Gender and Ultimatum in Pakistan: Revisited

Razzaque (2009) studied the role of gender in the ultimatum game by running experiments on students in various cities in Pakistan. He used standard confirmatory data analysis techniques, which work well in familiar contexts, where relevant hypotheses of interest are known in advance. Our goal in this paper is to demonstrate that exploratory data analysis is much better suited to the study of experimental data where the goal is to discover patterns of interest. Our exploratory re-analysis of the original data set of Razzaque (2009) leads to several new insights. While we re-confirm the main finding of Razzaque regarding the greater generosity of males, additional analysis suggests that this is driven by student subculture in Pakistan, and would not generalise to the population at large. In addition, we find strong effect of urbanisation. Our exploratory data analysis also offers considerable additional insights into the learning process that takes place over the course of a sequence of games. JEL Classification: C78, C81, C91, J16 Keywords: Ultimatum Game, Gender Differences, Exploratory Data Analysi

Gender and Misogyny in Classical Literature: Tracing the Cultural Roots of Misogynistic Attitudes and Their Modern Consequences

This paper looks into antiquity and determines the role of gender. Women representation in classical literature and what impact these early imprints cast on the later works. In Classical literature, Women are portrayed as the object of sex, rape and pleasures. Their individual existence is fairly vague but only in connection to male presence. This misogynistic approach took its roots in very early literature, and handed over to us with almost the same features. Female illustration in the Classical Literature is one of such misogynistic theories. Likewise, Reader’s response cannot be marginalized while discussing classical – gender – portrayal. Print-media shapes readers’ lives. How far is it true, needs to be carefully scrutinized in order to indulge in further discussion. Based upon these misogynistic theories, the inferences at the end are startling, presenting before us pathetic picture of social values and customs. We cannot negate the pen-power. No doubt it can mould reader’s mind according to writer’s thinking. The way writer thinks can become the way of thinking of the particular reader too. The whole process is very subtle and imperceptible, even reader himself cannot feel the changes happening in him by reading and absorbing the very ideas of the writer. This game of perception to the paradigm shift is played by the creators of literary caricatures. Statistics about women homicide leave a sour taste in our mouth. And an urge to replenish a whole new standard of writing upon sensitive issues. Continuing focus upon biased pieces of literature would at least diminish the effects, if cannot eliminate it altogether

Synthesis of pyrimidines, aromatic and heteroaromatic acids as Biginelli reaction catalysts

Aromatic as well as heteroaromatic acids were found to beexcellent catalysts for the Biginelli three component synthesisof dihydropyrimidinones. Benzoic acid, substituted benzoic acids, five- and six- membered heterocyclic acids can be used for this purpose

Comparison of protective effects of carvedilol and α-tocopherol on doxorubicin-induced cardiotoxocity

Background: Doxorubicin, an effective anticancer drug used to treat multiple solid tumours and childhood malignancies since many decades but its cardiac adverse effects limits its use in full therapeutic dose. The mechanism involved in cardiotoxicity is apoptosis of cardiomyocytes due to reactive oxidative stress. The study was conducted to compare the cardioprotective effects of carvedilol and α-Tocopherol and to detect myocardial injury at early stage.Methods: Cardiotoxicity was produced in a group of rabbits by single intravenous injection of doxorubicin; control group was treated with normal saline only. Third and fourth groups were pretreated with carvedilol 30 mg/kg bodyweight and α-Tocopherol 200 mg/kg bodyweight respectively for ten days before injection of doxorubicin.Results: Doxorubicin produced marked cardiotoxicity represented by raised levels of serum biomarkers (cTnI, LDH and CK-MB) and severe necrosis of cardiomyocytes on microscopic examination. Carvedilol and α-tocopherol pretreatment resulted in decreased serum levels of biomarkers and improved the histological picture of heart tissue.Conclusions: The outcome of doxorubicin chemotherapy can be made successful with the concurrent use of carvedilol or α-tocopherol. Although carvedilol has more pronounced cardioprotective effects perhaps due to its antioxidant activity in addition to antiapoptotic, antiproliferative and anti-inflammatory effects. Furthermore the quantitative cTnI estimation for detection of cardiotoxicity at early stage can lead to significant economic impact in management of cancer

Nitrofurantoin and Fosfomycin, effective oral empirical treatment options against multidrug resistant Escherichia coli

