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
[Objectives]This study was conducted to investigate the effects of soil selenium(Se)content and exogenous Se supplementation on crop Se levels and provide a theoretical basis for the production of Se-enriched agricult...[Objectives]This study was conducted to investigate the effects of soil selenium(Se)content and exogenous Se supplementation on crop Se levels and provide a theoretical basis for the production of Se-enriched agricultural products.[Methods]Thirty three representative townships in Xiayi County,Ningling County,and Yongcheng City were selected.Soil Se content and its impact on wheat Se level were measured.The effects of exogenous Se supplementation on the Se content of crops including soybean,wheat,peanut,corn,sweet potato,grape,crisp pear and apple were also investigated.[Results]Showed that the soil Se content in 18 townships of Xiayi County ranged from 0.08 to 0.34 mg/kg,and Se-adequate areas,Se-deficient areas and severely Se-deficient areas accounted for 27.78%,66.67%,and 0.055%of the total,respectively.In Ningling County,the soil Se content in 11 townships ranged from 0.13 to 0.23 mg/kg,and among them,Se-adequate areas and Se-deficient areas comprised 18.18%and 81.82%,respectively.In Yongcheng City,the soil Se content in 4 townships ranged from 0.16 to 0.23 mg/kg,and Se-adequate areas and Se-deficient areas constituted 75.00%and 25.00%,respectively.The soil Se content in 33 townships across Xiayi County,Ningling County,and Yongcheng City ranged from 0.07 to 0.34 mg/kg.However,the Se levels in wheat from all 33 townships fail to meet the standard for Se-enriched agricultural products.It indicates that relying solely on soil Se supply is insufficient for crops to achieve the Se level required for Se-enriched agricultural products.Exogenous Se supplementation could effectively increase the Se content in soybean,wheat,peanut,corn,sweet potato,grape,crisp pear,and apple.Among these,soybean,wheat and peanut showed the highest Se levels after supplementation,all exceeding 0.10 mg/kg and meeting the industry standard for Se-enriched agricultural products.[Conclusions]The results of this study indicates that exogenous Se supplementation can serve as a key measure for producing Se-enriched agricultural products.展开更多
Background:Investigators from low-,middle-,and high-income countries representing 6 continents contributed to the development of the Global Adolescent and Child Physical Activity Questionnaire(GAC-PAQ).The GAC-PAQ is ...Background:Investigators from low-,middle-,and high-income countries representing 6 continents contributed to the development of the Global Adolescent and Child Physical Activity Questionnaire(GAC-PAQ).The GAC-PAQ is designed to assess physical activity(PA)across all key domains(i.e.,school,chores,work/volunteering,transport,free time,outdoor time).It aimed to address multiple gaps in global PA surveillance(e.g.,omission of important PA domains,insufficient cultural adaptation,underrepresentation of rural areas in questionnaire validation studies).The purpose of this study was to assess the content validity of the GAC-PAQ among PA experts,8-to 17-year-olds,and one of their parents/guardians,and to discuss changes made to the questionnaire based on participants'feedback.Methods:Sixty-two experts in PA measurement and/or surveillance from 24 countries completed an online survey that included both closed-and open-ended questions about the content validity of the GAC-PAQ.The proportion of experts who agreed or strongly agreed with the items was calculated.Child-parent/guardian dyads from 15 countries(n=250;10-40 per country)participated in a structured cognitive interview to assess the clarity of the questions and response options,and they were encouraged to provide suggestions to improve clarity and facilitate completion of the questionnaire.Participating countries are:Aotearoa New Zealand,Brazil,Canada,China,Colombia,Czech Republic,India,Malawi,Mexico,Nepal,Nigeria,Spain,Sweden,Thailand,and the United Arab Emirates.Interviews were conducted in 13 different languages and structured by PA domain.Generic images were included to help participants in answering questions about PA intensity.Results:Expert agreement with the items for each domain exceeded 75%,and their qualitative feedback was used to revise the questionnaire before cognitive interviews.In general,participants found the questionnaire to be comprehensive.Adolescents(12-17 years)found it easier than children(8-11 years)to answer the questions.Several children struggled to answer questions about the duration and intensity of activities and/or concepts related to travel modes,active trips,and organized activities.Many parents/guardians were unsure about the frequency,duration,and intensity of their children's or adolescents'PA at school and/or recommended using more culturally relevant and appropriate images.Some participants misunderstood the concept of activities that“make you stronger”(intended to assess resistance activities)and/or struggled to differentiate between work,volunteering,and chores.Conclusion:Participants'feedback was used to develop a revised,simplified,and culturally adapted GAC-PAQ,which will be pilot-tested in all15 countries in an App that will include country-specific images and narration in local languages.Further research is needed to assess the reliability and validity of the revised GAC-PAQ.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
Sichuan sausages with moisture contents of 40%,45%,50%,and 60% were stored at-18℃for durations of 0,2,4,6,and 8 weeks to evaluate the effect of moisture content on the quality attributes of Sichuan sausages during fr...Sichuan sausages with moisture contents of 40%,45%,50%,and 60% were stored at-18℃for durations of 0,2,4,6,and 8 weeks to evaluate the effect of moisture content on the quality attributes of Sichuan sausages during frozen storage.Product indicators including pH,colour,thiobarbituric acid reactive substances(TBARS),total volatile basic nitrogen(TVB-N),texture,electronic nose(E-nose)response,and water-holding capacity(thawing and cooking losses)were determined.The results indicated that as storage time increased,water retention in Sichuan sausages with different moisture contents decreased,while the degree of protein and lipid oxidation increased.This led to an increase in pH value,a colour shift from red-bright to grey-brown,and a deterioration in palatability.Among the samples,sausages with 50% moisture content exhibited the lowest thawing and cooking losses,indicating superior water-holding capacity.After 8 weeks of storage,TBARS and TVB-N values for the 50% moisture group were 19.5%and 2.5%lower,respectively,than those of the 40%and 45%moisture groups,indicating a reduced degree of oxidation.Furthermore,Sichuan sausage with 50%moisture content demonstrated an appropriate pH and colour difference,along with excellent texture and flavour,as evidenced by its higher toughness and satisfactory hardness.In conclusion,Sichuan sausage with 50% moisture content demonstrated the highest overall quality under frozen storage conditions.展开更多
