Moisture-enabled electricity generation(MEG)has emerged as a promising sustainable energy harvesting technology,comparable to photovoltaics,thermoelectrics,and triboelectrics[1].MEGs generate electricity by converting...Moisture-enabled electricity generation(MEG)has emerged as a promising sustainable energy harvesting technology,comparable to photovoltaics,thermoelectrics,and triboelectrics[1].MEGs generate electricity by converting the chemical potential of moisture into electric energy through interactions with hygroscopic materials and nanostructured interfaces.Unlike solar or thermal harvesters,MEGs operate continuously by utilizing ubiquitous atmospheric moisture,granting them unique spatial and temporal adaptability.Despite nearly a decade of progress and the exploration of diverse material systems for MEG,the overall output power remains significantly limited due to inherently low charge carrier concentrations and restricted ion diffusion fluxes[2].As a result,standalone MEG devices often deliver low and unstable output,limiting practical applications.To enhance performance and versatility,recent efforts have explored hybridization of MEG with other ambient energy sources such as triboelectric or thermoelectric effects.展开更多
Soil moisture(SM)is a critical variable in terrestrial ecosystems,especially in arid and semi-arid areas where water sources are limited.Despite its importance,understanding the spatiotemporal variations and influenci...Soil moisture(SM)is a critical variable in terrestrial ecosystems,especially in arid and semi-arid areas where water sources are limited.Despite its importance,understanding the spatiotemporal variations and influencing factors of SM in these areas remains insufficient.This study investigated the spatiotemporal variations and influencing factors of SM in arid and semi-arid areas of China by utilizing the extended triple collation(ETC),Mann-Kendall test,Theil-Sen estimator,ridge regression analysis,and other relevant methods.The following findings were obtained:(1)at the pixel scale,the long-term monthly SM data from the European Space Agency Climate Change Initiative(ESA CCI)exhibited the highest correlation coefficient of 0.794 and the lowest root mean square error(RMSE)of 0.014 m^(3)/m^(3);(2)from 2000 to 2022,the study area experienced significant increase in annual average SM,with a rate of 0.408×10^(-3)m^(3)/(m^(3)•a).Moreover,higher altitudes showed a notable upward trend,with SM increasing rates at 0.210×10^(-3)m^(3)/(m^(3)•a)between 1000 and 2000 m,0.530×10^(-3)m^(3)/(m^(3)•a)between 2000 and 4000 m,and 0.760×10^(-3)m^(3)/(m^(3)•a)at altitudes above 4000 m;(3)land surface temperature(LST),root zone soil moisture(RSM)(10-40 cm depth),and normalized difference vegetation index(NDVI)were identified as the primary factors influencing annual average SM,which accounted for 34.37%,24.16%,and 22.64%relative contributions,respectively;and(4)absolute contribution of LST was more significant in subareas at higher altitudes,with average absolute contributions of 0.800×10^(-3)m^(3)/(m^(3)•a)between 2000 and 4000 m and 0.500×10^(-2) m^(3)/(m^(3)•a)above 4000 m.This study reveals the spatiotemporal variations and main influencing factors of SM in Chinese arid and semi-arid areas,highlighting the more pronounced absolute contribution of LST to SM in high-altitude areas,providing valuable insights for ecological research and water resource management in these areas.展开更多
Mountainous areas are the priority for forest restoration in semiarid regions,with hillslopes serving as the basic units of mountains.Precipitation is the only water source in these regions,and the uneven distribution...Mountainous areas are the priority for forest restoration in semiarid regions,with hillslopes serving as the basic units of mountains.Precipitation is the only water source in these regions,and the uneven distribution of hillslope soil moisture replenishment after precipitation determines vegetation survival and growth.Therefore,in this study experiments were performed on a hillslope in the Liupan Mountains,Ningxia Hui Autonomous Region,China,to quantify the unevenness of soil moisture replenishment.Soil water content(SWC)in the 0–60 cm layer and precipitation were monitored throughout the growing season in 2020 and 2021.The results showed that(1)Annual soil moisture replenishment was the highest at the mid-slope position,with an average of 309.9 mm,especially under moderate and heavy rain grade conditions,reaching 38.7% and 30.8% of the total replenishment,respectively;(2)Vertical replenishment played a dominant role in the total replenishment,accounting for 82.8%;lateral replenishment played an important but lesser role,accounting for up to 17.2% of the total replenishment;(3)Based on a soil moisture replenishment model established in this study,the maximal replenishment occurred at 90 m from the top of the slope;(4)The dominant factors contributing to the soil moisture replenishment were rainfall amount and saturated hydraulic conductivity(Ks).These findings suggest that attention should be given to both vertical and lateral soil moisture replenishment,and the mid-slope position could be preferred for site selection to achieve precise and integrated forest-water management on hillslopes in semi-arid mountainous regions.展开更多
The article examines the impact of increased aridization of the territory due to an increase in air temperature,reduced precipitation,and the formation of moisture deficiency on grain yields in Northern Kazakhstan.The...The article examines the impact of increased aridization of the territory due to an increase in air temperature,reduced precipitation,and the formation of moisture deficiency on grain yields in Northern Kazakhstan.The most important result of the work is the revealed inverse relationship between grain yields and the temperature of the growing season:low-yielding years are associated with high temperatures and droughts,and high-yielding years are associated with lower temperatures and an optimal ratio of heat and moisture.The novelty of this study is the use of the method of hydrological and climatic calculations in identifying the nature of temperature variability and precipitation in the territory of Northern Kazakhstan for the modern period(1991–2020)compared with the base period(1961–1990).At all the studied meteorological stations,there is a tendency for the average annual temperature and the temperature of the growing season to increase:in the forest-steppe zone with an average warming intensity of 0.3–0.33℃ per decade;in the steppe zone by 0.2–0.43℃ per decade;and in the growing season by 0.2–0.7℃ per decade.The air temperature in the steppe zone is rising more intensively than in the forest-steppe zone,and precipitation in the forest-steppe zone has changed more than in the steppe zone.An increase in the average annual air temperature during the growing season(May–August),combined with a shortage of atmospheric moisture or a constant amount of it,led to an increase in the degree of aridization of the territory,an increase in the frequency of droughts in the steppe zone of Northern Kazakhstan.展开更多
From 26 October to 2 November 2024,Spain experienced a record-breaking rainfall event,with the most intense episode appearing in Valencia Province.During the event,Turis station recorded a historic 24-hour precipitati...From 26 October to 2 November 2024,Spain experienced a record-breaking rainfall event,with the most intense episode appearing in Valencia Province.During the event,Turis station recorded a historic 24-hour precipitation of 710.8 mm,exceeding the national annual average.This resulting flood led to widespread disruption and significant societal impacts.Synoptic analyses reveal that the event was dominated by a deep cut-off low extending through the entire troposphere and persisting for approximately 186 h.Background conditions were characterized by upper-level divergence,mid-tropospheric warm advection,and a strong southeasterly low-level jet,which promoted vertical motion and sustained moisture transport.The steep,funnel-shaped terrain along the eastern Iberian coast further triggered and enhanced the local convection.A 10-day backward Lagrangian moisture tracing using the HYSPLIT model identified the Mediterranean Sea as the primary moisture source(78.1%),followed by northwestern Africa(8.5%)and central-eastern Europe/the Black Sea(6.2%).Low-level moisture transport was mainly driven by the cut-off low and a persistent Mediterranean high,while mid-to upper-level trajectories were associated with a preceding low-pressure system over the Mediterranean and the subtropical Atlantic high.These systems acted in sequence to relay moisture toward the Valencia region,and under the influence of the strongly rotating and convergent cut-off low—along with terrain-induced lifting—this moisture was rapidly uplifted,ultimately triggering the extreme rainfall event.展开更多
