This study uses the coupled atmosphere–surface climate feedback–response analysis method（CFRAM） to analyze the surface temperature biases in the Flexible Global Ocean–Atmosphere–Land System model, spectral versi...This study uses the coupled atmosphere–surface climate feedback–response analysis method（CFRAM） to analyze the surface temperature biases in the Flexible Global Ocean–Atmosphere–Land System model, spectral version 2（FGOALS-s2）in January and July. The process-based decomposition of the surface temperature biases, defined as the difference between the model and ERA-Interim during 1979–2005, enables us to attribute the model surface temperature biases to individual radiative processes including ozone, water vapor, cloud, and surface albedo; and non-radiative processes including surface sensible and latent heat fluxes, and dynamic processes at the surface and in the atmosphere. The results show that significant model surface temperature biases are almost globally present, are generally larger over land than over oceans, and are relatively larger in summer than in winter. Relative to the model biases in non-radiative processes, which tend to dominate the surface temperature biases in most parts of the world, biases in radiative processes are much smaller, except in the sub-polar Antarctic region where the cold biases from the much overestimated surface albedo are compensated for by the warm biases from nonradiative processes. The larger biases in non-radiative processes mainly lie in surface heat fluxes and in surface dynamics,which are twice as large in the Southern Hemisphere as in the Northern Hemisphere and always tend to compensate for each other. In particular, the upward/downward heat fluxes are systematically underestimated/overestimated in most parts of the world, and are mainly compensated for by surface dynamic processes including the increased heat storage in deep oceans across the globe.展开更多
Human agency has become increasingly limited in complex systems with increasingly automated decision-making capabilities.For instance,human occupants are passengers and do not have direct vehicle control in fully auto...Human agency has become increasingly limited in complex systems with increasingly automated decision-making capabilities.For instance,human occupants are passengers and do not have direct vehicle control in fully automated cars(i.e.,driverless cars).An interesting question is whether users are responsible for the accidents of these cars.Normative ethical and legal analyses frequently argue that individuals should not bear responsibility for harm beyond their control.Here,we consider human judgment of responsibility for accidents involving fully automated cars through three studies with seven experiments(N=2668).We compared the responsibility attributed to the occupants in three conditions:an owner in his private fully automated car,a passenger in a driverless robotaxi,and a passenger in a conventional taxi,where none of these three occupants have direct vehicle control over the involved vehicles that cause identical pedestrian injury.In contrast to normative analyses,we show that the occupants of driverless cars(private cars and robotaxis)are attributed more responsibility than conventional taxi passengers.This dilemma is robust across different contexts(e.g.,participants from China vs the Republic of Korea,participants with first-vs third-person perspectives,and occupant presence vs absence).Furthermore,we observe that this is not due to the perception that these occupants have greater control over driving but because they are more expected to foresee the potential consequences of using driverless cars.Our findings suggest that when driverless vehicles(private cars and taxis)cause harm,their users may face more social pressure,which public discourse and legal regulations should manage appropriately.展开更多
The global climate has changed substantially over the last 100 years,and associated changes in species distribution limits have occurred in recent decades.Climate change presents a challenge for biodiversity conservat...The global climate has changed substantially over the last 100 years,and associated changes in species distribution limits have occurred in recent decades.Climate change presents a challenge for biodiversity conservation on a global scale.The ability to detect changes in species distributions and attribute them to past climate change is crucial for the accurate prediction of future species distributions and for biodiversity conservation.This study proposes a technique for the quantitative detection of species distribution changes and their attribution to past climate change.An attribution value was defined to describe the extent to which the distributional changes for observed species could be attributed to climate change.The calculation thereof involved the following steps:(1)construction of a time series of observed species distributions and climatic factors,(2)estimation of the correlations between changes in species distributions and climatic factors,(3)prediction of changes in species distributions as driven by climatic factors,(4)estimation of the consistency between observed and predicted changes in species distributions,and(5)estimation of the attribution value.Furthermore,using nine snake species found in China as examples,we demonstrated in detail the practical application of this technique.This technique can be used to identify,based on global species distribution and climate data,the effects of climate change on species distributions over the past years on a global scale.展开更多
The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risk...The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.展开更多
Knee osteoarthritis(KOA),characterized by heterogeneous arthritic manifestations and complex peripheral joint disorder,is one of the leading causes of disability worldwide,which has become a high burden due to the mul...Knee osteoarthritis(KOA),characterized by heterogeneous arthritic manifestations and complex peripheral joint disorder,is one of the leading causes of disability worldwide,which has become a high burden due to the multifactorial nature and the deficiency of available disease-modifying treatments.The application of mesenchymal stem/stromal cells(MSCs)as therapeutic drugs has provided novel treatment options for diverse degenerative and chronic diseases including KOA.However,the complexity and specificity of the“live”cells have posed challenges for MSC-based drug development and the concomitant scale-up preparation from laboratory to industrialization.For instance,despite the considerable progress in ex vivo cell culture technology for fulfilling the robust development of drug conversion and clinical trials,yet significant challenges remain in obtaining regulatory approvals.Thus,there’s an urgent need for the research and development of MSC drugs for KOA.In this review,we provide alternative solution strategies for the preparation of MSC drugs on the basis of the principle of quality by design,including designing the cell production processes,quality control,and clinical applications.In detail,we mainly focus on the quality by design method for MSC manufacturing in standard cell-culturing factories for the treatment of KOA by using the Quality Target Product Profile as a starting point to determine potential critical quality attributes and to establish relationships between critical material attributes and critical process parameters.Collectively,this review aims to meet product performance and robust process design,and should help to reduce the gap between compliant products and the production of compliant good manufacturing practice.展开更多