This paper provides a systematic review of the histochemical localization,content characteristics,and influencing factors of saponins in Pseudostellaria heterophylla based on an extensive literature survey.It provides...This paper provides a systematic review of the histochemical localization,content characteristics,and influencing factors of saponins in Pseudostellaria heterophylla based on an extensive literature survey.It provides an in-depth analysis and summary of the effects of biological factors,environmental conditions,agronomic practices,processing methods,and continuous cropping obstacles on the synthesis of P.heterophylla saponins,as well as their underlying mechanisms.Based on identified gaps in the current literature,future research directions and prospects are proposed.The findings of this review offer valuable insights for advancing the understanding of the saponin biosynthesis mechanisms in P.heterophylla and for enhancing its quality.展开更多
Rock-ice avalanches in cold high-mountain regions pose severe hazards due to their high mobility,yet the quantitative controls of particle-size ratio and ice content remain insufficiently constrained.This study invest...Rock-ice avalanches in cold high-mountain regions pose severe hazards due to their high mobility,yet the quantitative controls of particle-size ratio and ice content remain insufficiently constrained.This study investigates their coupled effects using inclinedflume experiments and Discrete Element Method(DEM)simulations,covering three gravel sizes(2-5 mm,5-7 mm,7-10 mm)and four ice-content levels(0%,20%,40%,60%).Run-out distance,velocity,energy components,flow regime(Savage number),and segregation indexαwere quantified.Increasing ice content significantly enhances mobility,but with diminishing marginal effectiveness.From 0%to 40%ice content,run-out distance increases by 41%-86%,whereas the additional increase from 40%to 60%contributes only 12%-23%.Particle-size ratio strongly governs segregation intensity.Fine-gravel groups reach segregation indices ofα=0.92-0.98,indicating nearly complete upward migration of ice,whereas medium-gravel and coarse-gravel groups exhibit much weaker segregation,stabilizing atα=0.68-0.74 and 0.60-0.69.Savage number analyses reveal marked flow-regime transitions.At 0%ice content,Savage numbers reach 1.0-1.5,indicating a collisional regime.Increasing ice content suppresses collisionality,with Savage numbers decreasing to 0.03-0.07 at 60%ice content,consistent with dense-regime flow.DEM energy analyses confirm this regime shift:for finegravel mixtures,collision energy decreases by 14%,while sliding-friction energy increases by 33%as ice content increases from 0%to 60%,reflecting enhanced overburden effects imposed by upward-segregated ice layers.Medium and coarse mixtures exhibit weaker or opposite energy-shift patterns,demonstrating strong size dependence.Mechanistically,large particle-size contrasts promote strong segregation and form dense basal rock layers that increase basal friction and reduce mobility.When particle sizes are similar or ice content is high,segregation remains limited,allowing ice to mix into the basal layer,thereby reducing basal friction and enhancing mobility.This research quantitatively demonstrates how composition controls particle spatial distribution,flow regime,and energy dissipation,offering new mechanistic insights into the propagation and deposition behaviors of rock-ice avalanches and improving hazard assessment in vulnerable high-mountain regions.展开更多
Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.Howev...Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.展开更多
Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy a...Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy attribute-based encryption(CP-ABE)schemes still face cumbersome ciphertext re-encryption and insufficient oversight when handling dynamic attribute changes and cross-chain collaboration.To address these issues,we propose a dynamic permission attribute-encryption scheme for multi-chain collaboration.This scheme incorporates a multiauthority architecture for distributed attribute management and integrates an attribute revocation and granting mechanism that eliminates the need for ciphertext re-encryption,effectively reducing both computational and communication overhead.It leverages the InterPlanetary File System(IPFS)for off-chain data storage and constructs a cross-chain regulatory framework—comprising a Hyperledger Fabric business chain and a FISCO BCOS regulatory chain—to record changes in decryption privileges and access behaviors in an auditable manner.Security analysis shows selective indistinguishability under chosen-plaintext attack(sIND-CPA)security under the decisional q-Parallel Bilinear Diffie-Hellman Exponent Assumption(q-PBDHE).In the performance and experimental evaluations,we compared the proposed scheme with several advanced schemes.The results show that,while preserving security,the proposed scheme achieves higher encryption/decryption efficiency and lower storage overhead for ciphertexts and keys.展开更多
