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Image Splicing Forgery Detection Using Feature-Based of Sonine Functions and Deep Features

Computers, Materials & Continua 2024年第1期78卷 795-810页

作者： ala’a r.al-Shamasneh rabha W.Ibrahim Department of Computer Science College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Science and Mathematics Lebanese American UniversityBeirut11022801Lebanon Department of Mathematics Mathematics Research CenterNear East UniversityNicosia99138Turkey Information and Communication Technology research Group Scientific Research CenterAlayen UniversityDhi Qar64001Iraq

The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research *** of the most popular methods for manipulating digital images is image splicing,which involves copying a specific area from one image and pasting it into *** were made to mitigate the effects of image splicing,which continues to be a significant research *** study proposes a new splicing detectionmodel,combining Sonine functions-derived convex-based features and deep *** stages make up the proposed *** first step entails feature extraction,then classification using the“support vector machine”(SVM)to differentiate authentic and spliced *** proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image *** proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset“Chinese Academy of Sciences,Institute of Automation”which is a publicly available dataset for forgery *** experimental results show that,for image splicing forgery detection,the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy,precision,and ***,the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature *** the regions or locations where image tampering has taken place is limited by the *** research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.

关键词： Image forgery image splicing deep learning Sonine functions

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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

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Surgical management of metastatic neuroendocrine tumors:beyond the realm of evidence-based medicine

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Hepatobiliary Surgery and Nutrition 2024年第3期13卷 551-553页

作者： Jonathan r.Strosberg Taymeyah al-Toubah Department of Gastrointestinal oncology Moffitt Cancer Center&Research InstituteUniversity of South FloridaTampaFLUSA

How strong is the evidence supporting surgery for patients with metastatic neuroendocrine tumors(NETs)?This is the chief question from ***’comprehensive and balanced literature analysis on the surgical treatment of metastatic pancreatic NETs(1).No matter how many non-randomized studies demonstrate a survival advantage for patients undergoing cytoreductive hepatic surgery or resection of the primary pancreatic tumor,the methodological biases remain constant(2-9).Patients undergoing surgery tend to be younger and healthier,and their tumors tend to be lower grade,less aggressive,and fewer in number than patients undergoing other systemic or liver-directed *** other words,the survival advantage begins when the patient is selected as a surgical candidate and before the scalpel even touches the *** multivariate analyses can control for some confounding variables,it is impossible to control for *** a simple concept such as tumor burden is challenging to standardize:a patient with a few large tumors confined to a single hepatic lobe may have a vastly different prognosis than a patient with a similar burden of miliary tumors scattered throughout the liver(2).

关键词： Neuroendocrine tumor(NET) cytoreduction debulking surgery transplantation

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Simultaneously targeting extracellular vesicle trafficking and TGF-β receptor kinase activity blocks signaling hyperactivation and metastasis

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Signal Transduction and Targeted Therapy 2024年第1期9卷 308-325页

作者： Adilson Fonseca Teixeira Yanhong Wang Josephine Iaria Peter ten Dijke Hong-Jian Zhu Department of Surgery(The royal Melbourne Hospital) The University of MelbourneParkvilleVICAustralia Huagene Institute Kecheng Science and Technology ParkPukou DistrictNanjingJiangsuChina Department of Cell and Chemical Biology Oncode InstituteLeiden University Medical CenterLeidenThe Netherlands

Metastasis is the leading cause of cancer-related *** growth factor beta(TGF-β)signaling drives metastasis and is strongly enhanced during cancer ***,the use of on-target TGF-βsignaling inhibitors in the treatment of cancer patients remains unsuccessful,highlighting a gap in the understanding of TGF-βbiology that limits the establishment of efficient anti-metastatic ***,we show that TGF-βsignaling hyperactivation in breast cancer cells is required for metastasis and relies on increased small extracellular vesicle(sEV)*** sEV’s unique role,TGF-βsignaling levels induced by sEVs exceed the activity of matching concentrations of soluble ligand TGF-β.Further,genetic disruption of sEV secretion in highlymetastatic breast cancer cells impairs cancer cell aggressiveness by reducing TGF-βsignaling to nearly-normal ***,TGF-βsignaling activity in non-invasive breast cancer cells is inherently low,but can be amplified by sEVs,enabling invasion and metastasis of poorly-metastatic breast cancer *** the translational potential of inhibiting sEV trafficking in advanced breast cancers,treatment with dimethyl amiloride(DMA)decreases sEV secretion,TGF-βsignaling activity,and breast cancer progression in *** both the sEV trafficking and TGF-βsignaling by combining DMA and SB431542 at suboptimal doses potentiated this effect,normalizing the TGF-βsignaling in primary tumors to potently reduce circulating tumor cells,metastasis,and tumor ***,this study establishes sEVs as critical elements in TGF-βbiology,demonstrating the feasibility of inhibiting sEV trafficking as a new therapeutic approach to impair metastasis by normalizing TGF-βsignaling levels in breast cancer cells.

