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Geochemical characteristics of Holocene sediments from Chuadanga district, Bangladesh: Implications for weathering, climate, redox conditions, provenance and tectonic setting

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Chinese Journal Of Geochemistry 2014年第4期33卷 336-350页

作者： Ismail Hossain Krisna Kanta Roy Pradip Kumar Biswas Mahbubul Alam Md. Moniruzzaman farah deeba Department of Geology and Mining University of Rajshahi Bangladesh Institute of Mining Mineralogy and Metallurgy (IMMM) Joypurhat Bangladesh Water Resource Planning Division Institute of Water Modelling (IWM) Dhaka Bangladesh Institute of Nuclear Science and Technology (INST) Atomic Energy Commission Dhaka Bangladesh

The present research deals with the geochemical characteristics of the Holocene sediments from Alamdanga area, Chuadanga district, Bangladesh. Main goals of the study are to delineate source rock characteristics, degree of chemical weathering and sorting processes and behavior of redox conditions during deposition of the sediments. Geochemical characteristics of the sediments show comparatively a wide variation in accordance with stratigraphy in their major element contents(e.g. Si O2 69.46–82.13, Al2O3 2.28–8.88 in wt%), reflecting the distinctive provenance and in part an unstable period in terms of tectonic activity. Geochemical classification of the sediments shows mostly sub-arkose with few sub-litharenites. Some major and trace elements display comprehensible correlation with Al2O3 confirming their possible hydraulic fractionation. The chemical index of alteration(CIA*), W* index, index of compositional variability(ICV), plagioclase index of alteration(PIA*) values and the ratio of Si O2/Al2O3, suggest low degrees of chemical weathering in the source areas as well as immature to moderately mature the sediments. The sediments suggest semi-arid climatic trends within oxic deltaic depositional conditions during the Holocene, at 3–12 ka. Whole rock geochemistry and discrimination diagrams demonstrate the continental signature derivatives, which might have been derived from the felsic to intermediate igneous rocks(granitic plutonic rocks) as well as from quartzose sedimentary/metamorphic provenance. These typical sources are present in a vast region of the Himalayan belt and catchment areas of Ganges. The tectonic setting of the sediments demarcates typically passive margin with slightly continental arc system.

关键词：地球化学特征氧化还原条件化学风化沉积物构造背景孟加拉国气候趋势全新世

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Time-Dependent Nonplanar Dust-Ion-Acoustic Gardner Double Layers

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Open Journal of Acoustics 2011年第3期1卷 70-75页

作者： farah deeba A. A. Mamun 不详

A theoretical investigation has been made on the nonplanar (cylindrical and spherical) dust-ion-acoustic (DIA) double layers (DLs) in a dusty plasma system, containing inertial ions, Boltzmann electrons, and negatively charged stationary dust. In this investigation, in order to analyze the time dependent nonplanar DIA DLs, we have used the modified Gardner equation, which has been obtained by employing the reductive perturbation method. It has been found that the behaviors of DIA DLs have been significantly modified by the time period and the nonplanar geometry. The nonplanar DIA DLs has been found to be similar with planar DIA DLs only at large time scale and the cylindrical DIA DLs have been found to be smaller than the spherical DLs, but larger than the planar DLs.

关键词： Dust-Ion-Acoustic Double Layers Nonplanar

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Effect of aspirin on glucose-D transport in intestine of rat

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海南医学院学报 2009年第2期15卷 107-110页

作者： Mazhar Mushtaq farah deeba Khan M.Naeem Akhtar Saghir Ahmad Jafri Mehboob Bari Biochemistry Dept Medical FacultyUniversity of Lahore Institute of Molecular Biology and Biotechnology University of Lahore

Objective: The present study was designed to evaluate the effect a commonly prescribed Non Steroidal Anti Inflammatory Drug(NSAID) *** on brush border membrane in terms of changes in the intestinal transport level of glucose which is monosaccharide with absolute requirement in the body and hence its absorption is directly proportional on the morphology of the intestinal ***: Albino rats(Rattus Norvegicus) were divided into two different groups,Group I(Control),Group II(aspirin-treated,50 mg aspirin/kg of body weight).The treatment was continued for 28 *** the 29th day after overnight fasting,intestine was removed from animals of both *** in transport of glucose-D in intestine were ***: The results indicated a significant decrease in the transport of glucose-D in aspirin treated group as compared to the control ***: Cautious use of NSAID is recommended in commonly observed symptom such as headache and to those patients who are given as a prophylaxis for thrombosis.

