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Investigation for Uranium Dispersion Adjacent to Cretaceous Phosphatic Outcrops in al-Nassrieh Basin, Southern Palmyrides, Syria

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Journal of Earth Science 2016年第5期27卷 786-795页

作者： Mohamed al-hilal Ahmad al-ali Department of Geology the Atomic Energy Commission of Syria (AECS)

A detailed radiometric survey including soil gas radon measurements and field gammaray survey, in accordance with geochemical and geological investigations were carried out in alNassrieh Basin（central Syria）, for the purpose of uranium exploration. Thirty-six samples were collected from various lithofacies of the survey area and analyzed by γ-ray spectrometric technique for determining the concentration values of major radioelements. The relationships between the concentrations of equivalent e U, e Th, and their ratios were examined in order to define their trend of variations and evaluate the degree of uranium remobilization and redistribution. although the initial results indicate that uranium enrichment is mostly restricted to the Upper Cretaceous phosphate rocks, a considerable portion of uranium appeared to be leached out of the primary phosphatic source and dispersed in the adjacent recent sediments. Further, notable increases of radon level associated with relatively high values of uranium concentration and gamma count rates were found to be spatially correlated with the transition zone between the marine Paleogene and continental Neogene formations throughout the study basin. This observation demonstrates the importance of the concerned zone as a suitable geological environment for hosting probable uranium mineralization along a chemically reducing interface where surface water of terrestrial and marine origin mingled at depth and away from surficial conditions.

关键词： radiometric survey geochemistry uranium exploration AI-Nassrieh Basin Syria.

Exteriorized colon anastomosis for unprepared bowel: An alternative to routine colostomy

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World Journal of Gastroenterology 2007年第23期13卷 3215-3220页

作者： Sami K Asfar hilal M al-Sayer Talib H Juma Professor and Chairman Department of Surgery Faculty of Medicine Kuwait University and Mubarak Al-Kabeer Hospital Safat-13110 Kuwait Associate Professor Department of Surgery Faculty of Medicine Kuwait University and Department of Surgery Ameri Hospital Kuwait Consultant Surgeon Department of Surgery Ameri Hospital Kuwait

AIM: To see the possibility of avoiding routine colostomy in patients presenting with unprepared bowel. METHODS: The cohort is composed of 103 patients, of these, 86 patients presented as emergencies (self- inflected and iatrogenic colon injuries, stab wounds and blast injury of the colon, volvulus sigmoid, obstructing left colon cancer, and strangulated ventral hernia). Another 17 patients were managed electively for other colon pathologies. During laparotomy, the involved segment was resected and the two ends of the colon were brought out via a separate colostomy wound. One layer of interrupted 3/0 silk was used for colon anastomosis. The exteriorized segment was immediately covered with a colostomy bag. Between the 5th and 7th postoperative day, the colon was easily dropped into the peritoneal cavity. The defect in the abdominal wall was closed with interrupted nonabsorbable suture. The skin was left open for secondary closure. RESULTS: The mean hospital stay (± SD) was 11.5 ± 2.6 d (8-20 d). The exteriorized colon was successfully dropped back into the peritoneal cavity in all patients except two. One developed a leak from oesophago- jejunostomy and from the exteriorized colon. She subsequently died of sepsis and multiple organ failure (MOF). In a second patient the colon proximal to the exteriorized anastomosis prolapsed and developed severe serositis, an elective ileo-colic anastomosis (to the left colon) was successfully performed. CONCLUSION: Exteriorized colon anastomosis is simple, avoids the inconvenience of colostomy and can be an alternative to routine colostomy. It is suitable where colostomy is socially unacceptable or the facilities and care is not available.