Extreme ozone pollution events(EOPEs)are associated with synoptic weather patterns(SWPs)and pose severe health and ecological risks.However,a systematic investigation of themeteorological causes,transport pathways,and...Extreme ozone pollution events(EOPEs)are associated with synoptic weather patterns(SWPs)and pose severe health and ecological risks.However,a systematic investigation of themeteorological causes,transport pathways,and source contributions to historical EOPEs is still lacking.In this paper,the K-means clustering method is applied to identify six dominant SWPs during the warm season in the Yangtze River Delta(YRD)region from 2016 to 2022.It provides an integrated analysis of the meteorological factors affecting ozone pollution in Hefei under different SWPs.Using the WRF-FLEXPART model,the transport pathways(TPPs)and geographical sources of the near-surface air masses in Hefei during EOPEs are investigated.The results reveal that Hefei experienced the highest ozone concentration(134.77±42.82μg/m^(3)),exceedance frequency(46 days(23.23%)),and proportion of EOPEs(21 instances,47.7%)under the control of peripheral subsidence of typhoon(Type 5).Regional southeast winds correlated with the ozone pollution in Hefei.During EOPEs,a high boundary layer height,solar radiation,and temperature;lowhumidity and cloud cover;and pronounced subsidence airflow occurred over Hefei and the broader YRD region.The East-South(E_S)patterns exhibited the highest frequency(28 instances,65.11%).Regarding the TPPs and geographical sources of the near-surface air masses during historical EOPEs.The YRD was the main source for land-originating air masses under E_S patterns(50.28%),with Hefei,southern Anhui,southern Jiangsu,and northern Zhejiang being key contributors.These findings can help improve ozone pollution early warning and control mechanisms at urban and regional scales.展开更多
Based on the analysis of surface geological survey,exploratory well,gravity-magnetic-electric and seismic data,and through mapping the sedimentary basin and its peripheral orogenic belts together,this paper explores s...Based on the analysis of surface geological survey,exploratory well,gravity-magnetic-electric and seismic data,and through mapping the sedimentary basin and its peripheral orogenic belts together,this paper explores systematically the boundary,distribution,geological structure,and tectonic attributes of the Ordos prototype basin in the geological historical periods.The results show that the Ordos block is bounded to the west by the Engorwusu Fault Zone,to the east by the Taihangshan Mountain Piedmont Fault Zone,to the north by the Solonker-Xilamuron Suture Zone,and to the south by the Shangnan-Danfeng Suture Zone.The Ordos Basin boundary was the plate tectonic boundary during the Middle Proterozoic to Paleozoic,and the intra-continental deformation boundary in the Meso-Cenozoic.The basin survived as a marine cratonic basin covering the entire Ordos block during the Middle Proterozoic to Ordovician,a marine-continental transitional depression basin enclosed by an island arc uplift belt at the plate margin during the Carboniferous to Permian,a unified intra-continental lacustrine depression basin in the Triassic,and an intra-continental cratonic basin circled by a rift system in the Cenozoic.The basin scope has been decreasing till the present.The large,widespread prototype basin controlled the exploration area far beyond the present-day sedimentary basin boundary,with multiple target plays vertically.The Ordos Basin has the characteristics of a whole petroleum(or deposition)system.The Middle Proterozoic wide-rift system as a typical basin under the overlying Phanerozoic basin and the Cambrian-Ordovician passive margin basin and intra-cratonic depression in the deep-sited basin will be the important successions for oil and gas exploration in the coming years.展开更多
Normal forms have a significant role in the theory of relational database normalization.The definitions of normal forms are established through the functional dependency(FD)relationship between a prime or nonprime att...Normal forms have a significant role in the theory of relational database normalization.The definitions of normal forms are established through the functional dependency(FD)relationship between a prime or nonprime attribute and a key.However,determining whether an attribute is a prime attribute is a nondeterministic polynomial-time complete(NP-complete)problem,making it intractable to determine if a relation scheme is in a specific normal form.While the prime attribute problem is generally NP-complete,there are cases where identifying prime attributes is not challenging.In a relation scheme R(U,F),we partition U into four distinct subsets based on where attributes in U appear in F:U_(1)(attributes only appearing on the left-hand side of FDs),U_(2)(attributes only appearing on the right-hand side of FDs),U_(3)(attributes appearing on both sides of FDs),and U_(4)(attributes not present in F).Next,we demonstrate the necessary and sufficient conditions for a key to be the unique key of a relation scheme.Subsequently,we illustrate the features of prime attributes in U_(3) and generalize the features of common prime attributes.The findings lay the groundwork for distinguishing between complex and simple cases in prime attribute identification,thereby deepening the understanding of this problem.展开更多
BACKGROUND Not all neuropsychiatric(NP)manifestations in patients with systemic lupus erythematosus(SLE)are secondary to lupus.The clarification of the cause of NP symptoms influences therapeutic strategies for SLE.AI...BACKGROUND Not all neuropsychiatric(NP)manifestations in patients with systemic lupus erythematosus(SLE)are secondary to lupus.The clarification of the cause of NP symptoms influences therapeutic strategies for SLE.AIM To understand the attribution of psychiatric manifestations in a cohort of Chinese patients with SLE.METHODS This retrospective single-center study analyzed 160 inpatient medical records.Clinical diagnosis,which is considered the gold standard,was used to divide the subjects into a psychiatric SLE(PSLE)group(G1)and a secondary psychiatric symptoms group(G2).Clinical features were compared between these two groups.The sensitivity and specificity of the Italian attribution model were explored.RESULTS A total of 171 psychiatric syndromes were recorded in 138 patients,including 87 cases of acute confusional state,40 cases of cognitive dysfunction,18 cases of psychosis,and 13 cases each of depressive disorder and mania or hypomania.A total of 141(82.5%)syndromes were attributed to SLE.In contrast to G2 patients,G1 patients had higher SLE Disease Activity Index-2000 scores(21 vs 12,P=0.001),a lower prevalence of anti-beta-2-glycoprotein 1 antibodies(8.6%vs 25.9%,P=0.036),and a higher prevalence of anti-ribosomal ribonucleoprotein particle(rRNP)antibodies(39.0%vs 22.2%,P=0.045).The Italian attribution model exhibited a sensitivity of 95.0%and a specificity of 70.0%when the threshold value was set at 7.CONCLUSION Patients with PSLE exhibited increased disease activity.There is a correlation between PSLE and anti-rRNP antibodies.The Italian model effectively assesses multiple psychiatric manifestations in Chinese SLE patients who present with NP symptoms.展开更多
Estimation and attribution of evapotranspiration(ET)and its components under changing environment is still a challenge but is essential for understanding the mechanisms of water and energy transfer for regional water ...Estimation and attribution of evapotranspiration(ET)and its components under changing environment is still a challenge but is essential for understanding the mechanisms of water and energy transfer for regional water resources management.In this study,an improved hydrological model is developed to estimate evapotranspiration and its components,i.e.,evaporation(E)and transpiration(T)by integrated the advantages of hydrological modeling constrained by water balance and the water-carbon close relationships.Results show that the improved hydrological model could captures ET and its components well in the study region.During the past years,annual ET and E increase obviously about 2.40 and 1.42 mm/a,particularly in spring and summer accounting for 90%.T shows less increasement and mainly increases in spring while it decreases in summer.Precipitation is the dominant factor and contributes 74.1%and 90.0%increases of annual ET and E,while the attribution of T changes is more complex by coupling of the positive effects of precipitation,rising temperature and interactive influences,the negative effects of solar diming and elevated CO_(2).In the future,ET and its components tend to increase under most of the Shared Socioeconomic Pathways(SSP)scenarios except for T decreases under the very high emissions scenario(SSP5-8.5)based on the projections.From seasonal perspective,the changes of ET and the components are mainly in spring and summer accounting for 75%,while more slight changes are found in autumn and winter.This study highlights the effectiveness of estimating ET and its components by improving hydrological models within water-carbon coupling relationships,and more complex mechanisms of transpiration changes than evapotranspiration and evaporation changes under the interactive effects of climate variability and vegetation dynamics.Besides,decision makers should pay attention to the more increases in the undesirable E than desirable T.展开更多