This study aimed to explore the relationship between Soil-Plant Analysis Development(SPAD)values and key environmental factors in cucumber(Cucumis sativus L.)cultivation in a greenhouse.SPAD values,indicative of chlor...This study aimed to explore the relationship between Soil-Plant Analysis Development(SPAD)values and key environmental factors in cucumber(Cucumis sativus L.)cultivation in a greenhouse.SPAD values,indicative of chlorophyll content,reflect plant health and productivity.The analysis revealed strong positive correlations between SPADvalues and both indoor light intensity(ILI,r=0.59,p<0.001)and outdoor light intensity(OLI,r=0.62,p<0.001),suggesting that higher light intensities were associated with enhanced SPAD values.In contrast,significant negative correlations were found between SPAD values and soil temperature at 15-30 cm depth(ST1530,r=−0.47,p<0.001)and volumetric soil moisture content at the same depth(SM1530,r=−0.52,p<0.001),with higher soil temperatures(e.g.,28℃)and excessive moisture(e.g.,25%)leading to reduced SPAD values.Multiple regression analysis identified ST1530 and SM1530 as significant negative predictors of SPAD,with coefficients of−0.97(p=0.05)and−0.34(p=0.05),respectively,suggesting that increases in soil temperature and moisture result in lower SPAD values.Indoor light intensity(e.g.,600-800μmol/m^(2)/s)emerged as a significant positive contributor,with a coefficient of 0.01(p<0.001),highlighting its role in promoting chlorophyll synthesis.Additionally,relative humidity(r=0.27,p<0.01)showed a positive,although less pronounced,association with SPAD.These results underscore the importance of both direct and indirect environmental factors in influencing SPAD variability and,by extension,plant health and productivity in cucumber cultivation.展开更多
Drought significantly constrains vegetation growth and reduces terrestrial carbon sinks.Currently,the spatiotemporal patterns and mechanisms of the differential impacts of soil and meteorological droughts on vegetatio...Drought significantly constrains vegetation growth and reduces terrestrial carbon sinks.Currently,the spatiotemporal patterns and mechanisms of the differential impacts of soil and meteorological droughts on vegetation productivity remain inadequately understood.In this study,we analyzed soil moisture(SM),vapor pressure deficit(VPD),and gross primary productivity(GPP)to investigate their spatiotemporal patterns and the combined effects on GPP over China.The results revealed that:(1)Soil drought and meteorological drought generally exhibited temporally synchronous trends across China.(2)GPP was predominantly affected by the combined and synchronous effects of both SM and VPD,although their effects displayed directional variability differences in certain regions.(3)SM demonstrated a greater relative importance on GPP than VPD across more than half of the regions in China,whereas deciduous broadleaf forests were the only vegetation type primarily affected by VPD.(4)Under the lag effects,both SM and VPD exhibited bidirectional Granger causality with GPP,with the interaction between VPD and GPP proving more pronounced than that of SM.Our research provides valuable insights into the mechanisms through which SM and VPD influence GPP,contributing to improved predictions vegetation productivity and implementing ecological restoration.展开更多
Mn-based P2-type oxides are considered as promising cathodes for Na-ion batteries;however,they face significant challenges,including structural degradation when charged at high cutoff voltages and structural changes u...Mn-based P2-type oxides are considered as promising cathodes for Na-ion batteries;however,they face significant challenges,including structural degradation when charged at high cutoff voltages and structural changes upon storing in a humid atmosphere.In response to these issues,we have designed an oxide with co-doping of Cu and Al which can balance both cost and structural stability.The redox reaction of Cu^(2+/3+)can provide certain charge compensation,and the introduction of Al can further suppress the Jahn-Teller effect of Mn,thereby achieving superior long-term cycling performance.The ex-situ XRD testing indicates that Cu/Al co-doping can effectively suppress the phase transition of P2-O2 at high voltage,thereby explaining the improvement in electrochemical performance.DFT calculations reveal a high chemical tolerance to moisture,with lower adsorption energy for H_(2)O compared to pure Na_(0.67)Cu_(0.25)Mn_(0.75)O_(2).A representative Na_(0.67)Cu_(0.20)Al_(0.05)Mn_(0.75)O_(2)cathode demonstrates impressive reversible capacities of 148.7 mAh/g at 0.2 C,along with a remarkable capacity retention of 79.1%(2 C,500 cycles).展开更多
The source region of the Yellow River(SRYR),with its semi-humid to semi-arid climate,is crucial for understanding water resource dynamics.Precipitation is key for replenishing surface water and balancing the ecosystem...The source region of the Yellow River(SRYR),with its semi-humid to semi-arid climate,is crucial for understanding water resource dynamics.Precipitation is key for replenishing surface water and balancing the ecosystem’s water cycle.However,the soil moisture response to precipitation across climate zones and soil layers remains poorly understood due to limited long-term data.This study examines the response of soil moisture to precipitation at multiple time scales in the SRYR,using data from Maqu,Mado,Ngoring Lake sites,and the Maqu monitoring network(MMN),along with CN05.1 precipitation and GLEAM v3.8a soil moisture data.Results show that the semi-humid area requires more precipitation to trigger soil moisture responses compared to the semi-arid area in the SRYR.Surface soil at Maqu,MMN,Ngoring Lake,and Mado sites require at least 8.6,8.4,5.2,and 2.84 mm of precipitation,respectively,for effective replenishment.Significant responses to precipitation events were observed in soil layers at 40 cm and above in the semi-humid area,while at 20 cm and above in the semi-arid area.Precipitation volume is the primary factor influencing soil moisture,affecting both the increment and time lag to maximum moisture.Precipitation intensity and pre-rain moisture have no direct effect.In the central SRYR,accumulated precipitation has a greater impact.Root-zone soil moisture has a weaker correlation with precipitation compared to surface soil moisture but persists longer,responding for up to 10 days,while surface soil moisture responds more immediately but only lasts about 5 days.展开更多
Southerly moisture surges over the central South China Sea(SCS)are characterized by the strengthening of lowlevel southerlies that transport moisture northward from the Pacific or Indian Oceans to South China.These su...Southerly moisture surges over the central South China Sea(SCS)are characterized by the strengthening of lowlevel southerlies that transport moisture northward from the Pacific or Indian Oceans to South China.These surge events typically occur for days in the early-summer season(from April to June)and can lead to heavy rains in South China.This study categorizes surge events into three types of flow patterns and examines their multiscale variations and impacts on rainfall.The first type occurs mainly in April,with the southeasterlies enhanced by a deepening trough in South China and the western Pacific subtropical high established over the SCS.The second type of surge events mostly appears in June,featuring the prevailing southwesterlies of summer monsoon from the Indian Ocean during the active phases of intraseasonal oscillations.Most surge events exhibit semi-diurnal variations with morning and afternoon peaks of northward moisture fluxes.Specifically,the first type features a dominant afternoon peak,while the second type shows a dominant early-morning peak,which is induced by thermal contrast between the Indochina Peninsula and the SCS.In general,the surge events enhance moisture convergence and increase rainfall downstream in South China,but they show some regional differences.The second type strengthens moisture convergence and rainfall in coastal regions with a morning peak.In contrast,the first type enhances inland rainfall with a morning peak,while moisture divergence dominates coastal regions.The third type of surge events denotes transitional conditions between the first two types,in terms of atmospheric circulations,diurnal cycles,and rainfall patterns.These results highlight a diversity of regional moisture surges and related rainfall ranging from diurnal to sub-seasonal scales.展开更多