With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-...With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-to-end datamodem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity.Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers.For example,speech activity detection may quickly classify encoded signals as nonspeech signals and reject input waveforms.To address this issue,we propose a novel modulation method based on linear frequency modulation that encodes 3 bits per symbol by varying its frequency,shape,and phase,alongside a lightweightMobileNetV3-Small-based demodulator for efficient and accurate signal decoding on resource-constrained devices.This method leverages the unique characteristics of linear frequency modulation signals,making them more easily transmitted and decoded in speech channels.To ensure reliable data delivery over unstable voice links,we further introduce a robust framing scheme with delimiter-based synchronization,a sample-level position remedying algorithm,and a feedback-driven retransmission mechanism.We have validated the feasibility and performance of our system through expanded real-world evaluations,demonstrating that it outperforms existing advanced methods in terms of robustness and data transfer rate.This technology establishes the foundational infrastructure for reliable certificate delivery over voice channels,which is crucial for achieving strong caller authentication and preventing telephone fraud at its root cause.展开更多
Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automaticall...Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.展开更多
Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel a...Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.展开更多
Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either re...Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either require trusted setups or suffer from high communication overhead and low verification efficiency.This paper presents ISTIRDA,a DAS scheme that lets light clients certify availability by sampling small random codeword symbols.Built on ISTIR,an improved Reed–Solomon interactive oracle proof of proximity,ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication.This paper formalizes opening consistency and prove security with bounded error in the random oracle model,giving polylogarithmic verifier queries and no trusted setup.In a prototype compared with FRIDA under equal soundness,ISTIRDA reduces communication by 40.65%to 80%.For data larger than 16 MB,ISTIRDA verifies faster and the advantage widens;at 128 MB,proofs are about 60%smaller and verification time is roughly 25%shorter,while prover overhead remains modest.In peer-to-peer emulation under injected latency and loss,ISTIRDA reaches confidence more quickly and is less sensitive to packet loss and load.These results indicate that ISTIRDA is a scalable and provably secure DAS scheme suitable for high-throughput,large-block public blockchains,substantially easing bandwidth and latency pressure on lightweight nodes.展开更多
With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service...With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.展开更多
Urban forests are highly multifunctional and provide numerous ecological functions.Plant functional traits individually or jointly influence the ecological multifunctionality of tree species(TS-EMF)and can also modify...Urban forests are highly multifunctional and provide numerous ecological functions.Plant functional traits individually or jointly influence the ecological multifunctionality of tree species(TS-EMF)and can also modify TSEMF in response to environmental changes.However,there has been limited exploration of multitrait combinations for predicting TS-EMF across seasons and of trait thresholds that enhance TS-EMF.Here,for 10 dominant tree species in urban forests of Northeast China,14 traits were measured and four aboveground and three belowground ecological functions assessed in three seasons.Ecological functions and TS-EMF differed significantly throughout the seasons(P<0.05).Synergistic relationships were found between carbon sequestration and oxygen release,between cooling and humidification,and between organic carbon accumulation and nutrient cycling.Notably,aboveground multifunctionality played a leading role in TS-EMF.With seasonal changes,resource allocation shifted toward traits related to resource acquisition rather than conservation to maintain TS-EMF.The combination of traits that predicted TS-EMF varied by type,accounting for up to 66.45%of the variation.TS-EMF was primarily driven by leaf structure in spring and by nutrient accumulation in autumn.Leaf carbon content(LCC)consistently served as a stabilizing factor for predicting TS-EMF across seasons.At 36.5-36.8 mg g^(-1),LCC had its optimal effect on TS-EMF.Other traits in combination that positively influence total TS-EMF include leaf nitrogen content(3.43-3.45 mg g^(-1)),leaf phosphorus content(0.80-0.83 mg g^(-1)),and leaf area(65.86-68.43 cm^(2)).Within these specified trait thresholds,Morus alba and Quercus mongolica were identified as key species.These findings suggest that the trade-off between various ecological functions can be managed by altering plant traits across seasons.This approach could provide a theoretical foundation for enhancing the TS-EMF of urban forests through trait-based management,offering practical guidance for selecting tree species.展开更多
Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data co...Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.展开更多
Extreme climate events(e.g.,heatwaves and droughts)are becoming increasingly frequent due to global climate change,which inevitably affects tree growth and various other ecological processes.While the impacts of droug...Extreme climate events(e.g.,heatwaves and droughts)are becoming increasingly frequent due to global climate change,which inevitably affects tree growth and various other ecological processes.While the impacts of droughts on these processes have been widely evaluated,the effects of heatwaves on tree growth and soil water content(SWC)remain poorly understood,particularly those related to thinning treatment.In this study,we evaluated the impacts of the 2021 Pacific Northwest Heatwave and thinning on forest growth and SWC,as well as assessed how thinning might mitigate the heatwave's impacts in lodgepole pine forests in British Columbia,Canada.We measured meteorological data(air temperature,rainfall,solar radiation(SR),relative humidity(RH),and wind speed(W_(s)),sap flow,SWC,soil temperature(T_(s)),and tree diameters at the breast height(DBH)during the growing season(June–September)in the control(27,000 stems·ha^(-1)),lightly thinned(4,500 stems·ha^(-1)),and heavily thinned(1,100 stems·ha^(-1))experimental plots from 2018 to 2024.We found that thinning persistently and significantly(p<0.05)increased individual tree growth,with the most pronounced effects in the heavily thinned stands.The 2021 Pacific Northwest Heatwave led to an exceptionally hot growing season,significantly(p<0.05)reducing forest growth and SWC across all plots.Forest growth recovered in 2022 in the thinned plots but remained suppressed in the unthinned plots,suggesting that thinning effectively mitigated the impact of the heatwave on forest growth,while the heatwave's impacts were persistent in the unthinned plots.Our study highlights that thinning is a practical management strategy for improving tree growth and supporting climate change adaptation to extreme climate events.展开更多