关键词：阿司匹林葡萄糖传递过程小肠药物分析

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Impact of Elevated CO<sub>2</sub>on Wheat Growth and Yield under Free Air CO<sub>2</sub>Enrichment

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American Journal of Climate Change 2017年第4期6卷 573-596页

作者： Vivek Pandey Marisha Sharma farah deeba Vivek K. Maurya Sunil K. Gupta Satyendra P. Singh Aradhana Mishra Chandra Shekhar Nautiyal Plant Ecology & Environmental Sciences CSIR-National Botanical Research Institute Lucknow India Division of Plant Microbe Interaction CSIR-National Botanical Research Institute Lucknow India

Impact of elevated CO2 (free air CO2 enrichment) was studied on wheat (Triticum aestivum L. var Kundan) growth, yield and proteome. Elevated CO2 significantly impacted both underground (+24%) and aboveground (+15%) biomass. Grain weight/plant and harvest index were increased by 35% and 11.4%, respectively under high CO2. On the other hand, seed protein content was decreased by 19% under CO2 enrichment while seed starch and soluble sugar contents were increased by 8% and 23%, respectively. Wheat leaf proteomics revealed that 50 proteins were showing differential expression. Twenty proteins were more abundant while 30 were less abundant. Thirty two proteins were identified by MALDI TOF TOF. More abundant proteins were related to defense, photosynthesis, energy metabolism etc. While less abundant proteins were related to glycolysis and gluconeogenesis. Wheat grain proteomics revealed that out of 49 differentially abundant proteins, 24 were more in abundance and 25 were less in abundance in wheat grains under eCO2 condition. Thirty three proteins were identified and functionally characterized. They were found to be involved mainly in carbon metabolism, storage, defence and proteolysis. Gluten proteins are the major component of wheat storage proteins. Our results showed that both high and low molecular weight glutenins were more in eCO2 wheat seeds while there was no change in gliadin evels. This might alter wheat dough strength. Concentration of grain Cr and As was increased at eCO2 while that of Fe, Cu, Zn and Se were found to be decreased. Dynamics of carbon utilization and metabolic abilities of soil microbes under eCO2 were significantly altered. Our study showed that altered wheat seed composition is cause for concern vis-à-vis nutrition and health and for industries which may have implications for agriculturally dominated country like India.

关键词： Carbon Dioxide FACE Wheat Proteomics Grain Yield Soil Microbes

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Sedimentary facies analysis of a thin-bed-dominated turbidite succession in the Miocene Temburong Formation,Kampung Bebuloh,Labuan Island,Malaysia

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Journal of Palaeogeography 2024年第3期13卷 401-429页

作者： farah Syafira Burhanuddin Meor Hakif Amir Hassan Department of Geology Faculty of ScienceUniversiti Malaya50603 Kuala LumpurMalaysia

Thin-bedded turbidites are important hydrocarbon-bearing reservoirs in many mature fields throughout the *** type of turbidite depositional setting strongly influences vertical and horizontal continuity of the ***,distinguishing between lobe and levee associated thin-bedded turbidites remains challenging due to their generally similar facies *** study aims to contribute some insights to this problem by conducting a detailed bed-scale facies analysis on the Early Miocene Temburong Formation exposed at Kampung Bebuloh,Labuan Island,Malaysia,which may uncover features that can help in differentiating between both depositional *** facies are recognized in the Temburong Formation,which are interpreted as low density turbidites(F1-F4),hybrid event beds(F5),and sustained turbidites(F6).Detailed facies and ichnology analysis reveal features which are consistent with a lobe fringe deposit rather than leveeassociated environment interpretation,including tabular bed geometries,presence of hybrid event beds,and the absence of thick-bedded channel-fill *** facies associations are identified and interpreted as representing sub-environments within an overall lobe depositional setting.A diverse trace fossil assemblage,comprising the Nereites ichnofacies is consistent with a deep marine *** identification of the Paleodictyon and Nereites sub-ichnofacies indicates a distal turbidite system setting,most likely lobe *** on the thick accumulation of FA2 and FA3 and no distinct observable vertical trend,the Early Miocene Temburong Formation in Labuan is interpreted as turbidites deposited at the fringes of lobe complexes rather than a single lobe *** works into the Temburong Formation in SW Labuan proposed a middle slope to proximal basin floor setting where the thin-bedded intervals were interpreted as potentially representing either levee deposits or the fringes of confined lobe ***

关键词： Facies analysis Trace fossils Depositional lobes Hybrid event beds Lobe fringe Temburong formation Thin-bedded turbidite

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RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

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Computers, Materials & Continua 2024年第5期79卷 3175-3192页

作者： farah Mohammad Saad Al-Ahmadi Jalal Al-Muhtadi Center of Excellence in Information Assurance(CoEIA) King Saud UniversityRiyadh11543Saudi Arabia College of Computer&Information Sciences King Saud UniversityRiyadh11543Saudi Arabia