Objective: This study aims to quantify the potential impact of controlling major risk factors on liver cancer deaths in China from 2021 to 2050 under various intervention scenarios.Methods: We developed a macro-level ...Objective: This study aims to quantify the potential impact of controlling major risk factors on liver cancer deaths in China from 2021 to 2050 under various intervention scenarios.Methods: We developed a macro-level simulation model based on comparative risk assessment to estimate population attributable fractions and avoidable liver cancer deaths. Risk factor prevalence data were obtained from national surveys and epidemiological estimates. Three intervention scenarios for each risk factor were projected:elimination(Scenario 1), ambitious reduction(Scenario 2), and manageable targets aligned with national/global goals(Scenario 3). The impact of secondary prevention through liver cancer screening at different coverage was evaluated.Results: Between 2021 and 2050, liver cancer deaths in China are projected to reach 9.44 million in males and4.29 million in females. Eliminating hepatitis B virus and hepatitis C virus could prevent 65.62%(57.47%-73.77%)and 28.47%(24.93%-32.00%) of liver cancer deaths, respectively. Achieving manageable targets in reducing the prevalence of smoking and alcohol drinking could prevent 6.57%(5.75%-7.38%) and 0.85%(0.75%-0.96%) of liver cancer deaths, with a more pronounced effect observed in males. Eliminating high body mass index(BMI)could avert 45,000 male and 25,000 female deaths annually by 2050, while diabetes elimination could prevent60,000 male and 21,000 female deaths. Secondary prevention through liver cancer screening with 80% coverage could reduce liver cancer deaths by 3.59%(3.14%-4.04%) for the total population. Combining all interventions under Scenario 1 could prevent up to 88.39%(76.65%-99.81%) of male and 77.80%(67.42%-87.88%) of female liver cancer deaths by 2050.Conclusions: Comprehensive risk factor control could prevent over 80% of liver cancer deaths in China by2050. Secondary prevention through screening may offer modest additional benefits. These findings provide strong quantitative support for targeted, evidence-based interventions and underscore the need for policy action to address key risk factors.展开更多
Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribut...Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.展开更多
This study analyzes the risks of re-identification in Korean text data and proposes a secure,ethical approach to data anonymization.Following the‘Lee Luda’AI chatbot incident,concerns over data privacy have increase...This study analyzes the risks of re-identification in Korean text data and proposes a secure,ethical approach to data anonymization.Following the‘Lee Luda’AI chatbot incident,concerns over data privacy have increased.The Personal Information Protection Commission of Korea conducted inspections of AI services,uncovering 850 cases of personal information in user input datasets,highlighting the need for pseudonymization standards.While current anonymization techniques remove personal data like names,phone numbers,and addresses,linguistic features such as writing habits and language-specific traits can still identify individuals when combined with other data.To address this,we analyzed 50,000 Korean text samples from the X platform,focusing on language-specific features for authorship attribution.Unlike English,Korean features flexible syntax,honorifics,syllabic and grapheme patterns,and referential terms.These linguistic characteristics were used to enhance re-identification accuracy.Our experiments combined five machine learning models,six stopword processing methods,and four morphological analyzers.By using a tokenizer that captures word frequency and order,and employing the LSTM model,OKT morphological analyzer,and stopword removal,we achieved the maximum authorship attributions accuracy of 90.51%.This demonstrates the significant role of Korean linguistic features in re-identification.The findings emphasize the risk of re-identification through language data and call for a re-evaluation of anonymization methods,urging the consideration of linguistic traits in anonymization beyond simply removing personal information.展开更多
Detecting overlapping communities in attributed networks remains a significant challenge due to the complexity of jointly modeling topological structure and node attributes,the unknown number of communities,and the ne...Detecting overlapping communities in attributed networks remains a significant challenge due to the complexity of jointly modeling topological structure and node attributes,the unknown number of communities,and the need to capture nodes with multiple memberships.To address these issues,we propose a novel framework named density peaks clustering with neutrosophic C-means.First,we construct a consensus embedding by aligning structure-based and attribute-based representations using spectral decomposition and canonical correlation analysis.Then,an improved density peaks algorithm automatically estimates the number of communities and selects initial cluster centers based on a newly designed cluster strength metric.Finally,a neutrosophic C-means algorithm refines the community assignments,modeling uncertainty and overlap explicitly.Experimental results on synthetic and real-world networks demonstrate that the proposed method achieves superior performance in terms of detection accuracy,stability,and its ability to identify overlapping structures.展开更多
Objective:Knee osteoarthritis is one of the important causes of disability worldwide.This study aims to analyze the disease burden of knee osteoarthritis,attributable risk factors among Chinese residents from 1990 to ...Objective:Knee osteoarthritis is one of the important causes of disability worldwide.This study aims to analyze the disease burden of knee osteoarthritis,attributable risk factors among Chinese residents from 1990 to 2021,and predict the disease burden trend for 2035.Methods:Data related to knee osteoarthritis in China from 1990 to 2021,including the number of incident cases,incidence rate,number of prevalent cases,prevalence rate,and years lived with disability(YLDs),were collected from the Global Burden of Disease Study(GBD2021)database.Joinpoint regression analysis was used to assess time trends,and the Bayesian-Age-Period-Cohort(BAPC)regression model was employed for future predictions.Results:From 1990 to 2021,the number of incident cases of knee osteoarthritis among Chinese residents increased from 3.65 million to 8.51 million,a rise of 133.16%,with an average annual increase of 3.15%.The incidence rate increased from 310.33 per 100,000 to 598.31 per 100,000,a rise of 92.80%,with an average annual increase of 2.55%.The number of prevalent cases increased from 41.04 million to 110 million,a rise of 166.97%,with an average annual increase of 3.61%.The prevalence rate increased from 3488.78 per 100,000 to 7701.69 per 100,000,a rise of 120.76%,with an average annual increase of 3.00%.The number of YLDs increased from 1.34 million to 3.55 million,a rise of 165.32%,with an average annual increase of 3.59%.The YLD rate increased from 113.86 per 100,000 to 249.81 per 100,000,a rise of 119.39%,with an average annual increase of 2.99%.High BMI was the only significant attributable risk factor,with the proportion of YLDs it caused continuing to rise.Predictions for 2035:The number of incident cases is expected to decline slightly from 5.89 million in 2022 to 5.72 million in 2035.The number of prevalent cases is expected to peak at 72.42 million in 2029 and be around 72.69 million in 2035.The number of YLDs is expected to increase year by year,from 2.35 million in 2022 to 2.35 million in 2035.Conclusion:The study reveals the increasing prevalence and disease burden of knee osteoarthritis among Chinese residents,emphasizing the importance of interventions targeting controllable risk factors.Although the prediction shows a slight decline in the number of incident cases in 2035,the number of prevalent cases and years of disability are expected to remain high,indicating the need for continued monitoring and intervention.展开更多
