In China, most oil fields are continental sedimentation with strong heterogeneity, which on one side makes the reservoir prospecting and development more difficult, but on the other side provides more space for search...In China, most oil fields are continental sedimentation with strong heterogeneity, which on one side makes the reservoir prospecting and development more difficult, but on the other side provides more space for searching residual oil in matured fields. Time-lapse seismic reservoir monitoring technique is one of most important techniques to define residual oil distribution. According to the demand for and development of time-lapse seismic reservoir monitoring in China, purposeless repeated acquisition time-lapse seismic data processing was studied. The four key steps in purposeless repeated acquisition time-lapse seismic data processing, including amplitude-preserved processing with relative consistency, rebinning, match filtering and difference calculation, were analyzed by combining theory and real seismic data processing. Meanwhile, quality control during real time-lapse seismic processing was emphasized.展开更多
Underground gas storage(UGS)are an important guarantee for national energy strategic reserves,but the monitoring of gas reservoir distribution has always faced challenges.Time-lapse microgravity monitoring technology ...Underground gas storage(UGS)are an important guarantee for national energy strategic reserves,but the monitoring of gas reservoir distribution has always faced challenges.Time-lapse microgravity monitoring technology can infer the movement patterns of substances based on density changes at different times.Simulation results indicate that this technology provides strong support for the dynamic monitoring of UGS.However,in the process of processing time-lapse microgravity data,it is necessary to use field separation technology to obtain the gravity anomaly of the target body.In order to obtain more accurate and stable field separation results,this paper utilizes the low-rank nature of the regional field and the sparsity of the local field in potential field data,and adopts a method based on Robust Principal Component Analysis(RPCA)for field separation processing.In the study of the gas injection process in the Y21 UGS,microgravity measurement and processing results show that the areas with enriched natural gas in the UGS are approximately annular and located in the structural high-point areas,which basically match the geological structural characteristics.Due to the presence of boundary faults,according to the results of time-lapse microgravity,it is inferred that groundwater moves towards the structural high-point areas,and natural gas mainly moves towards the southwest direction,providing the direction of underground fluid movement during the gas injection process in the UGS.展开更多
In the leakage detection of reservoir dam bodies,traditional geophysical methods can only achieve one-time detection.Meanwhile,due to the non-uniqueness of geophysicalin version,how to improve the fidelity of geophysi...In the leakage detection of reservoir dam bodies,traditional geophysical methods can only achieve one-time detection.Meanwhile,due to the non-uniqueness of geophysicalin version,how to improve the fidelity of geophysical detection inversion profiles has become a key challenge in the industry.This study aims to construct a long-term real-time monitoring system using the time-lapse resistivity method,reveal the spatiotemporal evolution law of dam leakage,and provide technical support for accurate treatment.By integrating the Internet of Things(IoT),5G technology,and AI technology,real-time data acquisition,real-time transmission,and automatic inversionare realized.Through dynamic imaging analysis of the electrical anomaly characteristics of the leakage area and comparison between corresponding rainfall events,the leakage range and resistivity changes,the reliability and efficiency of dam leakagedete ction are significantly improved.This achieves long-termdynamic monitoring of dam leakage and provides a new perspective for the safe operation and maintenance of reservoirs.展开更多
Passive source imaging can reconstruct body wave reflections similar to those of active sources through seismic interferometry(SI).It has become a low-cost,environmentally friendly alternative to active source seismic...Passive source imaging can reconstruct body wave reflections similar to those of active sources through seismic interferometry(SI).It has become a low-cost,environmentally friendly alternative to active source seismic,showing great potential.However,this method faces many challenges in practical applications,including uneven distribution of underground sources and complex survey environments.These situations seriously affect the reconstruction quality of virtual shot records,resulting in unguaranteed imaging results and greatly limiting passive source seismic exploration applications.In addition,the quality of the reconstructed records is directly related to the time length of the noise records,but in practice it is often difficult to obtain long-term,high-quality noise segments containing body wave events.To solve the above problems,we propose a deep learning method for reconstructing passive source virtual shot records and apply it to passive source time-lapse monitoring.This method combines the UNet network and the BiLSTM(Bidirectional Long Short-Term Memory)network for extracting spatial features and temporal features respectively.It introduces the spatial attention mechanism to establish a hybrid SUNet-BiLSTM-Attention(SBA)network for supervised training.Through pre-training and fine-tuning training,the network can accurately reconstruct passive source virtual shot records directly from short-time noisy segments containing body wave events.The experimental results of theoretical data show that the virtual shot records reconstructed by the network have high resolution and signal to noise ratio(SNR),providing high-quality data for subsequent monitoring and imaging.Finally,to further validate the effectiveness of proposed method,we applied it to field data collected from gas storage in northwest China.The reconstruction results of field data effectively improve the quality of virtual records and obtain more reliable time-lapse imaging monitoring results,which have significant practical value.展开更多