关键词： Colostomy Exteriorized colon Colon injury Colon anastomosis Unprepared colon Obstructing colon cancer Volvulus sigmoid

Electrochemical sensor investigation of carbon-supported PdCoAg multimetal catalysts using sugar-containing beverages

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Frontiers of Chemical Science and Engineering 2020年第4期14卷 629-638页

作者： Firat Salman hilal C.Kazici hilal Kivrak Department of Chemical Engineering Faculty of EngineeringVan Yizinci Yil University65000 VanTurkey

Novel PdCoAg/C nanostructures were suc-cessfully synthesized by the polyol method in order to develop electrocatalysts,related to the glucose sensor performance of the high glycemic index in *** characterization of this novel PdCoAg/C electrocatalyst was performed by X-ray diffraction,scanning electron microscopy,transmission clectron microscopy,and high-resolution transmission electron microscopy equipped with energy dispersive *** characterization results revealed that electronic state of the PdCoAg/C electro-catalyst was modified by the addition of the third *** electrochemical performances of the sensor were investigated by cyclic voltammetry and differential pulse *** prepared enzyme-free sensor exhibited excellent catalytic activity against glucose with a wide detection range(0.005 to 0.35 mmol·L^-1),low limit of detection(0.003 mmol·L^-1),high sensitivity(4156.34μA·mmol^-1·L·cm^-3),and long-term stability(10 days)because of the synergistic effect between the termary *** glucose contents of several energy drinks,fruit juices,and carbonated beverages were analyzed using the novel PdCoAgNGCE/C sensor *** results indicate the feasibility for applications in the foods industry.

关键词： non-enzymatic glucose detection ternary metals glycemic index beverages

Exploration of the Townsend regime by discharge light emission in a gas discharge device

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Chinese Physics B 2014年第1期23卷 289-296页

作者： hilal Yucel Kurt Gazi University Faculty of Sciences Department of Physics 06500 Ankara Turkey

The Townsend discharge mechanism has been explored in a planar microelectronic gas discharge device （MGDD） with different applied voltages U and interelectrode distance d under various pressures in air. The anode and the cathode of the MGDD are formed by a transparent SnO2 covered glass and a GaAs semiconductor, respectively. In the experiments, the discharge is found to be unstable just below the breakdown voltage Ub, whereas the discharge passes through a homo- geneous stable Townsend mode beyond the breakdown voltage. The measurements are made by an electrical circuit and a CCD camera by recording the currents and light emission （LE） intensities. The intensity profiles, which are converted from the 3D light emission images along the semiconductor diameter, have been analysed for different system parameters. Dif- ferent instantaneous conductivity ~t regimes are found below and beyond the Townsend region. These regimes govern the current and spatio-temporal LE stabilities in the plasma system. It has been proven that the stable LE region increases up to 550 Torr as a function of pressure for small d. If the active area of the semiconductor becomes larger and the interlectrode distance d becomes smaller, the stable LE region stays nearly constant with pressure.

关键词： Townsend discharge breakdown microelectronic gas discharge device semiconductor cathode

Optimal Wavelet Neural Network-Based Intrusion Detection in Internet of Things Environment

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Computers, Materials & Continua 2023年第5期75卷 4467-4483页

作者： Heba G.Mohamed Fadwa alrowais Mohammed Abdullah al-Hagery Mesfer al Duhayyim Anwer Mustafa hilal Abdelwahed Motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box84428Riyadh 11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box84428Riyadh 11671Saudi Arabia Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

As the Internet of Things(IoT)endures to develop,a huge count of data has been *** IoT platform is rather sensitive to security challenges as individual data can be leaked,or sensor data could be used to cause *** typical intrusion detection system(IDS)studies can be frequently designed for working well on databases,it can be unknown if they intend to work well in altering network *** learning(ML)techniques are depicted to have a higher capacity at assisting mitigate an attack on IoT device and another edge system with reasonable *** article introduces a new Bird Swarm algorithm with Wavelet Neural Network for Intrusion Detection(BSAWNN-ID)in the IoT *** main intention of the BSAWNN-ID algorithm lies in detecting and classifying intrusions in the IoT *** BSAWNN-ID technique primarily designs a feature subset selection using the coyote optimization algorithm(FSS-COA)to attain ***,to detect intrusions,the WNN model is *** last,theWNNparameters are optimally modified by the use of *** experiment is performed to depict the better performance of the BSAWNNID *** resultant values indicated the better performance of the BSAWNN-ID technique over other models,with an accuracy of 99.64%on the UNSW-NB15 dataset.

