Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate predictio...Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.展开更多
Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods...Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model...This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model follows a“three-stage”and“two-subject”framework,incorporating a structured design for teaching content and assessment methods before,during,and after class.Practical results indicate that this approach significantly enhances teaching effectiveness and improves students’learning autonomy.展开更多
Purpose–This study summarizes the overall situation of the resources of the national science and technology innovation platform in the railway industry,including the distribution of platform types,supporting institut...Purpose–This study summarizes the overall situation of the resources of the national science and technology innovation platform in the railway industry,including the distribution of platform types,supporting institutions,construction sites,professional fields,etc.,to provide a reference for the further improvement and optimization of the national science and technology innovation platform system in the railway industry.Design/methodology/approach–Through literature review,field investigation,expert consultation and other methods,this paper systematically investigates and analyzes the development status of the national science and technology innovation platform in the railway industry.Findings–Taking the national science and technology innovation platform of the railway industry as the research object,this paper investigates and analyzes the construction,development and distribution of the national science and technology innovation platform of railway industry over the years.And the National Engineering Research Center of High-speed Railway and Urban Rail Transit System Technology was taken as an example to introduce its operation effect.Originality/value–China Railway has made great development achievements,with the construction and development of national science and technology innovation platform in the railway industry.In recent years,a large number of national science and technology innovation platforms have been built in the railway industry,which play an important role in railway technological innovation,standard setting and commodification,and Railway Sciences provide strong support for railway technology development.展开更多
Mooring cable tension is a crucial parameter for evaluating the safety and reliability of a floating platform mooring system.The real-time mooring tension in an actual marine environment has always been essential data...Mooring cable tension is a crucial parameter for evaluating the safety and reliability of a floating platform mooring system.The real-time mooring tension in an actual marine environment has always been essential data that mooring system designers aim to acquire.To address the need for long-term continuous monitoring of mooring tension in deep-sea marine environments,this paper presents a mooring cable tension monitoring method based on the principle of direct mechanical measurement.The developed tension monitoring sensors were installed and applied in the mooring system of the"Yongle"scientific experimental platform.Over the course of one year,a substantial amount of in-situ tension monitoring data was obtained.Under wave heights of up to 1.24 m,the mooring tension on the floating platform reached 16.5 tons.Through frequency domain and time domain analysis,the spectral characteristics of mooring tension,including waveinduced force,slow drift force,and mooring cable elastic restoring force,were determined.The mooring cable elastic restoring force frequency was approximately half of that of the wave signal.Due to the characteristics of the hinge connection structure of the dual module floating platform,under some specific working conditions the wave-induced force was the maximum of the three different frequency forces,and restoring force was the smallest.展开更多
Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satell...Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satellite imagery and aerial data, remote sensing allows researchers to assess the health and extent of mangrove forests over large areas and time periods, providing insights into changes due to environmental stressors like climate change, urbanization, and deforestation. Coupled with web-based platforms, this technology facilitates real-time data sharing and collaborative research efforts among scientists, policymakers, and conservationists. Thus, there is a need to grow this research interest among experts working in this kind of ecosystem. The aim of this paper is to provide a comprehensive literature review on the effective role of remote sensing and web-based platform in monitoring mangrove ecosystem. The research paper utilized the thematic approach to extract specific information to use in the discussion which helped realize the efficiency of digital monitoring for the environment. Web-based platforms and remote sensing represent a powerful tool for environmental monitoring, particularly in the context of forest ecosystems. They facilitate the accessibility of vital data, promote collaboration among stakeholders, support evidence-based policymaking, and engage communities in conservation efforts. As experts confront the urgent challenges posed by climate change and environmental degradation, leveraging technology through web-based platforms is essential for fostering a sustainable future for the forests of the world.展开更多
A data-driven model ofmultiple variable cutting(M-VCUT)level set-based substructure is proposed for the topology optimization of lattice structures.TheM-VCUTlevel setmethod is used to represent substructures,enriching...A data-driven model ofmultiple variable cutting(M-VCUT)level set-based substructure is proposed for the topology optimization of lattice structures.TheM-VCUTlevel setmethod is used to represent substructures,enriching their diversity of configuration while ensuring connectivity.To construct the data-driven model of substructure,a database is prepared by sampling the space of substructures spanned by several substructure prototypes.Then,for each substructure in this database,the stiffness matrix is condensed so that its degrees of freedomare reduced.Thereafter,the data-drivenmodel of substructures is constructed through interpolationwith compactly supported radial basis function(CS-RBF).The inputs of the data-driven model are the design variables of topology optimization,and the outputs are the condensed stiffness matrix and volume of substructures.During the optimization,this data-driven model is used,thus avoiding repeated static condensation that would requiremuch computation time.Several numerical examples are provided to verify the proposed method.展开更多
