Soil salt crust can change the structure of aeolian soil and improve its resistance to wind erosion. Four ions (Na
, Ca
, Cl
, and SO
) with high contents in aeolian soil were selected for a salt ...crust experiment. The experiment set a variety of gradients of soil salt contents and salt mixing ratios of Na
SO
and CaCl
. The physical properties of the salt crust were tested, and the wind erosion resistance of the salt crust was discussed. The results showed that the soil salt contents and salt mixing ratio influenced the resistance of the salt crust, especially in terms of its compressive strength and toughness. The former affected the compressive strength of the salt crust by changing the amount of cemented soil salt. The latter affected the kinds of crystals by changing the ion ratio, thus changing the structure of the salt crust and affecting its wind erosion resistance. The wind erosion resistance of the salt crust is complicated by the interaction between the soil salt content and salt mixing ratio. A multilayer crust can be formed in mixed salt, which has a strong wind erosion resistance. This result provides new findings on flowing sand soil and a new method for the treatment of flowing sand soil.
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When the photovoltaic (PV) system suffers shading problems caused by different degrees and areas, the shaded PV cells will consume electricity and generate heat, the corresponding bypass diode ...operating at a certain current will conduct, and a special magnetic field will be generated in space. In this study, a magnetostrictive-based shading detection method and device for the PV system are developed from theoretical, simulation, and physical experimental aspects. This study aims to detect the special magnetic field using magnetostrictive material with a certain response pattern under the magnetic field to detect and locate the shading problem of each module in the PV system. Theoretically, the analysis is carried out from the on–off situation of the bypass diodes of PV modules under different shading conditions and the response mechanism of magnetostrictive materials under the action of the magnetic field. During simulation, the finite element magnetic field simulations are performed for the diode and the series magnetic field coil, and the structural parameters of the magnetic field coil are designed based on the simulation results. After establishing the validation idea of the detection method in this study, the experimental platform is built and the experimental steps are designed. Finally, the feasibility of the method proposed in this study is verified, the detection range of the method is calculated, and the minimum spacing of adjacent magnetic field coils is determined by experimental validation. This study provides a novel magnetostrictive-based detection method, as well as a theoretical and experimental basis, for identifying and localizing PV system shading problems, and discusses the feasibility of shading detection at the system level.
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Preservatives are essential components in cosmetic products, but their safety issues have attracted widespread attention. There is an urgent need for safe and effective alternatives. Antimicrobial ...peptides (AMPs) are part of the innate immune system and have potent antimicrobial properties. Using machine learning-assisted rational design, we obtained a novel antibacterial peptide, IK-16-1, with significant antibacterial activity and maintaining safety based on β-defensins. IK-16-1 has broad-spectrum antimicrobial properties against Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans, and has no haemolytic activity. The use of IK-16-1 holds promise in the cosmetics industry, since it can serve as a preservative synergist to reduce the amount of other preservatives in cosmetics. This study verified the feasibility of combining computational design with artificial intelligence prediction to design AMPs, achieving rapid screening and reducing development costs.
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► We evaluate the sensitivity of the Xinanjiang RR (rainfall-runoff) model parameters. ► Model parameters are estimated in batch using a particle swarm optimization algorithm. ► We examine the ...correlations between state variables and streamflow. ► The Xinanjiang RR model coupled with a dual ensemble Kalman Filter provides accurate predictions of streamflow. ► The coupled model is applied to an ungauged basin.
