Numerical simulation of dispersion around a cubic building: characterization of wind-induced pressure coefficients on cube using Standard K-ε Model

The aim of the work is to determine the total pressure coefficient ( ), the behavior of pressure and pressure coefficient  near the walls of cube (roof and floor of cube) to study the effect of  on the behavior of flow and reverse flow on the top of cube due to wind tunnel, which allows the pressure due to the wind at the cube faces (negative pressure ( < 0) or overpressure (  > 0) compared to the static pressure (atmospheric pressure)) to be quantified. This coefficient depends on the wind velocity (W) upstream the cube (velocity in the wind tunnel), on the geometry of obstacle (which is cube used here) and on the attack angle of wind. DOI: 10.7176/MTM/9-2-06

Drone-Assisted Wireless Communications

In order to address the increased demand for any-time/any-where wireless connectivity, both academic and industrial researchers are actively engaged in the design of the fifth generation (5G) wireless communication networks. In contrast to the traditional bottom-up or horizontal design approaches, 5G wireless networks are being co-created with various stakeholders to address connectivity requirements across various verticals (i.e., employing a top-to-bottom approach). From a communication networks perspective, this requires obliviousness under various failures. In the context of cellular networks, base station (BS) failures can be caused either due to a natural or synthetic phenomenon. Natural phenomena such as earthquake or flooding can result in either destruction of communication hardware or disruption of energy supply to BSs. In such cases, there is a dire need for a mechanism through which capacity short-fall can be met in a rapid manner. Drone empowered small cellular networks, or so-called \quotes{flying cellular networks}, present an attractive solution as they can be swiftly deployed for provisioning public safety (PS) networks. While drone empowered self-organising networks (SONs) and drone small cell networks (DSCNs) have received some attention in the recent past, the design space of such networks has not been extensively traversed. So, the purpose of this thesis is to study the optimal deployment of drone empowered networks in different scenarios and for different applications (i.e., in cellular post-disaster scenarios and briefly in assisting backscatter internet of things (IoT)). To this end, we borrow the well-known tools from stochastic geometry to study the performance of multiple network deployments, as stochastic geometry provides a very powerful theoretical framework that accommodates network scalability and different spatial distributions. We will then investigate the design space of flying wireless networks and we will also explore the co-existence properties of an overlaid DSCN with the operational part of the existing networks. We define and study the design parameters such as optimal altitude and number of drone BSs, etc., as a function of destroyed BSs, propagation conditions, etc. Next, due to capacity and back-hauling limitations on drone small cells (DSCs), we assume that each coverage hole requires a multitude of DSCs to meet the shortfall coverage at a desired quality-of-service (QoS). Hence, we consider the clustered deployment of DSCs around the site of the destroyed BS. Accordingly, joint consideration of partially operating BSs and deployed DSCs yields a unique topology for such PS networks. Hence, we propose a clustering mechanism that extends the traditional Mat\'{e}rn and Thomas cluster processes to a more general case where cluster size is dependent upon the size of the coverage hole. As a result, it is demonstrated that by intelligently selecting operational network parameters such as drone altitude, density, number, transmit power and the spatial distribution of the deployment, ground user coverage can be significantly enhanced. As another contribution of this thesis, we also present a detailed analysis of the coverage and spectral efficiency of a downlink cellular network. Rather than relying on the first-order statistics of received signal-to-interference-ratio (SIR) such as coverage probability, we focus on characterizing its meta-distribution. As a result, our new design framework reveals that the traditional results which advocate lowering of BS heights or even optimal selection of BS height do not yield consistent service experience across users. Finally, for drone-assisted IoT sensor networks, we develop a comprehensive framework to characterize the performance of a drone-assisted backscatter communication-based IoT sensor network. A statistical framework is developed to quantify the coverage probability that explicitly accommodates a dyadic backscatter channel which experiences deeper fades than that of the one-way Rayleigh channel. We practically implement the proposed system using software defined radio (SDR) and a custom-designed sensor node (SN) tag. The measurements of parameters such as noise figure, tag reflection coefficient etc., are used to parametrize the developed framework