As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and ...As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.展开更多
Data center industries have been facing huge energy challenges due to escalating power consumption and associated carbon emissions.In the context of carbon neutrality,the integration of data centers with renewable ene...Data center industries have been facing huge energy challenges due to escalating power consumption and associated carbon emissions.In the context of carbon neutrality,the integration of data centers with renewable energy has become a prevailing trend.To advance the renewable energy integration in data centers,it is imperative to thoroughly explore the data centers’operational flexibility.Computing workloads and refrigeration systems are recognized as two promising flexible resources for power regulationwithin data centermicro-grids.This paper identifies and categorizes delay-tolerant computing workloads into three types(long-running non-interruptible,long-running interruptible,and short-running)and develops mathematical time-shifting models for each.Additionally,this paper examines the thermal dynamics of the computer room and derives a time-varying temperature model coupled to refrigeration power.Building on these models,this paper proposes a two-stage,multi-time scale optimization scheduling framework that jointly coordinates computing workloads time-shift in day-ahead scheduling and refrigeration power control in intra-day dispatch to mitigate renewable variability.A case study demonstrates that the framework effectively enhances the renewable-energy utilization,improves the operational economy of the data center microgrid,and mitigates the impact of renewable power uncertainty.The results highlight the potential of coordinated computing workloads and thermal system flexibility to support greener,more cost-effective data center operation.展开更多
文摘Earthquakes are highly destructive spatio-temporal phenomena whose analysis is essential for disaster preparedness and risk mitigation.Modern seismological research produces vast volumes of heterogeneous data from seismic networks,satellite observations,and geospatial repositories,creating the need for scalable infrastructures capable of integrating and analyzing such data to support intelligent decision-making.Data warehousing technologies provide a robust foundation for this purpose;however,existing earthquake-oriented data warehouses remain limited,often relying on simplified schemas,domain-specific analytics,or cataloguing efforts.This paper presents the design and implementation of a spatio-temporal data warehouse for seismic activity.The framework integrates spatial and temporal dimensions in a unified schema and introduces a novel array-based approach for managing many-to-many relationships between facts and dimensions without intermediate bridge tables.A comparative evaluation against a conventional bridge-table schema demonstrates that the array-based design improves fact-centric query performance,while the bridge-table schema remains advantageous for dimension-centric queries.To reconcile these trade-offs,a hybrid schema is proposed that retains both representations,ensuring balanced efficiency across heterogeneous workloads.The proposed framework demonstrates how spatio-temporal data warehousing can address schema complexity,improve query performance,and support multidimensional visualization.In doing so,it provides a foundation for integrating seismic analysis into broader big data-driven intelligent decision systems for disaster resilience,risk mitigation,and emergency management.
基金Supported by Shangqiu Science and Technology Project(20240036).
文摘[Objectives]This study was conducted to investigate the effects of soil selenium(Se)content and exogenous Se supplementation on crop Se levels and provide a theoretical basis for the production of Se-enriched agricultural products.[Methods]Thirty three representative townships in Xiayi County,Ningling County,and Yongcheng City were selected.Soil Se content and its impact on wheat Se level were measured.The effects of exogenous Se supplementation on the Se content of crops including soybean,wheat,peanut,corn,sweet potato,grape,crisp pear and apple were also investigated.[Results]Showed that the soil Se content in 18 townships of Xiayi County ranged from 0.08 to 0.34 mg/kg,and Se-adequate areas,Se-deficient areas and severely Se-deficient areas accounted for 27.78%,66.67%,and 0.055%of the total,respectively.In Ningling County,the soil Se content in 11 townships ranged from 0.13 to 0.23 mg/kg,and among them,Se-adequate areas and Se-deficient areas comprised 18.18%and 81.82%,respectively.In Yongcheng City,the soil Se content in 4 townships ranged from 0.16 to 0.23 mg/kg,and Se-adequate areas and Se-deficient areas constituted 75.00%and 25.00%,respectively.The soil Se content in 33 townships across Xiayi County,Ningling County,and Yongcheng City ranged from 0.07 to 0.34 mg/kg.However,the Se levels in wheat from all 33 townships fail to meet the standard for Se-enriched agricultural products.It indicates that relying solely on soil Se supply is insufficient for crops to achieve the Se level required for Se-enriched agricultural products.Exogenous Se supplementation could effectively increase the Se content in soybean,wheat,peanut,corn,sweet potato,grape,crisp pear,and apple.Among these,soybean,wheat and peanut showed the highest Se levels after supplementation,all exceeding 0.10 mg/kg and meeting the industry standard for Se-enriched agricultural products.[Conclusions]The results of this study indicates that exogenous Se supplementation can serve as a key measure for producing Se-enriched agricultural products.