In common practice in the oil fields,the injection of water and gas into reservoirs is a crucial technique to increase production.The control of the waterflooding front in oil/gas exploitation is a matter of great con...In common practice in the oil fields,the injection of water and gas into reservoirs is a crucial technique to increase production.The control of the waterflooding front in oil/gas exploitation is a matter of great concern to reservoir engineers.Monitoring the waterflooding front in oil/gas wells plays a very important role in adjusting the well network and later in production,taking advantage of the remaining oil po-tential and ultimately achieving great success in improving the recovery rate.For a long time,micro-seismic monitoring,numerical simulation,four-dimensional seismic and other methods have been widely used in waterflooding front monitoring.However,reconciling their reliability and cost poses a significant challenge.In order to achieve real-time,reliable and cost-effective monitoring,we propose an innovative method for waterflooding front monitoring through the similarity analysis of passive source time-lapse seismic images.Typically,passive source seismic data collected from oil fields have extremely low signal-to-noise ratio(SNR),which poses a serious problem for obtaining structural images.The proposed method aims to visualize and analyze underground changes by highlighting time-lapse images and provide a strategy for underground monitoring using long-term passive source data under low SNR conditions.First,we verify the feasibility of the proposed method by designing a theoretical model.Then,we conduct an analysis of the correlation coefficient(similarity)on the passive source time-lapse seismic imaging results to enhance the image differences and identify the simulated waterflooding fronts.Finally,the proposed method is applied to the actual waterflooding front monitoring tasks in Shengli Oilfield,China.The research findings indicate that the monitoring results are consistent with the actual devel-opment conditions,which in turn demonstrates that the proposed method has great potential for practical application and is very suitable for monitoring common development tasks in oil fields.展开更多
Hydrocarbon contamination from oil spills presents geoenvironmental and geoengineering challenges,notably in Eleme,Nigeria.This study integrates electrical resistivity tomography(ERT),soil total petroleum hydrocarbon(...Hydrocarbon contamination from oil spills presents geoenvironmental and geoengineering challenges,notably in Eleme,Nigeria.This study integrates electrical resistivity tomography(ERT),soil total petroleum hydrocarbon(TPH)analysis,and geotechnical testing for treated spill site monitoring and characterization over six months.Four 100 m ERT lines,L1 to L4,with spacings at 1.5 m,3 m,6 m,9 m,12 m,and 15 m,were established for the first and second sampling phases.Twenty-one soil samples,12 TPH,and 9 mechanical analyses,were obtained from 5 boreholes,BH1 to BH4,for the study site and the BH5 control site across the phases at 0.5 m,3.0 m,and 5.0 m depths along ERT lines.ERT results reveal resistivity reductions averaging 18%in shallow zones of active degradation,correlating with an average 41%TPH-decrease.Specific gravity averaged 2.49 in the spill soils,compared to 2.58 in control samples,reflecting hydrocarbon-induced density reductions of 3.5%.Particle size showed spill soils contained>50%fines,increasing water retention and reducing permeability by 30%.Consolidation tests highlighted increased compressibility,with settlements of 1.89 mm in spill soils versus 1.01 mm in control samples,indicating a 47%increase in settlement from hydrocarbon reduction.Correlation analysis shows slower consolidation at BH3(−0.62 Cv)with moderate settlement increase(0.25),while BH4 exhibits much higher compressibility(0.95)but minimal Cv impact(0.23),indicating increased structural weakness with higher residual TPH.Spill degradation reduced TPH by 19%-64%in shallow zones,with persistent contamination at deeper layers exceeding the regulatory limits,emphasising the need for ongoing monitoring and targeted remediation for long-term stability and sustainability.展开更多
Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from sei...Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.展开更多
This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data e...This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data exchanges transitioning from quasi-public to marketoriented operations.To address the complex dynamics among data exchanges,suppliers,and consumers,the authors develop a threestage Stackelberg game framework.In this model,the data exchange acts as a leader setting transaction commission rates,suppliers are intermediate leaders determining unit prices,and consumers are followers making purchasing decisions.Two pricing strategies are examined:the Independent Pricing Approach(IPA)and the novel Perfectly Competitive Pricing Approach(PCPA),which accounts for competition among data providers.Using backward induction,the study derives subgame-perfect equilibria and proves the existence and uniqueness of Stackelberg equilibria under both approaches.Extensive numerical simulations are carried out in the model,demonstrating that PCPA enhances data demander utility,encourages supplier competition,increases transaction volume,and improves the overall profitability and sustainability of data exchanges.Social welfare analysis further confirms PCPA’s superiority in promoting efficient and fair data markets.展开更多
Ovarian cancer(OC)is one of the leading causes of death related to gynecological cancer,with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers.Machine learning(ML)has the potent...Ovarian cancer(OC)is one of the leading causes of death related to gynecological cancer,with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers.Machine learning(ML)has the potential to process complex datasets and support decision-making in OC diagnosis.Nevertheless,traditional ML models tend to be biased,overfitting,noisy,and less generalized.Moreover,their black-box nature reduces interpretability and limits their practical clinical applicability.In this study,we introduce an explainable ensemble learning(EL)model,TreeX-Stack,based on a stacking architecture that employs tree-based learners such as Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),and Extreme Gradient Boosting(XGBoost)as base learners,and Logistic Regression(LR)as the meta-learner to enhance ovarian cancer(OC)diagnosis.Local Interpretable ModelAgnostic Explanations(LIME)are used to explain individual predictions,making the model outputs more clinically interpretable and applicable.The model is trained on the dataset that includes demographic information,blood test,general chemistry,and tumor markers.Extensive preprocessing includes handling missing data using iterative imputation with Bayesian Ridge and addressing multicollinearity by removing features with correlation coefficients above 0.7.Relevant features are then selected using the Boruta feature selection method.To obtain robust and unbiased performance estimates during hyperparameter tuning,nested cross-validation(CV)with grid search is employed,and all experiments are repeated five times to ensure statistical reliability.TreeX-Stack demonstrates excellent diagnostic performance,achieving an accuracy of 0.9027,a precision of 0.8673,a recall of 0.9391,and an F1-score of 0.9012.Feature-importance analyses using LIME and permutation importance highlight Human Epididymis Protein 4(HE4)as the most significant biomarker for OC.The combination of high predictive performance and interpretability makes TreeX-Stack a reliable tool for clinical decision support in OC diagnosis.展开更多
Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and...Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.展开更多
To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative t...To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative to build an efficient comprehensive management platform for regional air quality.In this paper,the specific practice in Zibo City,Shandong Province is as an example to systematically analyze the top-level design,technical implementation,and innovative application of a comprehensive management platform for regional air quality integrating"perception monitoring,data fusion,research judgment of early warnings,analysis of sources,collaborative dispatching,and evaluation assessment".Through the construction of an"sky-air-ground"integrated three-dimensional monitoring network,the platform integrates multi-source heterogeneous environmental data,and employs big data,cloud computing,artificial intelligence,CALPUFF/CMAQ,and other numerical model technologies to achieve comprehensive perception,precise prediction,intelligent source tracing,and closed-loop management of air pollution.The platform innovatively establishes a full-process closed-loop management mechanism of"data-early warning-disposition-evaluation",and achieves a fundamental transformation from passive response to active anticipation and from experience-based judgment to data driving in environmental supervision.The application results show that this platform significantly improves the scientific decision-making ability and collaborative execution efficiency of air pollution governance in Zibo City,providing a replicable and scalable comprehensive solution for similar industrial cities to achieve the continuous improvement of air quality.展开更多
tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years f...tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years from accumulating studies.However,repositories for cataloging the detailed information on tsRNA–disease associations are scarce.In this study,we provide a tsRNADisease database by integrating experimentally and computationally supported tsRNA–disease associations from manual curation of literatures and other related resources.tsRNADisease contains 5571 manually curated associations between 4759 tsRNAs and 166 diseases with experimental evidence from 346 studies.In addition,it also contains 5013 predicted associations between 1297 tsRNAs and 111 diseases.tsRNADisease provides a user-friendly interface to browse,retrieve,and download data conveniently.This database can improve our understanding of tsRNA deregulation in diseases and serve as a valuable resource for investigating the mechanism of disease-related tsRNAs.tsRNADisease is freely available at http://www.compgenelab.info/tsRNADisease.展开更多
0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has...0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has largely been discipline-based,relying on field investigations,data collection,experimental analyses,and data interpretation to study individual components of the Earth system.展开更多
Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.Howev...Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.展开更多
Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy a...Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy attribute-based encryption(CP-ABE)schemes still face cumbersome ciphertext re-encryption and insufficient oversight when handling dynamic attribute changes and cross-chain collaboration.To address these issues,we propose a dynamic permission attribute-encryption scheme for multi-chain collaboration.This scheme incorporates a multiauthority architecture for distributed attribute management and integrates an attribute revocation and granting mechanism that eliminates the need for ciphertext re-encryption,effectively reducing both computational and communication overhead.It leverages the InterPlanetary File System(IPFS)for off-chain data storage and constructs a cross-chain regulatory framework—comprising a Hyperledger Fabric business chain and a FISCO BCOS regulatory chain—to record changes in decryption privileges and access behaviors in an auditable manner.Security analysis shows selective indistinguishability under chosen-plaintext attack(sIND-CPA)security under the decisional q-Parallel Bilinear Diffie-Hellman Exponent Assumption(q-PBDHE).In the performance and experimental evaluations,we compared the proposed scheme with several advanced schemes.The results show that,while preserving security,the proposed scheme achieves higher encryption/decryption efficiency and lower storage overhead for ciphertexts and keys.展开更多
With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-...With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-to-end datamodem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity.Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers.For example,speech activity detection may quickly classify encoded signals as nonspeech signals and reject input waveforms.To address this issue,we propose a novel modulation method based on linear frequency modulation that encodes 3 bits per symbol by varying its frequency,shape,and phase,alongside a lightweightMobileNetV3-Small-based demodulator for efficient and accurate signal decoding on resource-constrained devices.This method leverages the unique characteristics of linear frequency modulation signals,making them more easily transmitted and decoded in speech channels.To ensure reliable data delivery over unstable voice links,we further introduce a robust framing scheme with delimiter-based synchronization,a sample-level position remedying algorithm,and a feedback-driven retransmission mechanism.We have validated the feasibility and performance of our system through expanded real-world evaluations,demonstrating that it outperforms existing advanced methods in terms of robustness and data transfer rate.This technology establishes the foundational infrastructure for reliable certificate delivery over voice channels,which is crucial for achieving strong caller authentication and preventing telephone fraud at its root cause.展开更多
Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(O...Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(OGTT),and fasting plasma glucose(FPG)screening techniques,which are invasive and limited in scale.Machine learning(ML)and deep neural network(DNN)models that use large datasets to learn the complex,nonlinear feature interactions,but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy.Conversely,DNN models are more robust,though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge.For predicting diabetes,this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory(BiLSTM)network with a bidirectional gated recurrent unit(BiGRU).A robust DL model,developed by combining various datasets with weighted coefficients,dense operations in the connection of deep layers,and the output aggregation using batch normalization and dropout functions to avoid overfitting.The goal of this hybrid model is better generalization and consistency among various datasets,which facilitates the effective management and early intervention.The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks.The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics.展开更多
Reducing carbon emissions is fundamental to achieving carbon neutrality.Existing studies have typically estimated emissions by predicting fossil fuel consumption across sectors under different socioeconomic scenarios;...Reducing carbon emissions is fundamental to achieving carbon neutrality.Existing studies have typically estimated emissions by predicting fossil fuel consumption across sectors under different socioeconomic scenarios;however,uncertainties in future development often lead to deviations from these assumptions.To address this limitation,this study proposes a data-driven approach for evaluating national carbon emissions using historical data.Countries with similar energy consumption patterns were selected as reference samples,and their emission pathways were analyzed to predict future emissions for countries that have not yet reached their peak.Key indicators,including peak levels,timing,plateau duration,and post-peak decline rates,were identified.The results indicate that the trends in unpeaked economies can be effectively assessed based on the emission patterns of countries with comparable energy structures.Applying this framework to China suggests a carbon peak between 2027 and 2030,in the range of 14.207 to 16.234 Gt,followed by a gradual decline from 2031 to 2036.Compared with the average results of the existing studies,the predicted minimum and maximum emissions show error margins of 10.1% and 1.41%,respectively.This study proposes a top-down methodology that provides a transparent,reproducible,and empirical framework for forecasting carbon emission pathways,thereby offering a scientific basis for assessing countries that have not yet reached their emissions peak.展开更多
Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel a...Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.展开更多
Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either re...Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either require trusted setups or suffer from high communication overhead and low verification efficiency.This paper presents ISTIRDA,a DAS scheme that lets light clients certify availability by sampling small random codeword symbols.Built on ISTIR,an improved Reed–Solomon interactive oracle proof of proximity,ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication.This paper formalizes opening consistency and prove security with bounded error in the random oracle model,giving polylogarithmic verifier queries and no trusted setup.In a prototype compared with FRIDA under equal soundness,ISTIRDA reduces communication by 40.65%to 80%.For data larger than 16 MB,ISTIRDA verifies faster and the advantage widens;at 128 MB,proofs are about 60%smaller and verification time is roughly 25%shorter,while prover overhead remains modest.In peer-to-peer emulation under injected latency and loss,ISTIRDA reaches confidence more quickly and is less sensitive to packet loss and load.These results indicate that ISTIRDA is a scalable and provably secure DAS scheme suitable for high-throughput,large-block public blockchains,substantially easing bandwidth and latency pressure on lightweight nodes.展开更多
文摘In China, most oil fields are continental sedimentation with strong heterogeneity, which on one side makes the reservoir prospecting and development more difficult, but on the other side provides more space for searching residual oil in matured fields. Time-lapse seismic reservoir monitoring technique is one of most important techniques to define residual oil distribution. According to the demand for and development of time-lapse seismic reservoir monitoring in China, purposeless repeated acquisition time-lapse seismic data processing was studied. The four key steps in purposeless repeated acquisition time-lapse seismic data processing, including amplitude-preserved processing with relative consistency, rebinning, match filtering and difference calculation, were analyzed by combining theory and real seismic data processing. Meanwhile, quality control during real time-lapse seismic processing was emphasized.
基金supported by Project funded by Projects of Sinopec(P25115).
文摘Underground gas storage(UGS)are an important guarantee for national energy strategic reserves,but the monitoring of gas reservoir distribution has always faced challenges.Time-lapse microgravity monitoring technology can infer the movement patterns of substances based on density changes at different times.Simulation results indicate that this technology provides strong support for the dynamic monitoring of UGS.However,in the process of processing time-lapse microgravity data,it is necessary to use field separation technology to obtain the gravity anomaly of the target body.In order to obtain more accurate and stable field separation results,this paper utilizes the low-rank nature of the regional field and the sparsity of the local field in potential field data,and adopts a method based on Robust Principal Component Analysis(RPCA)for field separation processing.In the study of the gas injection process in the Y21 UGS,microgravity measurement and processing results show that the areas with enriched natural gas in the UGS are approximately annular and located in the structural high-point areas,which basically match the geological structural characteristics.Due to the presence of boundary faults,according to the results of time-lapse microgravity,it is inferred that groundwater moves towards the structural high-point areas,and natural gas mainly moves towards the southwest direction,providing the direction of underground fluid movement during the gas injection process in the UGS.