Moisture accumulation within road pavements,particularly in unbound granular materials with or without thin sprayed seals,presents significant challenges in high-rainfall regions such as Queensland.This infiltration o...Moisture accumulation within road pavements,particularly in unbound granular materials with or without thin sprayed seals,presents significant challenges in high-rainfall regions such as Queensland.This infiltration often leads to various forms of pavement distress,eventually causing irreversible damage to the pavement structure.The moisture content within pavements exhibits considerable dynamism and directly influenced by environmental factors such as precipitation,air temperature,and relative humidity.This variability underscores the importance of monitoring moisture changes using real-time climatic data to assess pavement conditions for operational management or incorporating these effects during pavement design based on historical climate data.Consequently,there is an increasing demand for advanced,technology-driven methodologies to predict moisture variations based on climatic inputs.Addressing this gap,the present study employs five traditional machine learning(ML)algorithms,K-nearest neighbors(KNN),regression trees,random forest,support vector machines(SVMs),and gaussian process regression(GPR),to forecast moisture levels within pavement layers over time,with varying algorithm complexities.Using data collected from an instrumented road in Brisbane,Australia,which includes pavement moisture and climatic factors,the study develops predictive models to forecast moisture content at future time steps.The approach incorporates current moisture content,rather than averaged values,along with seasonality(both daily and annual),and key climatic factors to predict next step moisture.Model performance is evaluated using R2,MSE,RMSE,and MAPE metrics.Results show that ML algorithms can reliably predict long-term moisture variations in pavements,provided optimal hyperparameters are selected for each algorithm.The best-performing algorithms include KNN(the number of neighbours equals to 15),medium regression tree,medium random forest,coarse SVM,and simple GPR,with medium random forest outperforming the others.The study also identifies the optimal hyperparameter combinations for each algorithm,offering significant advancements in moisture prediction tools for pavement technology。展开更多
Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere.This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau,China...Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere.This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau,China,as well as in the related ecological and hydrological processes.However,the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques.Thus,this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected(GRD)data,the polarization decomposition parameters of Sentinel-1A single-look complex(SLC)data,the normalized difference vegetation index(NDVI)based on Sentinel-2B data,and the topographic factors based on digital elevation model(DEM)data.By combining these parameters with a machine learning model,we established a feature selection rule.A cumulative importance threshold was derived for feature variables,and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination(R^(2))and the unbiased root mean square error(ubRMSE).The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion,and the SHapley Additive exPlanations(SHAP)method was used to analyze the importance of these variables.The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion.Compared to the unfiltered model,the optimal feature combination led to a 0.09 increase in R^(2)and a 0.7%reduction in ubRMSE.Ultimately,the optimized model achieved a R²of 0.87 and an ubRMSE of 5.6%.Analysis revealed that soil particle size had significant impact on soil water retention capacity.The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable,demonstrating a significant positive correlation.Moreover,the microtopographical features of hummocks interfered with soil moisture estimation,indicating that such terrain effects warrant increased attention in future studies within the permafrost regions.The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau,but also exhibits high computational efficiency(with a relative time reduction of 18.5%),striking an excellent balance between accuracy and efficiency.This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data,offering critical insights for ecological conservation,water resource management,and climate change adaptation on the Qinghai-Xizang Plateau.展开更多
Proton exchange membrane fuel cell(PEMFC)is a promising clean energy source,but its performance and stability are vulnerable to the negative effects of humidity conditions.The gas diffusion substrate(GDS)plays a pivot...Proton exchange membrane fuel cell(PEMFC)is a promising clean energy source,but its performance and stability are vulnerable to the negative effects of humidity conditions.The gas diffusion substrate(GDS)plays a pivotal role in regulating the moisture and gas transport.The single pore structure of traditionally designed GDS often leads to the pathway competition between moisture and gas,which effects the efficiency of fuel cells.In this study,we report on a hierarchical fibrous paper with tunable hierarchical pores for a sustainable GDS.This design offers gas permeability under wet conditions,by separating the gas pathway from the moisture pathway,thus mitigating their pathway competition.In addition,this paper forms a multi-scale scaffold that absorbs moisture under high humidity conditions and releases it under dry conditions.It is allowed to maintain an optimal internal humidity and further enhances the humidity adaptability.Furthermore,the carbon footprint is only 15.97%,significantly lower than commercial alternatives.This feature makes it a sustainable solution to stabilize PEMFCs under diverse humidity conditions.展开更多
Composite wood products(i.e.,particleboard,medium density fiberboard,oriented strand board,plywood)used in cabinets,shelving,and base trim express varying degrees of thickness swelling when exposed to a sustained mois...Composite wood products(i.e.,particleboard,medium density fiberboard,oriented strand board,plywood)used in cabinets,shelving,and base trim express varying degrees of thickness swelling when exposed to a sustained moisture source.Thickness swelling occurs when cellulose fibers adsorb water molecules and swell after attaining a moisture content of 29%to 36%.Observations of thickness swelling were made to refine water loss duration estimates.Thickness swell height is the result of several intrinsic factors(wood species,density,adhesive resin,heat pressing conditions).This study examined an extrinsic factor,humidity,at elevated(>95%RH)and ambient(50%RH)conditions.Specimens subjected to moisture for longer periods(8-10 weeks)experienced gradual darkening from accumulated biomass and fungal deterioration of the wood surfaces.The study revealed that high humidity conditions expressed higher rates of thickness swelling and that estimates of water loss duration should consider the influence of ambient humidity during and following a water release.展开更多
