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Study of inter-well interference in shale gas reservoirs by a robust production data analysis method based on deconvolution

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Petroleum science 2024年第4期21卷 2502-2519页

作者： Wen-Chao Liu Cheng-Cheng Qiao Ping Wang Wen-Song Huang Xiang-Wen Kong Yu-Ping Sun He-Dong Sun Yue-Peng Jia School of Civil and Resource Engineering University of Science and Technology Beijing PetroChina research institute of Petroleum Exploration and Development

In order to overcome the defects that the analysis of multi-well typical curves of shale gas reservoirs is rarely applied to engineering, this study proposes a robust production data analysis method based on deconvolution, which is used for multi-well inter-well interference research. In this study, a multi-well conceptual trilinear seepage model for multi-stage fractured horizontal wells was established, and its Laplace solutions under two different outer boundary conditions were obtained. Then, an improved pressure deconvolution algorithm was used to normalize the scattered production data. Furthermore, the typical curve fitting was carried out using the production data and the seepage model solution. Finally,some reservoir parameters and fracturing parameters were interpreted, and the intensity of inter-well interference was compared. The effectiveness of the method was verified by analyzing the production dynamic data of six shale gas wells in Duvernay area. The results showed that the fitting effect of typical curves was greatly improved due to the mutual restriction between deconvolution calculation parameter debugging and seepage model parameter debugging. Besides, by using the morphological characteristics of the log-log typical curves and the time corresponding to the intersection point of the log-log typical curves of two models under different outer boundary conditions, the strength of the interference between wells on the same well platform was well judged. This work can provide a reference for the optimization of well spacing and hydraulic fracturing measures for shale gas wells.

关键词： Shale gas Inter-well interference Deconvolution Production data analysis Typical curves Multi-stage fractured horizontal well

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Radiative Blood-Based Hybrid Copper-Graphene Nanoliquid Flows along a Source-Heated Leaning Cylinder

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Computer Modeling in Engineering & sciences 2024年第4期139卷 1017-1037页

作者： Siti Nur Ainsyah Ghani Noor Fadiya Mohd Noor institute of Mathematical sciences Faculty of ScienceUniversiti MalayaKuala Lumpur50603Malaysia Center for data Analytics Consultancy&Services(UM-CDACS) Faculty of ScienceUniversiti MalayaKuala Lumpur50603Malaysia

Variant graphene,graphene oxides(GO),and graphene nanoplatelets(GNP)dispersed in blood-based copper(Cu)nanoliquids over a leaning permeable cylinder are the focus of this *** forms of graphene are highly beneficial in the biological and medical fields for cancer therapy,anti-infection measures,and drug *** non-Newtonian Sutterby(blood-based)hybrid nanoliquid flows are generalized within the context of the Tiwari-Das model to simulate the effects of radiation and heating *** governing partial differential equations are reformulated into a nonlinear set of ordinary differential equations using similar transformational *** equations are then transformed into boundary value problems through a shooting technique,followed by the implementation of the bvp4c tool in *** influences of various parameters on the model’s nondimensional velocity and temperature profiles,reduced skin friction,and reduced Nusselt number are presented for detailed *** results indicated that Cu-GNP/blood and Cu-GO/blood hybrid nanofluids exhibit the lowest and highest velocity distributions,respectively,for increased nanoparticles volume fraction,curvature parameter,Sutterby fluid parameter,Hartmann number,and wall permeability ***,opposite trends are observed for the temperature distribution for all considered parameters,except the mixed convection *** in the reduced skin friction magnitude and the reduced Nusselt number with higher values of graphene/GO/GNP nanoparticle volume fraction are also ***,GNP is identified as the superior heat conductor,with an average increase of approximately 5%and a peak of 7.8%in the reduced Nusselt number compared to graphene and GO nanoparticles in the Cu/blood nanofluids.