New Rules in the Amended Jordanian Landlords and Tenants Act No 17/2009

The Jordanian legislature amended the Landlords and Tenants Act No 17, 2009; it includes new rules which represent a significant change in the relationship between landlords and tenants. In an analytical and critical approach, this research explores these new rules, especially the rules concerning the gradual termination of lease contracts concluded before 31/8/2000, in a period from 12/21/2010 to 12/31/2015. The rules also stipulate an increase in the rent ranging from 1% to 6%. The study furthermore discusses the landlords\u27 rights such as the new procedures set in the amended Tenant Act, by which the landlord can undertake in case of the tenant\u27s refusal to evacuate or hand over the leased property in the due time The study also points out as to how the lease contract is considered an executed document, and the right to proceed to interim relief

Law in the Corona and post-Corona Era, Flexibility and Efficiency Test of the Qatari Laws: Horizontal Overview

Abstract Out of the sudden. Coronavirus – Covid-19 erupted few months ago to challenge all human traditions, health care systems, norms and life styles. Given the fact that the pandemic is still in the process of spreading, it seems early to predict the extent to which it will change human perspectives in dealing with universal crisis, and to say the least, Coronavirus pandemic will strictly tests human readiness and ability to foresee and face challenges presented by any factor threatening human beings worldwide. Despite the fact that Coronavirus pandemic is a global disaster that the world still struggling with, there is a story to tell about the State of Qatar legal response to the Covid-19 crisis. The Qatari experience in confronting Coronavirus crisis has many points of strengths and weaknesses. These strengths should be enhanced and adopted while weaknesses may be improved to ensure readiness, flexibility and efficiency while confronting challenges affecting society at large. The ongoing Covid-19 pandemic presents tremendous challenges to the state and its legal framework; almost all branches of law face these challenges at different levels. Applying a horizontal overview, this Article will briefly present and critically assess the Qatari legal response to the crisis to draw the attention of competent authorities to enhance, strengths, and overcome weaknesses, which ultimately will improve flexibility as well as efficiency of its national legal frameworks in times of crisis. Keywords: Law, Crisis, Covid-19, Coronavirus, Flexibility and Efficiency test, Qatar Law

A Weft Knit Data Glove

Rehabilitation of stoke survivors can be expedited by employing an exoskeleton. The exercises are designed such that both hands move in synergy. In this regard often motion capture data from the healthy hand is used to derive control behaviour for the exoskeleton. Therefore, data gloves can provide a low-cost solution for the motion capture of the joints in the hand. However, current data gloves are bulky, inaccurate or inconsistent. These disadvantages are inherited because the conventional design of a glove involves an external attachment that degrades overtime and causes inaccuracies. This paper presents a weft knit data glove whose sensors and support structure are manufactured in the same fabrication process thus removing the need for an external attachment. The glove is made by knitting multifilament conductive yarn and an elastomeric yarn using WholeGarment technology. Furthermore, we present a detailed electromechanical model of the sensors alongside its experimental validation. Additionally, the reliability of the glove is verified experimentally. Lastly, machine learning algorithms are implemented for classifying the posture of hand on the basis of sensor data histograms

Performance Analysis of UAV Enabled Disaster Recovery Networks: A Stochastic Geometric Framework Based on Cluster Processes

In this paper, we develop a comprehensive statistical framework to characterize and model large-scale unmanned aerial vehicle-enabled post-disaster recovery cellular networks. In the case of natural or man-made disasters, the cellular network is vulnerable to destruction resulting in coverage voids or coverage holes. Drone-based small cellular networks (DSCNs) can be rapidly deployed to fill such coverage voids. Due to capacity and back-hauling limitations on drone small cells (DSCs), each coverage hole requires a multitude of DSCs to meet the shortfall coverage at a desired quality-of-service. Moreover, ground users also tend to cluster in hot-spots in a post-disaster scenario. Motivated by this fact, we consider the clustered deployment of DSCs around the site of a destroyed BS. Joint consideration partially operating BSs and deployed DSCs yields a unique topology for such public safety networks. Borrowing tools from stochastic geometry, we develop a statistical framework to quantify the down-link performance of a DSCN. Our proposed clustering mechanism extends the traditional Matern and Thomas cluster processes to a more general case, where cluster size is dependent upon the size of the coverage hole. We then employ the newly developed framework to find closed-form expressions (later verified by Monte-Carlo simulations) to quantify the coverage probability, area spectral efficiency, and the energy efficiency for the down-link mobile user. Finally, we explore several design parameters (for both of the adopted cluster processes) that address optimal deployment of the network (i.e., number of drones per cluster, drone altitudes, and transmit power ratio between the traditional surviving base stations and the drone base stations)