To accurately estimate floods with hydrological models, the model parameters and the initial state variables must be known. Good estimations of parameters and initial state variables are required to enable the models to make accurate estimations. The Xinanjiang rainfall-runoff (RR) model has been widely used in humid and semi-humid regions in China. In this paper, we evaluate the sensitivity of the Xinanjiang RR model parameters and the correlation between the state variables and the output streamflow. In order to reduce the impact of streamflow data error, model structural error and parameter uncertainty, the Xinanjiang RR model is coupled with intelligent optimization algorithms and a data assimilation method to estimate the streamflow in the Luo River in China that was first considered as gauged, and then as ungauged for parameter calibration. Model parameters are estimated in batch using a particle swarm optimization algorithm and three variations of ensemble Kalman Filter data assimilation (the state variable assimilation, parameter assimilation and the dual assimilation at the same time) for the Luo River, and the ungauged basin. In this case, the model parameters are set equal to median values. The results show that when parameter values are determined by an optimization algorithm using 10years of data, the dual ensemble Kalman Filter notably improves the simulated results. When the parameters adopt the median, the state variable assimilation has little effect on the estimation results, but the parameter assimilation positively affects the simulated results. The dual ensemble Kalman Filter also notably improves the simulated results. The time scale of assimilation has little effect on simulation results for the dual assimilation, but it has a large effect on the state variable assimilation and the parameter assimilation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
With the rapid development of the nuclear resource industry, the development of high-efficiency adsorbents is the key to the separation and removal of uranium from aqueous solutions. In this paper, ...phenyl phosphonic acid was designed as a functional monomer, the aromatic building blocks such as benzene and triptycene as an external crosslinker. Through the “knitting” strategy, a novel phosphonic acid-based functionalized triptycene-based hyper-crosslinked porous polymer (TPP-BPP) and benzene-based hyper-crosslinked porous polymer (B-BPP) were prepared by the above monomer and crosslinker. The resulting polymer B-BPP has a much higher surface area (
S
BET
= 836.21 m
2
/g) than that of polymer TPP-BPP (
S
BET
= 30.60 m
2
/g) due to the large skeleton triptycene confines the void. Owing to a large number of phosphonic acid functional groups, the polymer TPP-BPP and B-BPP both have a good ability to remove uranium. The optimal adsorption pH is 7 and the adsorption time is 90 min. Under the best experimental conditions, the maximum adsorption capacity of TPP-BPP for uranium is 119.05 mg/g, and the maximum adsorption capacity of B-BPP for uranium is 222.72 mg/g. Furthermore, in the presence of coexisting ions Na
+
, Mg
2+
, Al
3+
, Co
2+
, Ni
2+
, NO
3−
, CO
3
2−
etc., they still have good selectivity to uranium ascribed to the strong complexation between UO
2
2+
and P=O groups anchored on the polymer, which was evidenced by experimental results. Also, it has good reusability and can still maintain a very high removal percentage after at least being recycled five times. Therefore, the convenient synthesis route for the novel phosphonic acid-based functionalized hyper-crosslinked porous polymer has a promising application prospect for uranium adsorption.
Graphical abstract
•Developed a new filter method – unscented weighted ensemble Kalman filter (UWEnKF).•UWEnKF is a highly effective and practical assimilation technique.•UWEnKF has better performance than EnKF.
A new ...data assimilation technique, unscented weighted ensemble Kalman filter (UWEnKF) was developed based on the scaled unscented transformation and ensemble Kalman filter (EnKF). In UWEnKF, the individual members selected are unequally weighted and symmetric about the expectation. To investigate the performance of UWEnKF, nine assimilation experiments with different ensemble sizes (161, 1601, 16001) and different assimilation frequencies (every 6 h, every 12 h, every 24 h) were designed to assimilate soil surface (5 cm) moisture data observed at station HY in the upper reaches of the Yellow River, in the northeastern of Tibetan plateau, China into the Richards equation. The results showed that the performance of the filter was greatly affected by random noise, and the filter was sensitive to ensemble size and assimilation frequency. Increasing the ensemble size reduced the effects of random noise on filter performance in several independent assimilation runs (i.e., it decreased the differences between the results of the several independent assimilation runs). Reducing the assimilation frequency also reduced the effects of random noise on filter performance. UWEnKF gave more accurate soil moisture model results than EnKF for all ensemble sizes and assimilation frequencies at all soil depths. Additionally, EnKF may have different performances according to different initial conditions, but not for UWEnKF. Precipitation and soil properties uncertainties had some impact on filter performance. Thus, UWEnKF is a better choice than EnKF, while it is more computationally demanding, for improving soil moisture predictions by assimilating data from many sources, such as satellite-observed soil moisture data, at a low assimilation frequency (e.g., every 24 h).
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
In this paper, a new type of carbazole-based hyper-crosslinked porous polymer (HCP-CP) was prepared through a very simple “knitted” method. The obtained novel polymer HCP-CP has a good adsorption ...property of methyl orange (MO) and methylene blue (MB) organic dyes in aqueous solution due to its high surface area and rich nitrogen atoms. The effect of pH, adsorption time, different initial concentration of dyes and adsorption reusability were investigated in detailed. The optimum pH is 8 for the adsorption of MB and 6 for the adsorption of MO, respectively. Under the best condition, the Langmuir model fitted the adsorption isotherm well and the adsorption behavior follow pseudo-second-order kinetics. The maximum adsorption capacity of HCP-CP for cationic dye MB (q
max
=751.88 mg/g) is more than twice higher than that of anionic dye MO (q
max
=274.73 mg/g). These capacity differences may be owing to the stronger electrostatic interaction between the negatively charged nitrogen atoms of HCP-CP with the cationic dye MB than that of MO. Moreover, the used polymer HCP-CP still retain a removal percentage above 92% after 5 times adsorption-desorption recycle. Therefore, this work provided a convenient synthetic route to develop a novel hyper-crosslinked polymer with high adsorption capacity for the entrapment of dyes from aqueous solution.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Global warming has intensified the intensity of compound drought‐hot extremes (CDHEs), posing more severe impacts on human societies and ecosystems than individual extremes. The vulnerability of ...global terrestrial ecosystems under CDHEs, along with its key influencing factors, remains poorly understood. Based on multiple remote sensing data, we construct a Vine Copula model to appraise vegetation vulnerability under CDHEs, and attribute it to climatic and biotic factors for five different vegetation types. High vulnerability is detected in central and southern regions of North America, eastern and southern regions of South America, Southern Africa, northern and western Europe, and northern and eastern Australia. The drier the climate, the higher will be the vulnerability. Furthermore, biodiversity and biomass are key biotic factors influencing the vulnerability of various vegetation types, such that ecosystems with richer biodiversity and higher biomass have lower vulnerability to CDHEs. The findings deepen understanding of terrestrial ecosystem response to CDHEs.
