الابحاث العلمية للطلاب |Students Research Articles

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يعرض هذا القسم الأبحاث المقبولة للنشر ضمن مبادرة نشر الأبحاث للطلبة في المجلات ذات التأثير المرتفع و التي اطلقتها عمادة البحث العلمي

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3D-Printed Nanocomposite Denture-Base Resins: Effect of ZrO2 Nanoparticles on the Mechanical and Surface Properties In Vitro
(2022) Alshaikh, Ali A.; Khattar, Abdulrahman; Ibrajim Almindil; Ali Alshaikh; Akhtar, Sultan; Khan, Soban Q.; Gad, Mohammed M.
Due to the low mechanical performances of 3D-printed denture base resins, ZrO2 nanoparticles (ZrO2NPs) were incorporated into different 3D-printed resins and their effects on the flexure strength, elastic modulus, impact strength, hardness, and surface roughness were evaluated. A total of 286 specimens were fabricated in dimensions per respective test and divided according to materials into three groups: heat-polymerized as a control group and two 3D-printed resins (NextDent and ASIGA) which were modified with 0.5 wt.%, 1 wt.%, 3 wt.%, and 5 wt.% ZrO2NPs. The flexure strength and elastic modulus, impact strength, hardness, and surface roughness (µm) were measured using the three-point bending test, Charpy’s impact test, Vickers hardness test, and a profilometer, respectively. The data were analyzed by ANOVA and Tukey’s post hoc test (α = 0.05). The results showed that, in comparison to heat-polymerized resin, the unmodified 3D-printed resins showed a significant decrease in all tested properties (p < 0.001) except surface roughness (p = 0.11). In between 3D-printed resins, the addition of ZrO2NPs to 3D-printed resins showed a significant increase in flexure strength, impact strength, and hardness (p < 0.05) while showing no significant differences in surface roughness and elastic modulus (p > 0.05). Our study demonstrated that the unmodified 3D-printed resins showed inferior mechanical behavior when compared with heat-polymerized acrylic resin while the addition of ZrO2NPs improved the properties of 3D-printed resins. Therefore, the introduced 3D-printable nanocomposite denture-base resins are suitable for clinical use.
A Comparative Study of the Effects of Distance Learning and Face-to-Face Learning during the COVID-19 Pandemic on Learning Mathematical Concepts in Primary Students of the Kingdom of Bahrain
(2023) Enas Anwar Tayfour; Mansour Saleh Rashed Alabdulaziz
The main objective of this study is to compare the effectiveness of face-to-face learning and distance learning in helping fourth-grade primary students learn mathematical concepts. The data were collected from 120 fourth-grade students selected purposively and divided into two groups: a control group comprising 60 students, who used a face-to-face programme in their third grade, and an experimental group comprising 60 students, who used a distance learning programme in their third grade. A diagnostic test was used to measure their understanding of previous mathematical concepts. The current research revealed two interesting results: First, there were no statistically significant differences (p-value < 0.05) in rounding and ordering numbers, space concept, perimeter concept, and graphs between the face-to-face mode and distance learning mode, where students’ results were almost similar. Second, there were statistically significant differences (p-value < 0.05) in the concepts of expanding pictures of numbers (verbal, analytic, and standard), compare numbers, basic arithmetic operations, units of measurement, geometric shapes, sides, and data visualisation in favour of the group of students who were taught in a face-to-face learning mode.
