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Provenance and depositional setting of the Late miocenePleistocene clastic sediments in the eastern Arabian Peninsula and western Iraq using rare earth elements geochemistry

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Acta Geochimica 2024年第2期43卷 399-410页

作者： salih m.Awadh Zinah S.Al-Ankaz mohanad R.A.Al-Owaidi John S.Armstrong-Altrin Department of Geology College of ScienceUniversity of BaghdadBaghdadIraq Department of Geology College of ScienceUniversity of WasitWasit 52011Iraq Department of Applied Geology College of ScienceUniversity of BabylonBabylon 51001Iraq Unidad de Procesos Oceanicos y Costeros Instituto de Ciencias del Mar y LimnologíaUniversidad Nacional Autonoma de MexicoCiudad Universitaria04510 Mexico CityCDMXMexico Department of marine Sciences Bharathidasan UniversityTiruchirapalliTamil Nadu 620024India

The sandstones of the Late miocene–Pliocene Dibdibba Formation in the Najaf–Karbala Plateau and Basra were examined to determine their source rocks and origin. The rare earth elements(REE) and trace elements(Sc, Co, V, and Th) concentrations in these sandstones revealed that they likely derived from a single source. The steep light rare earth elements(LREE) and flat, heavy rare earth element(HREE) patterns, negative Eu anomaly, and high ΣREE contents in sandstones suggest its derivation from a suggests that a passive continental margin environment and originated from felsic source rocks. The average concentration of ΣREE is 93.5 ppm, which is lower than that of the average crustal compositions like Upper Continental Crust and Post Archean Australian Shale. The higher proportion of LREE compared to HREE implies that these sandstones were recycled and derived from a distal source. The Th/Co, Th/Sc, La/Sc, La/Co, Eu/Eu*and(La/Lu)cn elemental ratios indicated that these Late miocene–Pliocene sandstones were derived from felsic rocks located in the marginal region of the Arabian Shield.

关键词： REE Dibdibba formation Clastic sediments Passive continental margin

Exploring the Implications of the Deformation Parameter and minimal Length in the Generalized Uncertainty Principle

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Journal of Quantum Information Science 2024年第1期14卷 1-14页

作者： mahgoub A. salih Taysir m. Elmahdi Department of Physics Faculty of Science and Arts in Al-Miznab Qassim University Al Miznab Saudi Arabia

The breakdown of the Heisenberg Uncertainty Principle occurs when energies approach the Planck scale, and the corresponding Schwarzschild radius becomes similar to the Compton wavelength. Both of these quantities are approximately equal to the Planck length. In this context, we have introduced a model that utilizes a combination of Schwarzschild’s radius and Compton length to quantify the gravitational length of an object. This model has provided a novel perspective in generalizing the uncertainty principle. Furthermore, it has elucidated the significance of the deforming linear parameter β and its range of variation from unity to its maximum value.

关键词： Generalized Uncertainty Principle Deformed Heisenberg Algebra minimal Length

Exogenous application of bio-stimulants and growth retardants improve nutrient absorption and fiber quality in upland cotton

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Journal of Cotton Research 2024年第2期7卷 137-147页

作者： Al-Khayri Jameel m. Arif muhammad Kareem Shadia Hama salih Anwar Adeel Dehghanisanij Hossein Emami Somayeh Yasmeen Azra Aftab Komal Negm mohamed Department of Agricultural Biotechnology College of Agriculture and Food SciencesKing Faisal University31982 Al‑AhsaSaudi Arabia Water Resources Zone Irrigation Department PunjabD.G.KhanPakistan Department of Biotechnology and Crop Science College of Agricultural Engineering SciencesUniversity of SulaimaniSulaimaniIraq Department of Agronomy PMAS-Arid Agriculture UniversityRawalpindiPakistan Agricultural Research Education and Extension OrganizationAgricultural Engineering Research InstituteKarajAlborzIran Department of Water Engineering University of TabrizTabrizIran Department of Agronomy Bahauddin Zakariya UniversityMultanPakistan Department of Plant Breeding and Genetics Bahauddin Zakariya UniversityMultanPakistan Agricultural Research Center Cotton Research InstituteGiza 12619Egypt

