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| 1 | Blood chemical changes and renal histological alterations induced by gentamicin in rats显示文摘 | Saud Alarifi Amin Al-Doaiss Saad Alkahtani S.A. Al-Farraj Mohammed Saad Al-Eissa B. Al-Dahmash Hamad Al-Yahya Mohammed Mubarak | 2011 | Saudi Journal of Biological Sciences2011，，1： | 1 |
| 2 | Gene expression of 49 kDa apyrase,cytoskeletal proteins,ATPase,ADPase and amino acid contents of Pisum sativum(L.)cells germinated in Euryops arabicus(Steud.ex Jaub.&Spach)water extract显示文摘The present research reports of quick and marked changes induced by plant extract of Euryops arabicus in the gene expression of 49-kDa apyrases,cytoskeletal proteins,ATPases,ADPase and amount of amino acid of pea(Pisum sativum L.var.Alaska).Pellets of cytoskeletals proteins(27000 xg)were probed with anti-apyrase antibody,biotinylated anti-rat,actin and alpha and beta-tubulin for Western blotting.ATPase and ADPase activities were determined based on the hydrolytic efficacy of adenine triphosphate and adenine diphosphate.By 72 hours,the abundance of apyrases,cytoskeletal proteins and amount of amino acid in pellets of 27000 xg of germinated pea seeds in E.arabicus extracts were sharply increased than those sown in distilled water.All the samples exhibited that the stems had more amount from apyrases,cytoskeletal proteins,amino acids and ATPase and ADPase activities than primary leaves and primary roots that were germinated either on E.arabicus water extract or in distilled water.Based on the enzyme’s capability to hydrolyse nucleotide triphosphate and nucleotide diphosphate as well as the direct association between expression of 49-kDa apyrase and cytoskeletal proteins,E.arabicus water extract had an important effect on plant germinations. | MAHMOUD MOUSTAFA SAAD ALAMRI HOIDA ZAKI NAGLAA LOUTFY TAREK TAHA ALI SHATI MOHAMED ALKAHTANI SAJDA SIDDIQI | 2019 | BIOCELL2019，43，2： | 1 |
| 3 | Energy Theft Identification Using Adaboost Ensembler in the Smart Grids显示文摘One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques. | Muhammad Irfan Nasir Ayub Faisal Althobiani Zain Ali Muhammad Idrees Saeed Ullah Saifur Rahman Abdullah Saeed Alwadie Saleh Mohammed Ghonaim Hesham Abdushkour Fahad Salem Alkahtani Samar Alqhtani Piotr Gas | 2022 | Computers, Materials & Continua2022，，7： | 1 |
| 4 | Formability of Materials with Small Tools in Incremental Forming显示文摘Single point incremental forming(SPIF)is an innovative sheet forming process with a high economic pay-off.The formability in this process can be maximized by executing forming with a tool of specific small radius,regarded as threshold critical radius.Its value has been reported as 2.2 mm for 1 mm thick sheet materials.However,with a change in the forming conditions specifically in the sheet thickness and step size,the critical radius is likely to alter due to a change in the bending condition.The main aim of the present study is to undertake this point into account and develop a relatively generic condition.The study is composed of experimental and numerical investigations.The maximum wall angle(θmax)without sheet fracturing is regarded as sheet formability.A number of sheet materials are formed to fracture and the trends correlating formability with normalized radius(i.e.,R/To where R is the tool-radius and To is the sheet thickness)are drawn.These trends confirm that there is a critical tool-radius(Rc)that maximizes the formability in SPIF.Furthermore,it is found that the critical radius is not fixed rather it shows dependence on the sheet thickness such that Rc=βTo,whereβvaries from 2.2 to 3.3 as the thickness increases from 1 mm to 3 mm.The critical radius,however,remains insensitive to variation in step size ranging from 0.3 mm to 0.7 mm.This is also observed that the selection of tool with R | Hongyu Wei G.Hussain X.Shi B.B.L Isidore Mohammed Alkahtani Mustufa Haider Abidi | 2020 | Chinese Journal of Mechanical Engineering2020，33，4： | 1 |
| 5 | Human-Animal Affective Robot Touch Classification Using Deep Neural Network显示文摘Touch gesture recognition is an important aspect in human-robot interaction,as it makes such interaction effective and realistic.The novelty of this study is the development of a system that recognizes human-animal affective robot touch(HAART)using a deep learning algorithm.The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015.The dataset was tested with numerous subjects performing different HAART gestures;each touch was performed on a robotic animal covered by a pressure sensor skin.A convolutional neural network algorithm is proposed to implement the touch recognition system from row inputs of the sensor devices.The leave-one-subject-out cross-validation method was used to validate and evaluate the proposed system.A comparative analysis between the results of the proposed system and the state-of-the-art performance is presented.Findings show that the proposed system could recognize the gestures in almost real time(after acquiring the minimum number of frames).According to the results of the leave-one-subject-out cross-validation method,the proposed algorithm could achieve a classification accuracy of 83.2%.It was also superior compared with existing systems in terms of classification ratio,touch recognition time,and data preprocessing on the same dataset.Therefore,the proposed system can be used in a wide range of real applications,such as image recognition,natural language recognition,and video clip classification. | Mohammed Ibrahim Ahmed Al-mashhadani Theyazn H.H.Aldhyani Mosleh Hmoud Al-Adhaileh Alwi M.Bamhdi Mohammed Y.Alzahrani Fawaz Waselallah Alsaade Hasan Alkahtani | 2021 | Computer Systems Science & Engineering2021，38，7： | 0 |