A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS)
(2023) Alamoudi, Abrar; Khan, Irfan Ullah; Hind A.Saif; Nourah Hasan Al Qahtani; Omran Al Dandan; Al Dandan, Omran; Ridha Albahrani; Al Bahrani, Ridha
One of the leading causes of female infertility is PCOS, which is a hormonal disorder affecting women of childbearing age. The common symptoms of PCOS include increased acne, irregular period, increase in body hair, and overweight. Early diagnosis of PCOS is essential to manage the symptoms and reduce the associated health risks. Nonetheless, the diagnosis is based on Rotterdam criteria, including a high level of androgen hormones, ovulation failure, and polycystic ovaries on the ultrasound image (PCOM). At present, doctors and radiologists manually perform PCOM detection using ovary ultrasound by counting the number of follicles and determining their volume in the ovaries, which is one of the challenging PCOS diagnostic criteria. Moreover, such physicians require more tests and checks for biochemical/clinical signs in addition to the patient’s symptoms in order to decide the PCOS diagnosis. Furthermore, clinicians do not utilize a single diagnostic test or specific method to examine patients. This paper introduces the data set that includes the ultrasound image of the ovary with clinical data related to the patient that has been classified as PCOS and non-PCOS. Next, we proposed a deep learning model that can diagnose the PCOM based on the ultrasound image, which achieved 84.81% accuracy using the Inception model. Then, we proposed a fusion model that includes the ultrasound image with clinical data to diagnose the patient if they have PCOS or not. The best model that has been developed achieved 82.46% accuracy by extracting the image features using MobileNet architecture and combine with clinical features.
A Deep-Learning Approach to Driver Drowsiness Detection
(2023) Halah Alabdulkarem; Alabdulkarem, Halah; Alomair, Fatimah; Aldossary, Dana; Alahmari, Manar; Alhumaidan, Munira; Alrassan, Shoog; Rahman, Atta; Mustafa Youldash; Zaman, Gohar
Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver’s eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems.
A DFT study of the electronic and optical properties of four 2D thin films
(2022) Nouf AlSaleem; Imen, Said; Noureddine, Sfina; Nouf, K. Al-Saleem; Taher, Ghrib
The optical properties of 2D semiconductors display a wealth of knowledge regarding their physicochemical parameters, including their electronic and phonon states, as well as the existence and kind of defects and imperfections. Furthermore, two-dimensional substances optical characteristics are critical within a few applications, such as optical fiber coatings and lens coverings. In this path, we compute the optical properties, e.g., absorption, conductivity, refractive index, and dielectric function of new orthorhombic SiX and SiX2 (X = As or P) 2D materials, employing density functional theory. Our HSE calculations demonstrate that the SiX and SiX2 monolayers are direct and indirect semiconductors. The imaginary part of the dielectric function, ϵ2(ω) leaves at approximately 16.00 eV, which correlates to the power loss function, according to our GGA simulations. Moreover, when the frequency of the light source is greater than the photon energy, which is ∼ 16.00 eV, our generalized gradient approximation results reveal that our thin films are translucent substances. Additionally, we have discovered that as As evolves P, the conductivity value rises. The primary absorption peaks of the SiX and SiX2 layers along the in-plane polarization are positioned in the ultra-violet spectrum of light, suggesting that they could be a good choice for producing superior photodetectors, according to the optical study.
A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction
(2022) Gollapalli, Mohammed; Atta-ur-Rahman; Musleh, Dhiaa; Ibrahim, Nehad; Khan, Muhammad-Adnan; Mehwash Farooqui; Atta, Ayesha; Khan, Muhammad-Aftab; Abdullah Omar Alturki; Iqbal, Tahir; Ahmed, Mohammed-Salih; Ahmed, Mohammed-Imran-B.; Almoqbil, Dakheel; Nabeel, Majd; Omer, Abdullah
The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems. Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide. Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road. To address this overwhelming problem, in this article, a cloud-based intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach. The aim of the study is to reduce the delay in the queues, the vehicles experience at different road junctions across the city. The proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things (IoT) sensors across the road. After due preprocessing over the cloud server, the proposed approach makes use of this data by incorporating the neuro-fuzzy engine. Consequently, it possesses a high level of accuracy by means of intelligent decision making with minimum error rate. Simulation results reveal the accuracy of the proposed model as 98.72% during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%, 95.84%, 97.56% and 98.03%, respectively. As far as the training phase analysis is concerned, the proposed scheme exhibits 99.214% accuracy. The proposed prediction model is a potential contribution towards smart cities environment.