Graph similarity learning aims to calculate the similarity between pairs of graphs.Existing unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph aug...Graph similarity learning aims to calculate the similarity between pairs of graphs.Existing unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph augmentation strategies,which can harm the semantic and structural information of graphs and overlook the rich structural information present in subgraphs.To address these issues,we propose a graph similarity learning model based on learnable augmentation and multi-level contrastive learning.First,to tackle the problem of random augmentation disrupting the semantics and structure of the graph,we design a learnable augmentation method to selectively choose nodes and edges within the graph.To enhance contrastive levels,we employ a biased random walk method to generate corresponding subgraphs,enriching the contrastive hierarchy.Second,to solve the issue of previous work not considering multi-level contrastive learning,we utilize graph convolutional networks to learn node representations of augmented views and the original graph and calculate the interaction information between the attribute-augmented and structure-augmented views and the original graph.The goal is to maximize node consistency between different views and learn node matching between different graphs,resulting in node-level representations for each graph.Subgraph representations are then obtained through pooling operations,and we conduct contrastive learning utilizing both node and subgraph representations.Finally,the graph similarity score is computed according to different downstream tasks.We conducted three sets of experiments across eight datasets,and the results demonstrate that the proposed model effectively mitigates the issues of random augmentation damaging the original graph’s semantics and structure,as well as the insufficiency of contrastive levels.Additionally,the model achieves the best overall performance.展开更多
Delineation of hydrocarbon-bearing sands and the extent of accumulation using seismic data is a reoccurring challenge for many fields.This study addressed the existing challenges of delineating a known hydrocarbon reg...Delineation of hydrocarbon-bearing sands and the extent of accumulation using seismic data is a reoccurring challenge for many fields.This study addressed the existing challenges of delineating a known hydrocarbon region for a thin-pay reservoir using conventional attributes extraction methods.The efficacy of applying iso-frequency extraction and spectral frequency blending in identifying thin-pay and thick-pay reservoirs on seismic was tested by utilizing 3D seismic data and well logs data of Terra field in the Western Niger Delta Basin.Well tops of all the reservoirs in the field were picked and two reservoirs that correspond to thin-and thick-pay reservoirs,namely A and F were identified respectively.The gross pay thickness of reservoir A is 18 ft while that of reservoir F is 96 ft.Conventional attribute extraction such as RMS amplitude,minimum amplitude,and average energy can be used to identify the hydrocarbon-bearing region in reservoir F but was not applicable for identifying the thin-pay reservoir A.This prompted the interest of using iso-frequency extractions and spectral frequency blending of three iso-frequency cubes of 12 Hz,30 Hz,and 70 Hz to get a spectral frequency RGB cube.The 12 Hz isofrequency can be used to partially identify hydrocarbon-bearing region in reservoir A while the 30Hz iso-frequency can be used to partially identify hydrocarbon-bearing region in reservoir F.The results show that time slices from the spectral frequency blended cube were able to delineate both the thin-pay and thick-pay hydrocarbon-bearing regions as high amplitude.The extractions also conformed to the structure of the two reservoirs.However,there seems to be a color difference in the amplitude display for both reservoirs.The thick-pay reservoir showed a red color on the time slice while the thin-pay reservoir showed a green color.This study has shown that spectral frequency blending is a more effective tool than conventional attributes extractions in identifying hydrocarbon-bearing region using seismic data.The methodology utilized in this study can be applied to other fields with similar challenges and for identifying prospective hydrocarbon bearing areas.展开更多
Numerous experimental and theoretical investigations have highlighted the power law behavior of the proton structure function F_(2)(x,Q^(2)),particularly the dependence of its power constant on various kinematic varia...Numerous experimental and theoretical investigations have highlighted the power law behavior of the proton structure function F_(2)(x,Q^(2)),particularly the dependence of its power constant on various kinematic variables.In this study,we analyze the proton structure function F_(2)employing the analytical solution of the Balitsky–Kovchegov equation,with a focus on the high Q^(2)regime and small x domains.Our results indicate that as Q^(2)increases,the slope parameterλ,which characterizes the growth rate of F_(2),exhibits a gradual decrease,approaching a limiting value ofλ≈0.41±0.01 for large Q^(2).We suggest that this behavior ofλmay be attributed to mechanisms such as gluon overlap and the suppression of phase space growth.To substantiate these conclusions,further high-precision electron–ion collision experiments are required,encompassing a broad range of Q^(2)and x.展开更多
The authors regret for the missing of copyright attributions in the captions of Fig.1(d)and Fig.7 in the original publication.Please note the corrections do not affect the experimental results and conclusions.In the o...The authors regret for the missing of copyright attributions in the captions of Fig.1(d)and Fig.7 in the original publication.Please note the corrections do not affect the experimental results and conclusions.In the originally published article,Fig.1(d)and Fig.7 were adapted from previously published figures in the cited literature.展开更多
Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA)...Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and authenticity.In this paper,a strategy is proposed to integrate three currently competitive WA's evaluation methods.First,a conventional evaluation method based on AEF statistical indicators is selected.Subsequent evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy c-means.Different AEF attributes contribute to a more accurate AEF classification to different degrees.The resulting dynamic weighting applied to these attributes improves the classification accuracy.Each evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation score.The integration in the proposed strategy takes the form of a score accumulation.Different cumulative score levels correspond to different final WA results.Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes.Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA evaluation.This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.展开更多
基金jointly supported by projects XDA11010402 GYHY201406001the National Basic Key Project (973) 2010CB428603 and 2010CB950400
文摘This study uses the coupled atmosphere–surface climate feedback–response analysis method（CFRAM） to analyze the surface temperature biases in the Flexible Global Ocean–Atmosphere–Land System model, spectral version 2（FGOALS-s2）in January and July. The process-based decomposition of the surface temperature biases, defined as the difference between the model and ERA-Interim during 1979–2005, enables us to attribute the model surface temperature biases to individual radiative processes including ozone, water vapor, cloud, and surface albedo; and non-radiative processes including surface sensible and latent heat fluxes, and dynamic processes at the surface and in the atmosphere. The results show that significant model surface temperature biases are almost globally present, are generally larger over land than over oceans, and are relatively larger in summer than in winter. Relative to the model biases in non-radiative processes, which tend to dominate the surface temperature biases in most parts of the world, biases in radiative processes are much smaller, except in the sub-polar Antarctic region where the cold biases from the much overestimated surface albedo are compensated for by the warm biases from nonradiative processes. The larger biases in non-radiative processes mainly lie in surface heat fluxes and in surface dynamics,which are twice as large in the Southern Hemisphere as in the Northern Hemisphere and always tend to compensate for each other. In particular, the upward/downward heat fluxes are systematically underestimated/overestimated in most parts of the world, and are mainly compensated for by surface dynamic processes including the increased heat storage in deep oceans across the globe.