Background Natural and synthetic plant growth regulators are essential for plant health,likewise these regulators also play a role in increasing organic production productivity and improving quality and yield stability.In the present study,we have evaluated the effects of foliar applied plant growth regulators,i.e.,moringa leaf extract(mLE)and mepiquat chloride(mC)alone and in combination mC and mLE on the conventional cotton cultivar(CIm 573)and transgenic one(CIm 598).The growth regulators were applied at the start of bloom,45 and 90 days after blooming.Results The application of mC and mLE at 90 days after blooming significantly improved the relative growth rate,net assimilation rate,the number of bolls per plant,and seed cotton yield.Likewise,the combined application of mLE and mC at 90 days after blooming significantly boosted the nitrogen uptake in locules,as well as the phosphorus and potassium uptake in the leaves of both cotton cultivars.The application of mLE alone has considerably improved the nitrogen uptake in leaves,and phosphorus and potassium contents in locules of Bt and conventional cotton cultivars.Similarly,Bt cotton treated with mLE at 90 days after blooming produced significantly higher ginning out turn and oil contents.Treatment in combination(mLE+mC)at 90 days after blooming produced considerably higher micronaire value,fiber strength,and staple length in conventional cultivar.Conclusion The natural growth enhancer,mLE is a rich source of minerals and zeatin,improving the nutrient absorption and quality of cotton fiber in both conventional and Bt cotton cultivars.

关键词： Bio-stimulant Cotton Fiber quality mepiquat chloride moringa leaf extract Potassium accumulation Oil contents

Tigris River Water Quality Quantifying Using the Iraq Water Quality Index (IraqWQI) and Some Statistical Techniques

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Engineering（科研） 2024年第6期16卷 149-166页

作者： Nadhir Al-Ansari Salam Hussein Ewaid Ali Chabuk Salwan Ali Abed mudhafar A. Salim Jan Laue Riyadh m. salih Department of Civil Environmental and Natural Resources Engineering Lule University of Technology Lule Sweden Technical College of Shatra Southern Technical University Basra Iraq Department of Environmental Engineering College of Engineering University of Babylon Babylon Iraq Department of Environment College of Science University of Al-Qadisiyah Diwaniyah Iraq Iraqi Organization for Conservation of Nature IOCN Baghdad Iraq National Center for Water Resources management Ministry of Water Resources Baghdad Iraq

Evaluation of water quality is important for the management of water resources. The current study is focused on the interpretation of the water quality monitoring data of the Tigris River in Iraq by the application of the principal component analysis (PCA), cluster analysis (CA), and water quality index (WQI). Twelve water quality parameters were taken from 14 stations along the river Ca2+, mg2+, Na+, K+, Cl−, SO42−, HCO3−, NO3−, TH, TDS, BOD5, and EC to apply the PCA and CA. The results show that the mean of all the parameters was under the standards except Ca2+, EC, mg2+, TH, and SO42−. The amount of EC is the critical factor that affects the river water quality. The PCA obtained one principal component responsible for 97% of the variation caused by different pollution sources. The CA divided the river into three regions of sampling stations with similar water quality, the best in the north, and the worst in the far south. In this paper, the computer-automated tool (IraqWQI) was presented and evaluated, which has been developed by authors to classify and measure the quality of Iraqi surface water. The proposed index is of hundred degrees and includes six variables for drinking water quality Cl−, TH, TDS, COD, DO, and total coliform (TC) according to the Iraqi specifications. The result of the IraqWQI application showed a decrease in the water quality of the river and its suitability for drinking in the south of the country. The best value of the index was (81.48, Good) in Fishkhabour during winter, and the worst value was (46.23, Bad) in Qurnah during summer. The result of this study proved the success and importance of using statistical techniques and WQI as useful tools for the management, control, and conservation of surface water. .

