Climate change affects distribution and persistence of species. However, forecasting species＇ re-sponses to these changes requires long-term data series that are often lacking in ecological studies.We used 15 years o...Climate change affects distribution and persistence of species. However, forecasting species＇ re-sponses to these changes requires long-term data series that are often lacking in ecological studies.We used 15 years of small mammal trapping data collected between 1978 and 2015 in 3 areas atDoSana National Park （southwest Spain） to （i） describe changes in species composition and （ii） test theassociation between local climate conditions and size of small mammal populations. Overall, 5 specieswere captured： wood mouse Apodemus sylvaticus, algerian mouse Mus spretus, greater white-toothed shrew Crocidura russula, garden dormouse Eliomys quercinus, and black rat Rattus rattus. Thetemporal pattern in the proportion of captures of each species suggests that the small mammal diver-sity declined with time. Although the larger species （e.g., E. quercinus）, better adapted to colder cli-mate, have disappeared from our trapping records, M. spretus, a small species inhabiting southwestEurope and the Mediterranean coast of Africa, currently is almost the only trapped species. We used 2-level hierarchical models to separate changes in abundance from changes in probability of captureusing records of A. sylvaticus in all 3 areas and of Mo spretus in 1. We found that heavy rainfall and lowtemperatures were positively related to abundance of A. sylvaticus, and that the number of extremelyhot days was negatively related to abundance of M. spretus. Despite other mechanisms are likely to beinvolved, our findings support the importance of climate for the distribution and persistence of thesespecies and raise conservation concerns about potential cascading effects in the Donana ecosystem.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
Investigations into the long-term creep behavior of Beishan granite in uniaxial compression were conducted.Four levels of axial stress(60,70,87,and 95 MPa)were applied to rock specimens.Contrasting with earlier resear...Investigations into the long-term creep behavior of Beishan granite in uniaxial compression were conducted.Four levels of axial stress(60,70,87,and 95 MPa)were applied to rock specimens.Contrasting with earlier research,the long-term creep data in this work present a substantial advancement in the time dimension.Except for the sample subjected to 60 MPa axial loading,which did not fail after a loading duration of 1650 d,the specimens under the other three stresses all failed after sustained constant loading durations of 1204,1023,and 839 d,respectively.A lower envelope of driving stress-ratio for crystalline rocks was obtained,tending towards approximately 0.45 over an infinite time scale.According to the experimental results,as axial stress increases,both the axial strain accumulated in the transient creep process and the strain rate associated with steady-state creep deformation increase exponentially;however,the share of steady-state creep strain remains nearly constant at about82.53%.A novel damage-based creep model was put forward.It provides an enhanced depiction of the comprehensive creep process in rocks,notably improving the accuracy in forecasting the accelerated creep phase,which significantly impacts the long-term stability of engineering structures.展开更多
While oceanic and coastal acidification has gained increased attention,long-term pH trends and their drivers in large freshwater systems remain poorly understood.The Laurentian Great Lakes are the world’s largest fre...While oceanic and coastal acidification has gained increased attention,long-term pH trends and their drivers in large freshwater systems remain poorly understood.The Laurentian Great Lakes are the world’s largest freshwater system,and in many ways resemble marine ecosystems.However,unlike the open ocean and coastal waters where pH has declined due to rising atmospheric CO_(2),no significant pH trends have been observed in the Laurentian Great Lakes,despite significant ecosystem changes driven partly by the invasion of dreissenid mussels.This study examined 41 years of field observations from Lake Michigan to investigate the long-term carbonate chemistry dynamics.Observational results revealed substantial declines in both total alkalinity(TA)and dissolved inorganic carbon(DIC)over the four decades.Mussel shell calcification emerged as the primary mechanism behind these declines,accounting for 97%and 47%of the observed changes in TA and DIC,respectively,lowering water column pH by 0.24 units.Elevated carbon accumulation in soft mussel tissues,coupled with long-term changes in the air-water pCO_(2)gradient during summer,significantly contributed to long-term DIC variations,explaining 18%and 28%of the lake-wide DIC loss.These two mechanisms also resulted in an overall pH increase of 0.09 and 0.12 units,largely offsetting the calcification-driven pH decrease.These findings bridge a gap in acidification research for large freshwater systems and provide valuable insights for comprehensive lake-wide management strategies.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