文摘In the leakage detection of reservoir dam bodies,traditional geophysical methods can only achieve one-time detection.Meanwhile,due to the non-uniqueness of geophysicalin version,how to improve the fidelity of geophysical detection inversion profiles has become a key challenge in the industry.This study aims to construct a long-term real-time monitoring system using the time-lapse resistivity method,reveal the spatiotemporal evolution law of dam leakage,and provide technical support for accurate treatment.By integrating the Internet of Things(IoT),5G technology,and AI technology,real-time data acquisition,real-time transmission,and automatic inversionare realized.Through dynamic imaging analysis of the electrical anomaly characteristics of the leakage area and comparison between corresponding rainfall events,the leakage range and resistivity changes,the reliability and efficiency of dam leakagedete ction are significantly improved.This achieves long-termdynamic monitoring of dam leakage and provides a new perspective for the safe operation and maintenance of reservoirs.
基金supported by the CNPC-SWPU Innovation Alliance Technology Cooperation Project(2020CX020000)the Natural Science Foundation of Sichuan Province(24NSFSC0808)the China Scholarship Council(202306440144).
文摘Passive source imaging can reconstruct body wave reflections similar to those of active sources through seismic interferometry(SI).It has become a low-cost,environmentally friendly alternative to active source seismic,showing great potential.However,this method faces many challenges in practical applications,including uneven distribution of underground sources and complex survey environments.These situations seriously affect the reconstruction quality of virtual shot records,resulting in unguaranteed imaging results and greatly limiting passive source seismic exploration applications.In addition,the quality of the reconstructed records is directly related to the time length of the noise records,but in practice it is often difficult to obtain long-term,high-quality noise segments containing body wave events.To solve the above problems,we propose a deep learning method for reconstructing passive source virtual shot records and apply it to passive source time-lapse monitoring.This method combines the UNet network and the BiLSTM(Bidirectional Long Short-Term Memory)network for extracting spatial features and temporal features respectively.It introduces the spatial attention mechanism to establish a hybrid SUNet-BiLSTM-Attention(SBA)network for supervised training.Through pre-training and fine-tuning training,the network can accurately reconstruct passive source virtual shot records directly from short-time noisy segments containing body wave events.The experimental results of theoretical data show that the virtual shot records reconstructed by the network have high resolution and signal to noise ratio(SNR),providing high-quality data for subsequent monitoring and imaging.Finally,to further validate the effectiveness of proposed method,we applied it to field data collected from gas storage in northwest China.The reconstruction results of field data effectively improve the quality of virtual records and obtain more reliable time-lapse imaging monitoring results,which have significant practical value.
基金supported by the CNPC-SWPU Innovation Alliance Technology Cooperation Project(2020CX020000)the National Natural Science Foundation of China(42022028)+1 种基金the Natural Science Foundation of Sichuan Province(24NSFSC0808)the China Scholarship Council(202306440144)。
文摘In common practice in the oil fields,the injection of water and gas into reservoirs is a crucial technique to increase production.The control of the waterflooding front in oil/gas exploitation is a matter of great concern to reservoir engineers.Monitoring the waterflooding front in oil/gas wells plays a very important role in adjusting the well network and later in production,taking advantage of the remaining oil po-tential and ultimately achieving great success in improving the recovery rate.For a long time,micro-seismic monitoring,numerical simulation,four-dimensional seismic and other methods have been widely used in waterflooding front monitoring.However,reconciling their reliability and cost poses a significant challenge.In order to achieve real-time,reliable and cost-effective monitoring,we propose an innovative method for waterflooding front monitoring through the similarity analysis of passive source time-lapse seismic images.Typically,passive source seismic data collected from oil fields have extremely low signal-to-noise ratio(SNR),which poses a serious problem for obtaining structural images.The proposed method aims to visualize and analyze underground changes by highlighting time-lapse images and provide a strategy for underground monitoring using long-term passive source data under low SNR conditions.First,we verify the feasibility of the proposed method by designing a theoretical model.Then,we conduct an analysis of the correlation coefficient(similarity)on the passive source time-lapse seismic imaging results to enhance the image differences and identify the simulated waterflooding fronts.Finally,the proposed method is applied to the actual waterflooding front monitoring tasks in Shengli Oilfield,China.The research findings indicate that the monitoring results are consistent with the actual devel-opment conditions,which in turn demonstrates that the proposed method has great potential for practical application and is very suitable for monitoring common development tasks in oil fields.
基金funded by the Petroleum Technology Development Fund(PTDF),Nigeria.(Award No.:PTDF/ED/OSS/PHD/NOA/1714/20).
