Tissue remodeling is one of the most important and crucial biological process. Process in which tissue reorganization and renovation takes place is called tissue remodeling. Mean of recovery in human beings is tissue remodeling in which damaged tissue are replaced completely with new tissue or through tissue repairmen types physiological and pathological tissue remodeling are two derivatives of Tissue remodeling. Normal Tissue remodeling is referred to as Physiological tissue remodeling, however abnormal process which may lead to a disease is known as pathological tissue remodeling. From past till now different techniques like histopathology and chemicals were being used to identify abnormality in tissues. Which is a time taking and costly processes. There is no such computational method which can be used for the identification of the physiological and pathological tissue remodeling. The current article aims to develop a classification model which has ability to classify weather the given sequence is physiological or pathological process. Three classifiers RF, ANN and SVM will be used for practice and evaluation of proposed classification model.

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Crimes and criminal activities are increasing day by day and there are no proper criteria to search, detect, identify, and predict these criminals. Despite various surveillance cameras in different areas still, crimes are at a peak. The police investigation department cannot efficiently detect the criminals in time. However, in many countries for the sake of public and private security, the initiation of security technologies has been employed for criminal identification or recognition with the help of footprint identification, fingerprint identification, facial recognition, or based on other suspicious activity detections through surveillance cameras. However, there are limited automated systems that can identify the criminals precisely and get the accurate or precise similarity between the recorded footage images with the criminals that already are available in the police criminal records. To make the police investigation department more effective, this research work presents the design of an automated criminal detection system for the prediction of criminals. The proposed system can predict criminals or possibilities of being criminal based on Lombrosso's Theory of Criminology about born criminals or the persons who look like criminals. A deep learning-based facial recognition approach was used that can detect or predict any person whether he is criminal, or not and that can also give the possibility of being criminal. For training, the ResNet50 model was used, which is based on CNN and SVM Classifiers for feature extracting from the dataset. Two different labeled based datasets were used, having different criminals and noncriminals images in the database. The proposed system could efficiently help the investigating officers in narrowing down the suspects' pool.

A novel idea of electromechanical inverter (EMI) is proposed. This inverter minimize the complexity and cost of conventional rotating magnetic field inverters. The electromechanical inverter works on phenomena of rotating magnetic field in which changing flux in the external coils through dc motor induced an emf in the output coil. For controlling the speed of dc motor and rotation of coils, the adaptive control is used to avoid the saturation in magnetic field. The adaptive control system that is used for the electromechanical inverter (EMI) is model-reference adaptive control (MRAC) which has four parts i.e plant, reference model, adaptation mechanism and control law. An adaptation mechanism is designed with MIT rule of MRAC. The authenticity of proposed control technique for electromechanical inverter is verified by simulation results. The simulation result shows efficacy of proposed adaptive control technique using MATLAB

Background: Uterine prolapse (UP) is a prevalent chronic problem in Pakistani women of reproductive age. A huge number of UP cases can be prevented with knowledge, attitudes, and practices. Objectives: The study aimed to create awareness among married women by KAP set. Place and Duration: This survey was conducted in District Dera Ismail Khan, during 2020-2021. Methodology: A total of 290 married women in the age group 20-40 years were participants in this survey. The participants were interviewed face-to-face using a semi-structured questionnaire. Results: The maximum (60.68%) women participated from rural areas than from urban areas (39.31%) who faced the UP. The majority (36.56%) of respondents responded that uterine prolapse is caused due to weakness in pelvic muscles followed by delivery by an unskilled person (27.58%), giving birth at an early age (20.68%), frequent childbearing (10.68%), and constipation (4.48%). Out of 290 participants, 47.58, 22.06 and 30.68% of the respondents had a positive, moderate, and negative attitude towards uterine prolapse, respectively. Conclusion: The respondents' knowledge, attitudes, and practices toward UP had a statistically significant relationship with their marital status, age, educational status, and occupational status. Keywords: Uterine prolapse; Married Women; Attitude; Awareness; Knowledge; Pakistan.