| 6 | Cloning and characterization of 66 kDa streptavidin-binding peptides(SBP)of Pisum sativum L.embryo specific to var.Alaska显示文摘The aim of the current research was to clone and to characterize the partial 66 kDa streptavidin-binding peptide(SBP)found in the germinated embryos of Pisum sativum L.var.Alaska.The pea(P.sativum var.Alaska)embryos possess prominent 66 kDa SBPs that gradually disappeared after few hours of germination in germinated embryos,but not in the cotyledons.The total RNA was isolated from embryos of P.sativum but could not be isolated from the cotyledons.The partial nucleotides sequences of 66 kDa SBPs of embryonic stalk(P.sativum var.Alaska)were cloned and identified using pMOSBlue vector.66 kDa(SBP)gene from the embryos of P.sativum var.Alaska possesses 327 bp having an open reading frame(ORF)region in a part of the gene that encoded for 108 amino acids.Alignment showed similarity among 66 kDa SBPs P.sativum var.Alaska,with P.sativum seed biotinylated protein(SBP65)and P.sativum sbp65a mRNA with DNA distance matrix between 0.0094 to 1.2676.MALDI-TOF mass spectrometry analysis of 66 kDa(SBP)proteins showed it had similar short peptides to 19 proteins found in different organisms,especially Convicilin precursor,and the seed biotinylated protein in P.sativum.The alignment results of both nucleotide sequences and amino acid residues either from cloning or MALDI-TOF-MS showed differences with related species,especially P.sativum.No mRNA was found in the cotyledons during seeds germination,which means no metabolic activities and this part may act only as food reservoirs for growing newly embryos. | MAHMOUD MOUSTAFA SAAD ALAMRI TAREK TAHA ALI SHATI SULAIMAN ALRUMMAN MOHAMED ALKAHTANI | 2019 | BIOCELL2019，43，3： | 0 |
| 7 | In vitro inhibitory analysis of consensus siRNAs against NS3 gene of hepatitis C virus 1a genotype显示文摘Objective: To explore inhibitory effects of genome-specific, chemically synthesized siRNAs(small interference RNA) against NS3 gene of hepatitis C virus(HCV) 1a genotype in stable Huh-7(human hepatoma) cells as well as against viral replication in serum-inoculated Huh-7 cells. Methods: Stable Huh-7 cells persistently expressing NS3 gene were produced under antibiotic gentamycin(G418) selection. The cell clones resistant to 1 000 μg antibiotic concentration(G418) were picked as stable cell clones. The NS3 gene expression in stable cell clone was confirmed by RT-PCR and Western blotting. siRNA cell cytotoxicity was determined by MTT cell proliferation assay. Stable cell lines were transfected with sequence specific siRNAs and their inhibitory effects were determined by RT-PCR, real-time PCR and Western blotting. The viral replication inhibition by siRNAs in serum inoculated Huh-7 cells was determined by real-time PCR. Results: RT-PCR and Western blot analysis confirmed NS3 gene and protein expression in stable cell lines on day 10, 20 and 30 post transfection. MTT cell proliferation assay revealed that at most concentrated dose tested(50 nmol/L), siRNA had no cytotoxic effects on Huh-7 cells and cell proliferation remained unaffected. As demonstrated by the siRNA time-dependent inhibitory analysis, siRNA NS3-is44 showed maximum inhibition of NS3 gene in stable Huh-7 cell clones at 24(80%, P=0.013) and 48 h(75%, P=0.002) post transfection. The impact of siRNAs on virus replication in serum inoculated Huh-7 cells also demonstrated significant decrease in viral copy number, where siRNA NS3-is44 exhibited 70%(P<0.05) viral RNA reduction as compared to NS3-is33, which showed a 64%(P<0.05) decrease in viral copy number. siRNA synergism(NS3-is33 + NS3-is44) decreased viral load by 84%(P<0.05) as compared to individual inhibition by each siRNA(i.e., 64%–70%(P<0.05) in serum-inoculated cells. Synthetic siRNAs mixture(NS5Bis88 + NS3-is33) targeting different region of HCV genome(NS5B and NS3) also decreased HCV viral load by 85%(P< 0.05) as compared to siRNA inhibitory effects alone(70% and 64% respectively, P<0.05). Conclusions: siRNAs directed against NS3 gene significantly decreased m RNA and protein expression in stable cell clones. Viral replication was also vividly decreased in serum infected Huh-7 cells. Stable Huh-7 cells expressing NS3 gene is helpful to develop anti-hepatitis C drug screening assays. siRNA therapeutic potential along with other anti-HCV agents can be considered against hepatitis C. | Imran Shahid Waleed Hassan Al Malki Mohammed Wanees Al Rabia Mohammed Hasan Mukhtar Shaia Saleh R.Almalki Saad Ahmed Alkahtani Sami S.Ashgar Hani S.Faidah Muhammad Hassan Hafeez | 2017 | Asian Pacific Journal of Tropical Medicine2017，10，7： | 0 |
