In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random vari...In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random variables,and another is for sub-orthogonal random variables.Both extend the strong law of large numbers for independent random variables under sublinear expectations to the non-independent case.展开更多
In this paper,we consider the fourth-order parabolic equation with p(x)Laplacian and variable exponent source ut+∆^(2)u−div(|■u|^(p(x)−2■u))=|u|^(q(x))−1u.By applying potential well method,we obtain global existence...In this paper,we consider the fourth-order parabolic equation with p(x)Laplacian and variable exponent source ut+∆^(2)u−div(|■u|^(p(x)−2■u))=|u|^(q(x))−1u.By applying potential well method,we obtain global existence,asymptotic behavior and blow-up of solutions with initial energy J(u_(0))≤d.Moreover,we estimate the upper bound of the blow-up time for J(u_(0))≤0.展开更多
The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risk...The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.展开更多
The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its l...The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its long-term forecasting capabilities.Highly idealized ensemble forecasts are produced taking into account the uncertainties in the initial states of the system,with specific attention to the structure of the initial errors in the tropical model.Three main types of experiments are explored:with random perturbations along the three Lyapunov vectors of the tropical model;along the two dominant Lyapunov vectors;and along the first Lyapunov vector only.When perturbations are introduced along all vectors,forecasting biases develop even if in a perfect model framework and with known initial uncertainty properties.Theses biases are considerably reduced only when the perturbations are introduced along the dominant Lyapunov vector.Furthermore,this perturbation strategy allows a reduced mean square error to be obtained at long lead times of a few years,as well as reliable ensemble forecasts across the whole time range.These very counterintuitive findings further underline the importance of appropriately controlling the initial error structure in the tropics through data assimilation.展开更多
Thermal-mechanical damage and deformation at the interface between shotcrete linings and the surrounding rock of tunnels under high-temperature and variable-temperature conditions are critical to the safe construction...Thermal-mechanical damage and deformation at the interface between shotcrete linings and the surrounding rock of tunnels under high-temperature and variable-temperature conditions are critical to the safe construction and operation of tunnel engineering.This study investigated the thermo-mechanical damage behavior of the composite interface between alkali-resistant glass fiber-reinforced concrete(ARGFRC)and granite,focusing on a plateau railway tunnel.Laboratory triaxial tests,laser scanning,XRD analysis,numerical simulations,and theoretical analyses were employed to investigate how different initial curing temperatures and joint roughness coefficient(JRC)influence interfacial damage behavior.The results indicate that an increase in interface roughness exacerbates the structural damage at the interface.At a JRC of 19.9 and a temperature of 70℃,crack initiation in granite was notably restrained when the confining pressure rose from 7 MPa to 10 MPa.Roughness-induced stress distribution at the interface was notably altered,although this effect became less pronounced under high confining pressure conditions.Additionally,during high-temperature curing,thermal stress concentration at the tips of micro-convex protrusions on the granite surface induced microcracks in the adjacent ARGFRC matrix,followed by deformation.These findings provide practical guidelines for designing concrete support systems to ensure tunnel structural safety in high-altitude regions with harsh thermal environments.展开更多
As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy...As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.展开更多
Vegetation plays an important role in the environmental transport behavior of organic pollutants,however,the different roles of crops and natural vegetation have been ignored in most previous studies.In this study,we ...Vegetation plays an important role in the environmental transport behavior of organic pollutants,however,the different roles of crops and natural vegetation have been ignored in most previous studies.In this study,we developed the BETR-Urban-Rural-Veg model to quantitatively evaluate the influences of both natural vegetation and crops on the multimedia transport processes of Phenanthrene(PHE)and Benzo(a)pyrene(BaP)in mainland of China.The geographic distribution of polycyclic aromatic hydrocarbon(PAH)emissions and concentrations were consistent,displaying higher levels in northern China while lower levels in southern China.Under seasonal simulations,for both natural vegetation and crops,PAH concentrations in winter and spring were 1.5 to 27-fold higher than in summer and autumn,especially for PHE.Owing to the higher leaf area index(LAI)of natural vegetation and harvesting of crops,the filter and sequestration effect of natural vegetation was stronger than crops,while the seasonal changes of PAH concentrations in crops were more significant than natural vegetation.Temperature,precipitation rates and LAI might have important influences on seasonal concentrations and overall persistence of PAHs.PHE was more sensitive to the impacts of seasonal environmental parameters.Under different landscape scenarios,average annual PAH concentrations in natural vegetation were always a little higher than those in crops,and the overall persistence of BaP was greatly affected increasing by 15.15%-16.47%.This improved model provides a useful tool for environmental management.The results of this study are expected to support land use plans and decision-making in China's mainland.展开更多