基金supported by the National Natural Science Foundation of China(72071143)。
文摘Human agency has become increasingly limited in complex systems with increasingly automated decision-making capabilities.For instance,human occupants are passengers and do not have direct vehicle control in fully automated cars(i.e.,driverless cars).An interesting question is whether users are responsible for the accidents of these cars.Normative ethical and legal analyses frequently argue that individuals should not bear responsibility for harm beyond their control.Here,we consider human judgment of responsibility for accidents involving fully automated cars through three studies with seven experiments(N=2668).We compared the responsibility attributed to the occupants in three conditions:an owner in his private fully automated car,a passenger in a driverless robotaxi,and a passenger in a conventional taxi,where none of these three occupants have direct vehicle control over the involved vehicles that cause identical pedestrian injury.In contrast to normative analyses,we show that the occupants of driverless cars(private cars and robotaxis)are attributed more responsibility than conventional taxi passengers.This dilemma is robust across different contexts(e.g.,participants from China vs the Republic of Korea,participants with first-vs third-person perspectives,and occupant presence vs absence).Furthermore,we observe that this is not due to the perception that these occupants have greater control over driving but because they are more expected to foresee the potential consequences of using driverless cars.Our findings suggest that when driverless vehicles(private cars and taxis)cause harm,their users may face more social pressure,which public discourse and legal regulations should manage appropriately.
文摘The global climate has changed substantially over the last 100 years,and associated changes in species distribution limits have occurred in recent decades.Climate change presents a challenge for biodiversity conservation on a global scale.The ability to detect changes in species distributions and attribute them to past climate change is crucial for the accurate prediction of future species distributions and for biodiversity conservation.This study proposes a technique for the quantitative detection of species distribution changes and their attribution to past climate change.An attribution value was defined to describe the extent to which the distributional changes for observed species could be attributed to climate change.The calculation thereof involved the following steps:(1)construction of a time series of observed species distributions and climatic factors,(2)estimation of the correlations between changes in species distributions and climatic factors,(3)prediction of changes in species distributions as driven by climatic factors,(4)estimation of the consistency between observed and predicted changes in species distributions,and(5)estimation of the attribution value.Furthermore,using nine snake species found in China as examples,we demonstrated in detail the practical application of this technique.This technique can be used to identify,based on global species distribution and climate data,the effects of climate change on species distributions over the past years on a global scale.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608904)the International Partnership Program of the Chinese Academy of Sciences(Grant Nos.060GJHZ2023079GC and 134111KYSB20160031)+1 种基金supported by the Office of Science,U.S.Department of Energy(DOE)Biological and Environmental Research as part of the Regional and Global Model Analysis program area through the Water Cycle and Climate Extremes Modeling(WACCEM)scientific focus areaoperated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830。
文摘The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.
基金Supported by Taishan Scholar Special Funding,No.tsqnz20240858Medical and Health Technology Project of Shandong Province,No.202402050122+4 种基金Science and Technology Development Plan of Jinan Municipal Health Commission,No.2024301008Clinical Medical Science and Technology Innovation Program of Jinan Science and Technology Bureau,No.202430055Natural Science Foundation of Jiangxi Province,No.20224BAB206077Gansu Provincial Hospital Intra-Hospital Research Fund Project,No.22GSSYB-6and the 2022 Master/Doctor/Postdoctoral Program of National Health Commission Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumor,No.NHCDP2022004 and No.NHCDP2022008.
文摘Knee osteoarthritis(KOA),characterized by heterogeneous arthritic manifestations and complex peripheral joint disorder,is one of the leading causes of disability worldwide,which has become a high burden due to the multifactorial nature and the deficiency of available disease-modifying treatments.The application of mesenchymal stem/stromal cells(MSCs)as therapeutic drugs has provided novel treatment options for diverse degenerative and chronic diseases including KOA.However,the complexity and specificity of the“live”cells have posed challenges for MSC-based drug development and the concomitant scale-up preparation from laboratory to industrialization.For instance,despite the considerable progress in ex vivo cell culture technology for fulfilling the robust development of drug conversion and clinical trials,yet significant challenges remain in obtaining regulatory approvals.Thus,there’s an urgent need for the research and development of MSC drugs for KOA.In this review,we provide alternative solution strategies for the preparation of MSC drugs on the basis of the principle of quality by design,including designing the cell production processes,quality control,and clinical applications.In detail,we mainly focus on the quality by design method for MSC manufacturing in standard cell-culturing factories for the treatment of KOA by using the Quality Target Product Profile as a starting point to determine potential critical quality attributes and to establish relationships between critical material attributes and critical process parameters.Collectively,this review aims to meet product performance and robust process design,and should help to reduce the gap between compliant products and the production of compliant good manufacturing practice.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,and 41975037)the National Key Research and Development Programof China(No.2022YFC3700303).
文摘Extreme ozone pollution events(EOPEs)are associated with synoptic weather patterns(SWPs)and pose severe health and ecological risks.However,a systematic investigation of themeteorological causes,transport pathways,and source contributions to historical EOPEs is still lacking.In this paper,the K-means clustering method is applied to identify six dominant SWPs during the warm season in the Yangtze River Delta(YRD)region from 2016 to 2022.It provides an integrated analysis of the meteorological factors affecting ozone pollution in Hefei under different SWPs.Using the WRF-FLEXPART model,the transport pathways(TPPs)and geographical sources of the near-surface air masses in Hefei during EOPEs are investigated.The results reveal that Hefei experienced the highest ozone concentration(134.77±42.82μg/m^(3)),exceedance frequency(46 days(23.23%)),and proportion of EOPEs(21 instances,47.7%)under the control of peripheral subsidence of typhoon(Type 5).Regional southeast winds correlated with the ozone pollution in Hefei.During EOPEs,a high boundary layer height,solar radiation,and temperature;lowhumidity and cloud cover;and pronounced subsidence airflow occurred over Hefei and the broader YRD region.The East-South(E_S)patterns exhibited the highest frequency(28 instances,65.11%).Regarding the TPPs and geographical sources of the near-surface air masses during historical EOPEs.The YRD was the main source for land-originating air masses under E_S patterns(50.28%),with Hefei,southern Anhui,southern Jiangsu,and northern Zhejiang being key contributors.These findings can help improve ozone pollution early warning and control mechanisms at urban and regional scales.