A Novel Ensemble-Based Technique for the Preemptive Diagnosis of Rheumatoid Arthritis Disease in the Eastern Province of Saudi Arabia Using Clinical Data
(2022) Aisha Alansari; Heba Alkhorasani; Alkhorasani, Heba; Alsubaii, Meelaf; Sakloua, Rasha; Alzahrani, Reem; Alsaleem, Yasmeen; Reem Alassaf; Alhamad, Nada; Mohammed Imran Basheer Ahmed; Alshobbar, Zainab; Alassaf, Reem; Farooqui, Mehwash; Ahmed, Mohammed Imran Basheer
Rheumatoid arthritis (RA) is a chronic inflammatory disease caused by numerous genetic and environmental factors leading to musculoskeletal system pain. RA may damage other tissues and organs, causing complications that severely reduce patients’ quality of life. According to the World Health Organization (WHO), over 1.71 billion individuals worldwide had musculoskeletal problems in 2021. Rheumatologists face challenges in the early detection of RA since its symptoms are similar to other illnesses, and there is no definitive test to diagnose the disease. Accordingly, it is preferable to profit from the power of computational intelligence techniques that can identify hidden patterns to diagnose RA early. Although multiple studies were conducted to diagnose RA early, they showed unsatisfactory performance, with the highest accuracy of 87.5% using imaging data. Yet, imaging data requires diagnostic tools that are challenging to collect and examine and are more costly. Recent studies indicated that neither a blood test nor a physical finding could early confirm the diagnosis. Therefore, this study proposes a novel ensemble technique for the preemptive prediction of RA and investigates the possibility of diagnosing the disease using clinical data before the symptoms appear. Two datasets were obtained from King Fahad University Hospital (KFUH), Dammam, Saudi Arabia, including 446 patients, with 251 positive cases of RA and 195 negative cases of RA. Two experiments were conducted where the former was developed without upsampling the dataset, and the latter was carried out using an upsampled dataset. Multiple machine learning (ML) algorithms were utilized to assemble the novel voting ensemble, including support vector machine (SVM), logistic regression (LR), and adaptive boosting (Adaboost). The results indicated that clinical laboratory tests fed to the proposed voting ensemble technique could accurately diagnose RA preemptively with an accuracy, recall, and precision of 94.03%, 96.00%, and 93.51%, respectively, with 30 clinical features when utilizing the original data and sequential forward feature selection (SFFS) technique. It is concluded that deploying the proposed model in local hospitals can contribute to introducing a method that aids medical specialists in preemptively diagnosing RA and stopping or delaying the course using clinical laboratory tests.
A novel green extraction and analysis technique for the comprehensive characterization of mangiferin in different parts of the fresh mango fruit (Mangifera indica)
(2022) Saeed Abdullah Aldossary; Salem Mohammed Bubshait; Aljawharah, Alqathama; Alaa, Aldarwish; Asma, Abuhassan; Leena, Alateeq; Rizwan Ahmad; Mohammed jaber G Aldholmi; Saeed, Aldossary
The study reports a novel, green, and effective ASE (accelerated solvent extraction) technique with a green, fast, and sensitive method of UPLC-MS/MS for a comprehensive characterization of whole mango fruit (epi-, endo-, Mesocarp, seed) under solvent, temperature, and origin effect in order to determine the inter- and intra-variation of MG(Mangiferin)-amount in fresh mango fruit. The ASE-MD (method development) showed the highest extract yield and more MG-amount in H2O at 80 °C (mesocarp fruit part). For MG-quantification, a fast and sensitive UPLC-MS/MS method (RT = 0.31/runtime = 1 min; m/z 421.10 → 81.00 amu) was developed. The UPLC-MS/MS-MV validated the method with high accuracy, precision, and r2 = 0.998. The intra-variation for MG was: mesocarp > endocarp > epicarp > seed. The inter-variation (MG-amount (mg/10g) in 12-mango cultivars) based on the sum of MG-amount/12-mango fruits parts: mesocarp (12.42)> epicarp (10.92)> endocarp (10.47)> seed (8.70) whereas, based on the largest MG-amount/individual mango fruit it was: mesocarp (5/12 fruits)> endocarp (4/12 fruits)> epicarp (3/12 fruits)> seed (0/12). The statistical models for PCA, K-mean cluster, and Pearson's analysis revealed significant correlation (P ≤ 0.005) for origin*solvent*temperature Vs MG-amount with a nonsignificant correlation for origin*MG-amount Vs extract yield. The ASE-UPLC-MS/MS comprehensively characterized inter-, and intra-variation of MG in different parts of mango fruit.