关键词： Hybrid nanofluid sutterby fluid tiwari-das model thermal radiation graphene graphene oxides graphene nanoplatelets

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Investigating the dynamics and interactions of surface features on Pine Island Glacier using remote sensing and deep learning

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Advances in Climate Change research 2024年第4期15卷 609-622页

作者： Qi ZHU Hua-Dong GUO Lu ZHANG Dong LIANG Zhe-Rong WU Zhuo-Ran LYU Xiao-Bing DU Key Laboratory of Digital Earth science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China International research Center of Big data for Sustainable Development Goals Beijing 100094China University of Chinese Academy of sciences Beijing 100049China School of Integrative Plant science Soil and Crop Sciences SectionCornell University Ithaca 14850USA

Pine Island Glacier(PIG),the largest glacier in the Amundsen Sea Embayment of West Antarctica,has contributed to over a quarter of the observed sea level rise around *** recent years,multiple observations have confirmed its continuous retreat,ice flow acceleration and profound surface *** these changes is crucial for accurately monitoring ice mass discharge and future Antarctic contributions to sea level ***,it is essential to investigate the complex interactions between these variables to comprehend how they collectively affect the overall stability of the intricate PIG *** this study,we utilized high-resolution remote sensing data and deep learning method to detect and analyze the spatio-temporal variations of surface melt,ice shelf calving,and ice flow velocity of the PIG from 2015 to *** explored the correlations among these factors to understand their long-term impacts on the glacier's *** findings reveal a retreat of 26.3 km and a mass loss of 1001.6 km²during ***,extensive surface melting was observed,particularly in the 2016/2017 and 2019/2020 melting *** data vividly illustrate prolonged and intense melting periods,correlating with a significant retreat in the glacier's terminus position in 2019/***,the comprehensive analysis of surface melting and the cumulative retreat of the ice shelf from 2017 to 2020 on the PIG shows atemporal relationship with subsequent significant changes in ice fow velocity,ranging from 10.9 to 12.2 m d^(-1),with an average acceleration rate of 12%.These empirical findings elucidate the intricate relationship among surface melt,ice flow velocity,and consequential glacier dynamics.A profound understanding of these interrelationships holds paramount importance in glacier dynamic changes and modeling,providing invaluable insights into potential glacier responses to global climate change.

关键词： Pine island glacier Calving front Surface melt Ice flow velocity Remote sensing

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research Progress in Enantioselective Radical Desymmetrization Reactions

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CCS Chemistry 2024年第7期6卷 1612-1627页

作者： Chang-Jiang Yang Lin Liu Qiang-Shuai Gu Xin-Yuan Liu Department of Chemistry and Dongguan Key Laboratory for data science and Intelligent Medicine Great Bay UniversityDongguan 523000 Shenzhen Grubbs institute Department of Chemistryand Guangming Advanced Research InstituteSouthern University of Science and TechnologyShenzhen 518055 Academy for Advanced Interdisciplinary Studies and Department of Chemistry Southern University of Science and TechnologyShenzhen 518055

Enantioselective radical desymmetrization is a highly effective approach for rapidly creating enantioenriched molecules,introducing dramatically increased structural complexity from readily available prochiral or meso compound *** strategic modes have been developed for these reactions,which differ in the nature of the stereo-determining *** first category deals primarily with the stereoselective desymmetrization of closed-shell radical precursors or functional reagents,whereas the second category achieves desymmetrization by stereoselectively functionalizing open-shell radical *** mini-review explores the research progress in this growing field,aiming to elucidate mechanistic scenarios related to stereochemical ***,it offers insights into the challenges and opportunities that lie ahead for further development.