The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies m...The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies make it possible to fabricate these highly spatially programmable structures and greatly enhance the freedom in their design.However,traditional analytical methods do not sufficiently reflect the actual vibration-damping mechanism of lattice structures and are limited by their high computational cost.In this study,a hybrid lattice structure consisting of various cells was designed based on quasi-static and vibration experiments.Subsequently,a novel parametric design method based on a data-driven approach was developed for hybrid lattices with engineered properties.The response surface method was adopted to define the sensitive optimization target.A prediction model for the lattice geometric parameters and vibration properties was established using a backpropagation neural network.Then,it was integrated into the genetic algorithm to create the optimal hybrid lattice with varying geometric features and the required wide-band vibration-damping characteristics.Validation experiments were conducted,demonstrating that the optimized hybrid lattice can achieve the target properties.In addition,the data-driven parametric design method can reduce computation time and be widely applied to complex structural designs when analytical and empirical solutions are unavailable.展开更多
On 20 April 1994,China made its first o!cial full-function connection to the World Wide Web through a 64-kilobyte international leased line,marking the country’s formal entry into the global digital age.The year 2024...On 20 April 1994,China made its first o!cial full-function connection to the World Wide Web through a 64-kilobyte international leased line,marking the country’s formal entry into the global digital age.The year 2024 marked the 30th anniversary of the country’s entry into the internet era.展开更多
The Underwater Acoustic(UWA)channel is bandwidth-constrained and experiences doubly selective fading.It is challenging to acquire perfect channel knowledge for Orthogonal Frequency Division Multiplexing(OFDM)communica...The Underwater Acoustic(UWA)channel is bandwidth-constrained and experiences doubly selective fading.It is challenging to acquire perfect channel knowledge for Orthogonal Frequency Division Multiplexing(OFDM)communications using a finite number of pilots.On the other hand,Deep Learning(DL)approaches have been very successful in wireless OFDM communications.However,whether they will work underwater is still a mystery.For the first time,this paper compares two categories of DL-based UWA OFDM receivers:the DataDriven(DD)method,which performs as an end-to-end black box,and the Model-Driven(MD)method,also known as the model-based data-driven method,which combines DL and expert OFDM receiver knowledge.The encoder-decoder framework and Convolutional Neural Network(CNN)structure are employed to establish the DD receiver.On the other hand,an unfolding-based Minimum Mean Square Error(MMSE)structure is adopted for the MD receiver.We analyze the characteristics of different receivers by Monte Carlo simulations under diverse communications conditions and propose a strategy for selecting a proper receiver under different communication scenarios.Field trials in the pool and sea are also conducted to verify the feasibility and advantages of the DL receivers.It is observed that DL receivers perform better than conventional receivers in terms of bit error rate.展开更多
When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding bia...When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding biased data selection,ameliorating overconfident models,and being flexible to varying practical objectives,especially when the training and testing data are not identically distributed.A workflow characterized by leveraging Bayesian methodology was proposed to address these issues.Employing a Multi-Layer Perceptron(MLP)as the foundational model,this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity,accuracy,and resistance to overfitting.The analysis revealed that,while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios,Bayesian neural networks showed great potential for preventing overfitting.Additionally,integrating decision thresholds through various evaluative principles offers insights for challenging decisions.Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data,employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics.Overall,the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation,showing improved robustness against overfitting and greater versatility in addressing practical challenges.This research contributes to the seismic liquefaction assessment field by providing a structured,adaptable methodology for accurate and reliable analysis.展开更多
In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD...In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD),with its emphasis on cross-functional teamwork,concurrent engineering,and data-driven decision-making,has been widely recognized for enhancing R&D efficiency and product quality.However,the unique characteristics of SOEs pose challenges to the effective implementation of IPD.The advancement of big data and artificial intelligence technologies offers new opportunities for optimizing IPD R&D management through data-driven decision-making models.This paper constructs and validates a data-driven decision-making model tailored to the IPD R&D management of SOEs.By integrating data mining,machine learning,and other advanced analytical techniques,the model serves as a scientific and efficient decision-making tool.It aids SOEs in optimizing R&D resource allocation,shortening product development cycles,reducing R&D costs,and improving product quality and innovation.Moreover,this study contributes to a deeper theoretical understanding of the value of data-driven decision-making in the context of IPD.展开更多
Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysi...Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysis methods have limitations in dealing with these complex and interrelated factors,and it is difficult to fully reveal the actual contribution of each factor to the production.Machine learning-based methods explore the complex mapping relationships between large amounts of data to provide datadriven insights into the key factors driving production.In this study,a data-driven PCA-RF-VIM(Principal Component Analysis-Random Forest-Variable Importance Measures)approach of analyzing the importance of features is proposed to identify the key factors driving post-fracturing production.Four types of parameters,including log parameters,geological and reservoir physical parameters,hydraulic fracturing design parameters,and reservoir stimulation parameters,were inputted into the PCA-RF-VIM model.The model was trained using 6-fold cross-validation and grid search,and the relative importance ranking of each factor was finally obtained.In order to verify the validity of the PCA-RF-VIM model,a consolidation model that uses three other independent data-driven methods(Pearson correlation coefficient,RF feature significance analysis method,and XGboost feature significance analysis method)are applied to compare with the PCA-RF-VIM model.A comparison the two models shows that they contain almost the same parameters in the top ten,with only minor differences in one parameter.In combination with the reservoir characteristics,the reasonableness of the PCA-RF-VIM model is verified,and the importance ranking of the parameters by this method is more consistent with the reservoir characteristics of the study area.Ultimately,the ten parameters are selected as the controlling factors that have the potential to influence post-fracturing gas production,as the combined importance of these top ten parameters is 91.95%on driving natural gas production.Analyzing and obtaining these ten controlling factors provides engineers with a new insight into the reservoir selection for fracturing stimulation and fracturing parameter optimization to improve fracturing efficiency and productivity.展开更多
In the last decade,shell biorefinery,a novel concept referring to the extraction of the main components of crustacean shells and the transformation of each component into valuable products,was proposed and has attract...In the last decade,shell biorefinery,a novel concept referring to the extraction of the main components of crustacean shells and the transformation of each component into valuable products,was proposed and has attracted increasing attentions.Chitin is one of main components of crustacean shells.Owing to the bio-fixed nitrogen element,chitin biomass has been regarded as a good candidate to produce nitrogen-containing chemicals.Among these,3-acetamido-5-acetylfuran(3A5AF)is an interesting furanic compound derived from the hydrolysis and sequential dehydration of chitin.Similar to cellulose-derived platform chemical 5-hydromethylfurfural(HMF),3A5AF is an emerging platform compound and also can be converted into various useful chemicals by oxidation,reduction,hydrolysis,substitution,and so on.This review showcases the recent advances in the synthesis of 3A5AF from chitin and N-acetyl glucosamine(NAG)employing various catalytic systems.The conversion of 3A5AF into valuable compounds was introduced then.There are still some challenges in this area,for example,the rational design of green and efficient catalytic systems for the synthesis of 3A5AF and its derivatives.The outlooks also were discussed at the end of the review.展开更多
The construction of centralized and standardized material databases is essential to support both scientific innovation and industrial application.However,for rare and precious metal materials,existing data resources a...The construction of centralized and standardized material databases is essential to support both scientific innovation and industrial application.However,for rare and precious metal materials,existing data resources are often decentralized.This results in persistent issues such as data silos and fragmentation,which significantly hinder efficient data utilization and collaboration.In response to these challenges,this study investigates the development of an integrated platform for sharing genetic data of rare and precious metal materials.The research begins by analyzing current trends in material data platforms,both domestically and internationally.These insights help inform the architectural design.The core of the platform consists of several key modules.Data resource integration is designed to aggregate and harmonize heterogeneous data from diverse sources.A structured data management system supports efficient storage and retrieval.A computational environment enables data analysis and modeling.A trusted sharing mechanism ensures security and access control.By integrating these functionalities,the platform aims to provide a unified ecosystem.This system facilitates open yet secure data exchange,promotes reproducibility,and enhances research efficiency.Finally,the article summarizes the initial implementation of the platform.It discusses its potential limitations and outlines future directions for development,including the integration of artificial intelligence tools and the expansion of data coverage.展开更多
This paper focuses on the numerical solution of a tumor growth model under a data-driven approach.Based on the inherent laws of the data and reasonable assumptions,an ordinary differential equation model for tumor gro...This paper focuses on the numerical solution of a tumor growth model under a data-driven approach.Based on the inherent laws of the data and reasonable assumptions,an ordinary differential equation model for tumor growth is established.Nonlinear fitting is employed to obtain the optimal parameter estimation of the mathematical model,and the numerical solution is carried out using the Matlab software.By comparing the clinical data with the simulation results,a good agreement is achieved,which verifies the rationality and feasibility of the model.展开更多