Tree growth variability is a key determinant of forest stabilities.Previous studies have shown that recent climate change has increased variability in tree growth,while others have challenged this viewpoint,leading to...Tree growth variability is a key determinant of forest stabilities.Previous studies have shown that recent climate change has increased variability in tree growth,while others have challenged this viewpoint,leading to ongoing debate in this field.Moreover,gaps remain in understanding the climatic mechanisms driving increased tree growth variability,particularly for species simultaneously limited by multiple climate factors.In this study,we assessed the temporal trends in variability of Picea purpurea radial growth and its linkage with growth-climate sensitivity utilizing dendrochronological methods.Our results revealed a significant increase in P.purpurea radial growth variability from 1960 to 2020,as indicated by continuous rises in the standard deviation,coefficient of variation,and mean sensitivity of tree-ring width indices.The increased frequency of extreme growth declines further supported this finding.Furthermore,moisture condition in July was identified as a key limiting factor of P.purpurea growth.Notably,the strengthening relationship between tree-ring width indices and vapor pressure deficit(VPD)suggests that the moisture sensitivity for P.purpurea growth has increased over the period 1960-2020.This enhanced sensitivity to VPD,whose interannual variability has also increased synchronously,may have contributed to the rise in P.purpurea growth variability.Additionally,the maximum temperature in May was positively correlated with P.purpurea growth;however,there is little evidence that this factor contributed to the observed increase in growth variability.These findings provide new insights into P.purpurea growth trends and improve our understanding of the potential future impacts of climate change on forest ecosystems.展开更多
Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although la...Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
Marine forecasting is critical for navigation safety and disaster prevention.However,traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial fe...Marine forecasting is critical for navigation safety and disaster prevention.However,traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features.To address the limitations,the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism.The model treats target forecast values as internal variables,and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer.Using a self-attention structure,the model captures correlations between exogenous variables and target sequences,explores intrinsic multi-dimensional relationships,and subsequently corrects endogenous variables with the mined exogenous features.The model’s performance is evaluated using metrics including MSE(Mean Squared Error),MAE(Mean Absolute Error),RMSE(Root Mean Square Error),MAPE(Mean Absolute Percentage Error),MSPE(Mean Square Percentage Error),and computational time,with TimeXer and PatchTST models serving as benchmarks.Experiment results show that the proposed model achieves lower errors and higher correction accuracy for both one-day and seven-day forecasts.展开更多
Variable stiffness composites present a promising solution for mitigating impact loads via varying the fiber volume fraction layer-wise,thereby adjusting the panel's stiffness.Since each layer of the composite may...Variable stiffness composites present a promising solution for mitigating impact loads via varying the fiber volume fraction layer-wise,thereby adjusting the panel's stiffness.Since each layer of the composite may be affected by a different failure mode,the optimal fiber volume fraction to suppress damage initiation and evolution is different across the layers.This research examines how re-allocating the fibers layer-wise enhances the composites'impact resistance.In this study,constant stiffness panels with the same fiber volume fraction throughout the layers are compared to variable stiffness ones by varying volume fraction layer-wise.A method is established that utilizes numerical analysis coupled with optimization techniques to determine the optimal fiber volume fraction in both scenarios.Three different reinforcement fibers(Kevlar,carbon,and glass)embedded in epoxy resin were studied.Panels were manufactured and tested under various loading conditions to validate results.Kevlar reinforcement revealed the highest tensile toughness,followed by carbon and then glass fibers.Varying reinforcement volume fraction significantly influences failure modes.Higher fractions lead to matrix cracking and debonding,while lower fractions result in more fiber breakage.The optimal volume fraction for maximizing fiber breakage energy is around 45%,whereas it is about 90%for matrix cracking and debonding.A drop tower test was used to examine the composite structure's behavior under lowvelocity impact,confirming the superiority of Kevlar-reinforced composites with variable stiffness.Conversely,glass-reinforced composites with constant stiffness revealed the lowest performance with the highest deflection.Across all reinforcement materials,the variable stiffness structure consistently outperformed its constant stiffness counterpart.展开更多
Tuberculosis(TB),one of the oldest infectious diseases caused by Mycobacterium tuberculosis,poses a considerable challenge to global public health.There are approximately 10 million new TB cases worldwide annually,and...Tuberculosis(TB),one of the oldest infectious diseases caused by Mycobacterium tuberculosis,poses a considerable challenge to global public health.There are approximately 10 million new TB cases worldwide annually,and TB claims the lives of nearly 3 million people each year,making it one of the leading causes of death from a single infectious disease[1].China ranks third globally in terms of TB burden,with approximately 733,000 TB cases reported in 2023[2].Based on the ecological model of health determinants developed by Whitehead and Dahlgren,health determinants can be classified into direct causes.展开更多
Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers of...Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers offer advantages such as reduced material usage,lower refrigerant charge,and compact structure.However,they also face challenges,including increased refrigerant pressure drop and smaller heat transfer area inside the tubes.This paper combines the advantages and disadvantages of both small and large-diameter tubes and proposes a combined-diameter heat exchanger,consisting of large and small diameters,for use in the indoor units of split-type air conditioners.There are relatively few studies in this area.In this paper,A theoretical and numerical computation method is employed to establish a theoretical-numerical calculation model,and its reliability is verified through experiments.Using this model,the optimal combined diameters and flow path design for a combined-diameter heat exchanger using R32 as the working fluid are derived.The results show that the heat transfer performance of all combined diameter configurations improves by 2.79%to 8.26%compared to the baseline design,with the coefficient of performance(COP)increasing from 4.15 to 4.27~4.5.These designs can save copper material,but at the cost of an increase in pressure drop by 66.86%to 131.84%.The scheme IIIH,using R32,is the optimal combined-diameter and flow path configuration that balances both heat transfer performance and economic cost.展开更多
This study investigates the impacts of mixing time,execution procedure,cement dosage(α),and total water-to-cement ratio(W_(Total)/C)on the mixing energy(E)of deep soil mixing(DSM)columns and how E influences the stre...This study investigates the impacts of mixing time,execution procedure,cement dosage(α),and total water-to-cement ratio(W_(Total)/C)on the mixing energy(E)of deep soil mixing(DSM)columns and how E influences the strength of treated sand.Columns with a diameter of 7.5 cm were constructed using three mixing times(130,190,and 250 s),two execution procedures(normal and zigzag),threeαvalues(300,400,and 500 kg/m^(3)),and three W_(Total)/C ratios(2.5,3.0,and 3.5).For comparison,equivalent laboratory samples were also examined.Results revealed that increasing the mixing time andα,adopting the zigzag execution procedure,and reducing the W_(Total)/C ratio increase E.Outcomes indicated that an increase in E from 0.49-0.70 kJ to 0.70-0.90 kJ,0.90-1.10 kJ,and 1.10-1.40 kJ improves the unconfined compressive strength(UCS)of columns on average by 66%,124%,and 179%,respectively,and the secant modulus by 61%,110%,and 152%.Average strain at maximum stress also rises from 0.68%to 0.75%,0.81%,and 0.84%,respectively.The study identified a threshold in the direct relationship between E and the strength ratio(λ),beyond whichλdid not increase significantly with further increases in E.Additionally,at low and high E levels,DSM samples mainly failed by crushing and cracking modes,respectively.In DSM columns withα=500 kg/m^(3)and W_(Total)/C=2.5,increasing average E from 0.77 kJ to 0.95 kJ,1.08 kJ,and 1.28 kJ resulted in a reduction of coefficients of variation of UCS from 30.4%to 27.8%,24.5%,and 21.1%,respectively.展开更多
An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of t...An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of the internal variables were considered in this ISV model,and the parameters were optimized by genetic algorithm.After validation,the ISV model was used to simulate the evolution of grain size(GS)and dynamic recrystallization(DRX)fraction during hot spinning via Abaqus and its subroutine Vumat.By comparing the simulated results with the experimental results,the application of the ISV model was proven to be reliable.Meanwhile,the strength of the thin-walled spun ZK61 tube increased from 303 to 334 MPa due to grain refinement by DRX and texture strengthening.Besides,some ultrafine grains(0.5μm)that played an important role in mechanical properties were formed due to the proliferation,movement,and entanglement of dislocations during the spinning process.展开更多
Accurate prediction of water level changes in reservoirs is crucial for optimizing the operation of reservoir projects and ensuring their safety.This study proposed a method for reservoir water level prediction based ...Accurate prediction of water level changes in reservoirs is crucial for optimizing the operation of reservoir projects and ensuring their safety.This study proposed a method for reservoir water level prediction based on CEEMDAN-FE and RUN-SVM-RBFNN algorithms.By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method and fuzzy entropy(FE)with the new and highly efficient Runge–Kuta optimizer(RUN),adaptive parameter optimization for the support vector machine(SVM)and radial basis function neural network(RBFNN)algorithms was achieved.Regression prediction was conducted on the two reconstructed sequences using SVM and RBFNN according to their respective features.This approach improved the accuracy and stability of predictions.In terms of accuracy,the combined model outperformed single models,with the determination coefficient,root mean square error,and mean absolute error values of 0.9975,0.2418 m,and 0.1616 m,respectively.In terms of stability,the model predicted more consistently in training and testing periods,with stable overall prediction accuracy and a better adaptive ability to complex datasets.The case study demonstrated that the combined prediction model effectively addressed the environmental factors affecting reservoir water levels,leveraged the strength of each predictive method,compensated for their limitations,and clarified the impacts of environmental factors on reservoir water levels.展开更多