基金Supported by the National Natural Science Foundation of China(42330810)Major Science and Technology Project of PetroChina Changqing Oilfield Company(ZDZX2021-01).
文摘Based on the analysis of surface geological survey,exploratory well,gravity-magnetic-electric and seismic data,and through mapping the sedimentary basin and its peripheral orogenic belts together,this paper explores systematically the boundary,distribution,geological structure,and tectonic attributes of the Ordos prototype basin in the geological historical periods.The results show that the Ordos block is bounded to the west by the Engorwusu Fault Zone,to the east by the Taihangshan Mountain Piedmont Fault Zone,to the north by the Solonker-Xilamuron Suture Zone,and to the south by the Shangnan-Danfeng Suture Zone.The Ordos Basin boundary was the plate tectonic boundary during the Middle Proterozoic to Paleozoic,and the intra-continental deformation boundary in the Meso-Cenozoic.The basin survived as a marine cratonic basin covering the entire Ordos block during the Middle Proterozoic to Ordovician,a marine-continental transitional depression basin enclosed by an island arc uplift belt at the plate margin during the Carboniferous to Permian,a unified intra-continental lacustrine depression basin in the Triassic,and an intra-continental cratonic basin circled by a rift system in the Cenozoic.The basin scope has been decreasing till the present.The large,widespread prototype basin controlled the exploration area far beyond the present-day sedimentary basin boundary,with multiple target plays vertically.The Ordos Basin has the characteristics of a whole petroleum(or deposition)system.The Middle Proterozoic wide-rift system as a typical basin under the overlying Phanerozoic basin and the Cambrian-Ordovician passive margin basin and intra-cratonic depression in the deep-sited basin will be the important successions for oil and gas exploration in the coming years.
文摘Normal forms have a significant role in the theory of relational database normalization.The definitions of normal forms are established through the functional dependency(FD)relationship between a prime or nonprime attribute and a key.However,determining whether an attribute is a prime attribute is a nondeterministic polynomial-time complete(NP-complete)problem,making it intractable to determine if a relation scheme is in a specific normal form.While the prime attribute problem is generally NP-complete,there are cases where identifying prime attributes is not challenging.In a relation scheme R(U,F),we partition U into four distinct subsets based on where attributes in U appear in F:U_(1)(attributes only appearing on the left-hand side of FDs),U_(2)(attributes only appearing on the right-hand side of FDs),U_(3)(attributes appearing on both sides of FDs),and U_(4)(attributes not present in F).Next,we demonstrate the necessary and sufficient conditions for a key to be the unique key of a relation scheme.Subsequently,we illustrate the features of prime attributes in U_(3) and generalize the features of common prime attributes.The findings lay the groundwork for distinguishing between complex and simple cases in prime attribute identification,thereby deepening the understanding of this problem.
基金Supported by STI2030-Major Projects,No.2021ZD0202001National Natural Science Foundation of China,No.T2341003Capital Funds for Health Improvement and Research,No.CFH 2022-2-4012.
文摘BACKGROUND Not all neuropsychiatric(NP)manifestations in patients with systemic lupus erythematosus(SLE)are secondary to lupus.The clarification of the cause of NP symptoms influences therapeutic strategies for SLE.AIM To understand the attribution of psychiatric manifestations in a cohort of Chinese patients with SLE.METHODS This retrospective single-center study analyzed 160 inpatient medical records.Clinical diagnosis,which is considered the gold standard,was used to divide the subjects into a psychiatric SLE(PSLE)group(G1)and a secondary psychiatric symptoms group(G2).Clinical features were compared between these two groups.The sensitivity and specificity of the Italian attribution model were explored.RESULTS A total of 171 psychiatric syndromes were recorded in 138 patients,including 87 cases of acute confusional state,40 cases of cognitive dysfunction,18 cases of psychosis,and 13 cases each of depressive disorder and mania or hypomania.A total of 141(82.5%)syndromes were attributed to SLE.In contrast to G2 patients,G1 patients had higher SLE Disease Activity Index-2000 scores(21 vs 12,P=0.001),a lower prevalence of anti-beta-2-glycoprotein 1 antibodies(8.6%vs 25.9%,P=0.036),and a higher prevalence of anti-ribosomal ribonucleoprotein particle(rRNP)antibodies(39.0%vs 22.2%,P=0.045).The Italian attribution model exhibited a sensitivity of 95.0%and a specificity of 70.0%when the threshold value was set at 7.CONCLUSION Patients with PSLE exhibited increased disease activity.There is a correlation between PSLE and anti-rRNP antibodies.The Italian model effectively assesses multiple psychiatric manifestations in Chinese SLE patients who present with NP symptoms.
基金supported by the Chongqing Natural Science Foundation Innovation-Driven Development Joint Funds(No.CSTB2025NSCQ-LZX0055)the Youth Innovation Promotion Association,CAS(No.2021385)+4 种基金the Fundamental Research Funds for the Central Universities of South-Central Minzu University(No.CZQ24028)the Hubei Provincial Natural Science Foundation of China(No.2023AFB782)the Program of China Scholarship Council(No.202407780001)the National Natural Science Foundation of China(No.51809008)the Fund for Academic Innovation Teams of South-Central Minzu University(No.XTZ24019).
文摘Estimation and attribution of evapotranspiration(ET)and its components under changing environment is still a challenge but is essential for understanding the mechanisms of water and energy transfer for regional water resources management.In this study,an improved hydrological model is developed to estimate evapotranspiration and its components,i.e.,evaporation(E)and transpiration(T)by integrated the advantages of hydrological modeling constrained by water balance and the water-carbon close relationships.Results show that the improved hydrological model could captures ET and its components well in the study region.During the past years,annual ET and E increase obviously about 2.40 and 1.42 mm/a,particularly in spring and summer accounting for 90%.T shows less increasement and mainly increases in spring while it decreases in summer.Precipitation is the dominant factor and contributes 74.1%and 90.0%increases of annual ET and E,while the attribution of T changes is more complex by coupling of the positive effects of precipitation,rising temperature and interactive influences,the negative effects of solar diming and elevated CO_(2).In the future,ET and its components tend to increase under most of the Shared Socioeconomic Pathways(SSP)scenarios except for T decreases under the very high emissions scenario(SSP5-8.5)based on the projections.From seasonal perspective,the changes of ET and the components are mainly in spring and summer accounting for 75%,while more slight changes are found in autumn and winter.This study highlights the effectiveness of estimating ET and its components by improving hydrological models within water-carbon coupling relationships,and more complex mechanisms of transpiration changes than evapotranspiration and evaporation changes under the interactive effects of climate variability and vegetation dynamics.Besides,decision makers should pay attention to the more increases in the undesirable E than desirable T.