关键词： Component Formatting Style Styling

The Synthetic and mechanical Properties of a Silica matrix Cermet Composite

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Journal of Wuhan University of Technology(materials Science) 2022年第3期37卷 423-428页

作者： Sufian H Humeedi Saadoon m Abdulkareem salih Y Darweesh Department of Physics College of ScienceTikrit UniversityTikritIraq Physics Department College of EducationSamaraa UniversitySamaraaIraq Physics Department College of Education TuzKhurmatuTikrit UniversityTikritIraq

A cermet composite was prepared by employing thermal spraying flame onto stainless steel surfaces of(304)type.Hence 25,50%of aluminum was mixed with matrix of(10%Al_(2)O_(3)-SiO_(2)).A spray system consistis of oxygen barrel,acetylene and spray gun that contains powders feeder at different spray distances(5,10,15,20 and 25 cm).The thermal treatment of the samples resulting from spraying was performed at 1100℃for 1.5 hr.Some physical examinations(porosity and adhesion),mechanical tests(hardness and wear rate)and Atomic Force microscope(AFm)test were performed before and after thermal treatment.The test results showed that the best mixing rate was(50%)and the best spray distance was(15cm)where the quality of coating layer improved after thermal treatment at 1100℃for 1.5 hr with a porosity ratio reduced from 11.2%to 6.6%,and adhesion strength improved with the increase in the alumina content to 50%.The results demonstrated that the best adhesion value after thermal treatment was 30.98 mPa.The hardness of coating layer increased from 45.78 to 54.88 HV;while the lowest rate of wear was at 50%of alumina with a spray distance of 15cm.Also,there was a clear improvement based on AFm within grain size,root mean square and roughness rate.

关键词： spraying by flame ceramic oxides composites atomic force microscope

Improved Transient Search Optimization with machine Learning Based Behavior Recognition on Body Sensor Data

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Computers, materials & Continua 2023年第5期75卷 4593-4609页

作者： Baraa Wasfi Salim Bzar Khidir Hussan Zainab salih Ageed Subhi R.m.Zeebaree ITm Department Technical College of AdministrationDuhok Polytechnic UniversityDuhokIraq Information System Engineering Department Erbil Technical Engineering CollegeErbil Polytechnic UniversityErbilIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

Recently,human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare systems.Even though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs,physical human action recognition(HAR)through body sensors gives useful information about the lifestyle and functionality of an individual.This article concentrates on the design of an Improved Transient Search Optimization with machine Learning based BehaviorRecognition(ITSOmLBR)technique using body sensor data.The presented ITSOmL-BR technique collects data from different body sensors namely electrocardiography(ECG),accelerometer,and magnetometer.In addition,the ITSOmL-BR technique extract features like variance,mean,skewness,and standard deviation.moreover,the presented ITSOmL-BR technique executes a micro neural network(mNN)which can be employed for long term healthcare monitoring and classification.Furthermore,the parameters related to the mNN model are optimally selected via the ITSO algorithm.The experimental result analysis of the ITSOmL-BR technique is tested on the mHEALTH dataset.The comprehensive comparison study reported a higher result for the ITSOmLBR approach over other existing approaches with maximum accuracy of 99.60%.

关键词： Behavior recognition transient search optimization machine learning healthcare sensors wearables

modified Wild Horse Optimization with Deep Learning Enabled Symmetric Human Activity Recognition model

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Computers, materials & Continua 2023年第5期75卷 4009-4024页

作者： Bareen Shamsaldeen Tahir Zainab salih Ageed Sheren Sadiq Hasan Subhi R.m.Zeebaree Accounting Department College of Administration and EconomicsUniversity of DuhokDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq ITm Department Technical College of AdministrativeDuhok Polytechnic UniversityDuhokIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

Traditional indoor human activity recognition(HAR)is a timeseries data classification problem and needs feature extraction.Presently,considerable attention has been given to the domain ofHARdue to the enormous amount of its real-time uses in real-time applications,namely surveillance by authorities,biometric user identification,and health monitoring of older people.The extensive usage of the Internet of Things(IoT)and wearable sensor devices has made the topic of HAR a vital subject in ubiquitous and mobile computing.The more commonly utilized inference and problemsolving technique in the HAR system have recently been deep learning(DL).The study develops amodifiedWild Horse Optimization withDLAided Symmetric Human Activity Recognition(mWHODL-SHAR)model.The major intention of the mWHODL-SHAR model lies in recognition of symmetric activities,namely jogging,walking,standing,sitting,etc.In the presented mWHODL-SHAR technique,the human activities data is pre-processed in various stages to make it compatible for further processing.A convolution neural network with an attention-based long short-term memory(CNNALSTm)model is applied for activity recognition.The mWHO algorithm is utilized as a hyperparameter tuning strategy to improve the detection rate of the CNN-ALSTm algorithm.The experimental validation of the mWHODL-SHAR technique is simulated using a benchmark dataset.An extensive comparison study revealed the betterment of themWHODL-SHAR technique over other recent approaches.