基金supported by a Project Grant(Grant No.PJT183705)an Early Career Investigator Prize(Grant No.ECP 184184)from the Canadian Institutes of Health Research+7 种基金a Prentice Institute Research Affiliate Fund Grant from the Prentice Institute for Global Population and Economy(Grant No.G00004116)a Te Herenga Waka Victoria University of Wellington Division of Science Health Engineering Architecture and Design Innovation Faculty Strategic Research Grant(Grant No.FSRG-SHEADI-10724)The Thailand Physical Activity Knowledge Development Centre(TPAK)/Thai Health Promotion Foundation provided funding for the cognitive interviews and pilot study in Thailand(Grant No.66-P1-0473)The University Pablo de Olavide provided a scholarship for 2 undergraduate students working on the project(codes PPI2207 and PPI2308)In the Czech Republicthe study was supported by Palacky University IGA(Grant No.IGA_FTK_2023_017)supported by the Division of Intramural Research at the National Institute on Minority Health and Health Disparities of the National Institutes of Healthsupported by the Key Project of the National Philosophy and Social Science Foundation of China(23&ZD197)。
文摘Background:Investigators from low-,middle-,and high-income countries representing 6 continents contributed to the development of the Global Adolescent and Child Physical Activity Questionnaire(GAC-PAQ).The GAC-PAQ is designed to assess physical activity(PA)across all key domains(i.e.,school,chores,work/volunteering,transport,free time,outdoor time).It aimed to address multiple gaps in global PA surveillance(e.g.,omission of important PA domains,insufficient cultural adaptation,underrepresentation of rural areas in questionnaire validation studies).The purpose of this study was to assess the content validity of the GAC-PAQ among PA experts,8-to 17-year-olds,and one of their parents/guardians,and to discuss changes made to the questionnaire based on participants'feedback.Methods:Sixty-two experts in PA measurement and/or surveillance from 24 countries completed an online survey that included both closed-and open-ended questions about the content validity of the GAC-PAQ.The proportion of experts who agreed or strongly agreed with the items was calculated.Child-parent/guardian dyads from 15 countries(n=250;10-40 per country)participated in a structured cognitive interview to assess the clarity of the questions and response options,and they were encouraged to provide suggestions to improve clarity and facilitate completion of the questionnaire.Participating countries are:Aotearoa New Zealand,Brazil,Canada,China,Colombia,Czech Republic,India,Malawi,Mexico,Nepal,Nigeria,Spain,Sweden,Thailand,and the United Arab Emirates.Interviews were conducted in 13 different languages and structured by PA domain.Generic images were included to help participants in answering questions about PA intensity.Results:Expert agreement with the items for each domain exceeded 75%,and their qualitative feedback was used to revise the questionnaire before cognitive interviews.In general,participants found the questionnaire to be comprehensive.Adolescents(12-17 years)found it easier than children(8-11 years)to answer the questions.Several children struggled to answer questions about the duration and intensity of activities and/or concepts related to travel modes,active trips,and organized activities.Many parents/guardians were unsure about the frequency,duration,and intensity of their children's or adolescents'PA at school and/or recommended using more culturally relevant and appropriate images.Some participants misunderstood the concept of activities that“make you stronger”(intended to assess resistance activities)and/or struggled to differentiate between work,volunteering,and chores.Conclusion:Participants'feedback was used to develop a revised,simplified,and culturally adapted GAC-PAQ,which will be pilot-tested in all15 countries in an App that will include country-specific images and narration in local languages.Further research is needed to assess the reliability and validity of the revised GAC-PAQ.
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.
基金Supported by National Modern Agricultural Industry Technology System Sichuan Pig Innovation Team(SCCXTD-2026-8)Sichuan Science and Technology Program(2025ZYD0049)"Challenge and Leadership"Project for Key Core Technologies of Sui Ning in Sichuan(2025SNKBZ19).
文摘Sichuan sausages with moisture contents of 40%,45%,50%,and 60% were stored at-18℃for durations of 0,2,4,6,and 8 weeks to evaluate the effect of moisture content on the quality attributes of Sichuan sausages during frozen storage.Product indicators including pH,colour,thiobarbituric acid reactive substances(TBARS),total volatile basic nitrogen(TVB-N),texture,electronic nose(E-nose)response,and water-holding capacity(thawing and cooking losses)were determined.The results indicated that as storage time increased,water retention in Sichuan sausages with different moisture contents decreased,while the degree of protein and lipid oxidation increased.This led to an increase in pH value,a colour shift from red-bright to grey-brown,and a deterioration in palatability.Among the samples,sausages with 50% moisture content exhibited the lowest thawing and cooking losses,indicating superior water-holding capacity.After 8 weeks of storage,TBARS and TVB-N values for the 50% moisture group were 19.5%and 2.5%lower,respectively,than those of the 40%and 45%moisture groups,indicating a reduced degree of oxidation.Furthermore,Sichuan sausage with 50%moisture content demonstrated an appropriate pH and colour difference,along with excellent texture and flavour,as evidenced by its higher toughness and satisfactory hardness.In conclusion,Sichuan sausage with 50% moisture content demonstrated the highest overall quality under frozen storage conditions.
基金Supported by Open Fund Project of the Engineering Technology Research Center of Characteristic Medicinal Plants of Fujian(PP202003).
文摘This paper provides a systematic review of the histochemical localization,content characteristics,and influencing factors of saponins in Pseudostellaria heterophylla based on an extensive literature survey.It provides an in-depth analysis and summary of the effects of biological factors,environmental conditions,agronomic practices,processing methods,and continuous cropping obstacles on the synthesis of P.heterophylla saponins,as well as their underlying mechanisms.Based on identified gaps in the current literature,future research directions and prospects are proposed.The findings of this review offer valuable insights for advancing the understanding of the saponin biosynthesis mechanisms in P.heterophylla and for enhancing its quality.