Plain Language Summary
Drought and hot‐related extremes often coincide or follow one another, known as compound drought‐hot extremes (CDHEs), adversely affecting various vegetation processes. Thus, investigating the response relationship between vegetation dynamics and CDHEs is critical for maintaining the sustainable development of ecosystems under the background of climate warming. High ecosystem vulnerability is detected in central and southern regions of North America, eastern and southern regions of South America, Southern Africa, northern and western Europe, and northern and eastern Australia. Climatic factors (precipitation, arid index, temperature, and radiation) dominate the vulnerability of all vegetation types. The drier the climate, the higher will be the vulnerability of vegetation to CDHEs. Furthermore, biodiversity and biomass are key biotic factors influencing the vulnerability. The results provide us with essential insights for managing and adapting terrestrial ecosystems against climate change and are of interest to a wide range of audiences, including but not limited to ecologists, hydrologists, and climatologists.
Key Points
A framework is developed to estimate the vegetation vulnerability in response to compound drought‐hot extremes and attribute it to various factors
Vegetation vulnerability is higher for grasses and shrubs than for evergreen and deciduous forests
Vegetation vulnerability is both affected by climate and biotic factors, but climate factors are more dominant
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Precipitation is the most common cause of flood. Accurate precipitation prediction is therefore important for flood forecasting and can be a key factor that increases lead time and the accuracy of ...early flood warning. In this study, the reforecast precipitation of the global ensemble forecast system (GEFS) was postprocessed using CSG EMOS (censored and shifted gamma distribution-based ensemble model output statistics) method to improve its reliability, and then used as the forcing data for Xinanjiang model to increase the lead time of flood forecasts in order to provide a more effective early flood warning based on an empirically water level–discharge curve at Wangjiaba section, Huaihe River basin, China. Three scenarios were set to demonstrate the importance of precipitation prediction in flood forecasting. The results showed that predicted precipitation became more reliable after postprocessing and this improvement increased as lead time expanded. It is also demonstrated that the postprocessed predicted precipitation brings the improvement for flood forecasting, then leads to the gain of early flood warning. However, this improvement becomes less significant by the increase of lead time and fades away when lead time reaches 7 d. In addition, the results of flood forecast and early warning in predicted precipitation scenario were not as good as those in observed precipitation scenario, indicating that substitution of predicted rainfall for observation requires further refinement in future.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
To better understand the mechanisms of the hydro-ecological cycle in the changing environments of the Yangtze River Basin (YZRB), it is valuable to investigate vegetation dynamics and their response ...to climate change. This study explored the spatial patterns of vegetation dynamics and the essential triggers of regional differences by analyzing vegetation variations in the 1982–2015 period at different time scales and the interannual variability of vegetation sensitivity to climate variability. The results showed that the normalized difference vegetation index (NDVI) increased significantly in the last three decades, but vegetation displayed great spatiotemporal variations at different time scales. The vegetation in the central part of the YZRB dominated by forests and shrublands was more sensitive to climate variability than vegetation in the source region of the YZRB, which was dominated by alpine meadows and tundra (AMT). The contribution of climate variables to the vegetation sensitivity index (VSI) had large spatial differences, but solar radiation and temperature were the dominant factors. Furthermore, 57.9% of the YZRB had increasing VSIs, especially in the south-central part. Consistent with the distributions of elevation and vegetation types, vegetation dynamics in the YZRB were divided into five spatial patterns, with the largest increasing NDVI trend in Region III and the largest VSI in Region IV. Moreover, the VSI exhibited fairly consistent dynamics in all subregions, but the contributions of climate variables to the VSI varied greatly among the different regions.
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