Study the Effect of Thickness on the Performance of PM6:Y6 Organic Solar Using SCAPS Simulation

In this study, organic solar cells (OSCs) with an active layer, a blend of polymer of non-fullerene (NFA) Y6 as an acceptor, and donor PBDB-T-2F as donor were simulated through the one-dimensional solar capacitance simulator (SCAPS-1D) software to examine the performance of this type of organic polymer thin-film solar cell by varying the thickness of the active layer. PFN-Br interfacial layer entrenched in OPV devices gives overall enhanced open-circuit voltage, short-circuit current density and fill factor thus improving device performance. PEDOT: PSS is an electro-conductive polymer solution that has been extensively utilized in solar cell devices as a hole transport layer (HTL) due to its strong hole affinity, good thermal and mechanical stability, high work function, and high transparency in the visible range. The structure of the organic solar cell is ITO/PEDOT: PSS/BTP-4F: PBDB-T-2F/PFN-Br/Ag. Firstly, the active layer thickness was optimized to 100 nm;after that, the active-layer thickness was varied up to 900 nm. The results of these simulations demonstrated that the active layer thickness improves efficiency significantly up to 500 nm, then it decreased with increasing the thickness of the active layer from 600 nm, also notice that the short circuit current and the fill factor decrease with increasing the active layer from 600 nm, while the open voltage circuit increased with increasing the thickness of the active layer. The optimum thickness is 500 nm.

Analysis of Temperature Trends and Variations in the Arabian Peninsula’s Upper Atmosphere

In this study, the trends of upper-air temperatures are analysed by utilising radiosonde observations for the barometric levels at 700, 500, 300, 200, 150, 100 and 50 hPa from five meteorological stations within the Arabian Peninsula from January 1986 to August 2015. The mean monthly variations of the temperatures at these levels are characterised and established. The magnitudes of the annual trends of the mean temperatures for each site for the selected barometric levels are studied and statistically tested using Mann-Kendall rank statistics at different significance levels. The temperature trends at different pressure levels show that the upper troposphere and lower stratosphere are warming, while the middle troposphere is cooling which is consistent with the findings of other studies. The variations in upper air temperature observed in this study can be attributed to a range of factors, including increasing greenhouse gas concentrations, changes in atmospheric circulation patterns, variations in solar activity, aerosols and volcanic eruptions, and land use and land cover change.

Studying the Effect of Different Gas-Phase Chemical Kinetic Mechanisms on the Formation of Oxidants, Nitrogen Compounds and Ozone in Arid Regions

CMAQ was implemented in the central region of Saudi Arabia and the effect of simulating models using various chemical mechanisms on selected oxidants, nitrogen species, and O3 was investigated. CB05TUCL predicted OH, MEPX, and NOz about 7%, 7.7%, and 8% more than CB05E51 respectively;however, there was no observable difference in the O3 predictions. The differences in variations of SAPRC07 mechanism (SAPRC07TB, SAPRC07TC, and SAPRC07TIC) for all parameters were less than 1%. RACM2 produced the highest OH and H2O2 concentrations. RACM2 enhanced OH production in the range of 24% - 32% and H2O2 by 9% over other mechanisms;these are comparatively less than the findings of other studies. Similarly, CB05 produced over 40% more PAN concentration than CB05. Moreover, PAN concentrations produced by all mechanisms were very high compared to other studies. SAPRC07 produced approximately 3% more mean surface O3 concentration than RACM2 and approximately 10% more than CB05. RACM2 O3 predictions were higher than CB05 by 7%. The predicted O3 concentrations by CB05, RACM2, and SAPRC07 were 6%, 11%, and 15% more than the average observed concentrations, which indicate that closest predictions to the observed values were by CB05. This study concludes that there is a wide variation of mechanisms with respect to the predictions of oxidants and nitrogen compounds;however, less variation is noticed in predictions of O3. For any air pollution control strategies and photochemical modeling studies in the current region or in any other arid regions, the CB05 mechanism is recommended.

Modeling Distribution of Selective Ions in Urban and Rural Areas Using Geographical Information System

The main objective of this study is to evaluate the distributions of selective ions in Makkah wells using GIS. The present study focuses on the presence and accumulation of several ions in the ground water of Makkah City. This study exhibits selected measurements of the levels and distribution of 4 ions (nitrate, nitrite, chloride and sulphate) in wells water using the Geographical Information System (GIS). The study covered 27 areas of Makkah City and its environs. Two layers were made using the Arc-Map program: the first layer was called internal wells (Central Makkah, urban) and the second layer was called external wells (rural). The total number of wells covered by this study was 145, and the samples were collected in different seasons. The samples were analyzed following standard procedures and compared with local and international standards. The results showed that the relative abundance of the major ions in the ground water was SO4 > Cl-1 > NO3 > NO2, with the presence of SO4 being dominant.

