The Quality Of Saudi Accreditation Standards For Distance Learning: Benchmarking And Expert Validation

The quality of distance learning is a concern among different stakeholders. An online learning degree is recognized in some countries while it is not accredited in others. Saudi Arabia is one of these countries that have skepticism in the quality of distance learning. It also has specific conditions for accrediting distance learning programs. Saudi Arabia recently has developed accreditation standards to ensure the quality of this learning mode but Saudi universities have not adopted the standards yet. Thus, the quality of these standards has not been tested yet. Therefore, this study investigates the quality of these standards by applying the methodology of benchmarking to compare their quality to frequently cited quality models for online learning and to aspirational countries in the West (US, UK, and Australia) and to peer countries in Asia (South Korea, Malaysia, and Sri Lanka) and Arabic Region (Jordan and United Arab Emirates (UAE)). It also explores the differences and similarities in the regulations of distance learning accreditation between these 8 countries and Saudi Arabia. The study also validates the standards in a survey design using experts’ rating to the relevance and importance of the Saudi standards for quality distance learning. The findings revealed an overall quality of the Saudi standards based on benchmarking and experts’ rating. Suggestions have been made to improve or change very few quality indicators. The regulations and rules for accrediting distance learning in Saudi Arabia are found to be strict in comparison to other countries. Therefore, the study also recommended policy makers in Saudi Arabia to adopt some of the regulations and standards of distance learning accreditation available in some of the aspirational and peer countries. Other recommendations have been suggested to different stakeholders including higher education institutions, instructional designers, and program directors

Modeling, Analysis and Optimization of the Gas-Phase Methanol Synthesis Process

Methanol synthesis has been the subject of many improvements over the last decades since it became more cost effective and scalable than earlier high pressure technology. The synthesis of methanol from syngas has conventionally been carried out in adiabatic quench-type reactor in the gas phase where the only way to moderate the temperature is to inject shots of syngas at various position of the rector. However, because of the highly exothermic behavior of methanol synthesis reactions, the dissipation of heat has been a bottle-neck in the reactor design, and reactor configurations have a tendency to be complicated. This dissertation is divided into three parts presents a mathematical model of double-tube methanol reactor which was developed through cooperation between Mitsubishi Heavy Industries (MHI) and Mitsubishi Gas Company (MGC), methanol synthesis process flowsheet was developed and fully integrated with the Genetic Algorithms that generated a set of optimal operating conditions with respect to upper and lower limits and several constraints, and a dynamic optimization approaches to derive the ideal operating conditions for a Lurgi type reactor in the presence of catalyst deactivation. The first part of dissertation concentrates on the Mitsubishi Methanol “superconverter” which has a design capability to efficiently remove the heat generated by the exothermic reactions in methanol synthesis and improves methanol production by at least 3% more than the conventional single-tube converter. This converter is operated under milder conditions, especially at the end of the reactor, allows the catalyst to last for a longer period. This leads to process intensification and allows for the use of a compact distillation step. In addition, this new design has the advantage of preheating the feed gas to the reactor by having the inner tubes replace the feed gas preheater. The predicted methanol concentration and temperature profiles indicate that an increase in temperature is accompanied with a reduction in the methanol equilibrium concentration and hence limiting the profitability in the industrial plant. The use of a double-tube reactor is shown to be able to overcome this limitation. The novelty lies in a process modification which employs an inner tube that is disposed in the reactor and then the catalyst is charged into a circular space surrounded by the reaction tube on one side and inner tube on the other side. Simulation studies show that this design allows the temperature to increase gradually and, hence, delays the equilibrium to be reached to the end of the reactor. In other words, more methanol is produced and less byproducts. The second part of the dissertation concentrates on a multi-objective optimization applied for the operating conditions of the methanol synthesis loop via a multi-stage fixed bed adiabatic reactor system with an additional inter-stage CO2 quenching stream to maximize methanol production while reducing CO2 emissions. The model prediction for the methanol synthesis loop at steady state showed good agreement against data from an existing commercial plant. Later, the process flowsheet was developed and fully integrated with the Genetic Algorithms Toolbox that generated a set of optimal operating conditions with respect to limits and linear constraints. The results showed methanol production was improved by injecting shots of carbon dioxide recovered from the reformer at various reactor locations. The third part of the dissertation concentrates on a dynamic optimization approach derived the ideal operating conditions for a Lurgi-type methanol reactor in the presence of catalyst deactivation are proposed to determine the optimal use of recycle ratio of CO2 and shell coolant temperature without violating any process constraints. This study proposes a new approach based on a hybrid algorithm combining genetic algorithm (GA) and generalized pattern search (GPS) derivative-free methodologies to provide a sufficiently good solution to this dynamic optimization problem. The hybrid GA-GPS algorithm has the advantage of sequentially combining GA and GPS logics; while GA, as the most popular evolutionary algorithm, effectively explore the landscape of the fitness function and identify promising areas of the search space, GPS efficiently search existing basins in order to find an approximately optimal solution. The simulation results showed that implementing the shell temperature trajectory derived by the proposed approach with 5% recycle ratio of CO2 increased the production of methanol by approximately 2.5% compared to the existing operating conditions