Optogenetic has been widely applied in various pathogenesis investigations of neuropathic diseases since its accurate and targeted regulation of neuronal activity.However,due to the mismatch between the soft tissues a...Optogenetic has been widely applied in various pathogenesis investigations of neuropathic diseases since its accurate and targeted regulation of neuronal activity.However,due to the mismatch between the soft tissues and the optical waveguide,the long-term neural regulation within soft tissue(such as brain and spinal cord)by implantable optical fibers is a large challenge.Herein,we designed a modulus selfadaptive hydrogel optical fiber(MSHOF)with tunable mechanical properties(Young’modulus was tunable in the range of 0.32-10.56MPa)and low light attenuation(0.12-0.21 dB/cm,472nm laser light),which adapts to light transmission under soft tissues.These advantages of MSHOF can ensure the effectiveness of optogenetic stimulation meanwhile safeguarding the safety of the brain/materials interaction interface.In addition,this work provides more design possibilities of MSHOF for photogenetic stimuli and has significant application prospects in photomedical therapy.展开更多
Harmful algal blooms(HABs)have emerged as a critical global environmental and ecological concern.Timely and accurate monitoring of the prevalent bloom-forming genera is crucial for HABmanagement.Conventional microscop...Harmful algal blooms(HABs)have emerged as a critical global environmental and ecological concern.Timely and accurate monitoring of the prevalent bloom-forming genera is crucial for HABmanagement.Conventional microscope-based methods are time-consuming,laborintensive,and specialized expertise-dependent,often making them impractical for largescale surveillance.Molecular methods,such as metabarcoding,provide efficient technical solutions;however,the lack of competent PCR primers and further field validation present obstacles to their wide use.Here,we successfully developed Aphanizomenon-specific primers and validated the application of environmental DNA(eDNA)metabarcoding for field-based monitoring of Aphanizomenon in 37 sites across lentic and lotic freshwater ecosystems in Beijing.The sensitivity and specificity tests of newly developed primers demonstrated high performance-comprehensive recovery of biodiversity in Aphanizomenon communities and high ratios(>95%)of Aphanizomenon sequences in datasets.We observed significant correlations between the sequence abundance derived fromeDNAmetabarcoding and the total cell density determined through microscopic identification across all the sampling sites,both in the spring(r=0.8086,p<0.0001)and summer(r=0.7902,p<0.0001),thus validating the utility of eDNA metabarcoding based on the newly developed primers for monitoring in the field.Further,we identified key environmental variables that were primary drivers responsible for the spatiotemporal distribution of Aphanizomenon abundance.These variables included temperature,total nitrogen,and dissolved oxygen in lentic ecosystems,and total phosphorus in lotic ecosystems.The method developed and validated here offers an accurate,efficient,and high-throughput tool for the monitoring of Aphanizomenon blooms in freshwater ecosystems.展开更多
As a crucial component of the Earth’s climate system,Antarctic sea ice has demonstrated significant variability over the satellite era.Here,we identify a remarkable decadal transition in the total Antarctic Sea Ice E...As a crucial component of the Earth’s climate system,Antarctic sea ice has demonstrated significant variability over the satellite era.Here,we identify a remarkable decadal transition in the total Antarctic Sea Ice Extent(SIE).The stage from 1979 to 2006 is characterized by high-frequency(i.e.,seasonal to interannual)temporal variability in SIE and zonal asymmetry in Sea Ice Concentration(SIC),which is primarily under the control of the Amundsen Sea Low(ASL).After 2007,however,sea ice changes exhibit a more spatially homogeneous pattern in SIC and a more temporally long-lasting mode in SIE.Further analysis reveals that sea ice-ocean interaction plays a major role in the low-frequency(i.e.,multiannual)variability of Antarctic sea ice from 2007−22.The related physical process is inferred to manifest as a strong coupling between the surface and the subsurface ocean layers,involving enhanced vertical convection and the downward delivery of the surface anomalies related to ice melting and freezing processes,thus maintaining the SIE anomalies for a longer time.Furthermore,this process mainly occurs in the Amundsen-Bellingshausen Sea(ABS)sector,and the weakened subsurface ocean stratification is the key factor triggering the coupling process in this region.We find that the Circumpolar Deep Water(CDW)over the ABS sector continued to shoal before 2007 and remained stable thereafter.It is speculated that the shoaling of the CDW may be a possible driver leading to the weakening of the subsurface stratification.展开更多
文摘In this paper,we establish some strong laws of large numbers,which are for nonindependent random variables under the framework of sublinear expectations.One of our main results is for blockwise m-dependent random variables,and another is for sub-orthogonal random variables.Both extend the strong law of large numbers for independent random variables under sublinear expectations to the non-independent case.
基金Supported by NSFC(No.12101482)the Natural Science Foundation of Shaanxi Province,China(No.2018JQ1052)。
文摘In this paper,we consider the fourth-order parabolic equation with p(x)Laplacian and variable exponent source ut+∆^(2)u−div(|■u|^(p(x)−2■u))=|u|^(q(x))−1u.By applying potential well method,we obtain global existence,asymptotic behavior and blow-up of solutions with initial energy J(u_(0))≤d.Moreover,we estimate the upper bound of the blow-up time for J(u_(0))≤0.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608904)the International Partnership Program of the Chinese Academy of Sciences(Grant Nos.060GJHZ2023079GC and 134111KYSB20160031)+1 种基金supported by the Office of Science,U.S.Department of Energy(DOE)Biological and Environmental Research as part of the Regional and Global Model Analysis program area through the Water Cycle and Climate Extremes Modeling(WACCEM)scientific focus areaoperated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830。
文摘The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.