Evaluation of Cavity Formation and the Use of Cut-off Wall to Reduce the Risk of Washing Subsurface Fine Material

This study shows the results of mapping numerous cavities and distress which appeared and detected in Qassim area, Saudi Arabia. This phenomenon was observed near a school building and residential area and became a serious risk to occupants and residents. The survey was carried out applying geotechnical techniques which included advancing rotary boreholes to depths of 23 m to 30 m with sampling and testing. The evaluation process also included resistivity imaging profiles using 2D electrical resistivity measurements. Results obtained from this research showed a thick top layer of silty clayey sand soil rich of gypsum and carbonate presenting a hazardous and high-risk soil type. The percentage of fines that are likely to be washed out as a result of chemical disintegration and exposure to significant hydraulic gradient was of great concern. Assessment was made using combined geotechnical and geophysical approach in addition to chemical tests. Based on the data collected and analysis of test results a practical solution was suggested to solve this problem. The use of “cut-off wall” in order to reduce the level of subsurface scour and cajuvity formation were found appropriate. The depth of the cut off wall was determined based on the subsurface geological profile. Advantages of this approach and concerns need to be considered in adopting typical solutions that are presented.

Long-Term Visibility Trends in the Riyadh Megacity, Central Arabian Peninsula and Their Possible Link to Solar Activity

In this study, atmospheric visibility (AV) data from Riyadh, Saudi Arabia (24.91˚N, 46.41˚E, 760 m), for the period 1976-2011 were utilized to investigate the interannual, monthly, and seasonal AV variations and trends. The magnitudes of these trends were characterized and tested using mann-kendall (MK) rank statistics at different significance levels. No significant trend in AV was observed during the 36-year period. However, a significant increase in the annual mean AV by 0.24 km per year for the period between 1976 and 1999 was found. For the period 1999-2011, AV decreased significantly by 0.16 km per year. The potential effects of air temperature and relative humidity on AV were investigated. While these two variables could explain the observed trend of AV over some periods, they failed to do so for the whole study period. To search for extraterrestrial causes for long-term AV variations, correlation analyses between the time series of cosmic ray (CR) data (measured by NM and muon detector) and solar activity (represented by sunspot number) and AV were conducted and showed that these two variables are able to explain the AV variations for the whole study period. Additionally, power spectra analyses were conducted to investigate periodicities in the AV time series. Several significant periodicities, such as 9.8, 5.2, 2.2, 1.7, and 1.3 years were recognized. The obtained periodicities were similar to those reported by several investigators and found in solar, interplanetary, and CR parameters. The spectral and correlation results suggested that, with the expected effects of terrestrial and meteorological conditions on AV, long-term AV variations can also be related to the solar activity and associated CR modulations.

On the Development of a Simple and Portable Ground-Based Infrared Imaging System for Lunar and Atmospheric Studies

A small, portable, infrared (wavelength of 7 - 14 μm) system has been designed and developed to study the thermal behavior of the lunar surface and for thermal remote sensing applications. The principal operation of the system depends on collecting large amounts of infrared light, using a modified Newtonian telescope. The light from the object is reflected by the primary mirror and the secondary mirror. This collected light is then focused into a thermal camera by using an intermediate germanium lens as a field lens to provide a real optical image on the camera sensor. Several observations have been obtained out using the developed system, and eliciting some interesting results. These include lunar observations during different phases and during partial lunar eclipse. The thermal behavior of the lunar surface was identified, proving the system’s functionality and performance. The developed system is, also, particularly suitable tool for outreach programs and students projects which can possibly offer useful learning and exploration opportunities for students in different applications. In this paper, a brief description about the developed system is provided. Some of the obtained results are illustrated. The future applications and improvements to the designed system are also summarized.