基金funded by the Natural Science Foundation of China(Grants No 42277127)。
文摘Rock-ice avalanches in cold high-mountain regions pose severe hazards due to their high mobility,yet the quantitative controls of particle-size ratio and ice content remain insufficiently constrained.This study investigates their coupled effects using inclinedflume experiments and Discrete Element Method(DEM)simulations,covering three gravel sizes(2-5 mm,5-7 mm,7-10 mm)and four ice-content levels(0%,20%,40%,60%).Run-out distance,velocity,energy components,flow regime(Savage number),and segregation indexαwere quantified.Increasing ice content significantly enhances mobility,but with diminishing marginal effectiveness.From 0%to 40%ice content,run-out distance increases by 41%-86%,whereas the additional increase from 40%to 60%contributes only 12%-23%.Particle-size ratio strongly governs segregation intensity.Fine-gravel groups reach segregation indices ofα=0.92-0.98,indicating nearly complete upward migration of ice,whereas medium-gravel and coarse-gravel groups exhibit much weaker segregation,stabilizing atα=0.68-0.74 and 0.60-0.69.Savage number analyses reveal marked flow-regime transitions.At 0%ice content,Savage numbers reach 1.0-1.5,indicating a collisional regime.Increasing ice content suppresses collisionality,with Savage numbers decreasing to 0.03-0.07 at 60%ice content,consistent with dense-regime flow.DEM energy analyses confirm this regime shift:for finegravel mixtures,collision energy decreases by 14%,while sliding-friction energy increases by 33%as ice content increases from 0%to 60%,reflecting enhanced overburden effects imposed by upward-segregated ice layers.Medium and coarse mixtures exhibit weaker or opposite energy-shift patterns,demonstrating strong size dependence.Mechanistically,large particle-size contrasts promote strong segregation and form dense basal rock layers that increase basal friction and reduce mobility.When particle sizes are similar or ice content is high,segregation remains limited,allowing ice to mix into the basal layer,thereby reducing basal friction and enhancing mobility.This research quantitatively demonstrates how composition controls particle spatial distribution,flow regime,and energy dissipation,offering new mechanistic insights into the propagation and deposition behaviors of rock-ice avalanches and improving hazard assessment in vulnerable high-mountain regions.
基金supported by the National Key R&D Program of China[Grant No.2023YFF0713600]the National Natural Science Foundation of China[Grant No.62275062]+3 种基金Project of Shandong Innovation and Startup Community of High-end Medical Apparatus and Instruments[Grant No.2023-SGTTXM-002 and 2024-SGTTXM-005]the Shandong Province Technology Innovation Guidance Plan(Central Leading Local Science and Technology Development Fund)[Grant No.YDZX2023115]the Taishan Scholar Special Funding Project of Shandong Provincethe Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai[Grant No.ZL202402].
文摘Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability,enabling comprehensive three-dimensional imaging of biological targets.However,the increasing demand for higher resolution and real-time imaging results in significant data volume,limiting data storage,transmission and processing efficiency of system.Therefore,there is an urgent need for an effective method to compress the raw data without compromising image quality.This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer.This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding.It combines the multiscale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers,achieving high-quality data compression and reconstruction.Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions,achieving a raw data compression ratio of up to 1:40.Furthermore,three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)values of reconstructed images reached 38.60 and 0.9583,effectively overcoming detail loss and artifacts introduced by raw data compression.All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost,enhancing computational efficiency and image quality.These advantages support the development of photoacoustic-computed tomography toward higher efficiency,real-time performance and intelligent functionality.
文摘Amid the increasing demand for data sharing,the need for flexible,secure,and auditable access control mechanisms has garnered significant attention in the academic community.However,blockchain-based ciphertextpolicy attribute-based encryption(CP-ABE)schemes still face cumbersome ciphertext re-encryption and insufficient oversight when handling dynamic attribute changes and cross-chain collaboration.To address these issues,we propose a dynamic permission attribute-encryption scheme for multi-chain collaboration.This scheme incorporates a multiauthority architecture for distributed attribute management and integrates an attribute revocation and granting mechanism that eliminates the need for ciphertext re-encryption,effectively reducing both computational and communication overhead.It leverages the InterPlanetary File System(IPFS)for off-chain data storage and constructs a cross-chain regulatory framework—comprising a Hyperledger Fabric business chain and a FISCO BCOS regulatory chain—to record changes in decryption privileges and access behaviors in an auditable manner.Security analysis shows selective indistinguishability under chosen-plaintext attack(sIND-CPA)security under the decisional q-Parallel Bilinear Diffie-Hellman Exponent Assumption(q-PBDHE).In the performance and experimental evaluations,we compared the proposed scheme with several advanced schemes.The results show that,while preserving security,the proposed scheme achieves higher encryption/decryption efficiency and lower storage overhead for ciphertexts and keys.
文摘With the popularization of new technologies,telephone fraud has become the main means of stealing money and personal identity information.Taking inspiration from the website authentication mechanism,we propose an end-to-end datamodem scheme that transmits the caller’s digital certificates through a voice channel for the recipient to verify the caller’s identity.Encoding useful information through voice channels is very difficult without the assistance of telecommunications providers.For example,speech activity detection may quickly classify encoded signals as nonspeech signals and reject input waveforms.To address this issue,we propose a novel modulation method based on linear frequency modulation that encodes 3 bits per symbol by varying its frequency,shape,and phase,alongside a lightweightMobileNetV3-Small-based demodulator for efficient and accurate signal decoding on resource-constrained devices.This method leverages the unique characteristics of linear frequency modulation signals,making them more easily transmitted and decoded in speech channels.To ensure reliable data delivery over unstable voice links,we further introduce a robust framing scheme with delimiter-based synchronization,a sample-level position remedying algorithm,and a feedback-driven retransmission mechanism.We have validated the feasibility and performance of our system through expanded real-world evaluations,demonstrating that it outperforms existing advanced methods in terms of robustness and data transfer rate.This technology establishes the foundational infrastructure for reliable certificate delivery over voice channels,which is crucial for achieving strong caller authentication and preventing telephone fraud at its root cause.