A Novel Metadata Based Multi-Label Document Classification Technique
(2023) Reem Alzaher; Ahmad, Munir; Rahman, Atta-ur; Zaman, Gohar; Ahmed, Mohammed-Salih; Mohammed Salih Ahmed; Ahmed, Mohammed-Imran-B.; Krishnasamy, Gomathi; Gomathi Krishnasamy; Alkharraa, Mariam; AlKhulaifi, Dania; Linah Saraireh; Salam, Asiya-A.; Saraireh, Linah; Gollapalli, Mohammed; Ahmed, Rashad
From the beginning, the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies, its growth rate is overwhelming. On a rough estimate, more than thirty thousand research journals have been issuing around four million papers annually on average. Search engines, indexing services, and digital libraries have been searching for such publications over the web. Nevertheless, getting the most relevant articles against the user requests is yet a fantasy. It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification. To overcome this issue, researchers are striving to investigate new techniques for the classification of the research articles especially, when the complete article text is not available (a case of non-open access articles). The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess, “to what extent metadata-based features can perform in contrast to content-based approaches.” In this regard, novel techniques for investigating multilabel classification have been proposed, developed, and evaluated on metadata such as the Title and Keywords of the articles. The proposed technique has been assessed for two diverse datasets, namely, from the Journal of universal computer science (J.UCS) and the benchmark dataset comprises of the articles published by the Association for computing machinery (ACM). The proposed technique yields encouraging results in contrast to the state-of-the-art techniques in the literature.
A novel stacking ensemble for detecting three types of diabetes mellitus using a Saudi Arabian dataset: Pre-diabetes, T1DM, and T2DM
(2022) Gollapalli, Mohammed; Alansari, Aisha; Alkhorasani, Heba; Rasha Sakloua; Reem Alzahrani; Alzahrani, Reem; Al-Hariri, Mohammed; Alfares, Maiadah; Reem Jaafar Al Argan; Al Argan, Reem; Albaker, Waleed
Glucose is the primary source of energy for cells, which are the building blocks of life. It is given to the body by insulin that carries out the metabolic tasks that keep people alive. Glucose level imbalance is a sign of diabetes mellitus (DM), a common type of chronic disease. It leads to long-term complications, such as blindness, kidney failure, and heart disease, having a negative impact on one's quality of life. In Saudi Arabia, a ten-fold increase in diabetic cases has been documented within the last three years. DM is broadly categorized as Type 1 Diabetes (T1DM), Type 2 Diabetes (T2DM), and Pre-diabetes. The diagnosis of the correct type is sometimes ambiguous to medical professionals causing difficulties in managing the illness progression. Intensive efforts have been made to predict T2DM. However, there is a lack of studies focusing on accurately identifying T1DM and Pre-diabetes. Therefore, this study aims to utilize Machine Learning (ML) to distinguish and predict the three types of diabetes based on a Saudi Arabian hospital dataset to control their progression. Four different experiments have been conducted to achieve the highest results, where several algorithms were used, including Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (K-NN), Decision Tree (DT), Bagging, and Stacking. In experiments 2, 3, and 4, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the dataset. The empirical results demonstrated promising results of the novel Stacking model that combined Bagging K-NN, Bagging DT, and K-NN, with a K-NN meta-classifier attaining an accuracy, weighted recall, weighted precision, and cohen's kappa score of 94.48%, 94.48%, 94.70%, and 0.9172, respectively. Five principal features were identified to significantly affect the model accuracy using the permutation feature importance, namely Education, AntiDiab, Insulin, Nutrition, and Sex.
