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Over the recent years, Schizophrenia has become a serious mental disorder that is affecting almost 21 million people globally. There are different symptoms to recognize schizophrenia from healthy people. It can affect the thinking pattern of the brain. Delusions, hallucinations, and disorganized speech are the common symptoms of Schizophrenia. In this study, we have used electroencephalography (EEG) signals to analyze and diagnose Schizophrenia using machine learning algorithms and found that temporal features performed well as compared to statistical features. EEG signals are the best way to analyze this disorder as they are intimately linked with human thinking patterns and provide information about brain activities. The present work proposes a Machine Learning (ML) model based on Logistic Regression (LR) along with two feature extraction libraries Time Series Feature Extraction Library (TSFEL) and MNE Python toolkit to diagnose Schizophrenia from EEG signals. The results are analyzed based on 5 different sampling techniques. The dataset was cross-validated using leave one subject out cross-validation (LOSOCV) using Scikit learn and achieve greater accuracy, sensitivity, specificity, macro average recall, and macro f1 score on temporal features respectively.

The massive multiple-input-multiple-output (MIMO) is a key enabling technology for the 5G cellular communication systems. In massive MIMO (M-MIMO) systems few hundred numbers of antennas are deployed at each base station (BS) to serve a relatively small number of single-antenna terminals with multiuser, providing higher data rate and lower latency. In this paper, an M-MIMO communication system with a large number of BS antennas with zero-forcing beamforming is proposed for the improved spectral efficiency performance of the system. The zero forcing beamforming technique is used to overcome the interference that limits the spectral efficiency of M-MIMO communication systems. The simulation results authenticate the improvement in the spectral efficiency of M-MIMO system. The spectral efficiency value using zero-forcing beamforming is near to the spectral efficiency value with the nointerference scenario.

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Aim: To assess the association between premature rupture of membrane and maternal blood vitamin D levels. Study Design: A cross-sectional study. Place and Duration: Abbasi Shaheed Hospital, Karachi Medical and Dental College from April 2020 to April 2021. Methodology: Over the duration of one year, a total of 100 patients with vitamin D levels, as well as obstetrical abnormalities and risk factors, were monitored. In 88 pregnant women, vitamin D deficiency was observed. Pregnant women who have a thyroid disorder, such as thyroiditis or Grave's disease, or who had calcium or parathyroid disease in the past, or who need cardiac medication therapy,& diuretic particularly calcium channel blockers were excluded from the study. Results: Vitamin D deficiency was found in 88 pregnant women out of 100. It was more prevalent among housewives (86.36 percent) and multiparous women (68.0 percent). Pregnancy complications were present in 33.0 percent of cases such as preeclampsia, gestational hypertension, and diabetes, and PROM was less prevalent in the deficient group. Conclusion: Our data indicate that pregnant females are at a greater risk of Vitamin D deficiency, and associated pregnancy complications. The correlation between maternal vitamin D levels & preterm rupture of the membrane was not statistically significant.