In the process of developing oil and gas resources in the Arctic,the impact of icebergs can pose a considerable threat to the structural safety of semi-submersible mooring platforms in ice regions.On the basis of the ...In the process of developing oil and gas resources in the Arctic,the impact of icebergs can pose a considerable threat to the structural safety of semi-submersible mooring platforms in ice regions.On the basis of the arbitrary Lagrangian Eulerian(ALE)algorithm,a numerical model for the interaction between an iceberg and a semi-submersible mooring platform is built in this work.First,a mooring system with a link element is designed and validated.An ice material model for the target iceberg is built and validated.A numerical model for the interaction between an iceberg and a semi-submersible mooring platform is then built.A parametric study(cable angle,tension angle and number of cables)is carried out to study the performance of the mooring system.The collision process between the semi-submersible mooring platform and the iceberg in the polar marine environment can be predicted by the present numerical model,and then the optimal mooring arrangement scheme can be obtained.The research results in this work can provide a reference for the design of mooring systems.展开更多
The impacts of lateral boundary conditions(LBCs)provided by numerical models and data-driven networks on convective-scale ensemble forecasts are investigated in this study.Four experiments are conducted on the Hangzho...The impacts of lateral boundary conditions(LBCs)provided by numerical models and data-driven networks on convective-scale ensemble forecasts are investigated in this study.Four experiments are conducted on the Hangzhou RDP(19th Hangzhou Asian Games Research Development Project on Convective-scale Ensemble Prediction and Application)testbed,with the LBCs respectively sourced from National Centers for Environmental Prediction(NCEP)Global Forecast System(GFS)forecasts with 33 vertical levels(Exp_GFS),Pangu forecasts with 13 vertical levels(Exp_Pangu),Fuxi forecasts with 13 vertical levels(Exp_Fuxi),and NCEP GFS forecasts with the vertical levels reduced to 13(the same as those of Exp_Pangu and Exp_Fuxi)(Exp_GFSRDV).In general,Exp_Pangu performs comparably to Exp_GFS,while Exp_Fuxi shows slightly inferior performance compared to Exp_Pangu,possibly due to its less accurate large-scale predictions.Therefore,the ability of using data-driven networks to efficiently provide LBCs for convective-scale ensemble forecasts has been demonstrated.Moreover,Exp_GFSRDV has the worst convective-scale forecasts among the four experiments,which indicates the potential improvement of using data-driven networks for LBCs by increasing the vertical levels of the networks.However,the ensemble spread of the four experiments barely increases with lead time.Thus,each experiment has insufficient ensemble spread to present realistic forecast uncertainties,which will be investigated in a future study.展开更多
As a high-value eudicot family,many famous horticultural crop genomes have been deciphered in Oleaceae.However,there are currently no bioinformatics platforms focused on empowering genome research in Oleaceae.Herein,w...As a high-value eudicot family,many famous horticultural crop genomes have been deciphered in Oleaceae.However,there are currently no bioinformatics platforms focused on empowering genome research in Oleaceae.Herein,we developed the first comprehensive Oleaceae Genome Research Platform(OGRP,https://oleaceae.cgrpoee.top/).In OGRP,70 genomes of 10 Oleaceae species and 46 eudicots and 366 transcriptomes involving 18 Oleaceae plant tissues can be obtained.We built 34 window-operated bioinformatics tools,collected 38 professional practical software programs,and proposed 3 new pipelines,namely ancient polyploidization identification,ancestral karyotype reconstruction,and gene family evolution.Employing these pipelines to reanalyze the Oleaceae genomes,we clarified the polyploidization,reconstructed the ancestral karyotypes,and explored the effects of paleogenome evolution on genes with specific biological regulatory roles.Significantly,we generated a series of comparative genomic resources focusing on the Oleaceae,comprising 108 genomic synteny dot plots,1952225 collinear gene pairs,multiple genome alignments,and imprints of paleochromosome rearrangements.Moreover,in Oleaceae genomes,researchers can efficiently search for 1785987 functional annotations,22584 orthogroups,29582 important trait genes from 74 gene families,12664 transcription factor-related genes,9178872 transposable elements,and all involved regulatory pathways.In addition,we provided downloads and usage instructions for the tools,a species encyclopedia,ecological resources,relevant literatures,and external database links.In short,ORGP integrates rich data resources and powerful analytical tools with the characteristic of continuous updating,which can efficiently empower genome research and agricultural breeding in Oleaceae and other plants.展开更多
基金The research work was financially supported by the National Natural Science Foundation of China(Grant Nos.51979238 and 52301338)the Sichuan Science and Technology Program(Grant Nos.2023NSFSC1953 and 2023ZYD0140).
文摘Mitigating vortex-induced vibrations(VIV)in flexible risers represents a critical concern in offshore oil and gas production,considering its potential impact on operational safety and efficiency.The accurate prediction of displacement and position of VIV in flexible risers remains challenging under actual marine conditions.This study presents a data-driven model for riser displacement prediction that corresponds to field conditions.Experimental data analysis reveals that the XGBoost algorithm predicts the maximum displacement and position with superior accuracy compared with Support vector regression(SVR),considering both computational efficiency and precision.Platform displacement in the Y-direction demonstrates a significant positive correlation with both axial depth and maximum displacement magnitude.The fourth point displacement exhibits the highest contribution to model prediction outcomes,showing a positive influence on maximum displacement while negatively affecting the axial depth of maximum displacement.Platform displacement in the X-and Y-directions exhibits competitive effects on both the riser’s maximum displacement and its axial depth.Through the implementation of XGBoost algorithm and SHapley Additive exPlanation(SHAP)analysis,the model effectively estimates the riser’s maximum displacement and its precise location.This data-driven approach achieves predictions using minimal,readily available data points,enhancing its practical field applications and demonstrating clear relevance to academic and professional communities.
