CHANNEL MODELING FOR FIFTH GENERATION CELLULAR NETWORKS AND WIRELESS SENSOR NETWORKS

In view of exponential growth in data traffic demand, the wireless communications industry has aimed to increase the capacity of existing networks by 1000 times over the next 20 years. A combination of extreme cell densification, more bandwidth, and higher spectral efficiency is needed to support the data traffic requirements for fifth generation (5G) cellular communications. In this research, the potential improvements achieved by using three major 5G enabling technologies (i.e., small cells, millimeter-wave spectrum, and massive MIMO) in rural and urban environments are investigated. This work develops SPM and KA-based ray models to investigate the impact of geometrical parameters on terrain-based multiuser MIMO channel characteristic. Moreover, a new directional 3D channel model is developed for urban millimeter-wave (mmW) small cells. Path-loss, spatial correlation, coverage distance, and coherence length are studied in urban areas. Exploiting physical optics (PO) and geometric optics (GO) solutions, closed form expressions are derived for spatial correlation. Achievable spatial diversity is evaluated using horizontal and vertical linear arrays as well as planar 2D arrays. In another study, a versatile near-ground field prediction model is proposed to facilitate accurate wireless sensor network (WSN) simulations. Monte Carlo simulations are used to investigate the effects of antenna height, frequency of operation, polarization, and terrain dielectric and roughness properties on WSNs performance

A Hybrid Clustering and Classification Technique for Forecasting Short-Term Energy Consumption

Electrical energy distributor companies in Iran have to announce their energy demand at least three 3-day ahead of the market opening. Therefore, an accurate load estimation is highly crucial. This research invoked methodology based on CRISP data mining and used SVM, ANN, and CBA-ANN-SVM (a novel hybrid model of clustering with both widely used ANN and SVM) to predict short-term electrical energy demand of Bandarabbas. In previous studies, researchers introduced few effective parameters with no reasonable error about Bandarabbas power consumption. In this research we tried to recognize all efficient parameters and with the use of CBA-ANN-SVM model, the rate of error has been minimized. After consulting with experts in the field of power consumption and plotting daily power consumption for each week, this research showed that official holidays and weekends have impact on the power consumption. When the weather gets warmer, the consumption of electrical energy increases due to turning on electrical air conditioner. Also, con-sumption patterns in warm and cold months are different. Analyzing power consumption of the same month for different years had shown high similarity in power consumption patterns. Factors with high impact on power consumption were identified and statistical methods were utilized to prove their impacts. Using SVM, ANN and CBA-ANN-SVM, the model was built. Sine the proposed method (CBA-ANN-SVM) has low MAPE 5 1.474 (4 clusters) and MAPE 5 1.297 (3 clusters) in comparison with SVM (MAPE 5 2.015) and ANN (MAPE 5 1.790), this model was selected as the final model. The final model has the benefits from both models and the benefits of clustering. Clustering algorithm with discovering data structure, divides data into several clusters based on similarities and differences between them. Because data inside each cluster are more similar than entire data, modeling in each cluster will present better results. For future research, we suggest using fuzzy methods and genetic algorithm or a hybrid of both to forecast each cluster. It is also possible to use fuzzy methods or genetic algorithms or a hybrid of both without using clustering. It is issued that such models will produce better and more accurate results. This paper presents a hybrid approach to predict the electric energy usage of weather-sensitive loads. The presented methodutilizes the clustering paradigm along with ANN and SVMapproaches for accurate short-term prediction of electric energyusage, using weather data. Since the methodology beinginvoked in this research is based on CRISP data mining, datapreparation has received a gr eat deal of attention in thisresear ch. Once data pre-processing was done, the underlyingpattern of electric energy consumption was extracted by themeans of machine learning methods to precisely forecast short-term energy consumption. The proposed approach (CBA-ANN-SVM) was applied to real load data and resulting higher accu-racy comparing to the existing models. 2018 American Institute of Chemical Engineers Environ Prog, 2018 https://doi.org/10.1002/ep.1293

Investigating the Efficacy of Sumac Topical Solution Against Permethrin-resistant Human Head Lice

Background: The present study aimed at determining the efficacy of applying Rhus coriaria (Sumac) solution for the treatment of Permethrin-resistant head louse in patients, who used permethrin for at least 2 consecutive periods, but have not been cured.Methods: This study is a before-after clinical trial performed on 100 patients with pediculosis aged between 2 and 50 years old and both sexes. All patients had used Permethrin at least twice consecutively (with at least 14 days interval) according to correct instructions (on the first and 7th day), but they have not been cured. Each patient received 60ml of Rhus coriaria solution for 3 consecutive days, and the treatment was repeated again for another 3 days; then, the patients were followed-up on the 4th, 10th, and14th days after the treatment.Results: The results showed a significant difference in the severity of head lice infection and itching before the treatment and 14 days after the treatment (P<0.001).Conclusion: Rhus coriaria solution was more effective in eliminating head-louse infestations on 4, 10, and 14 days after the treatment and itching disappeared in most of the patients, while negligible complications were observed

Estimation of Sediment Transport Rate of Karun River (Iran)