关键词： radical desymmetrization enantioselective desymmetrization radical functionalization transition metal catalysis asymmetric catalysis

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Shock Initiation Experiments with Modeling on a DNAN Based MeltCast Insensitive Explosive

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Defence Technology（防务技术） 2024年第2期32卷 655-662页

作者： Feichao Miao Dandan Li Yangfan Cheng Junjiong Meng Lin Zhou School of Chemical Engineering Anhui University of Science and TechnologyHuainan 232001China Composite Explosive research Department Xi'an Modern Chemistry Research InstituteXi'an 710065China State Key Laboratory of Explosion science and Technology Beijing Institute of TechnologyBeijing 100081China

2,4-dinitroanisole(DNAN)is a good replacement for 2,4,6-trinitrotoluene(TNT)in melt-cast explosives due to its superior *** the increasing use of DNAN-based melt-cast explosives,the prediction of reaction violence and hazard assessment of the explosives subjected to shock is of great *** study investigated the shock initiation characteristics for a DNAN-based melt-cast explosive,DHFA,using the one-dimensional Lagrangian *** embedded manganin gauges in the apparatus record the pressure histories at four Lagrangian positions and show that shock-todetonation transition in DHFA needs a high input shock *** experimental data are analyzed to calibrate the Ignition and Growth *** calibration is performed using an objective function based on both pressure history and the arrival time of *** agreement between experimental and calculated pressure histories indicates the high accuracy of the calibrated parameters with the optimization method.

关键词： 2,4-Dinitroanisole(DNAN) Shock initiation Insensitive explosives Ignition and growth model Equation of state(EOS)

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An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

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Computers, Materials & Continua 2024年第4期79卷 243-262页

作者： Yasmine M.Ibrahim Reem Essameldin Saad M.Darwish Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt Faculty of Computers and Information Technology Egyptian E-Learning University(EELU)Giza12611Egypt Faculty of Computers and data science Alexandria UniversityAlexandria21554Egypt

Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due tothe complex nature of language used in such platforms. Currently, several methods exist for classifying hatespeech, but they still suffer from ambiguity when differentiating between hateful and offensive content and theyalso lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm(WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs)for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to exploreand determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performanceof the MLP as fine-tuning. Additionally, in this approach, two separate MLP models are employed. One MLPis dedicated to predicting degrees of truth membership, while the other MLP focuses on predicting degrees offalse membership. The difference between these memberships quantifies uncertainty, indicating the degree ofindeterminacy in predictions. The experimental results indicate the superior performance of our model comparedto previous work when evaluated on the Davidson dataset.

关键词： Hate speech detection whale optimization neutrosophic sets social media forensics

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SDGSAT-1: Capabilities for Monitoring and Evaluating SDG Indicators

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空间科学学报 2024年第4期44卷 677-686页

作者： GUO Huadong DOU Changyong LIANG Dong JIANG Nijun TANG Yunwei MA Wenyong International research Center for Big data for Sustainable Development Beijing 100094 Key Laboratory of Digital Earth science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094 University of Chinese Academy of sciences Beijing 100049

SDGSAT-1,the world's first science satellite dedicated to assisting the United Nations 2030 Sustainable Development Agenda,has been operational for over two and a half *** provides valuable data to aid in implementing the Sustainable Development Goals *** its Open science Program,the satellite has maintained consistent operations and delivered free data to scientific and technological users from 88 *** program has produced a wealth of scientific output,with 72 papers,including 28 on data processing methods and 44 on applications for monitoring progress toward SDGs related to sustainable cities,clean energy,life underwater,climate action,and clean water and ***-1 is equipped with three key instruments:a multispectral imager,a thermal infrared spectrometer,and a glimmer imager,which have enabled ground-breaking research in a variety of domains such as water quality analysis,identification of industrial heat sources,assessment of environmental disaster impacts,and detection of forest *** precise measurements and ongoing monitoring made possible by this invaluable data significantly advance our understanding of various environmental *** are essential for making well-informed decisions on a local and global *** its application to academic research,SDGSAT-1 promotes global cooperation and strengthens developing countries'capacity to accomplish their sustainable development *** the satellite continues to gather and distribute data,it plays a pivotal role in developing strategies for environmental protection,disaster management and relief,and resource *** initiatives highlight the satellite's vital role in fostering international collaboration and technical innovation to advance scientific knowledge and promote a sustainable future.