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity *** hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by *** development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning ***,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive ***-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor *** this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious *** key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft *** advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for *** effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification *** performance evaluation of the proposed model revealed exemplary results across *** accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

关键词： Electricity theft smart grid RoBERTa GRU transfer learning

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Mapping lineaments using Landsat 8 OLI and SRTM data;a case study of the eastern part of the Ouarzazate Basin,Morocco

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Journal of Mountain Science 2024年第3期21卷 987-1003页

作者： MOUJANE Said ALGOUTI Ahmed ALGOUTI Abdellah farah Abdelouahed ABOULFARAJ Abdelfatah NAFOURI Imane Geoscience Geotourism Natural Hazards and Remote Sensing Laboratory(2GRNT) Department of GeologyFaculty of Sciences SemlaliaUniversity of Cadi AyyadBP 239040000MarrakechMorocco

The integration of remote sensing and geographic information system(GIS)was employed in this study to delineate the structural lineaments within the eastern section of the Ouarzazate Basin,situated between the southern front of the Central High Atlas and the northern slopes of the Eastern Anti-Atlas(also known as the Saghro Massif).To achieve this objective,Landsat 8 Operational Land Imager(OLI)and Shuttle Radar Topography Mission(SRTM)data were *** Component Analysis(PCA)was computed and a directional filter was applied to the first PCA and the panchromatic band(B8).Additionally,shading was applied to the SRTM data in four directions;N0°,N45°,N90°,N135°.After removing of the non-geological linear structures,the results obtained,using the automatic extraction method,allowed us to produce a synthetic map that included 1251 lineaments with an average length of 1331 m and was dominated by NE-SW,ENE-WSW and E-W directions,***,the high lineament density is clearly noted in the Anti-Atlas(Saghro Massif)and at the level of the northern part,extending from the Ait Ibrirne to Arg-Ali Oubourk *** lineament density can always be found around the major faults affecting this *** data collected during the field investigations and from geological maps show that the major direction of the faults and structural accidents range mostly between N45°,N70°and N75°.The correlation of remote sensing results with those collected in the field shows a similarity and coincidence with each *** these results,it is possible to consider the automatic extraction method as a supplementary kind that can serve classical geology by quickly enriching it with additional *** shown in this work,this method provides more information when applied in arid areas where the fields are well outcropped.

关键词： Remote sensing GIS Landsat 8 OLI SRTM Ouarzazate Basin Central High Atlas

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Phishing Attacks Detection Using EnsembleMachine Learning Algorithms

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Computers, Materials & Continua 2024年第7期80卷 1325-1345页

作者： Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Marwan Abu-Zanona Bassam Mohammad Elzaghmouri farah H.Zawaideh Mouiad Fadeil Alawneh Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26110Jordan Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjloun26767Jordan

Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based *** indicates a persistent rise in phishing ***,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to ***,it is imperative to utilize sophisticated algorithms to address this *** learning is a highly effective approach for identifying and uncovering these harmful *** learning(ML)approaches can identify common characteristics in most phishing *** this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing *** that,we used the normalization technique on the dataset to transform the range of all the features into the same *** findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best *** in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction ***,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

关键词： Social engineering attacks phishing attacks machine learning security artificial intelligence

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Optimizing wind farm layout for enhanced electricity extraction using a new hybrid PSO-ANN method

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Global Energy Interconnection 2024年第3期7卷 254-269页

作者： Mariam El Jaadi Touria Haidi Abdelaziz Belfqih Mounia farah Atar Dialmy LAGES Laboratory Hassania School of Public Works(EHTP)Casablanca 20230Morocco LESE Laboratory National School of Electricity and Mechanics(ENSEM)Casablanca 20100Morocco LAGCET Laboratory Hassania School of Public Works(EHTP)Casablanca 20230Morocco

With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind *** best wind turbine architecture in a wind farm has a major influence on the energy extraction *** paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization(PSO)and artificial neural networks(ANNs).The PSO method was used to explore the solution space and develop preliminary turbine layouts,and the ANN model was used to fine-tune the placements based on the predicted energy *** proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical *** efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches,giving exciting prospects for developing more efficient and sustainable wind *** integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both ***,this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency.A notable increase in power generation is observed across various *** percentage increase in the power generation ranged from approximately 7.7%to 11.1%.Owing to its versatility and adaptability to site-specific conditions,the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.

关键词： Layout optimization Turbine placement Wind energy Hybrid optimization Particle swarm optimization Artificial neural networks Renewable energy Energy efficiency

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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Computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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