| 8 | Addressing Economic Dispatch Problem with Multiple Fuels Using Oscillatory Particle Swarm Optimization显示文摘Economic dispatch has a significant effect on optimal economical operation in the power systems in industrial revolution 4.0 in terms of considerable savings in revenue.Various non-linearity are added to make the fossil fuel-based power systems more practical.In order to achieve an accurate economical schedule,valve point loading effect,ramp rate constraints,and prohibited operating zones are being considered for realistic scenarios.In this paper,an improved,and modified version of conventional particle swarm optimization(PSO),called Oscillatory PSO(OPSO),is devised to provide a cheaper schedule with optimum cost.The conventional PSO is improved by deriving a mechanism enabling the particle towards the trajectories of oscillatory motion to acquire the entire search space.A set of differential equations is implemented to expose the condition for trajectory motion in oscillation.Using adaptive inertia weights,this OPSO method provides an optimized cost of generation as compared to the conventional particle swarm optimization and other new meta-heuristic approaches. | Jagannath Paramguru Subrat Kumar Barik Ajit Kumar Barisal Gaurav Dhiman Rutvij HJhaveri Mohammed Alkahtani Mustufa Haider Abidi | 2021 | Computers, Materials & Continua2021，，12： | 0 |
| 9 | RSS-Based Selective Clustering Technique Using Master Node for WSN显示文摘Wireless sensor networks(WSN)are designed to monitor the physical properties of the target area.The received signal strength(RSS)plays a significant role in reducing sensor node power consumption during data transmission.Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span.This paper introduces the RSS-based energy-efficient selective clustering technique using a master node(RESCM)to improve energy utilization using a master node.The master node positioned at the center of the network area and base station(BS)is placed outside the network area.During cluster head(CH)selection,the node with a high RSS value is more likely to become CH.The network is divided into segments according to the distance from the master node.All nodes near BS or master node transmit their data using direct transmission without the clustering process.The simulation results showed that the RESCM method improves the total network lifespan effectively. | Vikram Rajpoot Vivek Tiwari Akash Saxena Prashant Chaturvedi Dharmendra Singh Rajput Mohammed Alkahtani Mustufa Haider Abidi | 2021 | Computers, Materials & Continua2021，，12： | 0 |
| 10 | Deep Neural Networks Based Approach for Battery Life Prediction显示文摘The Internet of Things(IoT)and related applications have witnessed enormous growth since its inception.The diversity of connecting devices and relevant applications have enabled the use of IoT devices in every domain.Although the applicability of these applications are predominant,battery life remains to be a major challenge for IoT devices,wherein unreliability and shortened life would make an IoT application completely useless.In this work,an optimized deep neural networks based model is used to predict the battery life of the IoT systems.The present study uses the Chicago Park Beach dataset collected from the publicly available data repository for the experimentation of the proposed methodology.The dataset is pre-processed using the attribute mean technique eliminating the missing values and then One-Hot encoding technique is implemented to convert it to numerical format.This processed data is normalized using the Standard Scaler technique.Moth Flame Optimization(MFO)Algorithm is then implemented for selecting the optimal features in the dataset.These optimal features are finally fed into the DNN model and the results generated are evaluated against the stateof-the-art models,which justify the superiority of the proposed MFO-DNN model. | Sweta Bhattacharya Praveen Kumar Reddy Maddikunta Iyapparaja Meenakshisundaram Thippa Reddy Gadekallu Sparsh Sharma Mohammed Alkahtani Mustufa Haider Abidi | 2021 | Computers, Materials & Continua2021，，11： | 0 |
| 11 | Classification and Categorization of COVID-19 Outbreak in Pakistan显示文摘Coronavirus is a potentially fatal disease that normally occurs in mammals and birds.Generally,in humans,the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person.Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses.In December 2019,a new variant,i.e.,a novel coronavirus(COVID-19)developed in Wuhan province,China.Since January 23,2020,the number of infected individuals has increased rapidly,affecting the health and economies of many countries,including Pakistan.The objective of this research is to provide a system to classify and categorize the COVID-19 outbreak in Pakistan based on the data collected every day from different regions of Pakistan.This research also compares the performance of machine learning classifiers(i.e.,Decision Tree(DT),Naive Bayes(NB),Support Vector Machine,and Logistic Regression)on the COVID-19 dataset collected in Pakistan.According to the experimental results,DT and NB classifiers outperformed the other classifiers.In addition,the classified data is categorized by implementing a Bayesian Regularization Artificial Neural Network(BRANN)classifier.The results demonstrate that the BRANN classifier outperforms state-of-the-art classifiers. | Amber Ayoub Kainaat Mahboob Abdul Rehman Javed Muhammad Rizwan Thippa Reddy Gadekallu Mustufa Haider Abidi Mohammed Alkahtani | 2021 | Computers, Materials & Continua2021，，10： | 0 |
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