Objective:To investigate the long-term prognosis and postoperative cosmetic outcomes of breast-conserving surgery combined with sentinel lymph node biopsy in patients with early-stage breast cancer,providing a referen...Objective:To investigate the long-term prognosis and postoperative cosmetic outcomes of breast-conserving surgery combined with sentinel lymph node biopsy in patients with early-stage breast cancer,providing a reference for the selection of clinical treatment plans.Methods:A retrospective analysis was conducted on the clinical data of 68 patients with early-stage breast cancer admitted from January 2022 to December 2025.Based on the surgical approach,patients were divided into an observation group(breast-conserving surgery+sentinel lymph node biopsy)and a control group(other surgical methods such as modified radical mastectomy/total mastectomy).Clinical and pathological characteristics,incidence of postoperative complications,follow-up prognosis,and satisfaction with cosmetic outcomes were compared between the two groups.Results:Among the 68 patients,41 were in the observation group and 27 in the control group.The average age of patients in the observation group was(54.32±8.15)years,while that in the control group was(62.45±9.76)years.The average tumor size in the observation group was(1.86±0.72)cm,compared to(3.21±1.45)cm in the control group.The incidence of postoperative complications in the observation group was 9.76%,significantly lower than that in the control group at 33.33%(P<0.05).The 6-month disease-free survival rate was 95.12%in the observation group and 88.89%in the control group,with no statistically significant difference between the two groups(P>0.05).The excellent and good rate of cosmetic outcomes in the observation group was 87.80%,significantly higher than that in the control group at 29.63%(P<0.05).Conclusion:Breast-conserving surgery combined with sentinel lymph node biopsy for early-stage breast cancer can achieve long-term prognostic outcomes comparable to those of traditional radical surgery,with the advantages of fewer postoperative complications and superior cosmetic results.This approach is worthy of clinical promotion and application,particularly for early-stage breast cancer patients who have a demand for preserving breast morphology.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
In this study,we use observations from the Sounding of the Atmosphere using Broadband Emission Radiometry(SABER)instrument onboard the Thermosphere–Ionosphere–Mesosphere Energetics and Dynamics(TIMED)satellite to de...In this study,we use observations from the Sounding of the Atmosphere using Broadband Emission Radiometry(SABER)instrument onboard the Thermosphere–Ionosphere–Mesosphere Energetics and Dynamics(TIMED)satellite to develop and apply a new local-time binning method to investigate the long-term evolution of mesospheric water vapor at high latitudes.The proposed method accounts for the gradual local-time drift of the SABER orbit by aligning seasonal observation windows and selecting samples observed at similar local times.This approach minimizes tidal aliasing and ensures more consistent sampling,yielding more reliable estimates of long-term water vapor trends at high latitudes.The results show that drying signals primarily appear in the polar regions.However,in the southern hemisphere,a drying trend is observed only in autumn,whereas winter and summer mainly show moistening trends.In contrast,the northern hemisphere exhibits drying signals in the polar regions during all seasons,showing a clear seasonal asymmetry.Additionally,the water vapor trend in the northern hemisphere is particularly pronounced in February(late winter),with moistening reaching up to+2.0 ppmv.The winter in the southern hemisphere(July–August)also shows moistening,but the trend is still weaker than in the northern hemisphere.These differences highlight the strong moistening trend in the northern hemisphere during winter and underscore the significant asymmetry in seasonal water vapor changes between the two hemispheres.These findings emphasize the limitations of water vapor trend estimates across different seasons and latitudes.Moreover,they provide new insights into the spatiotemporal variability associated with tidal structures,underscoring the importance of optimizing local-time sampling strategies for reliable long-term trend detection.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
Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the c...Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the common prosperity strategy. The research identifies key obstacles to income expansion, including an undiversified industrial structure, insufficient human capital, and a lack of robust social protection. These call for systemic solutions featuring institutional innovation, resource consolidation, and capability enhancement. Building on Zhejiang's experience as a common prosperity demonstration zone, the article constructs an integrated framework centered on four pillars: industrial empowerment, education upgrading, social security reinforcement, and digital coordination. It further offers concrete policy proposals involving the cultivation of localized industries, vocational skill training, enhanced safety nets, and the adoption of digital tools. The study thus offers both theoretical insights and practical paradigms for tackling the challenge of raising incomes in low-income rural areas.展开更多
Subarachnoid hemorrhage is a subtype of stroke that causes severe neurological damage and is associated with poor long-term prognosis.Cognitive impairment is a major manifestation of long-term neurological dysfunction...Subarachnoid hemorrhage is a subtype of stroke that causes severe neurological damage and is associated with poor long-term prognosis.Cognitive impairment is a major manifestation of long-term neurological dysfunction in patients with subarachnoid hemorrhage.However,there is notable absence of biological markers to predict long-term prognosis in this patient population.Given the aging-like neurocognitive phenomena associated with subarachnoid hemorrhage,this study postulates that telomere length,a recognized biomarker for aging,could be used as a prognostic indicator for subarachnoid hemorrhage.A left internal carotid artery intravascular puncture mouse model was used to simulate subarachnoid hemorrhage.Comprehensive neurological test scores were obtained through neurobehavioral assessments conducted at one-month intervals.Concurrently,the relative telomere length was analyzed by quantitative polymerase chain reaction,which was performed using DNA extracted from ear notch and brain tissue after each assessment.Furthermore,proteomic analysis was employed to investigate differential protein expression in hippocampal tissue.Subarachnoid hemorrhage mice exhibited persistent neurocognitive impairment over a prolonged period of time.There was a significant positive correlation between telomere length and neurological test scores,confirming the usefulness of telomere length as a prognostic indicator in subarachnoid hemorrhage.Hippocampal tissue from subarachnoid hemorrhage mice showed reduced expression of acetyl-coenzyme A synthetase-2 and abnormalities in the expression of proteins related to ribosomes,energy metabolism,and cellular signal transduction.This study confirmed telomere shortening in the brain and metabolic disturbances in the hippocampi of subarachnoid hemorrhage mice.Thus,telomere length is a predictive marker for long-term impairment of cognitive function in mice following experimental subarachnoid hemorrhage.展开更多
AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longit...AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longitudinal observational study,detailed medical histories of APAC patients and comprehensive ophthalmic examinations at final followup were collected.Logistic regression analysis was performed to identify predictors of blindness.Univariate and multivariate linear regression analyses were conducted to determine risk factors associated with visual outcomes.RESULTS:This study included 39 affected eyes of 31 subjects(26 females)with an average age of 74.1±8.0y.At 6.7±4.2y after APAC attack,2(5.7%)eyes had bestcorrected visual acuity(VA)worse than 3/60.Advanced glaucomatous visual field loss was observed in 15(39.5%)affected eyes and 5(25.0%)fellow eyes.Nine affected eyes(23.7%)had GON,and 11(28.9%)were blind.Six(15.4%)affected eyes and 2(9.1%)fellow eyes had suspicious progression.A significantly higher blindness rate in factory workers compared to office workers.Logistic regression identified that worse VA at attack(OR 10.568,95%CI 1.288-86.695;P=0.028)and worse early postoperative VA(OR 13.214,95%CI 1.157-150.881;P=0.038)were risk factors for blindness.Multivariate regression showed that longer duration of elevated intraocular pressure(P=0.004)and worse early postoperative VA(P=0.009)were associated with worse visual outcomes.CONCLUSION:Despite LE surgery,some APAC patients experience continued visual function deterioration.Lifelong monitoring is necessary.Target pressure and progression rates should be re-evaluated during follow-up.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