How to reduce peanut allergies has always been a main food safety concern.Plant polyphenol complex peanut sensitizing protein was proposed as a new desensitization strategy.Gallic acid(GA),as a natural plant polypheno...How to reduce peanut allergies has always been a main food safety concern.Plant polyphenol complex peanut sensitizing protein was proposed as a new desensitization strategy.Gallic acid(GA),as a natural plant polyphenol,has anti-inflammatory and immunomodulatory effects.Therefore,the aim of this study was to investigate the effect of GA on peanut protein(PP)sensitization under high moisture extrusion conditions.The contents of free sulfhydryl groups and disulfide bonds in the PP-GA complex were determined,and the structure of the complex was characterized by sodium dodecyl sulfate-polyacrylamide gel electrophoresis(SDS-PAGE)and Fourier transform infrared spectroscopy.The results showed that with increasing GA content,the number of free sulfhydryl groups increased while the number of disulfide bonds decreased.The secondary structure of PP-GA showed that the random coils andβ-turns were transformed toα-helices andβ-sheets.A BALB/c mouse model was also established,wherein Al(OH)3 was used as an adjuvant when the complex was administered via intraperitoneal injection,and the mice showed mild allergic symptoms and a decreased immune organ index.In addition,the serum levels of specific antibodies(immunoglobulin E(IgE),immunoglobulin G1(IgG1),and immunoglobulin G2a(IgG2a)),cytokines(interleukin-5(IL-5),interleukin 13(IL-13),and interferon gamma(IFN-γ))and histamine were reduced.In summary,this study proved that GA can relieve the sensitization of PP induced by high moisture extrusion.展开更多
Direct air capture(DAC)is a negative carbon emission technology that faces challenges in scalability and practical deployment due to its exorbitant costs.Hou et al.(2017)integrated DAC technology with fertilization.A ...Direct air capture(DAC)is a negative carbon emission technology that faces challenges in scalability and practical deployment due to its exorbitant costs.Hou et al.(2017)integrated DAC technology with fertilization.A multi-bed desorption system driven by water provides a competitive and sustainable carbon source for indoor agriculture.展开更多
Traditional studies on transforming selenate and selenite are often limited by static measurements and low spatial resolution.They do not fully consider the impact of moisture content.This paper uses the DGT(diffusive...Traditional studies on transforming selenate and selenite are often limited by static measurements and low spatial resolution.They do not fully consider the impact of moisture content.This paper uses the DGT(diffusive gradients in thin films)technique to deeply explore how moisture changes affect the transformation of selenate and selenite in the environment(changes in properties over time).First,representative soil samples(loess)are prepared,and their moisture content is adjusted.Fixed concentrations of selenate and selenite are added,and then the DGT device simulates their migration in the natural environment.The experiment covers drought,moisture,and high moisture environments,and the experiment is repeated under each condition to ensure the accuracy of the data.The sample quality is verified and further analyzed by ion chromatography(IC)and atomic absorption spectroscopy(AAS).This article uses DGT technology to study the influence of moisture content on the migration and transformation of selenate and selenite in soil.Results indicate that increased moisture content leads to higher concentrations,diffusion rates,and DGT capture efficiency of both selenium species,highlighting the importance of moisture in their environmental behavior.When the moisture content increased from 25%to 65%,the coefficient of variation of selenate and selenite increased.The DGT technique proved effective in capturing spatial heterogeneity and providing high-precision measurements,offering robust data to advance research on selenium behavior in soils.展开更多
Spatiotemporal forecasting of surface soil moisture(SSM)is recognized as a critical scientific issue in precision agricultural irrigation,regional drought monitoring,and early warning systems for extreme precipitation...Spatiotemporal forecasting of surface soil moisture(SSM)is recognized as a critical scientific issue in precision agricultural irrigation,regional drought monitoring,and early warning systems for extreme precipitation.However,long-term forecasting continues to pose formidable challenges because of the complexity observed across both the spatial and temporal scales.In this study,we used a daily SSM dataset at a 0.05°×0.05°spatial resolution over the Qilian Mountains,China and proposed a hybrid Convolutional Long Short-Term Memory(ConvLSTM)-Nudging model,which combined deep neural networks with data assimilation to increase the accuracy of long-term SSM forecasting.We trained and evaluated the SSM predictive performance of four models(Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),ConvLSTM,and ConvLSTM with Squeeze-and-Excitation(SE)attention mechanism(ConvLSTM-SE))in both short-term and long-term scenarios.The results showed that all the models perform well under short-term predictions,but the accuracy decrease substantially in long-term predictions.Therefore,we integrated Nudging technique during the long-term prediction phase to assimilate observational information and rectify model biases.Comprehensive evaluations demonstrate that Nudging significantly improves all the models,with ConvLSTM-Nudging achieving the best performance under the 200-d forecasting scenario.Relative to those of the best-performing ConvLSTM model for long-term forecasts,when observation noiseδ=0.00 and observation fraction obs=50.0%,the coefficient of determination(R2)of ConvLSTM-Nudging increases by approximately 82.1%,while its mean absolute error(MAE)and root mean squared error(RMSE)decrease by approximately 84.8%and 77.3%,respectively;the average Pearson correlation coefficient(r)improves by approximately 23.6%,and Bias is reduced by 98.1%.These results demonstrated that although pure deep learning models achieve high accuracy in the short-term predictions,they are prone to error accumulation and systematic drift in long-term autoregressive predictions.Integrating data assimilation with deep learning and continuously correcting the state through observation can effectively suppress long-term biases,thereby achieving robust long-term SSM forecasting.展开更多
Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables...Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.展开更多
On the basis of discussing the influencing mode of plant moisture stress on plant physiological process and the division of soil moisture availability range, the water suction values partitioning soil moisture were pu...On the basis of discussing the influencing mode of plant moisture stress on plant physiological process and the division of soil moisture availability range, the water suction values partitioning soil moisture were put forward, and then the corresponding water moistures under water stress were obtained by conversing together with characteristic curve of water moisture.展开更多
基金the financial support of the National Natural Science Foundation of China(No.22205165).
文摘Moisture-enabled electricity generation(MEG)has emerged as a promising sustainable energy harvesting technology,comparable to photovoltaics,thermoelectrics,and triboelectrics[1].MEGs generate electricity by converting the chemical potential of moisture into electric energy through interactions with hygroscopic materials and nanostructured interfaces.Unlike solar or thermal harvesters,MEGs operate continuously by utilizing ubiquitous atmospheric moisture,granting them unique spatial and temporal adaptability.Despite nearly a decade of progress and the exploration of diverse material systems for MEG,the overall output power remains significantly limited due to inherently low charge carrier concentrations and restricted ion diffusion fluxes[2].As a result,standalone MEG devices often deliver low and unstable output,limiting practical applications.To enhance performance and versatility,recent efforts have explored hybridization of MEG with other ambient energy sources such as triboelectric or thermoelectric effects.