关键词： SDGSAT-1 Earth observation Remote sensing SDGs

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DeepRisk:A deep learning approach for genome-wide assessment of common disease risk

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Fundamental research 2024年第4期4卷 752-760页

作者： Jiajie Peng Zhijie Bao Jingyi Lia Ruijiang Han Yuxian Wang Lu Han Jinghao Peng Tao Wang Jianye Hao Zhongyu Wei Xuequn Shang AI for science Interdisciplinary research Center Schoolof Computer ScienceNorthwestern Polyechnical UniversityXi'an 129China Key Laboratoryof Bg data Sorage and Management Northwestern Polytechnical UniversityMinisty of Industy and Information TechnologyX'an 129Chin College of Intelligence and Computing Tianjin UniversityTianjin 300072China School of data science Fudan UniversityShanghai 200433China research and Development institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518000China

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant *** widely applied,traditional polygenic risk scoring methods fall short,as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms(SNPs).This presents a limitation,as genetic diseases often arise from complex interactions between multiple *** address this challenge,we developed DeepRisk,a biological knowledge-driven deep learning method for modeling these complex,nonlinear associations among SNPs,to provide a more effective method for scoring the risk of common diseases with genome-wide genotype *** demonstrated that DeepRisk outperforms existing PRs-based methods in identifying individuals at high risk for four common diseases:Alzheimer's disease,inflammatory bowel disease,type 2diabetes,and breast cancer.

关键词： Disease risk prediction Deep learning Polygenic risk score Common disease risk Disease prevention

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Multi-objective evolutionary optimization for hardware-aware neural network pruning

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Fundamental research 2024年第4期4卷 941-950页

作者： Wenjing Hong Guiying Li Shengcai Liu Peng Yang Ke Tang Guangdong Provincial Key Laboratory of Brain-Inspired Inteligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China research institute of Trustworthy Autonomous Systems Southern University of Science and TechnologyShenzhen 518055China Department of Statistics and data science Southern University of Science and TechnologyShenzhen 518055China

Neural network pruning is a popular approach to reducing the computational complexity of deep neural *** recent years,as growing evidence shows that conventional network pruning methods employ inappropriate proxy metrics,and as new types of hardware become increasingly available,hardware-aware network pruning that incorporates hardware characteristics in the loop of network pruning has gained growing attention,Both network accuracy and hardware efficiency(latency,memory consumption,etc.)are critical objectives to the success of network pruning,but the conflict between the multiple objectives makes it impossible to find a single optimal *** studies mostly convert the hardware-aware network pruning to optimization problems with a single *** this paper,we propose to solve the hardware-aware network pruning problem with Multi-Objective Evolutionary Algorithms(MOEAs).Specifically,we formulate the problem as a multi-objective optimization problem,and propose a novel memetic MOEA,namely HAMP,that combines an efficient portfoliobased selection and a surrogate-assisted local search,to solve *** studies demonstrate the potential of MOEAs in providing simultaneously a set of alternative solutions and the superiority of HAMP compared to the state-of-the-art hardware-aware network pruning method.

关键词： Multi-objective optimization Evolutionary algorithm Neural network pruning Hardware-awaremachine learning Hardware efficiency

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KeyEE:Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt

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Big data Mining and Analytics 2024年第2期7卷 547-560页

作者： Junwen Duan Xincheng Liao Ying An Jianxin Wang Hunan Key Laboratory of Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Big data institute Central South UniversityChangsha 410083China

Event Extraction(EE)is a key task in information extraction,which requires high-quality annotated data that are often costly to *** classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained *** recent approaches have endeavored to address EE through a more data-efficient generative process,they often overlook event keywords,which are vital for *** tackle these challenges,we introduce KeyEE,a multi-prompt learning strategy that improves low-resource event extraction by Event Keywords Extraction(EKE).We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language *** the auxiliary sub-prompt,KeyEE learns event keywords knowledge implicitly,thereby reducing the dependence on annotated ***,we investigate and analyze various EKE sub-prompt strategies to encourage further research in this *** experiments on benchmark datasets ACE2005 and ERE show that KeyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results.

关键词： natural language processing Event Extraction(EE) Multi-Prompt Learning(MPL) low-resource

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