关键词： metastasis doses invasion

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Single image super-resolution:a comprehensive review and recent insight

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Frontiers of Computer Science 2024年第1期18卷 139-156页

作者： Hanadi al-MEKHLAFI Shiguang LIU College of Intelligence and Computing Tianjin UniversityTianjin 300350China

Super-resolution(Sr)is a long-standing problem in image processing and computer vision and has attracted great attention from researchers over the *** main concept of Sr is to reconstruct images from low-resolution(Lr)to high-resolution(Hr).It is an ongoing process in image technology,through up-sampling,de-blurring,and *** neural network(CNN)has been widely used to enhance the resolution of images in recent *** alternative methods use deep learning to improve the progress of image super-resolution based on ***,we review the recent findings of single image super-resolution using deep learning with an emphasis on distillation knowledge used to enhance image super-resolution.,it is also to highlight the potential applications of image super-resolution in security monitoring,medical diagnosis,microscopy image processing,satellite remote sensing,communication transmission,the digital multimedia industry and video ***,we present the challenges and assess future trends in super-resolution based on deep learning.

关键词： super-resolution deep learning single-image interpolation-based learning-based reconstruction-based

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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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Developing a Model for Parkinson’s Disease Detection Using Machine Learning algorithms

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Computers, Materials & Continua 2024年第6期79卷 4945-4962页

作者： Naif al Mudawi Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia

Parkinson’s disease(PD)is a chronic neurological condition that progresses over *** start to have trouble speaking,writing,walking,or performing other basic skills as dopamine-generating neurons in some brain regions are injured or *** patient’s symptoms become more severe due to the worsening of their signs over *** this study,we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk *** research worked on the publicly available dataset on PD,and the dataset consists of a set of significant characteristics of *** aim to apply soft computing techniques and provide an effective solution for medical professionals to diagnose PD *** research methodology involves developing a model using a machine learning *** the model selection,eight different machine learning techniques were adopted:Namely,random Forest(rF),Decision Tree(DT),Support Vector Machine(SVM),Naïve Bayes(NB),Light Gradient Boosting Machine(LightGBM),K-Nearest Neighbours(KNN),Extreme Gradient Boosting(XGBoost),and Logistic regression(Lr).Subsequently,the concentrated models were validated through 10-fold Cross-Validation and receiver operating Characteristic(roC)—Area Under the Curve(AUC).In addition,GridSearchCV was utilised to measure each algorithm’s best parameter;eventually,the models were trained through the hyperparameter tuning *** 98%accuracy,LightGBM had the highest accuracy in this ***,KNN,and SVM came in second with 96%***,the performance scores of NB and Lr were recorded to be 76%and 83%,*** is to be mentioned that after applying 10-fold cross-validation,the average performance score of LightGBM accounted for 93%.At the same time,the percentage of roC-AUC appeared at 0.92,which indicates that this LightGBM model reached a satisfactory ***,we extracted meaningful insights and figured out potential gaps on top of *** extracting meaningful in

关键词： Light GBM cross-validation roC-AUC Parkinson’s disease(PD) SVM and XGBoost

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The optimal semi-analytical modeling for the infinite-conductivity horizontal well performance under rectangular bounded reservoir based on a new instantaneous source function

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Petroleum 2024年第1期10卷 68-84页