A Proactive Attack Detection for Heating, Ventilation, and Air Conditioning (HVAC) System Using Explainable Extreme Gradient Boosting Model (XGBoost)
(2022) Khan, Irfan Ullah; Aslam, Nida; Rand AlEssa; AlFrayan, Dina; Dina AlFrayan; AlShuail, Noura A.; Alhawra Al Safwan
The advent of Industry 4.0 has revolutionized the life enormously. There is a growing trend towards the Internet of Things (IoT), which has made life easier on the one hand and improved services on the other. However, it also has vulnerabilities due to cyber security attacks. Therefore, there is a need for intelligent and reliable security systems that can proactively analyze the data generated by these devices and detect cybersecurity attacks. This study proposed a proactive interpretable prediction model using ML and explainable artificial intelligence (XAI) to detect different types of security attacks using the log data generated by heating, ventilation, and air conditioning (HVAC) attacks. Several ML algorithms were used, such as Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Ada Boost (AB), Light Gradient Boosting (LGBM), Extreme Gradient Boosting (XGBoost), and CatBoost (CB). Furthermore, feature selection was performed using stepwise forward feature selection (FFS) technique. To alleviate the data imbalance, SMOTE and Tomeklink were used. In addition, SMOTE achieved the best results with selected features. Empirical experiments were conducted, and the results showed that the XGBoost classifier has produced the best result with 0.9999 Area Under the Curve (AUC), 0.9998, accuracy (ACC), 0.9996 Recall, 1.000 Precision and 0.9998 F1 Score got the best result. Additionally, XAI was applied to the best performing model to add the interpretability in the black-box model. Local and global explanations were generated using LIME and SHAP. The results of the proposed study have confirmed the effectiveness of ML for predicting the cyber security attacks on IoT devices and Industry 4.0.
A putative cytotoxic serine protease from Salmonella typhimurium UcB5 recovered from undercooked burger
(2023) Kotb, Essam; El-Nogoumy, Baher A.; Alqahtani, Haifa A.; Ahmed, Asmaa A.; Haifa A. Alqahtani; Algarudi, Sakina M.; Almahasheer, Hanan
A putative virulence exoprotease designated as UcB5 was successfully purified from the bacterium Salmonella typhimurium to the electrophoretic homogeneity with 13.2-fold and 17.1% recovery by hydrophobic, ion-exchange, and gel permeation chromatography using Phenyl-Sepharose 6FF, DEAE-Sepharose CL-6B, and Sephadex G-75, respectively. By applying SDS-PAGE, the molecular weight was confirmed at 35 kDa. The optimal temperature, pH, and isoelectric point were 35 °C, 8.0, 5.6 ± 0.2, respectively. UcB5 was found to have a broad substrate specificity against almost all the tested chromogenic substrates with maximal affinity against N-Succ-Ala-Ala-Pro-Phe-pNA achieving Km of 0.16 mM, Kcat/Km of 3.01 × 105 S−1 M−1, and amidolytic activity of 28.9 µmol min−1 L−1. It was drastically inhibited by TLCK, PMSF, SBTI, and aprotinin while, DTT, β-mercaptoethanol, 2,2′-bipyridine, o-phenanthroline, EDTA, and EGTA had no effect, which suggested a serine protease-type. Also, it has shown a broad substrate specificity against a broad range of natural proteins including serum proteins. A cytotoxicity and electron microscopy study revealed that UcB5 could cause subcellular proteolysis that finally led to liver necrosis. For this, future research should focus on using a combination of external antiproteases and antimicrobial agents for the treatment of microbial diseases instead of using drugs alone.
A Real-Time Computer Vision Based Approach to Detection and Classification of Traffic Incidents
(2023) Basheer Ahmed, Mohammed Imran; Sarah Alsharif; Ahmed, Mohammed Salih; Bashayr Adnan Albin Saad; Alsharif, Sarah; Mohammed Imran Basheer Ahmed; Albin Saad, Bashayr Adnan; Alsabt, Reema; Atta-ur-Rahman; Gomathi Krishnasamy
To constructively ameliorate and enhance traffic safety measures in Saudi Arabia, a prolific number of AI (Artificial Intelligence) traffic surveillance technologies have emerged, including Saher, throughout the past years. However, rapidly detecting a vehicle incident can play a cardinal role in ameliorating the response speed of incident management, which in turn minimizes road injuries that have been induced by the accident’s occurrence. To attain a permeating effect in increasing the entailed demand for road traffic security and safety, this paper presents a real-time traffic incident detection and alert system that is based on a computer vision approach. The proposed framework consists of three models, each of which is integrated within a prototype interface to fully visualize the system’s overall architecture. To begin, the vehicle detection and tracking model utilized the YOLOv5 object detector with the DeepSORT tracker to detect and track the vehicles’ movements by allocating a unique identification number (ID) to each vehicle. This model attained a mean average precision (mAP) of 99.2%. Second, a traffic accident and severity classification model attained a mAP of 83.3% while utilizing the YOLOv5 algorithm to accurately detect and classify an accident’s severity level, sending an immediate alert message to the nearest hospital if a severe accident has taken place. Finally, the ResNet152 algorithm was utilized to detect the ignition of a fire following the accident’s occurrence; this model achieved an accuracy rate of 98.9%, with an automated alert being sent to the fire station if this perilous event occurred. This study employed an innovative parallel computing technique for reducing the overall complexity and inference time of the AI-based system to run the proposed system in a concurrent and parallel manner.