Intelligent Solar Forecasts: Modern Machine Learning Models & TinyML Role for Improved Solar Energy Yield Predictions

The advancement of sustainable energy sources necessitates the development of robust forecasting tools for efficient energy management. A prominent player in this domain, solar power, heavily relies on accurate energy yield predictions to optimize production, minimize costs, and maintain grid stability. This paper explores an innovative application of tiny machine learning to provide real-time, low-cost forecasting of solar energy yield on resource-constrained edge internet of things devices, such as micro-controllers, for improved residential and industrial energy management. To further contribute to the domain, we conduct a comprehensive evaluation of four prominent machine learning models, namely unidirectional long short-term memory, bidirectional gated recurrent unit, bidirectional long short-term memory, and simple bidirectional recurrent neural network, for predicting solar farm energy yield. Our analysis delves into the impacts of tuning the machine learning model hyperparameters on the performance of these models, offering insights to improve prediction accuracy and stability. Additionally, we elaborate on the challenges and opportunities presented by the implementation of machine learning on low-cost energy management control systems, highlighting the benefits of reduced operational expenses and enhanced grid stability. The results derived from this study offer significant implications for energy management strategies at both household and industrial scales, contributing to a more sustainable future powered by accurate and efficient solar energy forecasting

KINETIC CHANGES OF KIDNEY FUNCTION TESTS AMONG PATIENTSWITH KIDNEY FAILURE IN ROYAL MEDICAL SERVICES

Introduction: Kidney function tests are requested to monitor the general status of kidney health in general, and particularly to assess renal failure status. Objectives: To investigate the efficacy of therapeutic options for renal failure patients through studying changes in selected laboratory investigations.. Methodology: A retrospective study design was involved to review files of patients with kidney failure at Royal Medical Services. A total of 263 files were reviewed for kidney function tests over a period of three months. After the end of data collection, data were analyzed employing SPSS V20. The representation of data was as means and standard deviations. Kinetic changes were tested using paired T-test. Significance between variables was considered at an alpha < 0.05. Results Among study variable including Hematocrit (HCT), mean cell volume (MVC), blood urea nitrogen (BUN), creatinine, phosphorous, calcium, albumin, sodium, and potassium, there were insignificant changes except for BUN (p=0.004), sodium (p=0.013), and potassium (p=0.000). Conclusion From the results, following changes in the level of kidney function tests help in assessment of renal failure status as the progression of disease can be monitored. Understanding and comparing various laboratory findings help in better monitoring of clinical status of patients

Study The Overprescription Of Proton Pump Inhibitors And Their Relation With Recurrent Community Aquired Infections In Outpatient Refilled Prescriptions Of Chronic Diseases Patients

Background: proton pump inhibitors are widely used worldwide and studies have demonstrated that the use of PPIs to be associated with various diseases such as several types of infection. Study objectives: to explore the effect of using PPIs on patients through studying some inflammatory biomarkers including WBC, neutrophil count, ESR, CRP, and IL-6. Methods and subjects: a retrospective study design was followed to collect data from study participants. The study included 62 patients receiving PPIs and 60 persons without being prescribed for PPIs. A working sheet was created for each patient and included the following information: age, WBC, neutrophil count, ESR, CRP, and IL-6. Data analysis was carried out using SPSS version 20. The relationship between variables was tested using independent T test. Significance was considered at alpha level < 0.05. Study findings: age was not varied significantly between study group and control group. All inflammatory biomarkers under study were significantly elevated in study group compared with control group. Conclusions: the findings of the present study showed that the use of PPIs was associated significantly with increased inflammatory biomarkers. We think that health settings should pay much attention to the role of pharmacists and pharmacy doctors to increase the awareness about the use of PPIs