. Being easily accessible, non-invasive, and inexpensive, thyroid ultrasound (US) is a key investigation in the management of thyroid nodules.Methods: This ultrasound-based cross-sectional study was performed in the radiology department of a major tertiary care hospital. Every second patient visiting the emergency department was a potential candidate for a thyroid ultrasound. Patients having ages greater than 20 years were included in the study.Results: A total of 250 patients were included in the study. Out of these, 175 were female and 75 were male. The majority (54.80%) were in the age group 21-30 years. Nodules were found in 65 (26%) patients and in the majority of cases (67.7%) they were multiple in number. Associated lymphadenopathy was seen in only one patient. Thyroid nodules were more common in females as compared to males (75.38% versus 24.62%). According to Thyroid Imaging and Reporting Data System (TI-RADS) classification, the majority of the nodules were falling in TI-RADS 1 (74%) followed by TI-RADS 3 (9.60%) and 4A (8.80%).Conclusion: The thyroid nodules are more commonly seen in females as compared to males. A significant association is seen between the frequency of thyroid nodules and increasing age. The majority of thyroid nodules fall in TI-RADS 1 category followed by TI-RADS 3 and 4A

Deep Learning-based Method for Enhancing the Detection of Arabic Authorship Attribution using Acoustic and Textual-based Features

Authorship attribution (AA) is defined as the identification of the original author of an unseen text. It is found that the style of the author’s writing can change from one topic to another, but the author’s habits are still the same in different texts. The authorship attribution has been extensively studied for texts written in different languages such as English. However, few studies investigated the Arabic authorship attribution (AAA) due to the special challenges faced with the Arabic scripts. Additionally, there is a need to identify the authors of texts extracted from livestream broadcasting and the recorded speeches to protect the intellectual property of these authors. This paper aims to enhance the detection of Arabic authorship attribution by extracting different features and fusing the outputs of two deep learning models. The dataset used in this study was collected from the weekly livestream and recorded Arabic sermons that are available publicly on the official website of Al-Haramain in Saudi Arabia. The acoustic, textual and stylometric features were extracted for five authors. Then, the data were pre-processed and fed into the deep learning-based models (CNN architecture and its pre-trained ResNet34). After that the hard and soft voting ensemble methods were applied for combining the outputs of the applied models and improve the overall performance. The experimental results showed that the use of CNN with textual data obtained an acceptable performance using all evaluation metrics. Then, the performance of ResNet34 model with acoustic features outperformed the other models and obtained the accuracy of 90.34%. Finally, the results showed that the soft voting ensemble method enhanced the performance of AAA and outperformed the other method in terms of accuracy and precision, which obtained 93.19% and 0.9311 respectively

Knowledge of breast cancer and breast self-examination practice among Yemeni female school teachers in Malaysia

Background: Breast cancer is the most frequent cancer and the second cause of cancer deaths among women worldwide, including Yemeni women. The purpose of this study is to determine knowledge on breast cancer and breast self-examination (BSE) practice among Yemeni female school teachers in Klang Valley, Malaysia. Methodology: A cross-sectional study was conducted among 163 Yemeni female schoolteachers in Malaysia between April 2017- May 2017. The inclusion criteria for this study are teachers who were teaching at the selected primary and secondary Arabic schools, aged 20 years old age and above, and teachers who signed consent form to participate in the study. Teachers who had a previous history of breast cancer or who were pregnant or lactating were excluded from the study. A simple random sampling method was utilized and data were collected via self-administered questionnaire by using a validated questionnaire, which was developed for this study. The questionnaire consisted of four sections, background information of respondents, knowledge on breast cancer, health belief model, practices related to breast cancer screening. Result: The response rate derived in this study was100 %. The mean age of respondents was 32.8 ±7.23 years, 128(78%) of them were married, 26 (15.9%) had family history of breast cancer and 34 (20.9%) of them previously participated in breast cancer education program. The majority of respondents 131(79.9%) had heard/read about breast cancer screening, but only 43(26.2%) practiced breast self-examination and 136(82.9%) had intention to practice BSE in the future. This study showed majority of respondents 121(74.2%) and 104(63.8%) had low level of knowledge on breast cancer and BSE practice, respectively. Univariate analysis showed that hear/read about breast cancer screening, participated in breast cancer education programs, were statistically associated with knowledge of breast cancer (p=0.001) (p=0.005) respectively, Also, hear/read about breast cancer screening (p=0.01), participated in breast cancer education program (p=0.003), and education level (p=0.01), were statistically associated with BSE practice. Conclusions: The findings showed that knowledge of Yemeni female school teachers towards breast cancer and rate of BSE practice are low. Targeted education should be implemented to improve knowledge of breast cancer and BSE practice to improve breast cancer prevention among this group

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study’s primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study’s outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model’s diagnostic accuracy for heart disease