5G System Overview for Ongoing Smart Applications: Structure, Requirements, and Specifications.

Fifth-generation (5G) cellular networks are state-of-the-art wireless technologies revolutionizing all wireless systems. The fundamental goals of 5G are to increase network capacity, improve data rates, and reduce end-to-end latency. Therefore, 5G can support many devices connected to the Internet and realize the Internet of Things (IoT) vision. Though 5 G provides significant features for mobile wireless networks, some challenges still need to be addressed. Although 5 G offers valuable capabilities for mobile wireless networks, specific issues still need to be resolved. This article thoroughly introduces 5G technology, detailing its needs, infrastructure, features, and difficulties. In addition, it summarizes all the requirements and specifications of the 5G network based on the 3rd Generation Partnership Project (3GPP) Releases 15-17. Finally, this study discusses the key specifications challenges of 5G wireless networks.

Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach.

Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.

Intelligent Reflecting Surfaces Assisted UAV Communications for Massive Networks: Current Trends, Challenges, and Research Directions.

An intelligent reflecting surface (IRS) can intelligently configure wavefronts such as amplitude, frequency, phase, and even polarization through passive reflections and without requiring any radio frequency (RF) chains. It is predicted to be a revolutionizing technology with the capability to alter wireless communication to enhance both spectrum and energy efficiencies with low expenditure and low energy consumption. Similarly, unmanned aerial vehicle (UAV) communication has attained a significant interest by research fraternity due to high mobility, flexible deployment, and easy integration with other technologies. However, UAV communication can face obstructions and eavesdropping in real-time scenarios. Recently, it is envisaged that IRS and UAV can combine together to achieve unparalleled opportunities in difficult environments. Both technologies can achieve enhanced performance by proactively altering the wireless propagation through maneuver control and smart signal reflections in three-dimensional space. This study briefly discusses IRS-assisted UAV communications. We survey the existing literature on this emerging research topic for both ground and airborne scenarios. We highlight several emerging technologies and application scenarios for future wireless networks. This study goes one step further to elaborate research opportunities to design and optimize wireless systems with low energy footprint and at low cost. Finally, we shed some light on open challenges and future research directions for IRS-assisted UAV communication.

A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences.

In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Common Cold and other Acute Respiratory Infection (ARI) viruses. An investigation of the hyper-parameters including the optimizer type and the number of unit cells, was also performed to attain the best performance of the BRNN models. Results showed that the GRU BRNNs model was able to discriminate between SARS-CoV-2 and other classes of viruses with a higher overall classification accuracy of 96.8% as compared to that of the LSTM BRNNs model having a 95.8% overall classification accuracy. The best hyper-parameters producing the highest performance for both models was obtained when applying the SGD optimizer and an optimum number of unit cells of 80 in both models. This study proved that the proposed GRU BRNN model has a better classification ability for SARS-CoV-2 thus providing an efficient tool to help in containing the disease and achieving better clinical decisions with high precision.

Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach.

The sudden increase in patients with severe COVID-19 has obliged doctors to make admissions to intensive care units (ICUs) in health care practices where capacity is exceeded by the demand. To help with difficult triage decisions, we proposed an integration system Xtreme Gradient Boosting (XGBoost) classifier and Analytic Hierarchy Process (AHP) to assist health authorities in identifying patients' priorities to be admitted into ICUs according to the findings of the biological laboratory investigation for patients with COVID-19. The Xtreme Gradient Boosting (XGBoost) classifier was used to decide whether or not they should admit patients into ICUs, before applying them to an AHP for admissions' priority ranking for ICUs. The 38 commonly used clinical variables were considered and their contributions were determined by the Shapley's Additive explanations (SHAP) approach. In this research, five types of classifier algorithms were compared: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), Random Forest (RF), and Artificial Neural Network (ANN), to evaluate the XGBoost performance, while the AHP system compared its results with a committee formed from experienced clinicians. The proposed (XGBoost) classifier achieved a high prediction accuracy as it could discriminate between patients with COVID-19 who need ICU admission and those who do not with accuracy, sensitivity, and specificity rates of 97%, 96%, and 96% respectively, while the AHP system results were close to experienced clinicians' decisions for determining the priority of patients that need to be admitted to the ICU. Eventually, medical sectors can use the suggested framework to classify patients with COVID-19 who require ICU admission and prioritize them based on integrated AHP methodologies.

Split-leg modified lateral versus prone position in percutaneous nephrolithotomy: a prospective, randomized trial.

PURPOSE: Standard prone position (PP) during percutaneous nephrolithotomy (PNL) has multiple drawbacks. We aimed to compare PNLs performed in split-leg (SL) modified lateral position (MLP) and those performed in standard PP. METHODS: A prospective, randomized, unblind, double arm trial was conducted at a tertiary care academic medical center in Egypt, between November 2017 and October 2019. Adult patients with renal stones undergoing PNL were included. According to renal anatomy and stone complexity, stratified randomization was performed and study participants were allocated into either SL-MLP group or PP group. The stone free rate (SFR), total operative time, track formation time, fluoroscopy time, auxiliary procedures, and complications were compared. RESULTS: There were 61 patients in SL-MLP group and 63 patients in PP group. Both groups had similar baseline characteristics. The SFR was comparable between groups: 75.4% in SL-MLP group and 77.8% in PP group (p = 0.755). The mean total operative time was shorter and mean track formation time was longer in SL-MLP group (55.33 ± 20.73 vs. 98.49 ± 9.23, p < 0.001 and 7.89 ± 3.68 vs. 6.52 ± 1.77, p = 0.002). There was no significant difference in fluoroscopy time, total complication rates, hemoglobin reduction and need for blood transfusion between the groups. In SL-MLP group, all PNL procedures as well all the associated procedures were performed with the patients in the same position. CONCLUSION: SL-MLP PNL has a short operative time and similar SFR and complication rate compared to PP PNL. SL-MLP allowed antegrade and retrograde access to the urinary tract without patient repositioning.

Erectile function and gonadal hormones levels in men with end-stage renal disease: It's relevance to duration of haemodialysis.

In this study, we evaluated the relationship between haemodialysis (HD) duration and erectile function status and gonadal hormones serum levels in adult men with end-stage renal disease (ESRD). A total of 118 men with ESRD on chronic HD were eligible for analysis. The erectile dysfunction (ED) was defined and graded according to the international index of erectile function (IIEF-5) score. The serum levels of follicle stimulating hormones (FSH), luteinising hormone (LH), testosterone (TST), prolactin (PRL) and estradiol (E2) were measured using the standard laboratory technique. The mean age was 48.97 ± 14.68 years and mean duration of HD was 4.58 ± 3.03 years. The overall prevalence of ED was 78.8%; from them 31.2% had severe grade. The prevalence of ED was comparable in HD duration categories [≤5 years (79.7%), 5-10 years (76.5%), >10 years (80.0%); p > 0.05]. The percentage of abnormal serum levels of FSH, LH, TST, PRL, E2 were 5.1%, 1.6%, 18.6%, 90.7% and 0.0% respectively. No significant relationships were observed between HD duration and IIEF-5 score or gonadal hormones serum levels (p < 0.05). We concluded that HD duration has no effect on erectile function status and gonadal hormones serum levels. Other factors may be relevant to these conditions in this particular group of patients.