How Have Policy Makers Responded to the Current State of ICT in Schools in Saudi Arabia? A Qualitative Investigation

Previous research into Information and Communication Technology (ICT) in Saudi schools has not considered the role of the Ministry of Education or the Education Authority (EA). As a researcher, I decided to study their role in an attempt to understand the current state of ICT in Saudi schools from the perspectives of policy makers from both bodies. The aim of the study resulted in the generation of the following research question: What are the policy makers’ views about the current state of ICT in education in Saudi Arabia? As this research aims to discover and understand the current state of ICT in schools from the views and perspectives of policy makers, a qualitative methodology has been employed and interviews were used to collect the data. In total, five policy makers from both the Ministry of Education in KSA and the local education authority in Ar-Rass city participated. The findings show that the Ministry of Education and the education authority are significant factors in the failure of ICT in schools. The study concludes that, in order to handle issues that affect the successful use of ICT in education, departments of education need to develop their policies, strategies, plans and frameworks

Theoretical Limits of Photovoltaics Efficiency and Possible Improvements by Intuitive Approaches Learned from Photosynthesis and Quantum Coherence

In this review, we present and discussed the main trends in photovoltaics with emphasize on the conversion efficiency limits. The theoretical limits of various photovoltaics device concepts are presented and analyzed using a flexible detailed balance model where more discussion emphasize is toward the losses. Also, few lessons from nature and other fields to improve the conversion efficiency in photovoltaics are presented and discussed as well. From photosynthesis, the perfect exciton transport in photosynthetic complexes can be utilized for PVs. Also, we present some lessons learned from other fields like recombination suppression by quantum coherence. For example, the coupling in photosynthetic reaction centers is used to suppress recombination in photocells.Comment: 47 pages, 22 figures. arXiv admin note: text overlap with arXiv:1307.5093, arXiv:1105.4189 by other author

Bias adjustment of satellite-based precipitation estimation using artificial neural networks-cloud classification system over Saudi Arabia

Precipitation is a key input variable for hydrological and climate studies. Rain gauges can provide reliable precipitation measurements at a point of observations. However, the uncertainty of rain measurements increases when a rain gauge network is sparse. Satellite-based precipitation estimations SPEs appear to be an alternative source of measurements for regions with limited rain gauges. However, the systematic bias from satellite precipitation estimation should be estimated and adjusted. In this study, a method of removing the bias from the precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PERSIANN-CCS) over a region where the rain gauge is sparse is investigated. The method consists of monthly empirical quantile mapping of gauge and satellite measurements over several climate zones as well as inverse-weighted distance for the interpolation of gauge measurements. Seven years (2010–2016) of daily precipitation estimation from PERSIANN-CCS was used to test and adjust the bias of estimation over Saudi Arabia. The first 6 years (2010–2015) are used for calibration, while 1 year (2016) is used for validation. The results show that the mean yearly bias is reduced by 90%, and the yearly root mean square error is reduced by 68% during the validation year. The experimental results confirm that the proposed method can effectively adjust the bias of satellite-based precipitation estimations

How do microorganisms reach the stratosphere?

A number of studies have demonstrated that bacteria and fungi are present in the stratosphere. Since the tropopause is generally regarded as a barrier to the upward movement of particles it is difficult to see how such microorganisms can reach heights above 17 km. Volcanoes provide an obvious means by which this could be achieved, but these occur infrequently and any microorganisms entering the stratosphere from this source will rapidly fall out of the stratosphere. Here, we suggest mechanisms by which microorganisms might reach the stratosphere on a more regular basis; such mechanisms are, however, likely only to explain how micrometre to submicrometre particles could be elevated into the stratosphere. Intriguingly, clumps of bacteria of size in excess of 10 μm have been found in stratospheric samples. It is difficult to understand how such clumps could be ejected from the Earth to this height, suggesting that such bacterial masses may be incoming to Earth. We suggest that the stratospheric microflora is made up of two components: (a) a mixed population of bacteria and fungi derived from Earth, which can occasionally be cultured; and (b) a population made up of clumps of, viable but non-culturable, bacteria which are too large to have originated from Earth; these, we suggest, have arrived in the stratosphere from space. Finally, we speculate on the possibility that the transfer of bacteria from the Earth to the highly mutagenic stratosphere may have played a role in bacterial evolution

Cuckoo Search Inspired Hybridization of the Nelder-Mead Simplex Algorithm Applied to Optimization of Photovoltaic Cells

A new hybridization of the Cuckoo Search (CS) is developed and applied to optimize multi-cell solar systems; namely multi-junction and split spectrum cells. The new approach consists of combining the CS with the Nelder-Mead method. More precisely, instead of using single solutions as nests for the CS, we use the concept of a simplex which is used in the Nelder-Mead algorithm. This makes it possible to use the flip operation introduces in the Nelder-Mead algorithm instead of the Levy flight which is a standard part of the CS. In this way, the hybridized algorithm becomes more robust and less sensitive to parameter tuning which exists in CS. The goal of our work was to optimize the performance of multi-cell solar systems. Although the underlying problem consists of the minimization of a function of a relatively small number of parameters, the difficulty comes from the fact that the evaluation of the function is complex and only a small number of evaluations is possible. In our test, we show that the new method has a better performance when compared to similar but more compex hybridizations of Nelder-Mead algorithm using genetic algorithms or particle swarm optimization on standard benchmark functions. Finally, we show that the new method outperforms some standard meta-heuristics for the problem of interest

Quantum Confinement and Negative Heat Capacity

Thermodynamics dictates that the specific heat of a system is strictly non-negative. However, in finite classical systems there are well known theoretical and experimental cases where this rule is violated, in particular finite atomic clusters. Here, we show for the first time that negative heat capacity can also occur in finite quantum systems. The physical scenario on which this effect might be experimentally observed is discussed. Observing such an effect might lead to the design of new light harvesting nano devices, in particular a solar nano refrigerator.Comment: 8 pages, 5 figure

A Comparative Study of Student Performance Prediction using Pre-Course Data

Students at Saudi universities face difficulty registering for the right course since Student performance there is no support offered to students that uniquely consider each situation. Machine learning techniques could be applied to fill this gap by predicting grades of new courses for each student based on their historical data. This paper experiments with nine different prediction algorithms to predict course grades for public university students. The data-set includes grades for 215 students and 180 various courses. The models utilize grades obtained in semesters between the 2015 and 2018 academic years and evaluated on grades obtained in the 2019 academic year. Our result shows that the K-nearest neighbor with ZScore model outperforms the remaining models with respect to the Percentage of Tick Accuracy (PTA), which is the difference between two consecutive letter grades for the predicted letter grade and the observed letter grade. Our work achieved an 84% accuracy score in PTA2, where the difference between the predicted letter grade and the actual letter grade is less than or equal to two consecutive letter grades