Compartment Fire Toxicity: Measurements and Aspects of Modelling

Fire statistics from the UK and the USA attribute 60% to 70% of fire fatalities in dwellings to the inhalation of fire toxic smoke. The objective of this project was to provide more toxic yield data from typical compartment fires and in the process develop a methodology for faster generation of such data on bench scale apparatus. The models for overall toxicity assessment (for irritants and asphyxiants) were reviewed and the reported threshold limits for typical smoke toxicants, were collected, categorised and compared for increasing levels of harm. An extensive database was created of yields of toxic species from different materials and under different fire conditions. This highlighted the need for more yield data for under-ventilated fires in compartments. Eight full scale tests were carried out in a room enclosure with ventilation through a corridor to a front access door. Fire loads were wood pallets, cotton linen and towels, typical living room furniture and diesel. The fires were allowed to become fully developed before extinguishment by the local FRS team. Toxic concentrations were monitored in the hot layer and the corridor (through a heated sampling line) using a heated FTIR analyser, calibrated for 65 species. An emissions based model, developed as part of this work, was used to quantify the equivalence ratio and also the toxic species yields, even for the cases where the fuel mass loss rate was unknown. An important finding was the overwhelming contribution of Acrolein and Formaldehyde in most tests, in exceeding the impairment of escape threshold. The modified controlled atmosphere cone calorimeter showed comparable results to the full scale tests for lean burning combustion however it proved difficult at this stage to produce combustion in the rich burning regime and further development of the methodology is needed

Brain epilepsy seizure detection using bio-inspired krill herd and artificial alga optimized neural network approaches

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. Nowadays, Epilepsy is one of the chronic severe neurological diseases; it has been identified with the help of brain signal analysis. The brain signals are recorded with the help of electrocorticography (ECoG), Electroencephalogram (EEG). From the brain signal, the abnormal brain functions are a more challenging task. The traditional systems are consuming more time to predict unusual brain patterns. Therefore, in this paper, effective bio-inspired machine learning techniques are utilized to predict the epilepsy seizure from the EEG signal with maximum recognition accuracy. Initially, patient brain images are collected by placing the electrodes on their scalp. From the brain signal, different features are extracted that are analyzed with the help of the Krill Herd algorithm for selecting the best features. The selected features are processed using an artificial alga optimized general Adversarial Networks. The network recognizes the intricate and abnormal seizure patterns. Then the discussed state-of-art methods are examined simulation results

Oxidative stress contributes to cobalt oxide nanoparticles-induced cytotoxicity and DNA damage in human hepatocarcinoma cells.

BackgroundCobalt oxide nanoparticles (Co(3)O(4)NPs) are increasingly recognized for their utility in biological applications, magnetic resonance imaging, and drug delivery. However, little is known about the toxicity of Co(3)O(4)NPs in human cells.MethodsWe investigated the possible mechanisms of genotoxicity induced by Co(3)O(4)NPs in human hepatocarcinoma (HepG2) cells. Cell viability, reactive oxygen species (ROS), glutathione, thiobarbituric acid reactive substance, apoptosis, and DNA damage were assessed in HepG2 cells after Co(3)O(4)NPs and Co(2+) exposure.ResultsCo(3)O(4)NPs elicited a significant (P < 0.01) reduction in glutathione with a concomitant increase in lipid hydroperoxide, ROS generation, superoxide dismutase, and catalase activity after 24- and 48-hour exposure. Co(3)O(4)NPs had a mild cytotoxic effect in HepG2 cells; however, it induced ROS and oxidative stress, leading to DNA damage, a probable mechanism of genotoxicity. The comet assay showed a statistically significant (P < 0.01) dose- and time-related increase in DNA damage for Co(3)O(4)NPs, whereas Co(2+) induced less change than Co(3)O(4)NPs but significantly more than control.ConclusionOur results demonstrated that Co(3)O(4)NPs induced cytotoxicity and genotoxicity in HepG2 cells through ROS and oxidative stress

Data mining techniques for analyzing healthcare conditions of urban space-person lung using meta-heuristic optimized neural networks