Aspect-Based Sentiment Analysis for Social Multimedia:A Hybrid Computational Framework

People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events,public products and the latest affairs.People share their thoughts and feelings about various topics,including products,news,blogs,etc.In user reviews and tweets,sentiment analysis is used to discover opinions and feelings.Sentiment polarity is a term used to describe how sentiment is represented.Positive,neutral and negative are all examples of it.This area is still in its infancy and needs several critical upgrades.Slang and hidden emotions can detract from the accuracy of traditional techniques.Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories.Some existing strategies are domain-specific.The proposed model incorporates aspect extraction,association rule mining and the deep learning technique Bidirectional EncoderRepresentations from Transformers(BERT).Aspects are extracted using Part of Speech Tagger and association rulemining is used to associate aspects with opinion words.Later,classification was performed using BER.The proposed approach attained an average of 89.45%accuracy,88.45%precision and 85.98%recall on different datasets of products and Twitter.The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

Investigation of a Possible Link between Solar Activity and Climate Change in Saudi Arabia: Rainfall Patterns

In this study, annual, quarterly, and monthly mean precipitation data in Saudi Arabia were correlated with sunspot number (SSN) and galactic cosmic ray (CR) flux over 35 years (1985-2019). The results show that the strength, magnitude, proportion and statistical significance of the relationship between precipitation and the two variables varied by season and month. We find that mean annual precipitation in Saudi Arabia, from May to November, and summer and autumn are correlated with cosmic rays and inversely correlated with SSN. Correlations of varying intensities and scales were found during the remaining months and during winter and spring. The relationships between the rainfall and SSN and CR for each solar cycle were investigated and showed that for all three cycles, the annual rainfall over Saudi Arabia has a positive correlation with CR. Different results were obtained when the seasonal rainfall data correlated with the SSNs and CRs during each cycle. The results obtained, in terms of their strength and magnitude, are affected by terrestrial and extra-terrestrial factors. These factors have been briefly presented and discussed. These findings represent a step towards understanding the possible role of solar activity in climate change for future meteorological phenomenon forecasting, even if the physical mechanism is still poorly quantified.

The Role of Atmospheric Pressure, Temperature, and Humidity on Cosmic Ray Muons at a Low Latitude Station

This study aimed to investigate the relationship between atmospheric conditions and cosmic ray (CR) muons using daily and monthly CR data collected by the KAAU muon detector in Jeddah, Saudi Arabia between 2007 and 2012. Specifically, the study examined the effects of atmospheric pressure, air temperature, and relative humidity on CR muons at different time scales (annual, seasonal, and monthly). The results of the analysis revealed that atmospheric pressure and air temperature had a negative impact on CR muons, while relative humidity had a positive impact. Although air temperature and relative humidity had small mean values across all time scales, their coefficients varied significantly from month to month and season to season. In addition, the study conducted multivariable correlation analyses for each day, which showed that pressure coefficients had consistently negative mean values, while the temperature and humidity coefficients had varying effects, ranging from positive to negative values. The reasons for the variations in the coefficients are not yet fully understood, but the study proposed several possible terrestrial and extraterrestrial explanations. These findings provide important insights into the complex interactions between the Earth’s atmosphere and cosmic rays, which can contribute to a better understanding of the potential impacts of cosmic rays on the Earth’s climate and environment.

Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network optimization.This study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic RESs.The primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss mitigation.Additionally,a carbon tax is included in the objective function to reduce carbon emissions.Thorough scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal solutions.Simulation results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution quality.Notably,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)effectively.This research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local minima.GBBLCO emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

Decarbonising energy: The developing international activity in hydrogen technologies and fuel cells

Hydrogen technologies and fuel cells offer an alternative and improved solution for a decarbonised energy future.Fuel cells are electrochemical converters;transforming hydrogen (or energy sources containing hydrogen) and oxygen directly into electricity.The hydrogen fuel cell,invented in 1839,permits the generation of electrical energy with high efficiency through a non-combustion,electrochemical process and,importantly,without the emission ofits point of use.Hitherto,despite numerous efforts to exploit the obvious attractions of hydrogen technologies and hydrogen fuel cells,various challenges have been encountered,some of which are reviewed here.Now,however,given the exigent need to urgently seek low-carbon paths for humankind’s energy future,numerous countries are advancing the deployment of hydrogen technologies and hydrogen fuel cells not only for transport,but also as a means of the storage of excess renewable energy from,for example,wind and solar farms.Furthermore,hydrogen is also being blended into the natural gas supplies used in domestic heating and targeted in the decarbonisation of critical,large-scale industrial processes such as steel making.We briefly review specific examples in countries such as Japan,South Korea and the People’s Republic of China,as well as selected examples from Europe and North America in the utilization of hydrogen technologies and hydrogen fuel cells.