文摘Climate change affects distribution and persistence of species. However, forecasting species＇ re-sponses to these changes requires long-term data series that are often lacking in ecological studies.We used 15 years of small mammal trapping data collected between 1978 and 2015 in 3 areas atDoSana National Park （southwest Spain） to （i） describe changes in species composition and （ii） test theassociation between local climate conditions and size of small mammal populations. Overall, 5 specieswere captured： wood mouse Apodemus sylvaticus, algerian mouse Mus spretus, greater white-toothed shrew Crocidura russula, garden dormouse Eliomys quercinus, and black rat Rattus rattus. Thetemporal pattern in the proportion of captures of each species suggests that the small mammal diver-sity declined with time. Although the larger species （e.g., E. quercinus）, better adapted to colder cli-mate, have disappeared from our trapping records, M. spretus, a small species inhabiting southwestEurope and the Mediterranean coast of Africa, currently is almost the only trapped species. We used 2-level hierarchical models to separate changes in abundance from changes in probability of captureusing records of A. sylvaticus in all 3 areas and of Mo spretus in 1. We found that heavy rainfall and lowtemperatures were positively related to abundance of A. sylvaticus, and that the number of extremelyhot days was negatively related to abundance of M. spretus. Despite other mechanisms are likely to beinvolved, our findings support the importance of climate for the distribution and persistence of thesespecies and raise conservation concerns about potential cascading effects in the Donana ecosystem.
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financially supported by the China Atomic Energy Authority(CAEA)through the Geological Disposal Programthe National Natural Science Foundation of China(No.42307258)the China National Nuclear Corporation Fundamental Research Project(No.CNNC-JCYJ-202307)。
文摘Investigations into the long-term creep behavior of Beishan granite in uniaxial compression were conducted.Four levels of axial stress(60,70,87,and 95 MPa)were applied to rock specimens.Contrasting with earlier research,the long-term creep data in this work present a substantial advancement in the time dimension.Except for the sample subjected to 60 MPa axial loading,which did not fail after a loading duration of 1650 d,the specimens under the other three stresses all failed after sustained constant loading durations of 1204,1023,and 839 d,respectively.A lower envelope of driving stress-ratio for crystalline rocks was obtained,tending towards approximately 0.45 over an infinite time scale.According to the experimental results,as axial stress increases,both the axial strain accumulated in the transient creep process and the strain rate associated with steady-state creep deformation increase exponentially;however,the share of steady-state creep strain remains nearly constant at about82.53%.A novel damage-based creep model was put forward.It provides an enhanced depiction of the comprehensive creep process in rocks,notably improving the accuracy in forecasting the accelerated creep phase,which significantly impacts the long-term stability of engineering structures.
基金Supported by the National Natural Science Foundation of China(No.43277051)the Key Laboratory of Integrated Regulation and Resources Development of Shallow Lakes of Ministry of Education(No.B230203006).
文摘While oceanic and coastal acidification has gained increased attention,long-term pH trends and their drivers in large freshwater systems remain poorly understood.The Laurentian Great Lakes are the world’s largest freshwater system,and in many ways resemble marine ecosystems.However,unlike the open ocean and coastal waters where pH has declined due to rising atmospheric CO_(2),no significant pH trends have been observed in the Laurentian Great Lakes,despite significant ecosystem changes driven partly by the invasion of dreissenid mussels.This study examined 41 years of field observations from Lake Michigan to investigate the long-term carbonate chemistry dynamics.Observational results revealed substantial declines in both total alkalinity(TA)and dissolved inorganic carbon(DIC)over the four decades.Mussel shell calcification emerged as the primary mechanism behind these declines,accounting for 97%and 47%of the observed changes in TA and DIC,respectively,lowering water column pH by 0.24 units.Elevated carbon accumulation in soft mussel tissues,coupled with long-term changes in the air-water pCO_(2)gradient during summer,significantly contributed to long-term DIC variations,explaining 18%and 28%of the lake-wide DIC loss.These two mechanisms also resulted in an overall pH increase of 0.09 and 0.12 units,largely offsetting the calcification-driven pH decrease.These findings bridge a gap in acidification research for large freshwater systems and provide valuable insights for comprehensive lake-wide management strategies.