Several types of sediment transport equations have been developed for estimation of the river sediment materials during the past decades. The estimated sediment from these equations is very different, especially when they applied for a specific river. Therefore, choice of an equation for estimation of the river sediment load is not an easy task. In this study 10 important sediment transport equations namely; Meyer-Peter and Muller (1948), Einstein (1950), Bagnold (1966), Engelund and Hansen (1972), Toffaleti (1969), Yang (1996), Van Rijn (2004), Wiuff (1985), Samaga et al. (1986) and Beg (1995) are used to estimate sediment load of the Karun  River in Iran.  The estimated sediment load compared with the measured field data by using statistical criteria such as root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R2). Results showed that Engelund and Hansen formula can provide reliable estimates of sediment load of the Karun River which have high suspended sediment load concentration with RMSE of 3725 ton/day, MAE of 1058.82 ton/day and R2 of 0.41. Bagnold and Wiuff formulas estimated the total sediment load 280 % and 700% more than the measured values and the Van Rijn, Tofaleti and Bagnold formulas estimated the sediment load 99 %, 71% and 93 % lower than the measured values, respectively. The comparison indicated that Samaga, Einstein, Tofaleti and Yang equations with low accuracy are not suitable for estimation of sediment load of the Karun River. The main reason for this difference is related to fact that the Karun River carries fine sediment (wash load) which these equations not considered it

Influence of Boundary Conditions and Defects on the Buckling Behavior of SWCNTs via a Structural Mechanics Approach

The effects of boundary conditions and defects on the buckling behavior of SWCNTs are investigated using a structural mechanics model. Due to the application of carbon nanotubes in different fields such as NEMS, where they are subjected to different loading and boundary conditions, an investigation of buckling behavior of nanotubes with different boundary conditions is necessary. Critical buckling loads and the effects of vacancy and Stone-Wales defects were studied for zigzag and armchair nanotubes with various boundary conditions and aspect ratios (length/diameter). The comparison of our results with those of the buckling of shells with cutouts indicates that vacancy defects in carbon nanotubes can most likely be modeled as cutouts of the shells. Finally, a hybrid vacancy defect and Stone-Wales defect are also developed, and their effect on the critical buckling loads is studied

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK

Bilateral Medial Medullary Stroke: A Challenge in Early Diagnosis

Bilateral medial medullary stroke is a very rare type of stroke, with catastrophic consequences. Early diagnosis is crucial. Here, I present a young patient with acute vertigo, progressive generalized weakness, dysarthria, and respiratory failure, who initially was misdiagnosed with acute vestibular syndrome. Initial brain magnetic resonance imaging (MRI) that was done in the acute phase was read as normal. Other possibilities were excluded by lumbar puncture and MRI of cervical spine. MR of C-spine showed lesion at medial medulla; therefore a second MRI of brain was requested, showed characteristic “heart appearance” shape at diffusion weighted (DWI), and confirmed bilateral medial medullary stroke. Retrospectively, a vague-defined hyperintense linear DWI signal at midline was noted in the first brain MRI. Because of the symmetric and midline pattern of this abnormal signal and similarity to an artifact, some radiologists or neurologists may miss this type of stroke. Radiologists and neurologists must recognize clinical and MRI findings of this rare type of stroke, which early treatment could make a difference in patient outcome. The abnormal DWI signal in early stages of this type of stroke may not be a typical “heart appearance” shape, and other variants such as small dot or linear DWI signal at midline must be recognized as early signs of stroke. Also, MRI of cervical spine may be helpful if there is attention to brainstem as well

Near-Ground Channel Modeling for Distributed Cooperative Communications

© 2016 IEEE. A computationally efficient near-ground field prediction model is proposed to facilitate realistic wireless sensor network (WSN) simulations. In this model, using the principle of Fresnel zones, path loss is split into three segments. Certain propagation mechanisms dominate in each part. The distances that define the edges of each section are derived theoretically. The model is validated against several experimental datasets obtained in open and forested areas. It is noticed that the proposed model has higher accuracy compared to existing analytical near-ground propagation models. This enhancement is achieved by careful assessment of key features relevant to near-grazing propagation such as diffraction loss due to obstruction of the first Fresnel zone and higher order waves produced by terrain irregularities. Monte Carlo simulations are used to investigate the effects of antenna height, frequency of operation, polarization, and terrain dielectric and roughness properties on WSNs performance. It is realized that antenna height is by far the most influential geometric parameter to low-altitude WSNs connectivity and average number of neighbors

Directional channel modelling for millimetre wave communications in urban areas

© 2018, The Institution of Engineering and Technology. Integrating Kirchhoff approximation (KA) and a ray-tracing (RT) algorithm, this study develops a new directional threedimensional channel model for urban millimetre-wave small cells. The authors study the path loss, spatial correlation, coverage distance, and coherence length for line-of-sight (LOS), obstructed LOS, and non-LOS (NLOS) scenarios in urban areas. Exploiting physical optics and geometric optics solutions, closed-form expressions for spatial correlation are derived. It is deduced that LOS availability, frequency, and surface roughness scale highly impact spatial diversity. In addition, using antenna arrays of moderate gain at both sides of the link, even under NLOS conditions, a typical urban cell size of 200 m is achievable