A Review with Updated Perspectives on Nutritional and Therapeutic Benefits of Apricot and the Industrial Application of Its Underutilized Parts
(2022) Al-Soufi, Maryam Haroon; Fatima Taher Al-Abdulaziz; Fatimah Othman Al Ahmed; Al-Zuwaid, Safa Khalil; Hussah Abdullah al shwyeh; Al-Abdulaziz, Fatima Taher; Haifa Ali Nasser Alqahtani; Hellal, Khaoula; Mohd Nani, Nurul Hidayah; Zubaidi, Siti Norliyana; Asni, Nurul Syahidah Mio; Hamezah, Hamizah Shahirah; Kamal, Nurkhalida; Al-Muzafar, Hessah; Mediani, Ahmed
Fruits maintain the image as the richest sources of vitamins. Focusing on apricots, utilization of apricot species for many applications is possible due to its various benefits. Many research studies demonstrated different perspectives of apricot, especially in medical used as it can act as antioxidant, anti-inflammatory, and antimicrobial agents. Moreover, in the industrial sectors, apricots can be used in the production of biofuels and batteries. All components of the apricot fruit, including seeds and kernels have been found to possess significant interest. This review is to breach the knowledge gap regarding the key nutrients and chemicals of apricot fruit, contributing to its health-promoting properties to emphasize the noble importance of this fruit in the diet and in the management of several diseases. We also cover the application of apricots in the industry that could be developed as a promising and sustainable source.
A Robust Framework for MADS Based on DL Techniques on the IoT
(2021) Hussah Talal Alkhamees; Rachid Zagrouba
A State-of-the-Art Self-Cleaning System Using Thermomechanical Effect in Shape Memory Alloy for Smart Photovoltaic Applications
(2022) Ibrahim Khalil Almadani; Ibrahim Sufian Osman; Osman, Ibrahim Sufian
This research aims to present a state-of-the-art cleaning technology solution that effectively overcomes the dust accumulation issue for conventional photovoltaic systems. Although continuous innovations and advanced developments within renewable energy technologies have shown steady improvements over the past years, the dust accumulation issue remains one of the main factors hindering their efficiency and degradation rate. By harvesting abundant solar thermal energy, the presented self-cleaning system uses a unique thermomechanical property of Shape Memory Alloys to operate a solar-based thermomechanical actuator. Therefore, this study carries out different numerical and experimental validation tests to highlight the promising practicability of the developed self-cleaning system from thermal and mechanical perspectives. The results showed that the system has a life expectancy of over 20 years, which is closely equivalent to the life expectancy of conventional photovoltaic modules while operating under actual weather conditions in Dammam city. Additionally, the thermal to mechanical energy conversion efficiency reached 19.15% while providing average cleaning effectiveness of about 95%. The presented outcomes of this study add to the body of knowledge an innovative methodology for a unique solar-based self-cleaning system aimed toward smart and modern photovoltaic applications.