文摘Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.
文摘Missing data presents a crucial challenge in data analysis,especially in high-dimensional datasets,where missing data often leads to biased conclusions and degraded model performance.In this study,we present a novel autoencoder-based imputation framework that integrates a composite loss function to enhance robustness and precision.The proposed loss combines(i)a guided,masked mean squared error focusing on missing entries;(ii)a noise-aware regularization term to improve resilience against data corruption;and(iii)a variance penalty to encourage expressive yet stable reconstructions.We evaluate the proposed model across four missingness mechanisms,such as Missing Completely at Random,Missing at Random,Missing Not at Random,and Missing Not at Random with quantile censorship,under systematically varied feature counts,sample sizes,and missingness ratios ranging from 5%to 60%.Four publicly available real-world datasets(Stroke Prediction,Pima Indians Diabetes,Cardiovascular Disease,and Framingham Heart Study)were used,and the obtained results show that our proposed model consistently outperforms baseline methods,including traditional and deep learning-based techniques.An ablation study reveals the additive value of each component in the loss function.Additionally,we assessed the downstream utility of imputed data through classification tasks,where datasets imputed by the proposed method yielded the highest receiver operating characteristic area under the curve scores across all scenarios.The model demonstrates strong scalability and robustness,improving performance with larger datasets and higher feature counts.These results underscore the capacity of the proposed method to produce not only numerically accurate but also semantically useful imputations,making it a promising solution for robust data recovery in clinical applications.
基金supported in part by the Research Fund of Key Lab of Education Blockchain and Intelligent Technology,Ministry of Education(EBME25-F-08).
文摘Lightweight nodes are crucial for blockchain scalability,but verifying the availability of complete block data puts significant strain on bandwidth and latency.Existing data availability sampling(DAS)schemes either require trusted setups or suffer from high communication overhead and low verification efficiency.This paper presents ISTIRDA,a DAS scheme that lets light clients certify availability by sampling small random codeword symbols.Built on ISTIR,an improved Reed–Solomon interactive oracle proof of proximity,ISTIRDA combines adaptive folding with dynamic code rate adjustment to preserve soundness while lowering communication.This paper formalizes opening consistency and prove security with bounded error in the random oracle model,giving polylogarithmic verifier queries and no trusted setup.In a prototype compared with FRIDA under equal soundness,ISTIRDA reduces communication by 40.65%to 80%.For data larger than 16 MB,ISTIRDA verifies faster and the advantage widens;at 128 MB,proofs are about 60%smaller and verification time is roughly 25%shorter,while prover overhead remains modest.In peer-to-peer emulation under injected latency and loss,ISTIRDA reaches confidence more quickly and is less sensitive to packet loss and load.These results indicate that ISTIRDA is a scalable and provably secure DAS scheme suitable for high-throughput,large-block public blockchains,substantially easing bandwidth and latency pressure on lightweight nodes.
文摘With the accelerating aging process of China’s population,the demand for community elderly care services has shown diversified and personalized characteristics.However,problems such as insufficient total care service resources,uneven distribution,and prominent supply-demand contradictions have seriously affected service quality.Big data technology,with core advantages including data collection,analysis and mining,and accurate prediction,provides a new solution for the allocation of community elderly care service resources.This paper systematically studies the application value of big data technology in the allocation of community elderly care service resources from three aspects:resource allocation efficiency,service accuracy,and management intelligence.Combined with practical needs,it proposes optimal allocation strategies such as building a big data analysis platform and accurately grasping the elderly’s care needs,striving to provide operable path references for the construction of community elderly care service systems,promoting the early realization of the elderly care service goal of“adequate support and proper care for the elderly”,and boosting the high-quality development of China’s elderly care service industry.
基金supported by the National Natural Science Foundation(32130068,32271634,and 32071597)CAS Key Laboratory of Forest Ecology and Silviculture,Institute of Applied Ecology,Chinese Academy of Sciences(KLFES-2025)。
文摘Urban forests are highly multifunctional and provide numerous ecological functions.Plant functional traits individually or jointly influence the ecological multifunctionality of tree species(TS-EMF)and can also modify TSEMF in response to environmental changes.However,there has been limited exploration of multitrait combinations for predicting TS-EMF across seasons and of trait thresholds that enhance TS-EMF.Here,for 10 dominant tree species in urban forests of Northeast China,14 traits were measured and four aboveground and three belowground ecological functions assessed in three seasons.Ecological functions and TS-EMF differed significantly throughout the seasons(P<0.05).Synergistic relationships were found between carbon sequestration and oxygen release,between cooling and humidification,and between organic carbon accumulation and nutrient cycling.Notably,aboveground multifunctionality played a leading role in TS-EMF.With seasonal changes,resource allocation shifted toward traits related to resource acquisition rather than conservation to maintain TS-EMF.The combination of traits that predicted TS-EMF varied by type,accounting for up to 66.45%of the variation.TS-EMF was primarily driven by leaf structure in spring and by nutrient accumulation in autumn.Leaf carbon content(LCC)consistently served as a stabilizing factor for predicting TS-EMF across seasons.At 36.5-36.8 mg g^(-1),LCC had its optimal effect on TS-EMF.Other traits in combination that positively influence total TS-EMF include leaf nitrogen content(3.43-3.45 mg g^(-1)),leaf phosphorus content(0.80-0.83 mg g^(-1)),and leaf area(65.86-68.43 cm^(2)).Within these specified trait thresholds,Morus alba and Quercus mongolica were identified as key species.These findings suggest that the trade-off between various ecological functions can be managed by altering plant traits across seasons.This approach could provide a theoretical foundation for enhancing the TS-EMF of urban forests through trait-based management,offering practical guidance for selecting tree species.