基金supported by the Capital’s Funds for Health Improvement and Research (No. 2024-1G-4023)。
文摘Objective: This study aims to quantify the potential impact of controlling major risk factors on liver cancer deaths in China from 2021 to 2050 under various intervention scenarios.Methods: We developed a macro-level simulation model based on comparative risk assessment to estimate population attributable fractions and avoidable liver cancer deaths. Risk factor prevalence data were obtained from national surveys and epidemiological estimates. Three intervention scenarios for each risk factor were projected:elimination(Scenario 1), ambitious reduction(Scenario 2), and manageable targets aligned with national/global goals(Scenario 3). The impact of secondary prevention through liver cancer screening at different coverage was evaluated.Results: Between 2021 and 2050, liver cancer deaths in China are projected to reach 9.44 million in males and4.29 million in females. Eliminating hepatitis B virus and hepatitis C virus could prevent 65.62%(57.47%-73.77%)and 28.47%(24.93%-32.00%) of liver cancer deaths, respectively. Achieving manageable targets in reducing the prevalence of smoking and alcohol drinking could prevent 6.57%(5.75%-7.38%) and 0.85%(0.75%-0.96%) of liver cancer deaths, with a more pronounced effect observed in males. Eliminating high body mass index(BMI)could avert 45,000 male and 25,000 female deaths annually by 2050, while diabetes elimination could prevent60,000 male and 21,000 female deaths. Secondary prevention through liver cancer screening with 80% coverage could reduce liver cancer deaths by 3.59%(3.14%-4.04%) for the total population. Combining all interventions under Scenario 1 could prevent up to 88.39%(76.65%-99.81%) of male and 77.80%(67.42%-87.88%) of female liver cancer deaths by 2050.Conclusions: Comprehensive risk factor control could prevent over 80% of liver cancer deaths in China by2050. Secondary prevention through screening may offer modest additional benefits. These findings provide strong quantitative support for targeted, evidence-based interventions and underscore the need for policy action to address key risk factors.
基金supported by National Natural Science Foundation of China(No.62102449).
文摘Security attributes are the premise and foundation for implementing Attribute-Based Access Control(ABAC)mechanisms.However,when dealing with massive volumes of unstructured text big data resources,the current attribute management methods based on manual extraction face several issues,such as high costs for attribute extraction,long processing times,unstable accuracy,and poor scalability.To address these problems,this paper proposes an attribute mining technology for access control institutions based on hybrid capsule networks.This technology leverages transfer learning ideas,utilizing Bidirectional Encoder Representations from Transformers(BERT)pre-trained language models to achieve vectorization of unstructured text data resources.Furthermore,we have designed a novel end-to-end parallel hybrid network structure,where the parallel networks handle global and local information features of the text that they excel at,respectively.By employing techniques such as attention mechanisms,capsule networks,and dynamic routing,effective mining of security attributes for access control resources has been achieved.Finally,we evaluated the performance level of the proposed attribute mining method for access control institutions through experiments on the medical referral text resource dataset.The experimental results show that,compared with baseline algorithms,our method adopts a parallel network structure that can better balance global and local feature information,resulting in improved overall performance.Specifically,it achieves a comprehensive performance enhancement of 2.06%to 8.18%in the F1 score metric.Therefore,this technology can effectively provide attribute support for access control of unstructured text big data resources.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(RS-2023-00238866)Korea government(MOE)(2024 government collaboration type training project[Information security field],No.2024 personal information protection-002).
文摘This study analyzes the risks of re-identification in Korean text data and proposes a secure,ethical approach to data anonymization.Following the‘Lee Luda’AI chatbot incident,concerns over data privacy have increased.The Personal Information Protection Commission of Korea conducted inspections of AI services,uncovering 850 cases of personal information in user input datasets,highlighting the need for pseudonymization standards.While current anonymization techniques remove personal data like names,phone numbers,and addresses,linguistic features such as writing habits and language-specific traits can still identify individuals when combined with other data.To address this,we analyzed 50,000 Korean text samples from the X platform,focusing on language-specific features for authorship attribution.Unlike English,Korean features flexible syntax,honorifics,syllabic and grapheme patterns,and referential terms.These linguistic characteristics were used to enhance re-identification accuracy.Our experiments combined five machine learning models,six stopword processing methods,and four morphological analyzers.By using a tokenizer that captures word frequency and order,and employing the LSTM model,OKT morphological analyzer,and stopword removal,we achieved the maximum authorship attributions accuracy of 90.51%.This demonstrates the significant role of Korean linguistic features in re-identification.The findings emphasize the risk of re-identification through language data and call for a re-evaluation of anonymization methods,urging the consideration of linguistic traits in anonymization beyond simply removing personal information.
基金supported by the Natural Science Foundation of China(Grant No.72571150)。
文摘Detecting overlapping communities in attributed networks remains a significant challenge due to the complexity of jointly modeling topological structure and node attributes,the unknown number of communities,and the need to capture nodes with multiple memberships.To address these issues,we propose a novel framework named density peaks clustering with neutrosophic C-means.First,we construct a consensus embedding by aligning structure-based and attribute-based representations using spectral decomposition and canonical correlation analysis.Then,an improved density peaks algorithm automatically estimates the number of communities and selects initial cluster centers based on a newly designed cluster strength metric.Finally,a neutrosophic C-means algorithm refines the community assignments,modeling uncertainty and overlap explicitly.Experimental results on synthetic and real-world networks demonstrate that the proposed method achieves superior performance in terms of detection accuracy,stability,and its ability to identify overlapping structures.