基金supported by the Natural Science Foundation of Henan Province(252300421290)the National Natural Science Foundation of China(41771438)+1 种基金the Program for Innovative Research Team(in Science and Technology)of Henan University(22IRTSTHN010)the Postgraduate Education Reform and Quality Improvement Project of Henan Province(HNYJS2020JD14).
文摘Soil moisture(SM)is a critical variable in terrestrial ecosystems,especially in arid and semi-arid areas where water sources are limited.Despite its importance,understanding the spatiotemporal variations and influencing factors of SM in these areas remains insufficient.This study investigated the spatiotemporal variations and influencing factors of SM in arid and semi-arid areas of China by utilizing the extended triple collation(ETC),Mann-Kendall test,Theil-Sen estimator,ridge regression analysis,and other relevant methods.The following findings were obtained:(1)at the pixel scale,the long-term monthly SM data from the European Space Agency Climate Change Initiative(ESA CCI)exhibited the highest correlation coefficient of 0.794 and the lowest root mean square error(RMSE)of 0.014 m^(3)/m^(3);(2)from 2000 to 2022,the study area experienced significant increase in annual average SM,with a rate of 0.408×10^(-3)m^(3)/(m^(3)•a).Moreover,higher altitudes showed a notable upward trend,with SM increasing rates at 0.210×10^(-3)m^(3)/(m^(3)•a)between 1000 and 2000 m,0.530×10^(-3)m^(3)/(m^(3)•a)between 2000 and 4000 m,and 0.760×10^(-3)m^(3)/(m^(3)•a)at altitudes above 4000 m;(3)land surface temperature(LST),root zone soil moisture(RSM)(10-40 cm depth),and normalized difference vegetation index(NDVI)were identified as the primary factors influencing annual average SM,which accounted for 34.37%,24.16%,and 22.64%relative contributions,respectively;and(4)absolute contribution of LST was more significant in subareas at higher altitudes,with average absolute contributions of 0.800×10^(-3)m^(3)/(m^(3)•a)between 2000 and 4000 m and 0.500×10^(-2) m^(3)/(m^(3)•a)above 4000 m.This study reveals the spatiotemporal variations and main influencing factors of SM in Chinese arid and semi-arid areas,highlighting the more pronounced absolute contribution of LST to SM in high-altitude areas,providing valuable insights for ecological research and water resource management in these areas.
基金financially supported by the Central Public-Interest Scientific Institution Basal Research Fund of Chinese Academy of Forestry(CAFYBB2021ZW002)the National Key Research and Development Program of China(2022YFF1300404)the National Natural Science Foundation of China(U21A2005)。
文摘Mountainous areas are the priority for forest restoration in semiarid regions,with hillslopes serving as the basic units of mountains.Precipitation is the only water source in these regions,and the uneven distribution of hillslope soil moisture replenishment after precipitation determines vegetation survival and growth.Therefore,in this study experiments were performed on a hillslope in the Liupan Mountains,Ningxia Hui Autonomous Region,China,to quantify the unevenness of soil moisture replenishment.Soil water content(SWC)in the 0–60 cm layer and precipitation were monitored throughout the growing season in 2020 and 2021.The results showed that(1)Annual soil moisture replenishment was the highest at the mid-slope position,with an average of 309.9 mm,especially under moderate and heavy rain grade conditions,reaching 38.7% and 30.8% of the total replenishment,respectively;(2)Vertical replenishment played a dominant role in the total replenishment,accounting for 82.8%;lateral replenishment played an important but lesser role,accounting for up to 17.2% of the total replenishment;(3)Based on a soil moisture replenishment model established in this study,the maximal replenishment occurred at 90 m from the top of the slope;(4)The dominant factors contributing to the soil moisture replenishment were rainfall amount and saturated hydraulic conductivity(Ks).These findings suggest that attention should be given to both vertical and lateral soil moisture replenishment,and the mid-slope position could be preferred for site selection to achieve precise and integrated forest-water management on hillslopes in semi-arid mountainous regions.
文摘The article examines the impact of increased aridization of the territory due to an increase in air temperature,reduced precipitation,and the formation of moisture deficiency on grain yields in Northern Kazakhstan.The most important result of the work is the revealed inverse relationship between grain yields and the temperature of the growing season:low-yielding years are associated with high temperatures and droughts,and high-yielding years are associated with lower temperatures and an optimal ratio of heat and moisture.The novelty of this study is the use of the method of hydrological and climatic calculations in identifying the nature of temperature variability and precipitation in the territory of Northern Kazakhstan for the modern period(1991–2020)compared with the base period(1961–1990).At all the studied meteorological stations,there is a tendency for the average annual temperature and the temperature of the growing season to increase:in the forest-steppe zone with an average warming intensity of 0.3–0.33℃ per decade;in the steppe zone by 0.2–0.43℃ per decade;and in the growing season by 0.2–0.7℃ per decade.The air temperature in the steppe zone is rising more intensively than in the forest-steppe zone,and precipitation in the forest-steppe zone has changed more than in the steppe zone.An increase in the average annual air temperature during the growing season(May–August),combined with a shortage of atmospheric moisture or a constant amount of it,led to an increase in the degree of aridization of the territory,an increase in the frequency of droughts in the steppe zone of Northern Kazakhstan.
基金supported by the National Natural Science Foundation of China(42475008)Strategy Priority Research Program of Chinese Academy of Sciences(XDB0760400).
文摘From 26 October to 2 November 2024,Spain experienced a record-breaking rainfall event,with the most intense episode appearing in Valencia Province.During the event,Turis station recorded a historic 24-hour precipitation of 710.8 mm,exceeding the national annual average.This resulting flood led to widespread disruption and significant societal impacts.Synoptic analyses reveal that the event was dominated by a deep cut-off low extending through the entire troposphere and persisting for approximately 186 h.Background conditions were characterized by upper-level divergence,mid-tropospheric warm advection,and a strong southeasterly low-level jet,which promoted vertical motion and sustained moisture transport.The steep,funnel-shaped terrain along the eastern Iberian coast further triggered and enhanced the local convection.A 10-day backward Lagrangian moisture tracing using the HYSPLIT model identified the Mediterranean Sea as the primary moisture source(78.1%),followed by northwestern Africa(8.5%)and central-eastern Europe/the Black Sea(6.2%).Low-level moisture transport was mainly driven by the cut-off low and a persistent Mediterranean high,while mid-to upper-level trajectories were associated with a preceding low-pressure system over the Mediterranean and the subtropical Atlantic high.These systems acted in sequence to relay moisture toward the Valencia region,and under the influence of the strongly rotating and convergent cut-off low—along with terrain-induced lifting—this moisture was rapidly uplifted,ultimately triggering the extreme rainfall event.