作者： Firas A.A.al-Kabbawi Independent scholar LivingstonScotlandUNITED KINGDOM

The main objective of this study is to develop the optimal semi-analytical modeling for the infiniteconductivity horizontal well performance under rectangular bounded reservoir based on a new instantaneous source *** available semi-analytical infinite-conductivity models(ICMs)for horizontal well under rectangular bounded reservoir in literature were developed by applying superposition of pressures in space(SPS).A new instantaneous source function(i.e.,instantaneous uniform-flux segmentary source function under bounded reservoir)is derived to be used instead of SPS to develop the optimal semi-analytical *** new semi-analytical ICM is verified with ICM of Schlumberger[1]and with previous semi-analytical ICMs in terms of bottom hole pressure(BHP)profile and inflow rate distribution along the *** model is also validated with real horizontal wells in terms of inflow rate distribution along the *** results show that the developed model gives the optimal semi-analytical modeling for the infinite-conductivity horizontal well performance under rectangular bounded *** that,high computationalefficiency and high-resolution of wellbore discretization have been achieved(i.e.,wellbore segment number could be tens of hundreds depending on solution requirement).The results also show that at pseudosteady state(PSS)flow regime,inflow rate distribution along the wellbore by previous semi-analytical ICMs is stabilized U-shaped as performance of inflow rate distribution at late radial flow ***,the previous semi-analytical ICMs are incorrectly modeling inflow rate distribution at PSS flow regime due to the negative influence of applying *** optimal semi-analytical ICM is in a general form and real time domain,and can be applicable for 3D horizontal well and 2D vertical fracture well under infinite and rectangular bounded reservoirs,of uniform-flux and infinite-conductivity wellbore conditions at any time of well life.

关键词： Infinite-conductivity model Horizontal well performance Semi-analytical modeling rectangular bounded reservoir Instantaneous source functions Flow regimes

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Degradation pathways of amino acids during thermal utilization of biomass: a review

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Frontiers of Chemical Science and Engineering 2024年第7期18卷 67-95页

作者： Mubarak al-Kwradi Mohammednoor altarawneh Department of Chemical and Petroleum Engineering United Arab Emirates UniversityAl-Ain 15551United Arab Emirates

Amino acids are important nitrogen carriers in biomass and entail a broad spectrum of industrial uses,most notably as food additives,pharmaceutical ingredients,and raw materials for bio-based *** detailed information in regard to the fragmentation of amino acids is essential to comprehend the nitrogen transformation chemistry at conditions encountered during hydrothermal and pyrolytic degradation of *** underlying aim of this review is to survey literature studies pertinent to the complex structures of amino acids,their formation yields from various categories of biomass,and their fragmentation routes at elevated temperatures and in the aqueous *** predominant degradation reactions ensue in the decomposition of amino acids,namely deamination and ***,minor differences in structure can greatly affect the fate for each amino ***,amino acids,being nitrogen-rich compounds,play pivotal roles across various *** is a growing interest in producing varied types and configurations of amino *** fermentation appears to a viable approach to produce amino acids at an industrial *** innovative method under investigation is catalytic synthesis using renewable biomass as *** a method taps into the inherent nitrogen in biomass sources like chitin and proteins,eliminating the need for external nitrogen *** environmentally friendly approach is in line with green chemistry principles and has been gathering momentum in the scientific community.

关键词： amino acids pyrolysis biomass nitrogen mechanisms bioenergy

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Fast prediction of rain erosion in wind turbine blades using a data-based computational tool

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Journal of Hydrodynamics 2024年第3期36卷 504-518页

作者： Juan M.Gimenez Sergio r.Idelsohn Eugenio oñate Centre Internacional de Mètodes Numèrics en Enginyeria(CIMNE) Edifici C1 Campus Nord UPC C/Gran CapitàS/NBarcelonaSpain

Wind turbines(WTs)face a high risk of failure due to environmental factors like erosion,particularly in high-precipitation areas and offshore *** this paper we introduce a novel computational tool for the fast prediction of rain erosion damage on WT blades that is useful in operation and maintenance decision making *** approach is as follows:Pseudo-Direct Numerical Simulation(P-DNS)simulations of the droplet-laden flow around the blade section profile are employed to build a high-fidelity data set of impact statistics for potential operating *** this database as training data,a machine learning-based surrogate model provides the feature of the impact pattern over the 2-D section for given wind and rain *** this information,a fatigue-based model estimates the remaining lifetime and erosion damage for both homogeneous and coating-substrate blade *** prediction is done by quantifying the accumulated droplet impact energy and evaluating operative conditions over time periods for which the weather at the installation site is *** this work,we describe the modules that compose the prediction method,namely the database creation,the training of the surrogate model and their coupling to build the prediction ***,the method is applied to predict the remaining lifetime and erosion damage to the blade sections of a reference *** evaluate the reliability of the tool,several site locations(offshore,coastal,and inland),the coating material and the coating thickness of the blade are *** few minutes we are able to estimate erosion after many years of *** results are in good agreement with field observations,showing the promise of the new rain erosion prediction approach.

关键词： Wind turbine blades leading edge erosion fatigue Pseudo-Direct Numerical Simulation(P-DNS) machine learning eblader

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