基金This paper is the research result of“Research on Innovation of Evidence-Based Teaching Paradigm in Vocational Education under the Background of New Quality Productivity”(2024JXQ176)the Shandong Province Artificial Intelligence Education Research Project(SDDJ202501035),which explores the application of artificial intelligence big models in student value-added evaluation from an evidence-based perspective。
文摘Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金2024 Anqing Normal University University-Level Key Project(ZK2024062D)。
文摘This study examines the Big Data Collection and Preprocessing course at Anhui Institute of Information Engineering,implementing a hybrid teaching reform using the Bosi Smart Learning Platform.The proposed hybrid model follows a“three-stage”and“two-subject”framework,incorporating a structured design for teaching content and assessment methods before,during,and after class.Practical results indicate that this approach significantly enhances teaching effectiveness and improves students’learning autonomy.
文摘Purpose–This study summarizes the overall situation of the resources of the national science and technology innovation platform in the railway industry,including the distribution of platform types,supporting institutions,construction sites,professional fields,etc.,to provide a reference for the further improvement and optimization of the national science and technology innovation platform system in the railway industry.Design/methodology/approach–Through literature review,field investigation,expert consultation and other methods,this paper systematically investigates and analyzes the development status of the national science and technology innovation platform in the railway industry.Findings–Taking the national science and technology innovation platform of the railway industry as the research object,this paper investigates and analyzes the construction,development and distribution of the national science and technology innovation platform of railway industry over the years.And the National Engineering Research Center of High-speed Railway and Urban Rail Transit System Technology was taken as an example to introduce its operation effect.Originality/value–China Railway has made great development achievements,with the construction and development of national science and technology innovation platform in the railway industry.In recent years,a large number of national science and technology innovation platforms have been built in the railway industry,which play an important role in railway technological innovation,standard setting and commodification,and Railway Sciences provide strong support for railway technology development.
文摘Mooring cable tension is a crucial parameter for evaluating the safety and reliability of a floating platform mooring system.The real-time mooring tension in an actual marine environment has always been essential data that mooring system designers aim to acquire.To address the need for long-term continuous monitoring of mooring tension in deep-sea marine environments,this paper presents a mooring cable tension monitoring method based on the principle of direct mechanical measurement.The developed tension monitoring sensors were installed and applied in the mooring system of the"Yongle"scientific experimental platform.Over the course of one year,a substantial amount of in-situ tension monitoring data was obtained.Under wave heights of up to 1.24 m,the mooring tension on the floating platform reached 16.5 tons.Through frequency domain and time domain analysis,the spectral characteristics of mooring tension,including waveinduced force,slow drift force,and mooring cable elastic restoring force,were determined.The mooring cable elastic restoring force frequency was approximately half of that of the wave signal.Due to the characteristics of the hinge connection structure of the dual module floating platform,under some specific working conditions the wave-induced force was the maximum of the three different frequency forces,and restoring force was the smallest.
文摘Remote sensing and web-based platforms have emerged as vital tools in the effective monitoring of mangrove ecosystems, which are crucial for coastal protection, biodiversity, and carbon sequestration. Utilizing satellite imagery and aerial data, remote sensing allows researchers to assess the health and extent of mangrove forests over large areas and time periods, providing insights into changes due to environmental stressors like climate change, urbanization, and deforestation. Coupled with web-based platforms, this technology facilitates real-time data sharing and collaborative research efforts among scientists, policymakers, and conservationists. Thus, there is a need to grow this research interest among experts working in this kind of ecosystem. The aim of this paper is to provide a comprehensive literature review on the effective role of remote sensing and web-based platform in monitoring mangrove ecosystem. The research paper utilized the thematic approach to extract specific information to use in the discussion which helped realize the efficiency of digital monitoring for the environment. Web-based platforms and remote sensing represent a powerful tool for environmental monitoring, particularly in the context of forest ecosystems. They facilitate the accessibility of vital data, promote collaboration among stakeholders, support evidence-based policymaking, and engage communities in conservation efforts. As experts confront the urgent challenges posed by climate change and environmental degradation, leveraging technology through web-based platforms is essential for fostering a sustainable future for the forests of the world.
基金supported by the National Natural Science Foundation of China(Grant No.12272144).
文摘A data-driven model ofmultiple variable cutting(M-VCUT)level set-based substructure is proposed for the topology optimization of lattice structures.TheM-VCUTlevel setmethod is used to represent substructures,enriching their diversity of configuration while ensuring connectivity.To construct the data-driven model of substructure,a database is prepared by sampling the space of substructures spanned by several substructure prototypes.Then,for each substructure in this database,the stiffness matrix is condensed so that its degrees of freedomare reduced.Thereafter,the data-drivenmodel of substructures is constructed through interpolationwith compactly supported radial basis function(CS-RBF).The inputs of the data-driven model are the design variables of topology optimization,and the outputs are the condensed stiffness matrix and volume of substructures.During the optimization,this data-driven model is used,thus avoiding repeated static condensation that would requiremuch computation time.Several numerical examples are provided to verify the proposed method.