基金supported by the National Key R&D Program of China(Grant No.2023YFF0805100)。
文摘The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its long-term forecasting capabilities.Highly idealized ensemble forecasts are produced taking into account the uncertainties in the initial states of the system,with specific attention to the structure of the initial errors in the tropical model.Three main types of experiments are explored:with random perturbations along the three Lyapunov vectors of the tropical model;along the two dominant Lyapunov vectors;and along the first Lyapunov vector only.When perturbations are introduced along all vectors,forecasting biases develop even if in a perfect model framework and with known initial uncertainty properties.Theses biases are considerably reduced only when the perturbations are introduced along the dominant Lyapunov vector.Furthermore,this perturbation strategy allows a reduced mean square error to be obtained at long lead times of a few years,as well as reliable ensemble forecasts across the whole time range.These very counterintuitive findings further underline the importance of appropriately controlling the initial error structure in the tropics through data assimilation.
基金funded by the National Natural Science Foundation of China(Nos.52209130 and 52379100)Shandong Provincial Natural Science Foundation(No.ZR2024ME112).
文摘Thermal-mechanical damage and deformation at the interface between shotcrete linings and the surrounding rock of tunnels under high-temperature and variable-temperature conditions are critical to the safe construction and operation of tunnel engineering.This study investigated the thermo-mechanical damage behavior of the composite interface between alkali-resistant glass fiber-reinforced concrete(ARGFRC)and granite,focusing on a plateau railway tunnel.Laboratory triaxial tests,laser scanning,XRD analysis,numerical simulations,and theoretical analyses were employed to investigate how different initial curing temperatures and joint roughness coefficient(JRC)influence interfacial damage behavior.The results indicate that an increase in interface roughness exacerbates the structural damage at the interface.At a JRC of 19.9 and a temperature of 70℃,crack initiation in granite was notably restrained when the confining pressure rose from 7 MPa to 10 MPa.Roughness-induced stress distribution at the interface was notably altered,although this effect became less pronounced under high confining pressure conditions.Additionally,during high-temperature curing,thermal stress concentration at the tips of micro-convex protrusions on the granite surface induced microcracks in the adjacent ARGFRC matrix,followed by deformation.These findings provide practical guidelines for designing concrete support systems to ensure tunnel structural safety in high-altitude regions with harsh thermal environments.
基金Supported by Natural Science Foundation General Project of Heilongjiang Province(C2018050).
文摘As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.
基金supported by the National Natural Science Foundation of China(Nos.42107420,U23A20157,and U1910207)Shanxi Province Science Foundation for Young Scholars(No.20210302124363).
文摘Vegetation plays an important role in the environmental transport behavior of organic pollutants,however,the different roles of crops and natural vegetation have been ignored in most previous studies.In this study,we developed the BETR-Urban-Rural-Veg model to quantitatively evaluate the influences of both natural vegetation and crops on the multimedia transport processes of Phenanthrene(PHE)and Benzo(a)pyrene(BaP)in mainland of China.The geographic distribution of polycyclic aromatic hydrocarbon(PAH)emissions and concentrations were consistent,displaying higher levels in northern China while lower levels in southern China.Under seasonal simulations,for both natural vegetation and crops,PAH concentrations in winter and spring were 1.5 to 27-fold higher than in summer and autumn,especially for PHE.Owing to the higher leaf area index(LAI)of natural vegetation and harvesting of crops,the filter and sequestration effect of natural vegetation was stronger than crops,while the seasonal changes of PAH concentrations in crops were more significant than natural vegetation.Temperature,precipitation rates and LAI might have important influences on seasonal concentrations and overall persistence of PAHs.PHE was more sensitive to the impacts of seasonal environmental parameters.Under different landscape scenarios,average annual PAH concentrations in natural vegetation were always a little higher than those in crops,and the overall persistence of BaP was greatly affected increasing by 15.15%-16.47%.This improved model provides a useful tool for environmental management.The results of this study are expected to support land use plans and decision-making in China's mainland.
基金funded by the Science and Technology Program of Gansu Province(25JRRA487)the National Natural Science Foundation of China(42101072)the Key Research and Development Program of Gansu Province(22YF7FA029).
文摘Tree growth variability is a key determinant of forest stabilities.Previous studies have shown that recent climate change has increased variability in tree growth,while others have challenged this viewpoint,leading to ongoing debate in this field.Moreover,gaps remain in understanding the climatic mechanisms driving increased tree growth variability,particularly for species simultaneously limited by multiple climate factors.In this study,we assessed the temporal trends in variability of Picea purpurea radial growth and its linkage with growth-climate sensitivity utilizing dendrochronological methods.Our results revealed a significant increase in P.purpurea radial growth variability from 1960 to 2020,as indicated by continuous rises in the standard deviation,coefficient of variation,and mean sensitivity of tree-ring width indices.The increased frequency of extreme growth declines further supported this finding.Furthermore,moisture condition in July was identified as a key limiting factor of P.purpurea growth.Notably,the strengthening relationship between tree-ring width indices and vapor pressure deficit(VPD)suggests that the moisture sensitivity for P.purpurea growth has increased over the period 1960-2020.This enhanced sensitivity to VPD,whose interannual variability has also increased synchronously,may have contributed to the rise in P.purpurea growth variability.Additionally,the maximum temperature in May was positively correlated with P.purpurea growth;however,there is little evidence that this factor contributed to the observed increase in growth variability.These findings provide new insights into P.purpurea growth trends and improve our understanding of the potential future impacts of climate change on forest ecosystems.