文摘Hydrocarbon contamination from oil spills presents geoenvironmental and geoengineering challenges,notably in Eleme,Nigeria.This study integrates electrical resistivity tomography(ERT),soil total petroleum hydrocarbon(TPH)analysis,and geotechnical testing for treated spill site monitoring and characterization over six months.Four 100 m ERT lines,L1 to L4,with spacings at 1.5 m,3 m,6 m,9 m,12 m,and 15 m,were established for the first and second sampling phases.Twenty-one soil samples,12 TPH,and 9 mechanical analyses,were obtained from 5 boreholes,BH1 to BH4,for the study site and the BH5 control site across the phases at 0.5 m,3.0 m,and 5.0 m depths along ERT lines.ERT results reveal resistivity reductions averaging 18%in shallow zones of active degradation,correlating with an average 41%TPH-decrease.Specific gravity averaged 2.49 in the spill soils,compared to 2.58 in control samples,reflecting hydrocarbon-induced density reductions of 3.5%.Particle size showed spill soils contained>50%fines,increasing water retention and reducing permeability by 30%.Consolidation tests highlighted increased compressibility,with settlements of 1.89 mm in spill soils versus 1.01 mm in control samples,indicating a 47%increase in settlement from hydrocarbon reduction.Correlation analysis shows slower consolidation at BH3(−0.62 Cv)with moderate settlement increase(0.25),while BH4 exhibits much higher compressibility(0.95)but minimal Cv impact(0.23),indicating increased structural weakness with higher residual TPH.Spill degradation reduced TPH by 19%-64%in shallow zones,with persistent contamination at deeper layers exceeding the regulatory limits,emphasising the need for ongoing monitoring and targeted remediation for long-term stability and sustainability.
文摘Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.
基金supported by the National Natural Science Foundation of China[grant numbers 12171158,12371474 and 12571510]Fundamental Research Funds for the Central Universities[grant number 2025ECNU-WLJC006].
文摘This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data exchanges transitioning from quasi-public to marketoriented operations.To address the complex dynamics among data exchanges,suppliers,and consumers,the authors develop a threestage Stackelberg game framework.In this model,the data exchange acts as a leader setting transaction commission rates,suppliers are intermediate leaders determining unit prices,and consumers are followers making purchasing decisions.Two pricing strategies are examined:the Independent Pricing Approach(IPA)and the novel Perfectly Competitive Pricing Approach(PCPA),which accounts for competition among data providers.Using backward induction,the study derives subgame-perfect equilibria and proves the existence and uniqueness of Stackelberg equilibria under both approaches.Extensive numerical simulations are carried out in the model,demonstrating that PCPA enhances data demander utility,encourages supplier competition,increases transaction volume,and improves the overall profitability and sustainability of data exchanges.Social welfare analysis further confirms PCPA’s superiority in promoting efficient and fair data markets.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)under the grant number IMSIU-DDRSP2601.
文摘Ovarian cancer(OC)is one of the leading causes of death related to gynecological cancer,with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers.Machine learning(ML)has the potential to process complex datasets and support decision-making in OC diagnosis.Nevertheless,traditional ML models tend to be biased,overfitting,noisy,and less generalized.Moreover,their black-box nature reduces interpretability and limits their practical clinical applicability.In this study,we introduce an explainable ensemble learning(EL)model,TreeX-Stack,based on a stacking architecture that employs tree-based learners such as Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),and Extreme Gradient Boosting(XGBoost)as base learners,and Logistic Regression(LR)as the meta-learner to enhance ovarian cancer(OC)diagnosis.Local Interpretable ModelAgnostic Explanations(LIME)are used to explain individual predictions,making the model outputs more clinically interpretable and applicable.The model is trained on the dataset that includes demographic information,blood test,general chemistry,and tumor markers.Extensive preprocessing includes handling missing data using iterative imputation with Bayesian Ridge and addressing multicollinearity by removing features with correlation coefficients above 0.7.Relevant features are then selected using the Boruta feature selection method.To obtain robust and unbiased performance estimates during hyperparameter tuning,nested cross-validation(CV)with grid search is employed,and all experiments are repeated five times to ensure statistical reliability.TreeX-Stack demonstrates excellent diagnostic performance,achieving an accuracy of 0.9027,a precision of 0.8673,a recall of 0.9391,and an F1-score of 0.9012.Feature-importance analyses using LIME and permutation importance highlight Human Epididymis Protein 4(HE4)as the most significant biomarker for OC.The combination of high predictive performance and interpretability makes TreeX-Stack a reliable tool for clinical decision support in OC diagnosis.
基金supported by the International Partnership program of the Chinese Academy of Sciences(170GJHZ2023074GC)National Natural Science Foundation of China(42425706 and 42488201)+1 种基金National Key Research and Development Program of China(2024YFF0807902)Beijing Natural Science Foundation(8242041),and China Postdoctoral Science Foundation(2025M770353).