基金supported by National Natural Science Foundation of China(Grant No.52375380)National Key R&D Program of China(Grant No.2022YFB3402200)the Key Project of National Natural Science Foundation of China(Grant No.12032018).
文摘The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads.Additive-manufacturing technologies make it possible to fabricate these highly spatially programmable structures and greatly enhance the freedom in their design.However,traditional analytical methods do not sufficiently reflect the actual vibration-damping mechanism of lattice structures and are limited by their high computational cost.In this study,a hybrid lattice structure consisting of various cells was designed based on quasi-static and vibration experiments.Subsequently,a novel parametric design method based on a data-driven approach was developed for hybrid lattices with engineered properties.The response surface method was adopted to define the sensitive optimization target.A prediction model for the lattice geometric parameters and vibration properties was established using a backpropagation neural network.Then,it was integrated into the genetic algorithm to create the optimal hybrid lattice with varying geometric features and the required wide-band vibration-damping characteristics.Validation experiments were conducted,demonstrating that the optimized hybrid lattice can achieve the target properties.In addition,the data-driven parametric design method can reduce computation time and be widely applied to complex structural designs when analytical and empirical solutions are unavailable.
文摘On 20 April 1994,China made its first o!cial full-function connection to the World Wide Web through a 64-kilobyte international leased line,marking the country’s formal entry into the global digital age.The year 2024 marked the 30th anniversary of the country’s entry into the internet era.
基金funded in part by the National Natural Science Foundation of China under Grant 62401167 and 62192712in part by the Key Laboratory of Marine Environmental Survey Technology and Application,Ministry of Natural Resources,P.R.China under Grant MESTA-2023-B001in part by the Stable Supporting Fund of National Key Laboratory of Underwater Acoustic Technology under Grant JCKYS2022604SSJS007.
文摘The Underwater Acoustic(UWA)channel is bandwidth-constrained and experiences doubly selective fading.It is challenging to acquire perfect channel knowledge for Orthogonal Frequency Division Multiplexing(OFDM)communications using a finite number of pilots.On the other hand,Deep Learning(DL)approaches have been very successful in wireless OFDM communications.However,whether they will work underwater is still a mystery.For the first time,this paper compares two categories of DL-based UWA OFDM receivers:the DataDriven(DD)method,which performs as an end-to-end black box,and the Model-Driven(MD)method,also known as the model-based data-driven method,which combines DL and expert OFDM receiver knowledge.The encoder-decoder framework and Convolutional Neural Network(CNN)structure are employed to establish the DD receiver.On the other hand,an unfolding-based Minimum Mean Square Error(MMSE)structure is adopted for the MD receiver.We analyze the characteristics of different receivers by Monte Carlo simulations under diverse communications conditions and propose a strategy for selecting a proper receiver under different communication scenarios.Field trials in the pool and sea are also conducted to verify the feasibility and advantages of the DL receivers.It is observed that DL receivers perform better than conventional receivers in terms of bit error rate.
文摘When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding biased data selection,ameliorating overconfident models,and being flexible to varying practical objectives,especially when the training and testing data are not identically distributed.A workflow characterized by leveraging Bayesian methodology was proposed to address these issues.Employing a Multi-Layer Perceptron(MLP)as the foundational model,this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity,accuracy,and resistance to overfitting.The analysis revealed that,while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios,Bayesian neural networks showed great potential for preventing overfitting.Additionally,integrating decision thresholds through various evaluative principles offers insights for challenging decisions.Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data,employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics.Overall,the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation,showing improved robustness against overfitting and greater versatility in addressing practical challenges.This research contributes to the seismic liquefaction assessment field by providing a structured,adaptable methodology for accurate and reliable analysis.
文摘In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD),with its emphasis on cross-functional teamwork,concurrent engineering,and data-driven decision-making,has been widely recognized for enhancing R&D efficiency and product quality.However,the unique characteristics of SOEs pose challenges to the effective implementation of IPD.The advancement of big data and artificial intelligence technologies offers new opportunities for optimizing IPD R&D management through data-driven decision-making models.This paper constructs and validates a data-driven decision-making model tailored to the IPD R&D management of SOEs.By integrating data mining,machine learning,and other advanced analytical techniques,the model serves as a scientific and efficient decision-making tool.It aids SOEs in optimizing R&D resource allocation,shortening product development cycles,reducing R&D costs,and improving product quality and innovation.Moreover,this study contributes to a deeper theoretical understanding of the value of data-driven decision-making in the context of IPD.