关键词： Internet of things wavelet neural network security intrusion detection machine learning

Artificial Intelligence Based Optimal Functional Link Neural Network for Financial Data Science

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Computers, Materials & Continua 2022年第3期70卷 6289-6304页

作者： Anwer Mustafa hilal Hadeel alsolai Fahd Nal-Wesabi Mohammed Abdullah al-Hagery Manar Ahmed Hamza Mesfer al Duhayyim Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their *** science integrates the conventions of econometrics with the technological elements of data *** make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related *** technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient *** the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial *** becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time *** this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model *** proposed model involves preprocessing,feature selection,classification,and parameter *** the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the ***,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of ***,functional link neural network(FLNN)model is employed for the classification of the feature reduced ***,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented *** experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively.

关键词： Data science small and medium-sized enterprises business sectors financial crisis prediction intelligent systems artificial intelligence decision making machine learning

Intelligent Machine Learning Based EEG Signal Classification Model

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Computers, Materials & Continua 2022年第4期71卷 1821-1835页

作者： Mesfer al Duhayyim Haya Mesfer alshahrani Fahd N.al-Wesabi Mohammed Abdullah al-Hagery Anwer Mustafa hilal Abu Sarwar Zaman Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and *** systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,***(EEG)is a technique commonly applied in capturing brain *** is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and *** classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and *** this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI *** Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG ***,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG ***,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was *** outcomes were examined under distinct *** experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.

关键词： Brain computer interface EEG recognition human computer interface machine learning parameter tuning FSVM

Machine Learning Based Depression,Anxiety,and Stress Predictive Model During COVID-19 Crisis

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Computers, Materials & Continua 2022年第3期70卷 5803-5820页

作者： Fahd N.al-Wesabi Hadeel alsolai Anwer Mustafa hilal Manar Ahmed Hamza Mesfer al Duhayyim Noha Negm Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Computer Science Department King Khaled UniversityKSA Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt

Corona Virus Disease-2019(COVID-19)was reported at first in Wuhan city,China by December *** Health Organization(WHO)declared COVID-19 as a pandemic i.e.,global health crisis onMarch 11,*** outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread,not only affected the economic status of a number of countries,but it also resulted in increased levels of Depression,Anxiety,and Stress(DAS)among ***,there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear;with tremendously-limitingmeasures of social distancing and lockdown in force;and with high rates of new cases and *** this motivation,the current study aims at investigating theDAS levels among college students during COVID-19 lockdown since they are identified as a highly-susceptible *** current study proposes to develop Intelligent Feature Subset Selection withMachine Learning-based DAS predictive(IFSSML-DAS)*** presented IFSSML-DAS model involves data preprocessing,Feature Subset Selection(FSS),classification,and parameter ***,IFSSML-DAS model uses Group Gray Wolf Optimization based FSS(GGWO-FSS)technique to reduce the curse of *** addition,Beetle Swarm Optimization based Least Square Support Vector Machine(BSO-LSSVM)model is also employed for classification in which the weight and bias parameters of the LSSVM model are optimally adjusted using BSO *** performance of the proposed IFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different *** outcome of the study suggests the development of specialized programs to handleDAS among population so as to overcome COVID-19 crisis.

关键词： Psycho-social factors covid-19 crisis management predictive models decision making machine learning

Securing Arabic Contents algorithm for Smart Detecting of Illegal Tampering Attacks

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Computers, Materials & Continua 2022年第2期70卷 2879-2894页

作者： Mesfer al Duhayyim Manal Abdullah alohali Fahd N.al-Wesabi Anwer Mustafa hilal Mohammad Medani Manar Ahmed Hamza Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Themost common digital media exchanged via the Internet is in text *** Arabic language is considered one of themost sensitive languages of content modification due to the presence of diacritics that can cause a change in the *** this paper,an intelligent scheme is proposed for improving the reliability and security of the text exchanged via the *** core mechanism of the proposed scheme depends on integrating the hidden Markov model and zero text watermarking *** watermark key will be generated by utilizing the extracted features of the text analysis process using the third order and word level of the Markov *** Embedding and detection processes of the proposed scheme will be performed logically without the effect of the original *** proposed scheme is implemented using PHP with VS code *** simulation results,using varying sizes of standard datasets,show that the proposed scheme can obtain high reliability and provide better accuracy of the common illegal tampering *** results with other baseline techniques show the added value of the proposed scheme.

关键词： Tampering detection zero-watermarking soft computing text analysis hidden Markov model