文摘This study aimed to explore the relationship between Soil-Plant Analysis Development(SPAD)values and key environmental factors in cucumber(Cucumis sativus L.)cultivation in a greenhouse.SPAD values,indicative of chlorophyll content,reflect plant health and productivity.The analysis revealed strong positive correlations between SPADvalues and both indoor light intensity(ILI,r=0.59,p<0.001)and outdoor light intensity(OLI,r=0.62,p<0.001),suggesting that higher light intensities were associated with enhanced SPAD values.In contrast,significant negative correlations were found between SPAD values and soil temperature at 15-30 cm depth(ST1530,r=−0.47,p<0.001)and volumetric soil moisture content at the same depth(SM1530,r=−0.52,p<0.001),with higher soil temperatures(e.g.,28℃)and excessive moisture(e.g.,25%)leading to reduced SPAD values.Multiple regression analysis identified ST1530 and SM1530 as significant negative predictors of SPAD,with coefficients of−0.97(p=0.05)and−0.34(p=0.05),respectively,suggesting that increases in soil temperature and moisture result in lower SPAD values.Indoor light intensity(e.g.,600-800μmol/m^(2)/s)emerged as a significant positive contributor,with a coefficient of 0.01(p<0.001),highlighting its role in promoting chlorophyll synthesis.Additionally,relative humidity(r=0.27,p<0.01)showed a positive,although less pronounced,association with SPAD.These results underscore the importance of both direct and indirect environmental factors in influencing SPAD variability and,by extension,plant health and productivity in cucumber cultivation.
基金National Key Research and Development Program,No.2021xjkk0303。
文摘Drought significantly constrains vegetation growth and reduces terrestrial carbon sinks.Currently,the spatiotemporal patterns and mechanisms of the differential impacts of soil and meteorological droughts on vegetation productivity remain inadequately understood.In this study,we analyzed soil moisture(SM),vapor pressure deficit(VPD),and gross primary productivity(GPP)to investigate their spatiotemporal patterns and the combined effects on GPP over China.The results revealed that:(1)Soil drought and meteorological drought generally exhibited temporally synchronous trends across China.(2)GPP was predominantly affected by the combined and synchronous effects of both SM and VPD,although their effects displayed directional variability differences in certain regions.(3)SM demonstrated a greater relative importance on GPP than VPD across more than half of the regions in China,whereas deciduous broadleaf forests were the only vegetation type primarily affected by VPD.(4)Under the lag effects,both SM and VPD exhibited bidirectional Granger causality with GPP,with the interaction between VPD and GPP proving more pronounced than that of SM.Our research provides valuable insights into the mechanisms through which SM and VPD influence GPP,contributing to improved predictions vegetation productivity and implementing ecological restoration.
基金supported by National Natural Science Youth Foundation of China(No.22308294)National Natural Science Foundation of China(No.22179077)+1 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX23_1868)Qing Lan Project of Jiangsu University and the Funding for school-level research projects of Yancheng Institute of Technology.
文摘Mn-based P2-type oxides are considered as promising cathodes for Na-ion batteries;however,they face significant challenges,including structural degradation when charged at high cutoff voltages and structural changes upon storing in a humid atmosphere.In response to these issues,we have designed an oxide with co-doping of Cu and Al which can balance both cost and structural stability.The redox reaction of Cu^(2+/3+)can provide certain charge compensation,and the introduction of Al can further suppress the Jahn-Teller effect of Mn,thereby achieving superior long-term cycling performance.The ex-situ XRD testing indicates that Cu/Al co-doping can effectively suppress the phase transition of P2-O2 at high voltage,thereby explaining the improvement in electrochemical performance.DFT calculations reveal a high chemical tolerance to moisture,with lower adsorption energy for H_(2)O compared to pure Na_(0.67)Cu_(0.25)Mn_(0.75)O_(2).A representative Na_(0.67)Cu_(0.20)Al_(0.05)Mn_(0.75)O_(2)cathode demonstrates impressive reversible capacities of 148.7 mAh/g at 0.2 C,along with a remarkable capacity retention of 79.1%(2 C,500 cycles).
基金supported by the National Natural Science Foundation of China(Grant No.42325502,and 42275045)the West Light Foundation of the Chi-nese Academy of Sciences(Grant No.xbzg-zdsys-202215)+1 种基金the Sci-ence and Technology Research Plan of Gansu Province(Grant Nos.23JRRA654 and 20JR10RA070)iLEAPs(Integrated Land Ecosystem-Atmosphere Processes Study).
文摘The source region of the Yellow River(SRYR),with its semi-humid to semi-arid climate,is crucial for understanding water resource dynamics.Precipitation is key for replenishing surface water and balancing the ecosystem’s water cycle.However,the soil moisture response to precipitation across climate zones and soil layers remains poorly understood due to limited long-term data.This study examines the response of soil moisture to precipitation at multiple time scales in the SRYR,using data from Maqu,Mado,Ngoring Lake sites,and the Maqu monitoring network(MMN),along with CN05.1 precipitation and GLEAM v3.8a soil moisture data.Results show that the semi-humid area requires more precipitation to trigger soil moisture responses compared to the semi-arid area in the SRYR.Surface soil at Maqu,MMN,Ngoring Lake,and Mado sites require at least 8.6,8.4,5.2,and 2.84 mm of precipitation,respectively,for effective replenishment.Significant responses to precipitation events were observed in soil layers at 40 cm and above in the semi-humid area,while at 20 cm and above in the semi-arid area.Precipitation volume is the primary factor influencing soil moisture,affecting both the increment and time lag to maximum moisture.Precipitation intensity and pre-rain moisture have no direct effect.In the central SRYR,accumulated precipitation has a greater impact.Root-zone soil moisture has a weaker correlation with precipitation compared to surface soil moisture but persists longer,responding for up to 10 days,while surface soil moisture responds more immediately but only lasts about 5 days.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)National Natural Science Foundation of China(42475003)Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(SML2023SP209)。
文摘Southerly moisture surges over the central South China Sea(SCS)are characterized by the strengthening of lowlevel southerlies that transport moisture northward from the Pacific or Indian Oceans to South China.These surge events typically occur for days in the early-summer season(from April to June)and can lead to heavy rains in South China.This study categorizes surge events into three types of flow patterns and examines their multiscale variations and impacts on rainfall.The first type occurs mainly in April,with the southeasterlies enhanced by a deepening trough in South China and the western Pacific subtropical high established over the SCS.The second type of surge events mostly appears in June,featuring the prevailing southwesterlies of summer monsoon from the Indian Ocean during the active phases of intraseasonal oscillations.Most surge events exhibit semi-diurnal variations with morning and afternoon peaks of northward moisture fluxes.Specifically,the first type features a dominant afternoon peak,while the second type shows a dominant early-morning peak,which is induced by thermal contrast between the Indochina Peninsula and the SCS.In general,the surge events enhance moisture convergence and increase rainfall downstream in South China,but they show some regional differences.The second type strengthens moisture convergence and rainfall in coastal regions with a morning peak.In contrast,the first type enhances inland rainfall with a morning peak,while moisture divergence dominates coastal regions.The third type of surge events denotes transitional conditions between the first two types,in terms of atmospheric circulations,diurnal cycles,and rainfall patterns.These results highlight a diversity of regional moisture surges and related rainfall ranging from diurnal to sub-seasonal scales.