文摘Accurately assessing the relationship between tree growth and climatic factors is of great importance in dendrochronology.This study evaluated the consistency between alternative climate datasets(including station and gridded data)and actual climate data(fixed-point observations near the sampling sites),in northeastern China’s warm temperate zone and analyzed differences in their correlations with tree-ring width index.The results were:(1)Gridded temperature data,as well as precipitation and relative humidity data from the Huailai meteorological station,was more consistent with the actual climate data;in contrast,gridded soil moisture content data showed significant discrepancies.(2)Horizontal distance had a greater impact on the representativeness of actual climate conditions than vertical elevation differences.(3)Differences in consistency between alternative and actual climate data also affected their correlations with tree-ring width indices.In some growing season months,correlation coefficients,both in magnitude and sign,differed significantly from those based on actual data.The selection of different alternative climate datasets can lead to biased results in assessing forest responses to climate change,which is detrimental to the management of forest ecosystems in harsh environments.Therefore,the scientific and rational selection of alternative climate data is essential for dendroecological and climatological research.
文摘To address the severe challenges of PM_(2.5) and ozone co-control during the"14^(th) Five-Year Plan"period and to enhance the precision and intelligence level of air environment governance,it is imperative to build an efficient comprehensive management platform for regional air quality.In this paper,the specific practice in Zibo City,Shandong Province is as an example to systematically analyze the top-level design,technical implementation,and innovative application of a comprehensive management platform for regional air quality integrating"perception monitoring,data fusion,research judgment of early warnings,analysis of sources,collaborative dispatching,and evaluation assessment".Through the construction of an"sky-air-ground"integrated three-dimensional monitoring network,the platform integrates multi-source heterogeneous environmental data,and employs big data,cloud computing,artificial intelligence,CALPUFF/CMAQ,and other numerical model technologies to achieve comprehensive perception,precise prediction,intelligent source tracing,and closed-loop management of air pollution.The platform innovatively establishes a full-process closed-loop management mechanism of"data-early warning-disposition-evaluation",and achieves a fundamental transformation from passive response to active anticipation and from experience-based judgment to data driving in environmental supervision.The application results show that this platform significantly improves the scientific decision-making ability and collaborative execution efficiency of air pollution governance in Zibo City,providing a replicable and scalable comprehensive solution for similar industrial cities to achieve the continuous improvement of air quality.
基金supported by the National Natural Science Foundation of China(91959106)the Foundation of the Shanghai Municipal Education Commission(24RGZNC02)+4 种基金Shanghai Key Laboratory of Intelligent Information Processing,Fudan University(IIPL-2025-RD3-02)Key University Science Research Project of Anhui Province(2023AH030108)Climbing Peak Training Program for Innovative Technology team of Yijishan Hospital,Wannan Medical College(PF201904)Peak Training Program for Scientific Research of Yijishan Hospital,Wannan Medical College(GF2019G15)the talent project of the First Affiliated Hospital of Wannan Medical College(Yijishan Hospital of Wannan Medical College)(YR202422).
文摘tRNA-derived small RNAs(tsRNAs),as a class of regulatory small noncoding RNA,have been implicated in a wide variety of human diseases.Large amounts of tsRNA–disease associations have been identified in recent years from accumulating studies.However,repositories for cataloging the detailed information on tsRNA–disease associations are scarce.In this study,we provide a tsRNADisease database by integrating experimentally and computationally supported tsRNA–disease associations from manual curation of literatures and other related resources.tsRNADisease contains 5571 manually curated associations between 4759 tsRNAs and 166 diseases with experimental evidence from 346 studies.In addition,it also contains 5013 predicted associations between 1297 tsRNAs and 111 diseases.tsRNADisease provides a user-friendly interface to browse,retrieve,and download data conveniently.This database can improve our understanding of tsRNA deregulation in diseases and serve as a valuable resource for investigating the mechanism of disease-related tsRNAs.tsRNADisease is freely available at http://www.compgenelab.info/tsRNADisease.
基金supported by National Key R&D Program of China(No.2021YFF0501301)the National Natural Science Foundation of China(No.42172231)。
文摘0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has largely been discipline-based,relying on field investigations,data collection,experimental analyses,and data interpretation to study individual components of the Earth system.
基金supported by the National Key R&D Program of China[Grant No.2023YFF0713600]the National Natural Science Foundation of China[Grant No.62275062]+3 种基金Project of Shandong Innovation and Startup Community of High-end Medical Apparatus and Instruments[Grant No.2023-SGTTXM-002 and 2024-SGTTXM-005]the Shandong Province Technology Innovation Guidance Plan(Central Leading Local Science and Technology Development Fund)[Grant No.YDZX2023115]the Taishan Scholar Special Funding Project of Shandong Provincethe Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai[Grant No.ZL202402].
文摘Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.