基金supported by the Key Program of National Natural Science Foundation of China(Grant No.41930650)Young Scientists Fund of the National Natural Science Foundation of China(Grant No.42301310).
文摘Sustained and spatially explicit monitoring of the United Nations 2030 Agenda for Sustainable Development is critical for effectively tracking progress toward the global Sustainable Development Goals(SDGs).Although land cover information has long been recognized as an essential component for monitoring SDGs,a standardized scientific framework for identifying and prioritizing land cover related essential variables does not exist.Therefore,we propose a novel expert-and data-driven framework for identifying,refining,and selecting a priority list of Essential Land cover-related Variables for SDGs(ELcV4SDGs).This framework integrates methods including expert knowledge-based analysis,clustering of variables with similar attributes,and quantified index calculation to establish the priority list.Applying the framework to 15 specific SDG indicators,we found that the ELcV4SDGs priority list comprises three main categories,type and structure,pattern and intensity,and process and evolution of land cover,which are further divided into 19 subcategories and ultimately encompass 50 general variables.The ELcV4SDGs will support detailed spatial monitoring and enhance their scientific applications for SDG monitoring and assessment,thereby guiding future SDG priority actions and informing decision-making to advance the 2030 SDGs agenda at local,national,and global levels.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金supported by the National Key Research and Development Program Project(2023YFC3107804)Planning Fund Project of Humanities and Social Sciences Research of the Ministry of Education(24YJA880097)the Graduate Education Reform Project in North China University of Technology(217051360025XN095-17)。
文摘Marine forecasting is critical for navigation safety and disaster prevention.However,traditional ocean numerical forecasting models are often limited by substantial errors and inadequate capture of temporal-spatial features.To address the limitations,the paper proposes a TimeXer-based numerical forecast correction model optimized by an exogenous-variable attention mechanism.The model treats target forecast values as internal variables,and incorporates historical temporal-spatial data and seven-day numerical forecast results from traditional models as external variables based on the embedding strategy of TimeXer.Using a self-attention structure,the model captures correlations between exogenous variables and target sequences,explores intrinsic multi-dimensional relationships,and subsequently corrects endogenous variables with the mined exogenous features.The model’s performance is evaluated using metrics including MSE(Mean Squared Error),MAE(Mean Absolute Error),RMSE(Root Mean Square Error),MAPE(Mean Absolute Percentage Error),MSPE(Mean Square Percentage Error),and computational time,with TimeXer and PatchTST models serving as benchmarks.Experiment results show that the proposed model achieves lower errors and higher correction accuracy for both one-day and seven-day forecasts.
基金funded by the American University of Sharjah.United Arab Emirates award number EN 9502-FRG19-M-E75。
文摘Variable stiffness composites present a promising solution for mitigating impact loads via varying the fiber volume fraction layer-wise,thereby adjusting the panel's stiffness.Since each layer of the composite may be affected by a different failure mode,the optimal fiber volume fraction to suppress damage initiation and evolution is different across the layers.This research examines how re-allocating the fibers layer-wise enhances the composites'impact resistance.In this study,constant stiffness panels with the same fiber volume fraction throughout the layers are compared to variable stiffness ones by varying volume fraction layer-wise.A method is established that utilizes numerical analysis coupled with optimization techniques to determine the optimal fiber volume fraction in both scenarios.Three different reinforcement fibers(Kevlar,carbon,and glass)embedded in epoxy resin were studied.Panels were manufactured and tested under various loading conditions to validate results.Kevlar reinforcement revealed the highest tensile toughness,followed by carbon and then glass fibers.Varying reinforcement volume fraction significantly influences failure modes.Higher fractions lead to matrix cracking and debonding,while lower fractions result in more fiber breakage.The optimal volume fraction for maximizing fiber breakage energy is around 45%,whereas it is about 90%for matrix cracking and debonding.A drop tower test was used to examine the composite structure's behavior under lowvelocity impact,confirming the superiority of Kevlar-reinforced composites with variable stiffness.Conversely,glass-reinforced composites with constant stiffness revealed the lowest performance with the highest deflection.Across all reinforcement materials,the variable stiffness structure consistently outperformed its constant stiffness counterpart.
基金supported by the National Natural Science Foundation of China(82574173,82003516)Jiangsu Provincial Natural Science Foundation(BK20251958)+2 种基金Jiangsu Provincial Medical Key Discipline(ZDXK202250)Top Talent Awards Project Fund(RDF-TP-0023,RDF-TP-0030)Postgraduate Research Fund(PGRS2112022)at Xi'an Jiaotong-Liverpool University.