Intelligent Autonomous-Robot Control for Medical Applications

The COVID-19 pandemic has shown that there is a lack of healthcare facilities to cope with a pandemic.This has also underscored the immediate need to rapidly develop hospitals capable of dealing with infectious patients and to rapidly change in supply lines to manufacture the prescription goods(including medicines)that is needed to prevent infection and treatment for infected patients.The COVID-19 has shown the utility of intelligent autonomous robots that assist human efforts to combat a pandemic.The artificial intelligence based on neural networks and deep learning can help to fight COVID-19 in many ways,particularly in the control of autonomous medic robots.Health officials aim to curb the spread of COVID-19 among medical,nursing staff and patients by using intelligent robots.We propose an advanced controller for a service robot to be used in hospitals.This type of robot is deployed to deliver food and dispense medications to individual patients.An autonomous line-follower robot that can sense and follow a line drawn on the floor and drive through the rooms of patients with control of its direction.These criteria were met by using two controllers simultaneously:a deep neural network controller to predict the trajectory of movement and a proportional-integral-derivative(PID)controller for automatic steering and speed control.

Application of 2-D geoelectrical resistivity tomography for subsurface cavity detection in the eastern part of Saudi Arabia

The presence of natural voids and cavities in subsurface karstic limestones causes severe problem for civil engineering and environmental management. The presence of such features hinders the extension of urbanizations particularly in the new metropolitan. The eastern part of Saudi Arabia contains various types of karstic limestone, sinkholes, solution cavities and voids. In this context, geophysical methods particularly electrical resistivity technique is used as a cost-effective solution for investigating subsurface caves, voids, and shallow weathered zones. 2-D electrical resistivity data sets have been acquired along seven profiles in the new urbanization at AI Hassa area. Data processing has been carried out taking into consideration the response of synthetic models, which simulates physical models of the most common karstic features in the area. The results are very useful to determine the extension of shallow weathered zones and to locate different cavities underneath them. The hard limestone bedrock can also be detected and traced along the surveyed profiles.

Possible Association between Space Weather Variables, and the World’s COVID-19 Cases

This study aims to investigate the influential role of space weather parameters on the transmission of COVID-19. Solar radio flux, interplanetary magnetic field, Dst index, sunspot number, and solar wind speed were utilized to represent the space weather variables. The association of the considered variables to the number of the confirmed COVID-19 cases worldwide along with five geographical categories, i.e. Asia, Europe, Africa, South, and North America, were investigated for a period ranging from 20 January 2020 to 5 August 2021 using Pearson linear tests as well as the non-parametric Spearman’s and Kendall’s rank correlation tests. Pearson linear tests showed that the number of confirmed COVID-19 cases worldwide and the chosen geographical categories have a significant correlation to interplanetary magnetic strength, solar radio flux F10.7, and sunspot number. When the confirmed COVID-19 cases reported in the Asia continent were excluded, the solar wind speed correlated significantly with the number of COVID-19 cases reported elsewhere in the world and the other geographical categories. The non-parametric Kendall and Pearson tests showed that the world’s COVID-19 cases and the other geographical categories had significant correlations with the interplanetary magnetic field, radio flux F10.7, sunspot number, and the solar wind speed, but not with the Dst index.

Correlation Analyses between Ultraviolet Radiation, Global Solar Radiation, and Metrological Variables and the COVID-19 Cases in Arid Climate

The transmission of infectious diseases is influenced by several meteorological factors. In this study, the influence of several such factors in the transmission of COVID-19 (from 26 March 2020 to 29 July 2021) in the arid weather of Riyadh, Saudi Arabia was investigated using the Spearman and Kendall rank tests. The factors considered were the average, maximum, and minimum values of air temperatures, air pressure, wind speed, relative humidity, absolute humidity, dew point temperatures, and the average values of the global solar radiation and ultraviolet radiation at bands A and B. The data on meteorological factors were obtained from the King Abdulaziz City for Science and Technology (KACST) weather station, whereas the data on the daily COVID-19 cases were obtained from the official webpage of the Saudi Arabian Ministry of Health (MOH). The results revealed that air temperature (average, minimum, and maximum) average and maximum wind speed, maximum dew point temperature, global solar radiation, and ultraviolet radiation at A and B bands are positively associated with the daily number of COVID-19 cases reported in Riyadh. However, relative humidity, atmospheric pressure (averages, minimum, and maximum) is anti-correlated with the number of daily COVID-19 cases, while absolute humidity exerts no influence. These results are in total agreement with some of the previously established studies and are either contradicted partly or totally with others conducted at several locations around the world. The results could help not only epidemiologists understand the behavior of COVID-19 against meteorological variables but also national and international organizations and healthcare policymakers devise control strategies to combat the virus.