关键词： Human activity recognition symmetry deep learning machine learning pattern recognition time series classification

Automated Colonic Polyp Detection and Classification Enabled Northern Goshawk Optimization with Deep Learning

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Computers, materials & Continua 2023年第5期75卷 3677-3693页

作者： mohammed Jasim mohammed Jasim Bzar Khidir Hussan Subhi R.m.Zeebaree Zainab salih Ageed Engineering College Al-Kitab UniversityKirkukIraq Information System Engineering Department Erbil Technical Engineering CollegeErbil Polytechnic UniversityErbilIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq

The major mortality factor relevant to the intestinal tract is the growth of tumorous cells(polyps)in various parts.more specifically,colonic polyps have a high rate and are recognized as a precursor of colon cancer growth.Endoscopy is the conventional technique for detecting colon polyps,and considerable research has proved that automated diagnosis of image regions that might have polyps within the colon might be used to help experts for decreasing the polyp miss rate.The automated diagnosis of polyps in a computer-aided diagnosis(CAD)method is implemented using statistical analysis.Nowadays,Deep Learning,particularly throughConvolution Neural networks(CNN),is broadly employed to allowthe extraction of representative features.This manuscript devises a new Northern Goshawk Optimization with Transfer Learning model for Colonic Polyp Detection and Classification(NGOTL-CPDC)model.The NGOTL-CPDC technique aims to investigate endoscopic images for automated colonic polyp detection.To accomplish this,the NGOTL-CPDC technique comprises of adaptive bilateral filtering(ABF)technique as a noise removal process and image pre-processing step.Besides,the NGOTL-CPDC model applies the Faster SqueezeNet model for feature extraction purposes in which the hyperparameter tuning process is performed using the NGO optimizer.Finally,the fuzzy Hopfield neural network(FHNN)method can be employed for colonic poly detection and classification.A widespread simulation analysis is carried out to ensure the improved outcomes of the NGOTL-CPDC model.The comparison study demonstrates the enhancements of the NGOTL-CPDC model on the colonic polyp classification process on medical test images.

关键词： Biomedical imaging artificial intelligence colonic polyp classification medical image classification computer-aided diagnosis

Solar Radiation Prediction Using Satin Bowerbird Optimization with modified Deep Learning

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Computer Systems Science & Engineering 2023年第9期46卷 3225-3238页

作者： Sheren Sadiq Hasan Zainab salih Agee Bareen Shamsaldeen Tahir Subhi R.m.Zeebaree ITm Department Technical College of AdministrativeDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Accounting Department College of Adminstration and EconomicsUniversity of DuhokDuhokIraq Energy Eng.Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

Solar energy will be a great alternative to fossil fuels since it is clean and renewable.The photovoltaic(PV)mechanism produces sunbeams’green energy without noise or pollution.The PV mechanism seems simple,seldom malfunctioning,and easy to install.PV energy productivity significantly contributes to smart grids through many small PV mechanisms.Precise solar radiation(SR)prediction could substantially reduce the impact and cost relating to the advancement of solar energy.In recent times,several SR predictive mechanism was formulated,namely artificial neural network(ANN),autoregressive moving average,and support vector machine(SVm).Therefore,this article develops an optimal modified Bidirectional Gated Recurrent Unit Driven Solar Radiation Prediction(OmBGRU-SRP)for energy management.The presented OmBGRU-SRP technique mainly aims to accomplish an accurate and time SR prediction process.To accomplish this,the presented OmBGRU-SRP technique performs data preprocessing to normalize the solar data.Next,the mBGRU model is derived using BGRU with an attention mechanism and skip connections.At last,the hyperparameter tuning of the mBGRU model is carried out using the satin bowerbird optimization(SBO)algorithm to attain maximum prediction with minimum error values.The SBO algorithm is an intelligent optimization algorithm that simulates the breeding behavior of an adult male Satin Bowerbird in the wild.many experiments were conducted to demonstrate the enhanced SR prediction performance.The experimental values highlighted the supremacy of the OmBGRU-SRP algorithm over other existing models.

关键词： Solar radiation prediction deep learning parameter optimization energy management sustainability