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.
文摘Objective:To investigate the long-term prognosis and postoperative cosmetic outcomes of breast-conserving surgery combined with sentinel lymph node biopsy in patients with early-stage breast cancer,providing a reference for the selection of clinical treatment plans.Methods:A retrospective analysis was conducted on the clinical data of 68 patients with early-stage breast cancer admitted from January 2022 to December 2025.Based on the surgical approach,patients were divided into an observation group(breast-conserving surgery+sentinel lymph node biopsy)and a control group(other surgical methods such as modified radical mastectomy/total mastectomy).Clinical and pathological characteristics,incidence of postoperative complications,follow-up prognosis,and satisfaction with cosmetic outcomes were compared between the two groups.Results:Among the 68 patients,41 were in the observation group and 27 in the control group.The average age of patients in the observation group was(54.32±8.15)years,while that in the control group was(62.45±9.76)years.The average tumor size in the observation group was(1.86±0.72)cm,compared to(3.21±1.45)cm in the control group.The incidence of postoperative complications in the observation group was 9.76%,significantly lower than that in the control group at 33.33%(P<0.05).The 6-month disease-free survival rate was 95.12%in the observation group and 88.89%in the control group,with no statistically significant difference between the two groups(P>0.05).The excellent and good rate of cosmetic outcomes in the observation group was 87.80%,significantly higher than that in the control group at 29.63%(P<0.05).Conclusion:Breast-conserving surgery combined with sentinel lymph node biopsy for early-stage breast cancer can achieve long-term prognostic outcomes comparable to those of traditional radical surgery,with the advantages of fewer postoperative complications and superior cosmetic results.This approach is worthy of clinical promotion and application,particularly for early-stage breast cancer patients who have a demand for preserving breast morphology.
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.2022YFF0503703)the National Natural Science Foundation of China(Grant Nos.42130203,42275133,and 42241135).
文摘In this study,we use observations from the Sounding of the Atmosphere using Broadband Emission Radiometry(SABER)instrument onboard the Thermosphere–Ionosphere–Mesosphere Energetics and Dynamics(TIMED)satellite to develop and apply a new local-time binning method to investigate the long-term evolution of mesospheric water vapor at high latitudes.The proposed method accounts for the gradual local-time drift of the SABER orbit by aligning seasonal observation windows and selecting samples observed at similar local times.This approach minimizes tidal aliasing and ensures more consistent sampling,yielding more reliable estimates of long-term water vapor trends at high latitudes.The results show that drying signals primarily appear in the polar regions.However,in the southern hemisphere,a drying trend is observed only in autumn,whereas winter and summer mainly show moistening trends.In contrast,the northern hemisphere exhibits drying signals in the polar regions during all seasons,showing a clear seasonal asymmetry.Additionally,the water vapor trend in the northern hemisphere is particularly pronounced in February(late winter),with moistening reaching up to+2.0 ppmv.The winter in the southern hemisphere(July–August)also shows moistening,but the trend is still weaker than in the northern hemisphere.These differences highlight the strong moistening trend in the northern hemisphere during winter and underscore the significant asymmetry in seasonal water vapor changes between the two hemispheres.These findings emphasize the limitations of water vapor trend estimates across different seasons and latitudes.Moreover,they provide new insights into the spatiotemporal variability associated with tidal structures,underscoring the importance of optimizing local-time sampling strategies for reliable long-term trend detection.