The Possible Relationship between the Atmospheric Electric Field and High Energy Charged Particles and the COVID-19 Cases in the Central Arabian Peninsula

Background: Since the beginning of the global COVID-19 pandemic, several studies have been carried out to investigate its spread, with a wide range of factors to understand the influence of the factors that contribute to its spread and to reduce the ongoing threat of COVID-19 pandemic. Methods: In this study, the relationships between the Earth’s electric field and cosmic ray charged particles of different energy ranges and the daily confirmed COVID-19 infections in Riyadh, Saudi Arabia have been investigated using non-parametric statistical tests. The data covered the period between 3 April 2020 and 1st August 2021 and were obtained from the King Abdulaziz City for Science and Technology (KACST) CARPET detector, Riyadh, Saudi Arabia. The electric fields data were obtained from electric field monitor (EFM) deployed on the rooftop of the KACST laboratory. The data of the daily COVID-19 cases were obtained from the official webpage of the Saudi Arabian Ministry of Health (MOH). Results: The results revealed that that the number of COVID-19 cases is correlated with cosmic ray charged particles and anti-correlated with the Earth’s electric field. Conclusion: While the exact mechanism explaining the influence of Earth’s electric field and cosmic rays variations on the reported number of COVID-19 cases is not yet established, the results presented in this study can add additional knowledge to our understanding of the effects of additional factors on influenza activities.

Determination of gamma-ray parameters for polyethylene glycol of different molecular weights

Mass attenuation coefficient(μ_m) for polyethylene glycol(PEG) of different molecular weights was determined by using NaI(Tl) scintillator and Win Xcom mixture rule at gamma energies of 59.5, 302.9, 356.0, 661.7, 1173.2 and 1332.5 keV. The total atomic, molecular and electronic cross sections, half-value layer, effective atomic and electron numbers, mass energy-absorption coefficients and kerma relative to air are calculated. The energy and compositional dependence of μ_m values, and the related radiation absorption parameters, are evaluated and discussed. The experimental results agree well with the theoretical ones, within an uncertainty of 1% in the effective atomic number for all PEG samples at the designated energies.

Deep Learning Control for Autonomous Robot

Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these approaches.Autonomous cars are one such application.This is expected to have a significant and revolutionary influence on society.Integration with smart cities,new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving automobiles.The autonomous automobile,often known as selfdriving systems or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predetermined routes without human involvement.Cars are on the verge of evolving into autonomous robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-economic implications.However,in order for these automobiles to become a reality,they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate action.The majority of self-driving car technologies are based on computer systems that automate vehicle control parts.From forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully automated driving,these technological components have a wide range of capabilities.A self-driving car combines a wide range of sensors,actuators,and cameras.Recent researches on computer vision and deep learning are used to control autonomous driving systems.For self-driving automobiles,lane-keeping is crucial.This study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the lane.We propose an advanced control for a selfdriving robot by using two controllers simultaneously.Convolutional neural networks(CNNs)are employed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering control.This study uses a Raspberry PI based camera to control the robot car.

The Distribution and Accretion of Some Heavy Metals in Makkah Wells

The aim of this study is to determine the types of heavy metals frequently present in Makkah wells and the possible environmental causes of their distribution and accumulation. Makkah lies in a mountain range dominated by different types of rocks from the Precambrian and Lower Paleozoic eras, as well as subordinate sedimentary rocks and basaltic lava flow from the Tertiary and Quaternary periods. Natural contaminants in Makkah wells water can be attributed to the unique location. Many epidemiological studies have identified associations between the ingestion of wells water contaminated with heavy metals and increased risk of some illnesses. This study presents exclusive information on the levels and distribution of 9 heavy metals—arsenic, barium, cadmium, chromium, cobalt, copper, lead, mercury, and selenium—in the wells water in various rural and urban areas of Makkah city. These naturally occurring elements are considered significant markers of water purity. More than 160 wells were involved in this three-year investigation. Water samples were collected during different seasons in order to assess any changes in the distribution and concentration of these heavy metals throughout the year. The collected water samples were filtered and digested before analysis using inductively coupled plasma mass spectrometry (ICP/MS). We found the following sequence of concentrations of heavy metals in the studied wells: Ba > Se >Cr > As > Co > Cu> Hg > Pb > Cd. Arsenic, barium, chromium, and selenium were the most abundant contaminants in the wells studied. The concentrations of the other heavy metals ranged from non-detectable to 3 μg/L. Although low, these values are also reported in comparisons with the local and international strict values and standards which govern the maximum contaminant levels permitted for long-term daily consumption.