文摘Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy attribute-based encryption(CP-ABE)schemes still face cumbersome ciphertext re-encryption and insufficient oversight when handling dynamic attribute changes and cross-chain collaboration.To address these issues,we propose a dynamic permission attribute-encryption scheme for multi-chain collaboration.This scheme incorporates a multiauthority architecture for distributed attribute management and integrates an attribute revocation and granting mechanism that eliminates the need for ciphertext re-encryption,effectively reducing both computational and communication overhead.It leverages the InterPlanetary File System(IPFS)for off-chain data storage and constructs a cross-chain regulatory framework—comprising a Hyperledger Fabric business chain and a FISCO BCOS regulatory chain—to record changes in decryption privileges and access behaviors in an auditable manner.Security analysis shows selective indistinguishability under chosen-plaintext attack(sIND-CPA)security under the decisional q-Parallel Bilinear Diffie-Hellman Exponent Assumption(q-PBDHE).In the performance and experimental evaluations,we compared the proposed scheme with several advanced schemes.The results show that,while preserving security,the proposed scheme achieves higher encryption/decryption efficiency and lower storage overhead for ciphertexts and keys.
文摘With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-to-end datamodem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity.Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers.For example,speech activity detection may quickly classify encoded signals as nonspeech signals and reject input waveforms.To address this issue,we propose a novel modulation method based on linear frequency modulation that encodes 3 bits per symbol by varying its frequency,shape,and phase,alongside a lightweightMobileNetV3-Small-based demodulator for efficient and accurate signal decoding on resource-constrained devices.This method leverages the unique characteristics of linear frequency modulation signals,making them more easily transmitted and decoded in speech channels.To ensure reliable data delivery over unstable voice links,we further introduce a robust framing scheme with delimiter-based synchronization,a sample-level position remedying algorithm,and a feedback-driven retransmission mechanism.We have validated the feasibility and performance of our system through expanded real-world evaluations,demonstrating that it outperforms existing advanced methods in terms of robustness and data transfer rate.This technology establishes the foundational infrastructure for reliable certificate delivery over voice channels,which is crucial for achieving strong caller authentication and preventing telephone fraud at its root cause.
基金supported by the School of Digital Science,Universiti Brunei Darussalam,Brunei.
文摘Artificial Intelligence(AI)in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease,which include hemoglobin A1c(HbA1c),oral glucose tolerance test(OGTT),and fasting plasma glucose(FPG)screening techniques,which are invasive and limited in scale.Machine learning(ML)and deep neural network(DNN)models that use large datasets to learn the complex,nonlinear feature interactions,but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy.Conversely,DNN models are more robust,though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge.For predicting diabetes,this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory(BiLSTM)network with a bidirectional gated recurrent unit(BiGRU).A robust DL model,developed by combining various datasets with weighted coefficients,dense operations in the connection of deep layers,and the output aggregation using batch normalization and dropout functions to avoid overfitting.The goal of this hybrid model is better generalization and consistency among various datasets,which facilitates the effective management and early intervention.The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks.The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics.
基金The National Natural Science Foundation of China(No.52470211)Special Foundation of Jiangsu Province Science and Technology Plan(No.BZ2024017)RECLAIM Network Plus Project(No.EP/W034034/1).
文摘Reducing carbon emissions is fundamental to achieving carbon neutrality.Existing studies have typically estimated emissions by predicting fossil fuel consumption across sectors under different socioeconomic scenarios;however,uncertainties in future development often lead to deviations from these assumptions.To address this limitation,this study proposes a data-driven approach for evaluating national carbon emissions using historical data.Countries with similar energy consumption patterns were selected as reference samples,and their emission pathways were analyzed to predict future emissions for countries that have not yet reached their peak.Key indicators,including peak levels,timing,plateau duration,and post-peak decline rates,were identified.The results indicate that the trends in unpeaked economies can be effectively assessed based on the emission patterns of countries with comparable energy structures.Applying this framework to China suggests a carbon peak between 2027 and 2030,in the range of 14.207 to 16.234 Gt,followed by a gradual decline from 2031 to 2036.Compared with the average results of the existing studies,the predicted minimum and maximum emissions show error margins of 10.1% and 1.41%,respectively.This study proposes a top-down methodology that provides a transparent,reproducible,and empirical framework for forecasting carbon emission pathways,thereby offering a scientific basis for assessing countries that have not yet reached their emissions peak.
文摘Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.
基金supported in part by the Research Fund of Key Lab of Education Blockchain and Intelligent Technology,Ministry of Education(EBME25-F-08).
文摘Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either require trusted setups or suffer from high communication overhead and low verification efficiency.This paper presents ISTIRDA,a DAS scheme that lets light clients certify availability by sampling small random codeword symbols.Built on ISTIR,an improved Reed–Solomon interactive oracle proof of proximity,ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication.This paper formalizes opening consistency and prove security with bounded error in the random oracle model,giving polylogarithmic verifier queries and no trusted setup.In a prototype compared with FRIDA under equal soundness,ISTIRDA reduces communication by 40.65%to 80%.For data larger than 16 MB,ISTIRDA verifies faster and the advantage widens;at 128 MB,proofs are about 60%smaller and verification time is roughly 25%shorter,while prover overhead remains modest.In peer-to-peer emulation under injected latency and loss,ISTIRDA reaches confidence more quickly and is less sensitive to packet loss and load.These results indicate that ISTIRDA is a scalable and provably secure DAS scheme suitable for high-throughput,large-block public blockchains,substantially easing bandwidth and latency pressure on lightweight nodes.