基金supported by Natural Science Foundation of Qinghai Province(2025-ZJ-994M)Scientific Research Innovation Capability Support Project for Young Faculty(SRICSPYF-BS2025007)National Natural Science Foundation of China(62566050).
文摘Multivariate anomaly detection plays a critical role in maintaining the stable operation of information systems.However,in existing research,multivariate data are often influenced by various factors during the data collection process,resulting in temporal misalignment or displacement.Due to these factors,the node representations carry substantial noise,which reduces the adaptability of the multivariate coupled network structure and subsequently degrades anomaly detection performance.Accordingly,this study proposes a novel multivariate anomaly detection model grounded in graph structure learning.Firstly,a recommendation strategy is employed to identify strongly coupled variable pairs,which are then used to construct a recommendation-driven multivariate coupling network.Secondly,a multi-channel graph encoding layer is used to dynamically optimize the structural properties of the multivariate coupling network,while a multi-head attention mechanism enhances the spatial characteristics of the multivariate data.Finally,unsupervised anomaly detection is conducted using a dynamic threshold selection algorithm.Experimental results demonstrate that effectively integrating the structural and spatial features of multivariate data significantly mitigates anomalies caused by temporal dependency misalignment.
基金supported by the British Columbia Ministry of Forces through long-term annual contracts with University of British Columbia(Okanagan)(No.RE25SIR242)the Natural Sciences and Engineering Research Council of Canada(NSERC),Discovery Grants Program(No.RGPIN-2021-02628)+1 种基金supported by the China Postdoctoral Science Foundation(No.2024M760387)Heilongjiang Postdoctoral Financial Assistance(No.LBH-Z24062)。
文摘Extreme climate events(e.g.,heatwaves and droughts)are becoming increasingly frequent due to global climate change,which inevitably affects tree growth and various other ecological processes.While the impacts of droughts on these processes have been widely evaluated,the effects of heatwaves on tree growth and soil water content(SWC)remain poorly understood,particularly those related to thinning treatment.In this study,we evaluated the impacts of the 2021 Pacific Northwest Heatwave and thinning on forest growth and SWC,as well as assessed how thinning might mitigate the heatwave's impacts in lodgepole pine forests in British Columbia,Canada.We measured meteorological data(air temperature,rainfall,solar radiation(SR),relative humidity(RH),and wind speed(W_(s)),sap flow,SWC,soil temperature(T_(s)),and tree diameters at the breast height(DBH)during the growing season(June–September)in the control(27,000 stems·ha^(-1)),lightly thinned(4,500 stems·ha^(-1)),and heavily thinned(1,100 stems·ha^(-1))experimental plots from 2018 to 2024.We found that thinning persistently and significantly(p<0.05)increased individual tree growth,with the most pronounced effects in the heavily thinned stands.The 2021 Pacific Northwest Heatwave led to an exceptionally hot growing season,significantly(p<0.05)reducing forest growth and SWC across all plots.Forest growth recovered in 2022 in the thinned plots but remained suppressed in the unthinned plots,suggesting that thinning effectively mitigated the impact of the heatwave on forest growth,while the heatwave's impacts were persistent in the unthinned plots.Our study highlights that thinning is a practical management strategy for improving tree growth and supporting climate change adaptation to extreme climate events.
基金supported by the National Science Foundation of China(No.62171387)the Science and Technology Program of Sichuan Province(No.2024NSFSC0468)the China Postdoctoral Science Foundation(No.2019M663475).
文摘As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.
基金supported by Science and Technology Standard Project of Guangdong Electric Power Design Institute(ER11301W,ER11811W).
文摘Data center industries have been facing huge energy challenges due to escalating power consumption and associated carbon emissions.In the context of carbon neutrality,the integration of data centers with renewable energy has become a prevailing trend.To advance the renewable energy integration in data centers,it is imperative to thoroughly explore the data centers’operational flexibility.Computing workloads and refrigeration systems are recognized as two promising flexible resources for power regulationwithin data centermicro-grids.This paper identifies and categorizes delay-tolerant computing workloads into three types(long-running non-interruptible,long-running interruptible,and short-running)and develops mathematical time-shifting models for each.Additionally,this paper examines the thermal dynamics of the computer room and derives a time-varying temperature model coupled to refrigeration power.Building on these models,this paper proposes a two-stage,multi-time scale optimization scheduling framework that jointly coordinates computing workloads time-shift in day-ahead scheduling and refrigeration power control in intra-day dispatch to mitigate renewable variability.A case study demonstrates that the framework effectively enhances the renewable-energy utilization,improves the operational economy of the data center microgrid,and mitigates the impact of renewable power uncertainty.The results highlight the potential of coordinated computing workloads and thermal system flexibility to support greener,more cost-effective data center operation.