A strong smartphone authentication model to control cellular network access using blockchain
(2021) Shikah J. Alsunaid; Abdullah M. Almuhaideb
In an era that requests facilitating access to most services by providing the online platforms, the need to own a small electronic device is increasing. The smartphone is the ideal choice for most people due to its multifunctionality. On the other hand, the rate of physical crimes on these devices raised, especially theft and counterfeiting. As a solution to mitigate these crimes, stolen or counterfeit devices are prevented from connecting to the cellular network, but is this solution effective in mitigating the problem? To answer this question, this paper conducts a comprehensive survey on the current solutions tackled the smartphone theft or counterfeit issues, and it provides a taxonomy classify the current solutions into three categories: manufacturers solutions, cellular service provider solutions, and third-party solutions. The proposed smartphone authentication model for cellular network authentication utilizes the permissioned Blockchain network and consists of three protocols, which are smartphone registration protocol, ownership transfer protocol, and cellular network authentication protocol. In order to overcome the two serious physical crimes (theft and counterfeiting), the paper identifies five main requirements that must be met in any cellular network authentication approach, namely ability to detect counterfeit IMEI, ability to identify the device owner, ability to block the service on the stolen/lost devices, less processing time, and acceptable performance. The comparisons with related solutions showed that the proposed work meets all the requirements to mitigate smartphone theft and counterfeiting crimes.
A study of empathy levels among nursing interns: a cross-sectional study
(2023) Suaad Ghazwani; Alshowkan, Amira; AlSalah, Nagla
Empathy is one of the therapeutic communication techniques used to help the client feel better. However, there are a few studies have investigated level of empathy among enrollers at nursing colleges. The aim was to examine the level of self-reported empathy among nursing interns.
A survey of elastase-producing bacteria and characteristics of the most potent producer, Priestia megaterium gasm32
(2023) Ghadah A. AlShaikh-Mubarak; Essam Kotb; Alabdalall, Amira H.; Aldayel, Munirah F.
Ninety-one elastase-producing bacterial isolates were recovered from different localities of the Eastern Province of Saudi Arabia. Elastase from the best isolate Priestia megaterium gasm32, from luncheon samples was purified to electrophoretic homogeneity using DEAE-Sepharose CL-6B and Sephadex G-100 chromatographic techniques. The recovery was 17.7%, the purification fold was 11.7x, and the molecular mass was 30 kDa. Enzymatic activity was highly repressed by Ba2+ and almost completely lost by EDTA, but it was greatly stimulated by Cu2+ ions, suggesting a metalloprotease type. The enzyme was stable at 45°C and pH 6.0–10.0 for 2 hours. Ca2+ ions considerably enhanced the stability of the heat-treated enzyme. The Vmax and Km against the synthetic substrate elastin–Congo red were 6.03 mg/mL, and 8.82 U/mg, respectively. Interestingly, the enzyme showed potent antibacterial activity against many bacterial pathogens. Under SEM, most bacterial cells showed loss of integrity, damage, and perforation. SEM micrographs also showed a time-dependent gradual breakdown of elastin fibers exposed to elastase. After 3 hours, intact elastin fibers disappeared, leaving irregular pieces. Given these good features, this elastase may be a promising candidate for treating damaged skin fibers with the inhibition of contaminating bacteria.
A Systematic Literature Review on Cyber Threat Intelligence for Organizational Cybersecurity Resilience
(2023) Manal Alghamdi; Sarah Alsuayyid; Hayfa Al-Muhaisen; Al-Muhaisen, Hayfa; Almuhaideb, Abdullah M.
Cybersecurity is a significant concern for businesses worldwide, as cybercriminals target business data and system resources. Cyber threat intelligence (CTI) enhances organizational cybersecurity resilience by obtaining, processing, evaluating, and disseminating information about potential risks and opportunities inside the cyber domain. This research investigates how companies can employ CTI to improve their precautionary measures against security breaches. The study follows a systematic review methodology, including selecting primary studies based on specific criteria and quality valuation of the selected papers. As a result, a comprehensive framework is proposed for implementing CTI in organizations. The proposed framework is comprised of a knowledge base, detection models, and visualization dashboards. The detection model layer consists of behavior-based, signature-based, and anomaly-based detection. In contrast, the knowledge base layer contains information resources on possible threats, vulnerabilities, and dangers to key assets. The visualization dashboard layer provides an overview of key metrics related to cyber threats, such as an organizational risk meter, the number of attacks detected, types of attacks, and their severity level. This relevant systematic study also provides insight for future studies, such as how organizations can tailor their approach to their needs and resources to facilitate more effective collaboration between stakeholders while navigating legal/regulatory constraints related to information sharing.

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