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Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the common prosperity strategy. The research identifies key obstacles to income expansion, including an undiversified industrial structure, insufficient human capital, and a lack of robust social protection. These call for systemic solutions featuring institutional innovation, resource consolidation, and capability enhancement. Building on Zhejiang's experience as a common prosperity demonstration zone, the article constructs an integrated framework centered on four pillars: industrial empowerment, education upgrading, social security reinforcement, and digital coordination. It further offers concrete policy proposals involving the cultivation of localized industries, vocational skill training, enhanced safety nets, and the adoption of digital tools. The study thus offers both theoretical insights and practical paradigms for tackling the challenge of raising incomes in low-income rural areas.
基金National Natural Science Foundation of China,No.81901336(to JM).
文摘Subarachnoid hemorrhage is a subtype of stroke that causes severe neurological damage and is associated with poor long-term prognosis.Cognitive impairment is a major manifestation of long-term neurological dysfunction in patients with subarachnoid hemorrhage.However,there is notable absence of biological markers to predict long-term prognosis in this patient population.Given the aging-like neurocognitive phenomena associated with subarachnoid hemorrhage,this study postulates that telomere length,a recognized biomarker for aging,could be used as a prognostic indicator for subarachnoid hemorrhage.A left internal carotid artery intravascular puncture mouse model was used to simulate subarachnoid hemorrhage.Comprehensive neurological test scores were obtained through neurobehavioral assessments conducted at one-month intervals.Concurrently,the relative telomere length was analyzed by quantitative polymerase chain reaction,which was performed using DNA extracted from ear notch and brain tissue after each assessment.Furthermore,proteomic analysis was employed to investigate differential protein expression in hippocampal tissue.Subarachnoid hemorrhage mice exhibited persistent neurocognitive impairment over a prolonged period of time.There was a significant positive correlation between telomere length and neurological test scores,confirming the usefulness of telomere length as a prognostic indicator in subarachnoid hemorrhage.Hippocampal tissue from subarachnoid hemorrhage mice showed reduced expression of acetyl-coenzyme A synthetase-2 and abnormalities in the expression of proteins related to ribosomes,energy metabolism,and cellular signal transduction.This study confirmed telomere shortening in the brain and metabolic disturbances in the hippocampi of subarachnoid hemorrhage mice.Thus,telomere length is a predictive marker for long-term impairment of cognitive function in mice following experimental subarachnoid hemorrhage.
文摘AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longitudinal observational study,detailed medical histories of APAC patients and comprehensive ophthalmic examinations at final followup were collected.Logistic regression analysis was performed to identify predictors of blindness.Univariate and multivariate linear regression analyses were conducted to determine risk factors associated with visual outcomes.RESULTS:This study included 39 affected eyes of 31 subjects(26 females)with an average age of 74.1±8.0y.At 6.7±4.2y after APAC attack,2(5.7%)eyes had bestcorrected visual acuity(VA)worse than 3/60.Advanced glaucomatous visual field loss was observed in 15(39.5%)affected eyes and 5(25.0%)fellow eyes.Nine affected eyes(23.7%)had GON,and 11(28.9%)were blind.Six(15.4%)affected eyes and 2(9.1%)fellow eyes had suspicious progression.A significantly higher blindness rate in factory workers compared to office workers.Logistic regression identified that worse VA at attack(OR 10.568,95%CI 1.288-86.695;P=0.028)and worse early postoperative VA(OR 13.214,95%CI 1.157-150.881;P=0.038)were risk factors for blindness.Multivariate regression showed that longer duration of elevated intraocular pressure(P=0.004)and worse early postoperative VA(P=0.009)were associated with worse visual outcomes.CONCLUSION:Despite LE surgery,some APAC patients experience continued visual function deterioration.Lifelong monitoring is necessary.Target pressure and progression rates should be re-evaluated during follow-up.
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.