© 2020, Springer Science+Business Media, LLC, part of Springer Nature. Urban computing is one of the effective fields that have ability to collect the large volume of data, integrate and analyze the data in urban space. The urban space faces several issues such as traffic congestion, more energy consumption, air pollution and so on. Among the several problems, air pollution is one of the major issues because it creates several health issues. So, this paper introduces the meta-heuristic optimized neural network to analyze patient health to predict different diseases. Initially, patient data are collected, normalized by applying a min–max normalization process. Then different features are extracted and Hilbert–Schmidt Independence Criterion based features are selected. Further patient\u27s health condition is analyzed and classified into a normal and abnormal person. The classification process is done by applying the harmony optimized modular neural network. Here the system efficiency is evaluated using simulation results, which ensures maximum accuracy of 98.9% -ELT-COPD and 98% -NIH clinical dataset

Plugging efficiency of flaky and fibrous lost circulation materials in different carrier fluid systems

Lost circulation is one of the most significant contributors to wellbore instability and causes an increase in drilling operation costs. It is also a major contributor to the nonproductive time and must be minimized for improved economic and operational performance. The objective of this research is to provide tools and information about specific loss circulation management techniques that drillers can use to minimize lost circulation. This study involves comprehensive approaches to test the plugging efficiency of three different lost circulation materials (LCMs) from two groups of materials (flaky and fibrous). It also highlights the carrier fluids (drilling fluids) and the determination of the optimum drilling fluid properties. Different fracture sizes and the effect of the various LCM are analyzed. The impact of LCM’s shape, size, and physical and chemical properties along with the fracture sizes is discussed. Examining the particle size distribution before and after mixing with the fluids shows the capability of the materials in plugging the fracture while maintaining the minimum porosity and permeability of the plug. It also helps to strengthen the fracture gradient of the formation by knowing the actual particle sizes. The primary objective of this work is to precisely study and analyze these factors on the three LCMs along with different carrier fluids to investigate their plugging efficiency and potentially resolve or minimize the severity of the lost circulation problem

Proposed Questions to Assess the Extent of Knowledge in Understanding the Radiology Report Language

Radiotherapy and diagnostic imaging play a significant role in medical care. The amount of patient participation and communication can be increased by helping patients understand radiology reports. There is insufficient information on how to measure a patient’s knowledge of a written radiology report. The goal of this study is to design a tool that will measure patient literacy of radiology reports. A radiological literacy tool was created and evaluated as part of the project. There were two groups of patients: control and intervention. A sample radiological report was provided to each group for reading. After reading the report, the groups were quizzed to see how well they understood the report. The participants answered the questions and the correlation between the understanding of the radiology report and the radiology report literacy questions was calculated. The correlations between radiology report literacy questions and radiology report understanding for the intervention and control groups were 0.522, p \u3c 0.001, and 0.536, p \u3c 0.001, respectively. Our radiology literacy tool demonstrated a good ability to measure the awareness of radiology report understanding (area under the receiver operator curve in control group (95% CI: 0.77 (0.71–0.81)) and intervention group (95% CI: 0.79 (0.74–0.84))). We successfully designed a tool that can measure the radiology literacy of patients. This tool is one of the first to measure the level of patient knowledge in the field of radiology understanding

Characterization of Sunn hemp begomovirus and its geographical origin based on in silico structural and functional analysis of recombinant coat protein

Sequence alignment of the 897 bp amplicon obtained from a diseased sunn hemp (Crotalaria juncea L.) plant DNA revealed a complete 771 bp coat protein (CP) gene flanked by 3’ regions of the AV2 and AC3 genes. Southern hybridization using (α-32P) dCTP labeled (CP) gene probe of Indian tomato leaf curl virus (IToLCV) demonstrated the association of begomovirus with the leaf curl disease of sunn hemp. Phylogenetic data suggested that, the AV2, CP and AC3 genes have closest genetic relationship with begomovirus isolates from India, China and Bangladesh, respectively. In silico recombination analysis elucidated a 297 nucleotides hot spot (346 to 643 nucleotides) within AV2 overlapping region of CP gene, amenable to genetic rearrangements, with lineage from tomato leaf curl virus Bangalore (ToLCuVB) and Indian cassava mosaic virus-Ind (ICMV) as major and minor parents, respectively. Thus, it is concluded that the recombinant CP genes related to begomoviruses are evolved from the Indian isolates, causing broad host specificity and molecular diversity among the related begomoviruses across the geographical limits of Southeast Asia.Keywords: Begomovirus, sunn hemp, coat protein, recombination, phylogenetic analysis, in silico analysi