Weak ankle plantarflexors, in particular gastrocnemius, may have an important role in the presence of knee hyperextension. The results of this study did not support a role for weak hamstrings or quadriceps in knee hyperextension during gait. Further research is needed to clarify the role of gastrocnemius during the stance phase and to determine if strengthening weak gastrocnemius reduces knee hyperextension.

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With the tremendous demand for connectivity anywhere and anytime, existing network architectures should be modified. To cope with the challenges that arise due to the increasing flood of devices/users and a diverse range of application requirements, new technologies and concepts must be integrated to enable their benefits. Service providers and business companies are looking for new areas of research to enhance overall system performance. This paper gives a detailed survey about the recent 5G technologies, the solutions they provide, and the effect caused by their addition to current cellular networks. It is based on the three most important 5G concepts: Device to Device (D2D), Network Slicing (NS), and Mobile Edge Computing (MEC). This study proposes to design the future 5G networks by the integration of all three technologies. It is believed that spectrum efficiency, energy efficiency, and overall throughput will be greatly improved by using D2D. The system delay and computational load will be reduced as tasks will be handled by edge routers located at the base stations. Thus offloading the core network and the system capital expenses and operational expenses will be reduced significantly by slicing the network.

This review is aiming to discuss the risk factors which lead to the occurrence of PUD during the period from July 2018 to August 2018. The present review was conducted by searching in Medline, Embase, Web of Science, Science Direct, BMJ journal and Google Scholar for, researches, review articles and reports, published over the past years. Books published on peptic ulcers and on the pathogenesis of human disease were also included., were searched up to August 2018 for published and unpublished studies and without language restrictions, the selected studies were summarized and un reproducible studies were excluded. If several studies had similar findings, we randomly selected one or two to avoid repetitive results. On the basis of findings and results this review found the H. Pylori and the use of NSAIDs are the most common risk factors for developing PUD, and also the genetic, stress and comorbidity increase the risk of PUD occurrence so successful eradication and prevention of the risk factors should be conducted to prevent the presence of PUD and is complication.

[Purpose] The purpose of our study was to investigate the effect of different positions on pulmonary function test (PFT) values such as forced vital capacity (FVC) and forced expiratory volume in one second (FEV1) of asthmatic patients .[Subjects and Methods] Thirty subjects with severe asthma aged between 20–39 years were enrolled after they had signed a written consent. Subjects were selected using the inclusion criteria, and PFT were randomly administered. Spirometer measurements (FVC, FEV1) were taken in the supine, side lying on right, side lying on left, sitting and standing positions. Each measurement was taken three times, and the average values were analyzed. [Results] One- way analysis of variance (ANOVA) and Tukey’s Test (post hoc) for pair- wise comparison indicated that there was a significant difference in the FEV1 values of the asthmatic patients however a significant difference was obtained between standing and supine positions. There was also a significant difference in the FVC values between the standing and supine lying position in the pair -wise comparison. [Conclusion] This study showed standing is the best position for measuring FEV1 and FVC of asthmatic subjects. The more upright the position, the higher the FEV1 and FVC will be.

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Potato leaf disease detection in an early stage is challenging because of variations in crop species, crop diseases symptoms and environmental factors. These factors make it difficult to detect potato leaf diseases in the early stage. Various machine learning techniques have been developed to detect potato leaf diseases. However, the existing methods cannot detect crop species and crop diseases in general because these models are trained and tested on images of plant leaves of a specific region. In this research, a multi-level deep learning model for potato leaf disease recognition has developed. At the first level, it extracts the potato leaves from the potato plant image using the YOLOv5 image segmentation technique. At the second level, a novel deep learning technique has been developed using a convolutional neural network to detect the early blight and late blight potato diseases from potato leaf images. The proposed potato leaf disease detection model was trained and tested on a potato leaf disease dataset. The potato leaf disease dataset contains 4062 images collected from the Central Punjab region of Pakistan. The proposed deep learning technique achieved 99.75% accuracy on the potato leaf disease dataset. The performance of the proposed techniques was also evaluated on the PlantVillage dataset. The proposed technique is also compared with the state-of-the-art models and achieved significantly concerning the accuracy and computational cost.

The most aggressive form of brain tumor is gliomas, which leads to concise life when high grade. The early detection of glioma is important to save the life of patients. MRI is a commonly used approach for brain tumors evaluation. However, the massive amount of data provided by MRI prevents manual segmentation in a reasonable time, restricting the use of accurate quantitative measurements in clinical practice. An automatic and reliable method is required that can segment tumors accurately. To achieve end-to-end brain tumor segmentation, a hybrid deep learning model RMU-Net is proposed. The architecture of MobileNetV2 is modified by adding residual blocks to learn in-depth features. This modified Mobile Net V2 is used as an encoder in the proposed network, and upsampling layers of U-Net are used as the decoder part. The proposed model has been validated on BraTS 2020, BraTS 2019, and BraTS 2018 datasets. The RMU-Net achieved the dice coefficient scores for WT, TC, and ET of 91.35%, 88.13%, and 83.26% on the BraTS 2020 dataset, 91.76%, 91.23%, and 83.19% on the BraTS 2019 dataset, and 90.80%, 86.75%, and 79.36% on the BraTS 2018 dataset, respectively. The performance of the proposed method outperforms with less computational cost and time as compared to previous methods.