ObjectiveThe present study is designed to monitor antibiotic susceptibility pattern of Escherichia coli to assist in forecasting empirical therapy of urinary tract infection.MethodologyIt is a retrospective cross sectional study. It was carried out at Dow Diagnostic Research and Reference Laboratory for a period of 3 months from February 2017 to April 2017. A data of total 5000 urine culture and sensitivity test reports was taken from the medical record. The data was analyzed by SPSS version 16.ResultsOut of 5000 urine samples processed, 1565 showed significant bacterial growth. Escherichia coli was the most common pathogen isolated. Meropenem, Amikacin, Fosfomycin and Nitrofurantoin respectively were found to be the most sensitive antibiotics against Escherichia coli.Conclusion Fosfomycin and Nitrofurantoin are effective oral antibiotics against Escherichia coli causing urinary tract infection. The present study may help clinicians in making rational choice of empirical treatment of the patients

Predictive analytics in maternal health:a machine learning approach for classification of preeclampsia

The condition known as preeclampsia is a hypertensive disorder that occurs during pregnancy and has serious implications for both the mother and the fetus. Proper management of the condition depends on early detection of preeclampsia to make a correct prognosis. In this study, we classify pre-eclampsia using three datasets: two of which are the public datasets acquired from Mendeley and Kaggle, respectively, while the third is a real-world clinical dataset obtained from a local hospital. Recursive feature elimination, principal component analysis, correlation-based feature selection, and particle swarm optimization were used to select significant features from the predictor variables of the public datasets. To improve the classification performance, several models were created, with an emphasis on ensemble learning methods. Specifically, we propose three models: the alternative classification models include the Soft Decision Fusion Model, which applies soft-voting; the Stacking-Based Classifier, which is an ensemble stacking; and the Hybrid Soft Stacking Model. These models were assessed in detail concerning their quantitative indicators for the AUC-ROC criterion. The performance of our proposed models in the public datasets was an AUC-ROC of more than 95% and in the clinical dataset an even higher 96%. These ensemble methods accurately show that they have effective results in improving the precision and reliability of pre-eclampsia forecasts. With the help of real and public clinical data, the present work presents an effective and ecological approach that can help healthcare professionals make appropriate and timely decisions about the management of pre-eclampsia. In particular, the results of the Hybrid Soft Stacking Model look quite convincing in terms of predictive value, so the model could be considered a useful tool in the clinical context

Enhanced model for gestational diabetes mellitus prediction using a fusion technique of multiple algorithms with explainability

High glucose levels during pregnancy cause Gestational Diabetes Mellitus (GDM). The risks include cesarean deliveries, long-term type 2 diabetes, fetal macrosomia, and infant respiratory distress syndrome. These risks highlight the need for accurate GDM prediction. This research proposes a novel fusion model for early GDM prediction. It uses conventional Machine Learning (ML) and advanced Deep Learning (DL) algorithms. Subsequently, it combines the strengths of both ML and DL algorithms using various ensemble techniques. It incorporates a meta-classifier that further reinforces its robust prediction performance. The dataset is split into training and testing sets in a 70/30 ratio. The initial steps involve exploratory analysis and data preprocessing techniques such as iterative imputation and feature engineering. Subsequently, oversampling is applied to the training set to address class imbalance which ensures the model learns effectively. The testing set remains imbalanced to maintain the credibility of the model’s performance evaluation. The fusion model achieves an accuracy of 98.21%, precision of 97.72%, specificity of 98.64%, recall of 97.47%, F1 score of 97.59%, and an Accuracy Under the Curve (AUC) of 99.91%. The model exhibits efficiency with an average processing time of 0.06 s to predict GDM. These results outperform the previous studies using the same GDM prediction dataset and demonstrate the model's superior performance. Additionally, Explainable Artificial Intelligence (XAI) techniques are utilized to interpret the model’s decisions. They highlight the most influential features in GDM prediction and ensures transparency. The proposed fusion model can facilitate proactive GDM prediction to elevate GDM management and maternal–fetal health outcomes

Perceptions and Performance of dental students using Conventional and Virtual Microscopy in Oral Pathology

Virtual microscopy for showing histopathological slides have been in use for quite some time. This method of teaching is considered to be a good replacement for conventional microscopy using glass slides. This study was carried out to analyze the perception and performance of dental students regarding the use of conventional microscopic slides and virtual slides in relation to teaching and learning. Material and methods: Eighty undergraduate dental students who had studies the subject of oral pathology as a compulsory subject at Watim Dental college were invited to participate in the study. Students not willing to take part in the study were excluded. The questionnaires were duly filled and test was taken by the students using either virtual slides or glass slides. The data was collected and analyzed using SPSS 20. Results: A total of eighty undergraduate students participated in the study. The results showed that dental students had a higher acceptance rate (all P-value<0.001) for cases taught via virtual microscopy and they out performed in cases shown on virtual slides (p<0.01). Conclusion: In this study the students preferred teaching of oral histopathology via virtual slides over conventional glass slides and it also contributed more to their learning. Keyword: Conventional microscopy, dental students, virtual microscopy, oral patholog