文摘Tuberculosis(TB),one of the oldest infectious diseases caused by Mycobacterium tuberculosis,poses a considerable challenge to global public health.There are approximately 10 million new TB cases worldwide annually,and TB claims the lives of nearly 3 million people each year,making it one of the leading causes of death from a single infectious disease[1].China ranks third globally in terms of TB burden,with approximately 733,000 TB cases reported in 2023[2].Based on the ecological model of health determinants developed by Whitehead and Dahlgren,health determinants can be classified into direct causes.
基金supported by Supported by the Scientific Research Foundation for High-Level Talents of Zhoukou Normal University(ZKNUC2024018).
文摘Energy shortage has become one of themost concerning issues in the world today,and improving energy utilization efficiency is a key area of research for experts and scholars worldwide.Small-diameter heat exchangers offer advantages such as reduced material usage,lower refrigerant charge,and compact structure.However,they also face challenges,including increased refrigerant pressure drop and smaller heat transfer area inside the tubes.This paper combines the advantages and disadvantages of both small and large-diameter tubes and proposes a combined-diameter heat exchanger,consisting of large and small diameters,for use in the indoor units of split-type air conditioners.There are relatively few studies in this area.In this paper,A theoretical and numerical computation method is employed to establish a theoretical-numerical calculation model,and its reliability is verified through experiments.Using this model,the optimal combined diameters and flow path design for a combined-diameter heat exchanger using R32 as the working fluid are derived.The results show that the heat transfer performance of all combined diameter configurations improves by 2.79%to 8.26%compared to the baseline design,with the coefficient of performance(COP)increasing from 4.15 to 4.27~4.5.These designs can save copper material,but at the cost of an increase in pressure drop by 66.86%to 131.84%.The scheme IIIH,using R32,is the optimal combined-diameter and flow path configuration that balances both heat transfer performance and economic cost.
文摘This study investigates the impacts of mixing time,execution procedure,cement dosage(α),and total water-to-cement ratio(W_(Total)/C)on the mixing energy(E)of deep soil mixing(DSM)columns and how E influences the strength of treated sand.Columns with a diameter of 7.5 cm were constructed using three mixing times(130,190,and 250 s),two execution procedures(normal and zigzag),threeαvalues(300,400,and 500 kg/m^(3)),and three W_(Total)/C ratios(2.5,3.0,and 3.5).For comparison,equivalent laboratory samples were also examined.Results revealed that increasing the mixing time andα,adopting the zigzag execution procedure,and reducing the W_(Total)/C ratio increase E.Outcomes indicated that an increase in E from 0.49-0.70 kJ to 0.70-0.90 kJ,0.90-1.10 kJ,and 1.10-1.40 kJ improves the unconfined compressive strength(UCS)of columns on average by 66%,124%,and 179%,respectively,and the secant modulus by 61%,110%,and 152%.Average strain at maximum stress also rises from 0.68%to 0.75%,0.81%,and 0.84%,respectively.The study identified a threshold in the direct relationship between E and the strength ratio(λ),beyond whichλdid not increase significantly with further increases in E.Additionally,at low and high E levels,DSM samples mainly failed by crushing and cracking modes,respectively.In DSM columns withα=500 kg/m^(3)and W_(Total)/C=2.5,increasing average E from 0.77 kJ to 0.95 kJ,1.08 kJ,and 1.28 kJ resulted in a reduction of coefficients of variation of UCS from 30.4%to 27.8%,24.5%,and 21.1%,respectively.
基金supported by the National Natural Science Foundation of China(No.51905123)Major Scientific and Technological Innovation Program of Shandong Province,China(Nos.2020CXGC010303,2022ZLGX04)Key R&D Programme of Shandong Province,China(No.2022JMRH0308).
文摘An internal state variable(ISV)model was established according to the experimental results of hot plane strain compression(PSC)to predict the microstructure evolution during hot spinning of ZK61 alloy.The effects of the internal variables were considered in this ISV model,and the parameters were optimized by genetic algorithm.After validation,the ISV model was used to simulate the evolution of grain size(GS)and dynamic recrystallization(DRX)fraction during hot spinning via Abaqus and its subroutine Vumat.By comparing the simulated results with the experimental results,the application of the ISV model was proven to be reliable.Meanwhile,the strength of the thin-walled spun ZK61 tube increased from 303 to 334 MPa due to grain refinement by DRX and texture strengthening.Besides,some ultrafine grains(0.5μm)that played an important role in mechanical properties were formed due to the proliferation,movement,and entanglement of dislocations during the spinning process.
基金supported by the National Key R&D Program of China(Grant No.2022YFC3005401)the National Natural Science Foundation of China(Grant No.52239009)。
文摘Accurate prediction of water level changes in reservoirs is crucial for optimizing the operation of reservoir projects and ensuring their safety.This study proposed a method for reservoir water level prediction based on CEEMDAN-FE and RUN-SVM-RBFNN algorithms.By integrating the adaptive complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method and fuzzy entropy(FE)with the new and highly efficient Runge–Kuta optimizer(RUN),adaptive parameter optimization for the support vector machine(SVM)and radial basis function neural network(RBFNN)algorithms was achieved.Regression prediction was conducted on the two reconstructed sequences using SVM and RBFNN according to their respective features.This approach improved the accuracy and stability of predictions.In terms of accuracy,the combined model outperformed single models,with the determination coefficient,root mean square error,and mean absolute error values of 0.9975,0.2418 m,and 0.1616 m,respectively.In terms of stability,the model predicted more consistently in training and testing periods,with stable overall prediction accuracy and a better adaptive ability to complex datasets.The case study demonstrated that the combined prediction model effectively addressed the environmental factors affecting reservoir water levels,leveraged the strength of each predictive method,compensated for their limitations,and clarified the impacts of environmental factors on reservoir water levels.