基金funded by the Key Research and Development Program of Shaanxi,China(No.2024GX-YBXM-503)the National Natural Science Foundation of China(No.51974254)。
文摘Hydraulic fracturing technology has achieved remarkable results in improving the production of tight gas reservoirs,but its effectiveness is under the joint action of multiple factors of complexity.Traditional analysis methods have limitations in dealing with these complex and interrelated factors,and it is difficult to fully reveal the actual contribution of each factor to the production.Machine learning-based methods explore the complex mapping relationships between large amounts of data to provide datadriven insights into the key factors driving production.In this study,a data-driven PCA-RF-VIM(Principal Component Analysis-Random Forest-Variable Importance Measures)approach of analyzing the importance of features is proposed to identify the key factors driving post-fracturing production.Four types of parameters,including log parameters,geological and reservoir physical parameters,hydraulic fracturing design parameters,and reservoir stimulation parameters,were inputted into the PCA-RF-VIM model.The model was trained using 6-fold cross-validation and grid search,and the relative importance ranking of each factor was finally obtained.In order to verify the validity of the PCA-RF-VIM model,a consolidation model that uses three other independent data-driven methods(Pearson correlation coefficient,RF feature significance analysis method,and XGboost feature significance analysis method)are applied to compare with the PCA-RF-VIM model.A comparison the two models shows that they contain almost the same parameters in the top ten,with only minor differences in one parameter.In combination with the reservoir characteristics,the reasonableness of the PCA-RF-VIM model is verified,and the importance ranking of the parameters by this method is more consistent with the reservoir characteristics of the study area.Ultimately,the ten parameters are selected as the controlling factors that have the potential to influence post-fracturing gas production,as the combined importance of these top ten parameters is 91.95%on driving natural gas production.Analyzing and obtaining these ten controlling factors provides engineers with a new insight into the reservoir selection for fracturing stimulation and fracturing parameter optimization to improve fracturing efficiency and productivity.
基金support of the National Natural Science Foundation of China(22408032)the Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202000826 and KJQN202300836)+2 种基金Research Start-up Funding project of Chongqing Technology and Business University(1856011)Science and Technology Project of Chongqing Technology and Business University(2152027)Graduate Innovative Research Project from Chongqing Technology and Business University(yjscxx2024-284-29).
文摘In the last decade,shell biorefinery,a novel concept referring to the extraction of the main components of crustacean shells and the transformation of each component into valuable products,was proposed and has attracted increasing attentions.Chitin is one of main components of crustacean shells.Owing to the bio-fixed nitrogen element,chitin biomass has been regarded as a good candidate to produce nitrogen-containing chemicals.Among these,3-acetamido-5-acetylfuran(3A5AF)is an interesting furanic compound derived from the hydrolysis and sequential dehydration of chitin.Similar to cellulose-derived platform chemical 5-hydromethylfurfural(HMF),3A5AF is an emerging platform compound and also can be converted into various useful chemicals by oxidation,reduction,hydrolysis,substitution,and so on.This review showcases the recent advances in the synthesis of 3A5AF from chitin and N-acetyl glucosamine(NAG)employing various catalytic systems.The conversion of 3A5AF into valuable compounds was introduced then.There are still some challenges in this area,for example,the rational design of green and efficient catalytic systems for the synthesis of 3A5AF and its derivatives.The outlooks also were discussed at the end of the review.
基金funded by the Major Science and Technology Projects in Yunnan Province(202502AD080002)Yunnan Fundamental Research Projects(202201AT070161)Yunnan Province High-Level Talent Introduction Program(C619300A023).
文摘The construction of centralized and standardized material databases is essential to support both scientific innovation and industrial application.However,for rare and precious metal materials,existing data resources are often decentralized.This results in persistent issues such as data silos and fragmentation,which significantly hinder efficient data utilization and collaboration.In response to these challenges,this study investigates the development of an integrated platform for sharing genetic data of rare and precious metal materials.The research begins by analyzing current trends in material data platforms,both domestically and internationally.These insights help inform the architectural design.The core of the platform consists of several key modules.Data resource integration is designed to aggregate and harmonize heterogeneous data from diverse sources.A structured data management system supports efficient storage and retrieval.A computational environment enables data analysis and modeling.A trusted sharing mechanism ensures security and access control.By integrating these functionalities,the platform aims to provide a unified ecosystem.This system facilitates open yet secure data exchange,promotes reproducibility,and enhances research efficiency.Finally,the article summarizes the initial implementation of the platform.It discusses its potential limitations and outlines future directions for development,including the integration of artificial intelligence tools and the expansion of data coverage.
基金National Natural Science Foundation of China(Project No.:12371428)Projects of the Provincial College Students’Innovation and Training Program in 2024(Project No.:S202413023106,S202413023110)。
文摘This paper focuses on the numerical solution of a tumor growth model under a data-driven approach.Based on the inherent laws of the data and reasonable assumptions,an ordinary differential equation model for tumor growth is established.Nonlinear fitting is employed to obtain the optimal parameter estimation of the mathematical model,and the numerical solution is carried out using the Matlab software.By comparing the clinical data with the simulation results,a good agreement is achieved,which verifies the rationality and feasibility of the model.