文摘Objective:Knee osteoarthritis is one of the important causes of disability worldwide.This study aims to analyze the disease burden of knee osteoarthritis,attributable risk factors among Chinese residents from 1990 to 2021,and predict the disease burden trend for 2035.Methods:Data related to knee osteoarthritis in China from 1990 to 2021,including the number of incident cases,incidence rate,number of prevalent cases,prevalence rate,and years lived with disability(YLDs),were collected from the Global Burden of Disease Study(GBD2021)database.Joinpoint regression analysis was used to assess time trends,and the Bayesian-Age-Period-Cohort(BAPC)regression model was employed for future predictions.Results:From 1990 to 2021,the number of incident cases of knee osteoarthritis among Chinese residents increased from 3.65 million to 8.51 million,a rise of 133.16%,with an average annual increase of 3.15%.The incidence rate increased from 310.33 per 100,000 to 598.31 per 100,000,a rise of 92.80%,with an average annual increase of 2.55%.The number of prevalent cases increased from 41.04 million to 110 million,a rise of 166.97%,with an average annual increase of 3.61%.The prevalence rate increased from 3488.78 per 100,000 to 7701.69 per 100,000,a rise of 120.76%,with an average annual increase of 3.00%.The number of YLDs increased from 1.34 million to 3.55 million,a rise of 165.32%,with an average annual increase of 3.59%.The YLD rate increased from 113.86 per 100,000 to 249.81 per 100,000,a rise of 119.39%,with an average annual increase of 2.99%.High BMI was the only significant attributable risk factor,with the proportion of YLDs it caused continuing to rise.Predictions for 2035:The number of incident cases is expected to decline slightly from 5.89 million in 2022 to 5.72 million in 2035.The number of prevalent cases is expected to peak at 72.42 million in 2029 and be around 72.69 million in 2035.The number of YLDs is expected to increase year by year,from 2.35 million in 2022 to 2.35 million in 2035.Conclusion:The study reveals the increasing prevalence and disease burden of knee osteoarthritis among Chinese residents,emphasizing the importance of interventions targeting controllable risk factors.Although the prediction shows a slight decline in the number of incident cases in 2035,the number of prevalent cases and years of disability are expected to remain high,indicating the need for continued monitoring and intervention.
文摘Graph similarity learning aims to calculate the similarity between pairs of graphs.Existing unsupervised graph similarity learning methods based on contrastive learning encounter challenges related to random graph augmentation strategies,which can harm the semantic and structural information of graphs and overlook the rich structural information present in subgraphs.To address these issues,we propose a graph similarity learning model based on learnable augmentation and multi-level contrastive learning.First,to tackle the problem of random augmentation disrupting the semantics and structure of the graph,we design a learnable augmentation method to selectively choose nodes and edges within the graph.To enhance contrastive levels,we employ a biased random walk method to generate corresponding subgraphs,enriching the contrastive hierarchy.Second,to solve the issue of previous work not considering multi-level contrastive learning,we utilize graph convolutional networks to learn node representations of augmented views and the original graph and calculate the interaction information between the attribute-augmented and structure-augmented views and the original graph.The goal is to maximize node consistency between different views and learn node matching between different graphs,resulting in node-level representations for each graph.Subgraph representations are then obtained through pooling operations,and we conduct contrastive learning utilizing both node and subgraph representations.Finally,the graph similarity score is computed according to different downstream tasks.We conducted three sets of experiments across eight datasets,and the results demonstrate that the proposed model effectively mitigates the issues of random augmentation damaging the original graph’s semantics and structure,as well as the insufficiency of contrastive levels.Additionally,the model achieves the best overall performance.
文摘Delineation of hydrocarbon-bearing sands and the extent of accumulation using seismic data is a reoccurring challenge for many fields.This study addressed the existing challenges of delineating a known hydrocarbon region for a thin-pay reservoir using conventional attributes extraction methods.The efficacy of applying iso-frequency extraction and spectral frequency blending in identifying thin-pay and thick-pay reservoirs on seismic was tested by utilizing 3D seismic data and well logs data of Terra field in the Western Niger Delta Basin.Well tops of all the reservoirs in the field were picked and two reservoirs that correspond to thin-and thick-pay reservoirs,namely A and F were identified respectively.The gross pay thickness of reservoir A is 18 ft while that of reservoir F is 96 ft.Conventional attribute extraction such as RMS amplitude,minimum amplitude,and average energy can be used to identify the hydrocarbon-bearing region in reservoir F but was not applicable for identifying the thin-pay reservoir A.This prompted the interest of using iso-frequency extractions and spectral frequency blending of three iso-frequency cubes of 12 Hz,30 Hz,and 70 Hz to get a spectral frequency RGB cube.The 12 Hz isofrequency can be used to partially identify hydrocarbon-bearing region in reservoir A while the 30Hz iso-frequency can be used to partially identify hydrocarbon-bearing region in reservoir F.The results show that time slices from the spectral frequency blended cube were able to delineate both the thin-pay and thick-pay hydrocarbon-bearing regions as high amplitude.The extractions also conformed to the structure of the two reservoirs.However,there seems to be a color difference in the amplitude display for both reservoirs.The thick-pay reservoir showed a red color on the time slice while the thin-pay reservoir showed a green color.This study has shown that spectral frequency blending is a more effective tool than conventional attributes extractions in identifying hydrocarbon-bearing region using seismic data.The methodology utilized in this study can be applied to other fields with similar challenges and for identifying prospective hydrocarbon bearing areas.
基金supported by the National Key R&D Program of China(Grant Nos.2024YFE0109800 and 2024YFE0109802)the National Natural Science Foundation of China(Grant No.12305127)the International Partnership Program of the Chinese Academy of Sciences(Grant No.016GJHZ2022054FN)。
文摘Numerous experimental and theoretical investigations have highlighted the power law behavior of the proton structure function F_(2)(x,Q^(2)),particularly the dependence of its power constant on various kinematic variables.In this study,we analyze the proton structure function F_(2)employing the analytical solution of the Balitsky–Kovchegov equation,with a focus on the high Q^(2)regime and small x domains.Our results indicate that as Q^(2)increases,the slope parameterλ,which characterizes the growth rate of F_(2),exhibits a gradual decrease,approaching a limiting value ofλ≈0.41±0.01 for large Q^(2).We suggest that this behavior ofλmay be attributed to mechanisms such as gluon overlap and the suppression of phase space growth.To substantiate these conclusions,further high-precision electron–ion collision experiments are required,encompassing a broad range of Q^(2)and x.
文摘The authors regret for the missing of copyright attributions in the captions of Fig.1(d)and Fig.7 in the original publication.Please note the corrections do not affect the experimental results and conclusions.In the originally published article,Fig.1(d)and Fig.7 were adapted from previously published figures in the cited literature.
基金supported in part by the National Natural Science Foundation of China under Grant 62171228in part by the National Key R&D Program of China under Grant 2021YFE0105500in part by the Program of China Scholarship Council under Grant 202209040027。
文摘Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain models.It is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and authenticity.In this paper,a strategy is proposed to integrate three currently competitive WA's evaluation methods.First,a conventional evaluation method based on AEF statistical indicators is selected.Subsequent evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy c-means.Different AEF attributes contribute to a more accurate AEF classification to different degrees.The resulting dynamic weighting applied to these attributes improves the classification accuracy.Each evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation score.The integration in the proposed strategy takes the form of a score accumulation.Different cumulative score levels correspond to different final WA results.Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes.Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA evaluation.This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.