基金supported by the National Key Research and Development Program of China(Nos.2021YFA1201302 and 2021YFA1201300)the National Natural Science Foundation of China(Nos.52303033,52173029)+1 种基金Shanghai Sailing Program(No.23YF1400400)the Natural Science Foundation of Shanghai(No.21ZR1400500).
文摘Optogenetic has been widely applied in various pathogenesis investigations of neuropathic diseases since its accurate and targeted regulation of neuronal activity.However,due to the mismatch between the soft tissues and the optical waveguide,the long-term neural regulation within soft tissue(such as brain and spinal cord)by implantable optical fibers is a large challenge.Herein,we designed a modulus selfadaptive hydrogel optical fiber(MSHOF)with tunable mechanical properties(Young’modulus was tunable in the range of 0.32-10.56MPa)and low light attenuation(0.12-0.21 dB/cm,472nm laser light),which adapts to light transmission under soft tissues.These advantages of MSHOF can ensure the effectiveness of optogenetic stimulation meanwhile safeguarding the safety of the brain/materials interaction interface.In addition,this work provides more design possibilities of MSHOF for photogenetic stimuli and has significant application prospects in photomedical therapy.
基金supported by the National Key R&D Program of China(No.2021YFC3200102)Yunnan Collaborative Innovation Center for Plateau Lake Ecology and Environmental Healththe Key Research and Development Project of Shandong Province(No.2020CXGC011202).
文摘Harmful algal blooms(HABs)have emerged as a critical global environmental and ecological concern.Timely and accurate monitoring of the prevalent bloom-forming genera is crucial for HABmanagement.Conventional microscope-based methods are time-consuming,laborintensive,and specialized expertise-dependent,often making them impractical for largescale surveillance.Molecular methods,such as metabarcoding,provide efficient technical solutions;however,the lack of competent PCR primers and further field validation present obstacles to their wide use.Here,we successfully developed Aphanizomenon-specific primers and validated the application of environmental DNA(eDNA)metabarcoding for field-based monitoring of Aphanizomenon in 37 sites across lentic and lotic freshwater ecosystems in Beijing.The sensitivity and specificity tests of newly developed primers demonstrated high performance-comprehensive recovery of biodiversity in Aphanizomenon communities and high ratios(>95%)of Aphanizomenon sequences in datasets.We observed significant correlations between the sequence abundance derived fromeDNAmetabarcoding and the total cell density determined through microscopic identification across all the sampling sites,both in the spring(r=0.8086,p<0.0001)and summer(r=0.7902,p<0.0001),thus validating the utility of eDNA metabarcoding based on the newly developed primers for monitoring in the field.Further,we identified key environmental variables that were primary drivers responsible for the spatiotemporal distribution of Aphanizomenon abundance.These variables included temperature,total nitrogen,and dissolved oxygen in lentic ecosystems,and total phosphorus in lotic ecosystems.The method developed and validated here offers an accurate,efficient,and high-throughput tool for the monitoring of Aphanizomenon blooms in freshwater ecosystems.
基金supported by the National Natural Science Foundation China(Grant No.42176222).
文摘As a crucial component of the Earth’s climate system,Antarctic sea ice has demonstrated significant variability over the satellite era.Here,we identify a remarkable decadal transition in the total Antarctic Sea Ice Extent(SIE).The stage from 1979 to 2006 is characterized by high-frequency(i.e.,seasonal to interannual)temporal variability in SIE and zonal asymmetry in Sea Ice Concentration(SIC),which is primarily under the control of the Amundsen Sea Low(ASL).After 2007,however,sea ice changes exhibit a more spatially homogeneous pattern in SIC and a more temporally long-lasting mode in SIE.Further analysis reveals that sea ice-ocean interaction plays a major role in the low-frequency(i.e.,multiannual)variability of Antarctic sea ice from 2007−22.The related physical process is inferred to manifest as a strong coupling between the surface and the subsurface ocean layers,involving enhanced vertical convection and the downward delivery of the surface anomalies related to ice melting and freezing processes,thus maintaining the SIE anomalies for a longer time.Furthermore,this process mainly occurs in the Amundsen-Bellingshausen Sea(ABS)sector,and the weakened subsurface ocean stratification is the key factor triggering the coupling process in this region.We find that the Circumpolar Deep Water(CDW)over the ABS sector continued to shoal before 2007 and remained stable thereafter.It is speculated that the shoaling of the CDW may be a possible driver leading to the weakening of the subsurface stratification.