基金the financial and intellectual support provided by Queensland University of Technology(QUT),Australia,through its Higher Degree Research Program,which played a crucial role in the successful completion of this research study
文摘Moisture accumulation within road pavements,particularly in unbound granular materials with or without thin sprayed seals,presents significant challenges in high-rainfall regions such as Queensland.This infiltration often leads to various forms of pavement distress,eventually causing irreversible damage to the pavement structure.The moisture content within pavements exhibits considerable dynamism and directly influenced by environmental factors such as precipitation,air temperature,and relative humidity.This variability underscores the importance of monitoring moisture changes using real-time climatic data to assess pavement conditions for operational management or incorporating these effects during pavement design based on historical climate data.Consequently,there is an increasing demand for advanced,technology-driven methodologies to predict moisture variations based on climatic inputs.Addressing this gap,the present study employs five traditional machine learning(ML)algorithms,K-nearest neighbors(KNN),regression trees,random forest,support vector machines(SVMs),and gaussian process regression(GPR),to forecast moisture levels within pavement layers over time,with varying algorithm complexities.Using data collected from an instrumented road in Brisbane,Australia,which includes pavement moisture and climatic factors,the study develops predictive models to forecast moisture content at future time steps.The approach incorporates current moisture content,rather than averaged values,along with seasonality(both daily and annual),and key climatic factors to predict next step moisture.Model performance is evaluated using R2,MSE,RMSE,and MAPE metrics.Results show that ML algorithms can reliably predict long-term moisture variations in pavements,provided optimal hyperparameters are selected for each algorithm.The best-performing algorithms include KNN(the number of neighbours equals to 15),medium regression tree,medium random forest,coarse SVM,and simple GPR,with medium random forest outperforming the others.The study also identifies the optimal hyperparameter combinations for each algorithm,offering significant advancements in moisture prediction tools for pavement technology。
基金supported by the Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology(13230550)the Coal Industry Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring,Anhui University of Science and Technology(KSXTJC202305)+1 种基金the State Key Laboratory of Geodesy and Earth's Dynamics,Innovation Academy for Precision Measurement Science and Technology(SKLGED2023-5-1)the China Postdoctoral Science Foundation(2023M733604).
文摘Soil moisture is a key parameter in the exchange of energy and water between the land surface and the atmosphere.This parameter plays an important role in the dynamics of permafrost on the Qinghai-Xizang Plateau,China,as well as in the related ecological and hydrological processes.However,the region's complex terrain and extreme climatic conditions result in low-accuracy soil moisture estimations using traditional remote sensing techniques.Thus,this study considered parameters of the backscatter coefficient of Sentinel-1A ground range detected(GRD)data,the polarization decomposition parameters of Sentinel-1A single-look complex(SLC)data,the normalized difference vegetation index(NDVI)based on Sentinel-2B data,and the topographic factors based on digital elevation model(DEM)data.By combining these parameters with a machine learning model,we established a feature selection rule.A cumulative importance threshold was derived for feature variables,and those variables that failed to meet the threshold were eliminated based on variations in the coefficient of determination(R^(2))and the unbiased root mean square error(ubRMSE).The eight most influential variables were selected and combined with the CatBoost model for soil moisture inversion,and the SHapley Additive exPlanations(SHAP)method was used to analyze the importance of these variables.The results demonstrated that the optimized model significantly improved the accuracy of soil moisture inversion.Compared to the unfiltered model,the optimal feature combination led to a 0.09 increase in R^(2)and a 0.7%reduction in ubRMSE.Ultimately,the optimized model achieved a R²of 0.87 and an ubRMSE of 5.6%.Analysis revealed that soil particle size had significant impact on soil water retention capacity.The impact of vegetation on the estimated soil moisture on the Qinghai-Xizang Plateau was considerable,demonstrating a significant positive correlation.Moreover,the microtopographical features of hummocks interfered with soil moisture estimation,indicating that such terrain effects warrant increased attention in future studies within the permafrost regions.The developed method not only enhances the accuracy of soil moisture retrieval in the complex terrain of the Qinghai-Xizang Plateau,but also exhibits high computational efficiency(with a relative time reduction of 18.5%),striking an excellent balance between accuracy and efficiency.This approach provides a robust framework for efficient soil moisture monitoring in remote areas with limited ground data,offering critical insights for ecological conservation,water resource management,and climate change adaptation on the Qinghai-Xizang Plateau.
基金supported by the National Natural Science Foundation of China(Nos.U23A6005,22208112,and 32171721)the National Natural Science Foundation of China(No.22308109)+2 种基金Guangdong Basic and Applied Basic Research Foundation(No.2024A1515010678)the Fundamental Research Funds for the Central Universities(SCUT:2023ZYGXZR045)the State Key Laboratory of Pulp&Paper Engineering(Nos.2023ZD01,2023C02).
文摘Proton exchange membrane fuel cell(PEMFC)is a promising clean energy source,but its performance and stability are vulnerable to the negative effects of humidity conditions.The gas diffusion substrate(GDS)plays a pivotal role in regulating the moisture and gas transport.The single pore structure of traditionally designed GDS often leads to the pathway competition between moisture and gas,which effects the efficiency of fuel cells.In this study,we report on a hierarchical fibrous paper with tunable hierarchical pores for a sustainable GDS.This design offers gas permeability under wet conditions,by separating the gas pathway from the moisture pathway,thus mitigating their pathway competition.In addition,this paper forms a multi-scale scaffold that absorbs moisture under high humidity conditions and releases it under dry conditions.It is allowed to maintain an optimal internal humidity and further enhances the humidity adaptability.Furthermore,the carbon footprint is only 15.97%,significantly lower than commercial alternatives.This feature makes it a sustainable solution to stabilize PEMFCs under diverse humidity conditions.
