Mohamed E. Mostafa

Bio: Mohamed E. Mostafa is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Physics & Combustion. The author has an hindex of 9, co-authored 35 publications receiving 663 citations. Previous affiliations of Mohamed E. Mostafa include Cardiff University & Cairo University.

Applications of nanofluids in solar energy: A review of recent advances

Abstract: Solar energy systems (SESs) are considered as one of the most important alternatives to conventional fossil fuels, due to its ability to convert solar energy directly into heat and electricity without any negative environmental impact such as greenhouse gas emissions. Utilizing nanofluid as a potential heat transfer fluid with superior thermophysical properties is an effective method to enhance the thermal performance of solar energy systems. The purpose of this review paper is the investigation of the recent advances in the nanofluids’ applications in solar energy systems, i.e., solar collectors (SCs), photovoltaic/thermal (PV/T) systems, solar thermoelectric devices, solar water heaters, solar-geothermal combined cooling heating and power system (CCHP), evaporative cooling for greenhouses, and water desalination.

The significance of pelletization operating conditions: An analysis of physical and mechanical characteristics as well as energy consumption of biomass pellets

Abstract: Biomass raw materials are widely regarded as a significant source of renewable energy, which significantly reduces the dependence on traditional fossil fuels, especially in the case of countries that are able to obtain biomass from various sources. Recently, pelletization has been widely used for mass and energy densification of biomass to overcome the problems associated with raw material use. As the global pellet market has developed quickly, the use of wood residues became no longer sufficient to fulfill the market needs. The standards of pellets provide limits for both physical and mechanical characteristics of produced pellets. Characteristics of produced pellets depend mainly on the feedstock characteristics as particle size and moisture content and operating conditions as applied pressure and die temperature. Thus, this paper provides rich information on the factors affecting the physical and mechanical properties of granules included in pellets. The main goal of the paper is to review the latest and comprehensive research on the physical and mechanical properties of most types of single and mixed pellets from biomass. The analysis of the effect of properties, adhesives, humidity, pressure and temperature as well as the physical and mechanical properties of the pellets studied was carried out. In addition, the critical and optimal values of various factors for different materials in which the following is of importance: high quality of pellets and biomass from which they are produced were analyzed. The principle and effect of applying post-production conditions as steam explosion and torrefaction on the characteristics of the pellets were reviewed in details. Moreover, this study proposes some recommendations for further development of the pelletization analysis and characteristics.

Kinetic Parameters Determination of Biomass Pyrolysis Fuels Using TGA and DTA Techniques

Abstract: A TG/DTG and DTA measurements are used to determine the kinetics of the thermal decomposition of two Egyptian biomasses (sugarcane bagasse and cotton stalks powders) at three heating rates of 10, 15 and 20 °C/min. Two distinct reaction zones were observed for the two biomasses. The direct Arrhenius plot method and integral method were applied to (TG/DTG) analysis for determination of kinetic parameters: activation energy, pre-exponential factor, and order of reaction. The weight loss curve showed that pyrolysis of sugarcane bagasse and cotton stalks took place mainly in the range of 200–500 °C. Also, the activation energy of a phase transition can be calculated directly from the DTA thermogram of each biomass material. Heating rates had little effect on the pyrolysis process, but the peak of the weight loss rate in the DTG curves shifted towards higher temperature with heating rate. The activation energy of the sugarcane bagasse powder obtained by the direct Arrhenius plot method are 48.25, 57.15 and 45.35 kJ/mol for the heat rate of 10, 15 and 20 °C/min, respectively. On the other side, the integral method shows larger values of the activation energy for sugarcane bagasse (82.5, 78.5 and 56.7 kJ/mol for the heat rate of 10, 15 and 20 °C/min, respectively). The activation energy of the cotton stalks powder obtained by the direct Arrhenius plot method are 100, 80 and 68 kJ/mol for the heat rate 10, 15 and 20 °C/min, respectively, but the integral method shows larger values of activation energy (100, 107 and 101 kJ/mol for the heat rate of 10, 15 and 20 °C/min, respectively). The calculated activation energy by DTA analysis was found to be 81.77 and 84.75 kJ/mol for sugarcane bagasse and cotton stalks, respectively. These values are, to some extent, in agreement with the data obtained by direct and integral methods. The cotton stalks are more reactive than the sugarcane bagasse.

On average, a professional rugby union player is more likely than not to sustain a concussion after 25 matches

Abstract: Objectives To investigate concussion injury rates, the likelihood of sustaining concussion relative to the number of rugby union matches and the risk of subsequent injury following concussion. Methods A four-season (2012/2013–2015/2016) prospective cohort study of injuries in professional level (club and international) rugby union. Incidence (injuries/1000 player-match-hours), severity (days lost per injury) and number of professional matches conferring a large risk of concussion were determined. The risk of injury following concussion was assessed using a survival model. Results Concussion incidence increased from 7.9 (95% CI 5.1 to 11.7) to 21.5 injuries/1000 player-match-hours (95% CI 16.4 to 27.6) over the four seasons for combined club and international rugby union. Concussion severity was unchanged over time (median: 9 days). Players were at a greater risk of sustaining a concussion than not after an exposure of 25 matches (95% CI 19 to 32). Injury risk (any injury) was 38% greater (HR 1.38; 95% CI 1.21 to 1.56) following concussion than after a non-concussive injury. Injuries to the head and neck (HR 1.34; 95% CI 1.06 to 1.70), upper limb (HR 1.59; 95% CI 1.19 to 2.12), pelvic region (HR 2.07; 95% CI 1.18 to 3.65) and the lower limb (HR 1.60; 95% CI 1.21 to 2.10) were more likely following concussion than after a non-concussive injury. Conclusion Concussion incidence increased, while severity remained unchanged, during the 4 years of this study. Playing more than 25 matches in the 2015/2016 season meant that sustaining concussion was more likely than not sustaining concussion. The 38% greater injury risk after concussive injury (compared with non-concussive injury) suggests return to play protocols warrant investigation.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Standards of Medical Care in Diabetes

Abstract: XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.