基金financially supported by the Open Project Program of Shandong Marine Aerospace Equipment Technological Innovation Center,Ludong University(Grant Nos.MAETIC202209 and MAETIC202201)Shandong Provincial Natural Science Foundation(Grant No.ZR2022QE092)+2 种基金China Postdoctoral Science Foundation(Grant No.2023M730829)Open Fund of the State Key Laboratory of Industrial Equipment Structural Analysis(Grant No.GZ23109)the National Natural Science Foundation of China(Grant Nos.52001284 and 52192694).
文摘In the process of developing oil and gas resources in the Arctic,the impact of icebergs can pose a considerable threat to the structural safety of semi-submersible mooring platforms in ice regions.On the basis of the arbitrary Lagrangian Eulerian(ALE)algorithm,a numerical model for the interaction between an iceberg and a semi-submersible mooring platform is built in this work.First,a mooring system with a link element is designed and validated.An ice material model for the target iceberg is built and validated.A numerical model for the interaction between an iceberg and a semi-submersible mooring platform is then built.A parametric study(cable angle,tension angle and number of cables)is carried out to study the performance of the mooring system.The collision process between the semi-submersible mooring platform and the iceberg in the polar marine environment can be predicted by the present numerical model,and then the optimal mooring arrangement scheme can be obtained.The research results in this work can provide a reference for the design of mooring systems.
基金supported by the Strategic Research and Consulting Project of the Chinese Academy of Engineering[grant number 2024-XBZD-14]the National Natural Science Foundation of China[grant numbers 42192553 and 41922036]the Fundamental Research Funds for the Central Universities–Cemac“GeoX”Interdisciplinary Program[grant number 020714380207]。
文摘The impacts of lateral boundary conditions(LBCs)provided by numerical models and data-driven networks on convective-scale ensemble forecasts are investigated in this study.Four experiments are conducted on the Hangzhou RDP(19th Hangzhou Asian Games Research Development Project on Convective-scale Ensemble Prediction and Application)testbed,with the LBCs respectively sourced from National Centers for Environmental Prediction(NCEP)Global Forecast System(GFS)forecasts with 33 vertical levels(Exp_GFS),Pangu forecasts with 13 vertical levels(Exp_Pangu),Fuxi forecasts with 13 vertical levels(Exp_Fuxi),and NCEP GFS forecasts with the vertical levels reduced to 13(the same as those of Exp_Pangu and Exp_Fuxi)(Exp_GFSRDV).In general,Exp_Pangu performs comparably to Exp_GFS,while Exp_Fuxi shows slightly inferior performance compared to Exp_Pangu,possibly due to its less accurate large-scale predictions.Therefore,the ability of using data-driven networks to efficiently provide LBCs for convective-scale ensemble forecasts has been demonstrated.Moreover,Exp_GFSRDV has the worst convective-scale forecasts among the four experiments,which indicates the potential improvement of using data-driven networks for LBCs by increasing the vertical levels of the networks.However,the ensemble spread of the four experiments barely increases with lead time.Thus,each experiment has insufficient ensemble spread to present realistic forecast uncertainties,which will be investigated in a future study.
基金supported by the National Natural Science Foundation of China(32470676 and 32170236)Central Guidance on Local Science and Technology Development Fund of Hebei Province(246Z2508G)+2 种基金Hebei Natural Science Foundation(C2020209064)Tangshan Science and Technology Program Project(21130217C)Key research project of North China University of Science and Technology(ZD-YG-202313-23).
文摘As a high-value eudicot family,many famous horticultural crop genomes have been deciphered in Oleaceae.However,there are currently no bioinformatics platforms focused on empowering genome research in Oleaceae.Herein,we developed the first comprehensive Oleaceae Genome Research Platform(OGRP,https://oleaceae.cgrpoee.top/).In OGRP,70 genomes of 10 Oleaceae species and 46 eudicots and 366 transcriptomes involving 18 Oleaceae plant tissues can be obtained.We built 34 window-operated bioinformatics tools,collected 38 professional practical software programs,and proposed 3 new pipelines,namely ancient polyploidization identification,ancestral karyotype reconstruction,and gene family evolution.Employing these pipelines to reanalyze the Oleaceae genomes,we clarified the polyploidization,reconstructed the ancestral karyotypes,and explored the effects of paleogenome evolution on genes with specific biological regulatory roles.Significantly,we generated a series of comparative genomic resources focusing on the Oleaceae,comprising 108 genomic synteny dot plots,1952225 collinear gene pairs,multiple genome alignments,and imprints of paleochromosome rearrangements.Moreover,in Oleaceae genomes,researchers can efficiently search for 1785987 functional annotations,22584 orthogroups,29582 important trait genes from 74 gene families,12664 transcription factor-related genes,9178872 transposable elements,and all involved regulatory pathways.In addition,we provided downloads and usage instructions for the tools,a species encyclopedia,ecological resources,relevant literatures,and external database links.In short,ORGP integrates rich data resources and powerful analytical tools with the characteristic of continuous updating,which can efficiently empower genome research and agricultural breeding in Oleaceae and other plants.