文摘Composite wood products(i.e.,particleboard,medium density fiberboard,oriented strand board,plywood)used in cabinets,shelving,and base trim express varying degrees of thickness swelling when exposed to a sustained moisture source.Thickness swelling occurs when cellulose fibers adsorb water molecules and swell after attaining a moisture content of 29%to 36%.Observations of thickness swelling were made to refine water loss duration estimates.Thickness swell height is the result of several intrinsic factors(wood species,density,adhesive resin,heat pressing conditions).This study examined an extrinsic factor,humidity,at elevated(>95%RH)and ambient(50%RH)conditions.Specimens subjected to moisture for longer periods(8-10 weeks)experienced gradual darkening from accumulated biomass and fungal deterioration of the wood surfaces.The study revealed that high humidity conditions expressed higher rates of thickness swelling and that estimates of water loss duration should consider the influence of ambient humidity during and following a water release.
基金supported by the Natural Science Foundation of China(32072139)Liaoning Province Natural Science Foundation Project(2022-MS-307).
文摘How to reduce peanut allergies has always been a main food safety concern.Plant polyphenol complex peanut sensitizing protein was proposed as a new desensitization strategy.Gallic acid(GA),as a natural plant polyphenol,has anti-inflammatory and immunomodulatory effects.Therefore,the aim of this study was to investigate the effect of GA on peanut protein(PP)sensitization under high moisture extrusion conditions.The contents of free sulfhydryl groups and disulfide bonds in the PP-GA complex were determined,and the structure of the complex was characterized by sodium dodecyl sulfate-polyacrylamide gel electrophoresis(SDS-PAGE)and Fourier transform infrared spectroscopy.The results showed that with increasing GA content,the number of free sulfhydryl groups increased while the number of disulfide bonds decreased.The secondary structure of PP-GA showed that the random coils andβ-turns were transformed toα-helices andβ-sheets.A BALB/c mouse model was also established,wherein Al(OH)3 was used as an adjuvant when the complex was administered via intraperitoneal injection,and the mice showed mild allergic symptoms and a decreased immune organ index.In addition,the serum levels of specific antibodies(immunoglobulin E(IgE),immunoglobulin G1(IgG1),and immunoglobulin G2a(IgG2a)),cytokines(interleukin-5(IL-5),interleukin 13(IL-13),and interferon gamma(IFN-γ))and histamine were reduced.In summary,this study proved that GA can relieve the sensitization of PP induced by high moisture extrusion.
基金supported by the National Natural Science Foundation of China(No.52276022).
文摘Direct air capture(DAC)is a negative carbon emission technology that faces challenges in scalability and practical deployment due to its exorbitant costs.Hou et al.(2017)integrated DAC technology with fertilization.A multi-bed desorption system driven by water provides a competitive and sustainable carbon source for indoor agriculture.
文摘Traditional studies on transforming selenate and selenite are often limited by static measurements and low spatial resolution.They do not fully consider the impact of moisture content.This paper uses the DGT(diffusive gradients in thin films)technique to deeply explore how moisture changes affect the transformation of selenate and selenite in the environment(changes in properties over time).First,representative soil samples(loess)are prepared,and their moisture content is adjusted.Fixed concentrations of selenate and selenite are added,and then the DGT device simulates their migration in the natural environment.The experiment covers drought,moisture,and high moisture environments,and the experiment is repeated under each condition to ensure the accuracy of the data.The sample quality is verified and further analyzed by ion chromatography(IC)and atomic absorption spectroscopy(AAS).This article uses DGT technology to study the influence of moisture content on the migration and transformation of selenate and selenite in soil.Results indicate that increased moisture content leads to higher concentrations,diffusion rates,and DGT capture efficiency of both selenium species,highlighting the importance of moisture in their environmental behavior.When the moisture content increased from 25%to 65%,the coefficient of variation of selenate and selenite increased.The DGT technique proved effective in capturing spatial heterogeneity and providing high-precision measurements,offering robust data to advance research on selenium behavior in soils.
基金funded by the National Natural Science Foundation of China(42461053)the Department of Education of Gansu Province:Higher Education Innovation Fund Project(2023B-064)+1 种基金the Youth Doctoral Fund Project(2024QB-014)the Natural Science Foundation of Gansu Province(25JRRA012).
文摘Spatiotemporal forecasting of surface soil moisture(SSM)is recognized as a critical scientific issue in precision agricultural irrigation,regional drought monitoring,and early warning systems for extreme precipitation.However,long-term forecasting continues to pose formidable challenges because of the complexity observed across both the spatial and temporal scales.In this study,we used a daily SSM dataset at a 0.05°×0.05°spatial resolution over the Qilian Mountains,China and proposed a hybrid Convolutional Long Short-Term Memory(ConvLSTM)-Nudging model,which combined deep neural networks with data assimilation to increase the accuracy of long-term SSM forecasting.We trained and evaluated the SSM predictive performance of four models(Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),ConvLSTM,and ConvLSTM with Squeeze-and-Excitation(SE)attention mechanism(ConvLSTM-SE))in both short-term and long-term scenarios.The results showed that all the models perform well under short-term predictions,but the accuracy decrease substantially in long-term predictions.Therefore,we integrated Nudging technique during the long-term prediction phase to assimilate observational information and rectify model biases.Comprehensive evaluations demonstrate that Nudging significantly improves all the models,with ConvLSTM-Nudging achieving the best performance under the 200-d forecasting scenario.Relative to those of the best-performing ConvLSTM model for long-term forecasts,when observation noiseδ=0.00 and observation fraction obs=50.0%,the coefficient of determination(R2)of ConvLSTM-Nudging increases by approximately 82.1%,while its mean absolute error(MAE)and root mean squared error(RMSE)decrease by approximately 84.8%and 77.3%,respectively;the average Pearson correlation coefficient(r)improves by approximately 23.6%,and Bias is reduced by 98.1%.These results demonstrated that although pure deep learning models achieve high accuracy in the short-term predictions,they are prone to error accumulation and systematic drift in long-term autoregressive predictions.Integrating data assimilation with deep learning and continuously correcting the state through observation can effectively suppress long-term biases,thereby achieving robust long-term SSM forecasting.
基金supported by the National Natural Science Foundation of China(No.42061065)the Third Xinjiang Comprehensive Scientific Expedition,China(No.2022xjkk03010102).
文摘Root zone soil moisture(RZSM)plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth.However,the correlations between RZSM and its associated variables,including surface soil moisture(SSM),often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques.Therefore,this study presents a hybrid convolutional neural network(CNN)-long short-term memory neural network(LSTM)-attention(CLA)model for predicting RZSM.Owing to the scarcity of soil moisture(SM)observation data,the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM.Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model.The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models.This was particularly notable at the depth of 80–100 cm,where the fitness(R^(2))reached nearly 0.9298.Moreover,the root mean square error of the CLA model was reduced by 49%and 57%compared with those of the LSTM and CNN-LSTM models,respectively.This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone.
文摘On the basis of discussing the influencing mode of plant moisture stress on plant physiological process and the division of soil moisture availability range, the water suction values partitioning soil moisture were put forward, and then the corresponding water moistures under water stress were obtained by conversing together with characteristic curve of water moisture.
