Objectives: To assess the effect of pelvic organ prolapse (POP) and/or stress urinary incontinence (SUI) on various domains of female sexual functions in patients before and after reconstructive surgery for these pelvic floor disorders. Methods: We conducted a quasi-experimental study of 126 women aged 25-65 years, presenting with POP / SUI, from January 1st 2019 to December 31st 2019 at Aga Khan University Hospital. POP surgery was performed only in patients with symptomatic POP ≥ stage 2 according to POP-Q (quantification). Sexual functions were assessed using Female Sexual Function Index (FSFI) questionnaire, among sexually active women at baseline and 18 months after surgery. Results: Mean age of the participants was 51.6, with a mean parity of four. Out of 126 patients, 31 patients underwent vaginal hysterectomy, pelvic floor repair and mid-urethral sling (MUS), 55 had vaginal hysterectomy with pelvic floor repair, 12 women had only pelvic floor repair and 10 patients had uterine suspension surgery for prolapse, while 18 patients underwent MUS operation alone for SUI. There was a statistically significant difference in female sexual functions after surgery for POP and/or SUI (p<0.01). This improvement was observed in both total and individual scores of each domain of FSFI with an overall improvement in sexual function from a mean of 18.5 pre-surgery to 20.8 post-surgery. Conclusions: This study reveals that women sexual functions are affected by POP and SUI and improve remarkably after reconstructive surgeries for these pelvic floor disorders. doi: https://doi.org/10.12669/pjms.37.4.3892 How to cite this:Abrar S, Mohsin R, Saleem H. Surgery for pelvic organ prolapse and stress urinary incontinence and female sexual functions: A quasi-experimental study. Pak J Med Sci. 2021;37(4):---------. doi: https://doi.org/10.12669/pjms.37.4.3892 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Signal frequency estimation is a fundamental problem in signal processing. Deep learning is a fundamental method to solve this problem. This paper used five deep learning methods and three datasets including different singles Single Tone (ST), Linear- Frequency-Modulated (LFM), and Quadratic-Frequency-Modulated (QFM). This signal is affected by Additive White Gaussian (AWG) noise and Additive Symmetric alpha Stable (SαS) noise. Geometric SNR (GSNR) is used to determine the impulsiveness of noise in a Gaussian and SαS noise mixture. Deep learning methods are Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Bi-Direction Long Short-Term Memory (BiLSTM), and Convolution Neural Network (1D-CNN & 2D-CNN). When compared to a deep learning classifier with few layers to get on high accuracy and complexity reduces for Instantaneous Frequency (IF) estimation, Linear Chirp Rate (LCR) estimation, and Quadratic Chirp Rate (QCR) estimation. IF estimation of ST signals, IF and LCR estimation of LFM signals, and IF, LCR, and QCR estimation of QFM signals. The accuracy of the ST dataset in GRU is 58.09, LSTM is 46.61, BiLSTM is 45.95, 1D-CNN is 51.48, and 2D-CNN is 54.13. The accuracy of the LFM dataset in GRU is 82.89, LSTM is 66.28, BiLSTM is 20%, 1D-CNN is 74.79, and 2D-CNN is 98.26. The accuracy of the QFM dataset in GRU is 78.76, LSTM is 67.8, BiLSTM is 69.91, 1D-CNN is 75.8, and 2D-CNN is 98.2. The results show that 2D-CNN is better than other methods for parameter estimation in LFM signals and QFM signals, and the GRU is better for parameter estimation in ST signals.

Coronary artery disease (CAD) is leading cause of mortality in the developing countries. This study was to determine the prevalence and awareness of risk factors for CAD in apparently healthy Government servants employed in the Azad Jammu and Kashmir (AJK) Secretariat Muzaffarabad. A cross-sectional community based survey was conducted involving 515 Government servants. The prevalence of CAD risk factors was assessed on the basis of questionnaire and medical examination. The self-reported risk factors, blood pressure and anthropometric data were recorded. Blood samples were obtained for laboratory investigation of blood sugar and cholesterol. Chi square test was used to find the association of different risk factors with hypertension. The odds ratios were calculated by applying multivariate logistic regression. Physical inactivity (53.98%) was the most dominant risk factor in the study group. The prevalence of hypertension on questionnaire was 16.3% and on medical examination was 21.94%; 32.62% were overweight or obese; 33.20% were smoking and 49.90% have exposure to cigarette; 31.68% of urban and 49.80% rural population employees had no CAD risk factors; 32.58% urban and 17.79% of rural population had more than two CAD risk factors. The most prevalent risk factors of CAD in our study population were physical inactivity, high cholesterol, obesity, hypertension, smoking and its exposure. Most of the government servants in AJK were aware of the major CAD risk factors. CAD was more prevalent in the employees of the urban community.

The typical scheme used to generated cryptographic key is a fuzzy extractor. The fuzzy extractor is the extraction of a stable data from biometric data or noisy data based on the error correction code (ECC) method. Forward error correction includes two ways are blocked and convolutional coding used for error control coding. "Bose_Chaudhuri_Hocquenghem" (BCH) is one of the error correcting codes employ to correct errors in noise data. In this paper use fuzzy extractor scheme to find strong key based on BCH coding, face recognition data used SVD method and hash function. Hash_512 converted a string with variable length into a string of fixed length, it aims to protect information against the threat of repudiation.