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Ultrafast High-temperature Sintering (UHS) has been successfully applied to fabricate the silicon nitride (Si3N4) bulks, as the first attempt of ultra-rapid consolidation of a non-oxide ceramics. At a heating rate of 875 °C/min, the bulk Si3N4 ceramic with a relative density greater than 96 % and an α-β phase transformation degree above 80 % could be obtained within 300 s. The effects of ultrafast heating on the liquid phase sintering (LPS) were also comparatively studied. Results showed that, the ultrafast heating rate and high temperature under UHS might promote the LPS system evolving to a nonequilibrium state. By comparing with other pressureless sintering processes with much lower heating rates, UHS apart from reducing the processing time, and it is also an effective method to form a bimodal microstructure composed of interlocked rod-like β-Si3N4 grains.

International audience; There have been considerable interests in the lunar-based synthetic aperture radar (LBSAR) for monitoring large-scale geoscience phenomena. However, the signal distortions given rise by lunar orbital perturbations, especially the apsidal precession, are particularly severe in the LBSAR. The apsidal precession effects can induce a coordinate drift of the LBSAR, which can further lead to the variation in the range history of the LBSAR. As a result, LBSAR's image performance might be affected. In this letter, we thoroughly investigate whether the apsidal precession effects cause the phase decorrelation in the signal of the LBSAR, and how such effects impact the LBSAR imaging. The theoretical result shows that the impact of the lunar apsidal precession mainly results in the first-order and second-order Doppler errors, which further influence the geometric location and focusing quality along the azimuth direction. Numerical simulations using the point target response show good consistency with the theoretical analysis. To this end, the lunar apsidal precession effects deserve special care in the LBSAR for high imaging quality.

Mining activities carried out above an aquifer, especially in the hanging wall of normal faults, generally involve the risk of water inrush. For a better understanding of the mechanism inducing groundwater outburst through fault floor, the analytical and numerical simulation methods were applied to investigate the process of fault activation driven by mining and hydraulic loads. The influences of the advancing distance and fault dip were conducted. The results exhibit good agreement between the theoretical calculation and the numerical simulation. The fault activation by induced tangential stress change has a positive correlation with the advancing distance and a negative correlation with the fault dip angle. The stress concentration factor increases significantly when the excavation process enters the horizontal range of fault projection, following the stress peak and failure zone approaching the coal seam from deep to shallow along the fault. For the cases with a constant advancing distance, the smaller the fault dip is, the earlier the water-inrush channel will be formed. The research results in this study reveal the theoretical mechanism of fault activation and groundwater inrush of coal seam floor above a confined aquifer and thus provide valuable insights for the prevention and risk assessment of mine water disasters in underground engineering.

International audience; A deep mixture model was developed to retrieve land surface temperatures (LSTs) from infrared atmospheric sounding interferometer (IASI) observations. The IASI brightness temperature (Tb) data and the Advanced Very High Resolution Radiometer onboard MetOp (AVHRR/MetOp) LST data were randomly divided into training and test datasets, and a deep mixture model was constructed to simulate radiation transmission in order to invert the LST. The constructed model could evaluate dataset characteristics that included global features, local features, and time-domain predictions, covering most of the features of the satellite dataset. For the test datasets, the root mean square error (RMSE) indicated that the LST in Algeria and South Africa could be retrieved with an error of less than 2 K and 2.5 K, respectively. Compared with the AVHRR/MetOp LST product in March and December 2019 for Algeria and South Africa, the LST could be retrieved with the maximum RMSE of 2.5 K. The LST retrievals at nighttime had an RMSE of less than 2.0 K, which was superior to those retrieved during daytime for Algeria. This deep mixture model can be applied to time-series temperature prediction. INDEX TERMS Hyperspectral thermal infrared, land surface temperature retrieval, IASI, deep learning.

The focus of this research was on the catalytic reduction of nitrate to nitrogen gas for the water conservation. Zero-valent iron (Fe0) with bimetallic catalyst that carrier supported palladium (Pd...

This article presents a new index, polarization-conversion ratio (PCR) to characterize depolarized bistatic scattering from rough surfaces with dielectric inhomogeneity and spatial anisotropy. We then investigate the dependence of PCR on both surface and radar parameters. Numerical results show that the distribution of PCR on the scattering plane varies with the polarization state of the incident wave and incident angle. The PCR clusters more in the cross-plane for horizontally polarized incidence. However, for vertically polarized incidence, the PCR disperses as “triangular shape” on the whole scattering plane with a sharp valley occurring in the incident plane. The following points can be drawn: 1) the inhomogeneity effectively enhances the PCR in the cross-plane; 2) the effect of anisotropy on the PCR is relatively weak, because the scattering is less affected by correlation length; 3) the impacts of surface rms height on the PCR are negative on the whole scattering plane; and 4) as the background permittivity increases, at the horizontally polarized incidence, the PCR is enhanced in the backward and forward regions, while at vertically polarized incidence, it is enhanced in the incident plane and the forward region. As is demonstrated, the PCR is an effective measure of the sensitivity of depolarization, making it potentially useful as a new reliable index for surface parameter inversion.

Land surface temperature (LST) is a comprehensive embodiment of surface energy balance and land surface processes. The spatial and temporal variation of LST is of great significance for studying surface characteristics and climate change. In this study, the spatiotemporal variations of LST in China from 2003 to 2018 is examined by using the continuous and derivable annual temperature cycle model. The trends of the annual mean and annual amplitude of LST is detected using the Mann-Kendall test and Theil-Sen estimator. In addition, we have further revealed the correlation between normalized difference vegetation index (NDVI) and LST in different land cover types. The results show that the annual mean LST presents a spatial distribution pattern of high values in the southern regions and low values in the northern regions and that the factors of altitude and land cover type also affect the LST's spatial distribution. The annual amplitude of the LST presents a spatial distribution pattern of high values in the northern regions and low values in the southern regions. In the majority of instances, the phase of LST in China was positioned between the 175th and 205th day of each year. Both the annual mean and annual amplitude of the LST have a mean increasing trend in China with a rate of 0.02 K/year, and the areas with large significant changes accounted for 8.6% out of the total area, with a mean rate of approximately 0.05 K/year, in this period. Significant changes in the annual mean LST were correlated with the change in vegetation coverage and the impact of land cover types on the interannual variations of LST is also determined in this study. While the increase in vegetation coverage in barren land exhibits a clearly recognizable upward trend in the annual mean LST, the improved vegetation coverage in the grassland region presents a downward trend in annual mean LST.

With the continuous development and progress of new energy electric vehicles, high-capacity nickel-rich layered oxides are widely used in lithium-ion battery cathode materials, and their cycle performance and safety performance have also attracted more and more attention. In this experiment, a single crystal LiNi0.83Co0.12Mn0.05O2 cathode material was synthesized by co-precipitation and high-temperature solid-phase methods, and its crystal structure, morphology, composition, element distribution and electrochemical performance were systematically studied. The results show that the synthesized single crystal particles have good morphology, uniform element distribution, low Li+/Ni2+ ion mixing degree, and well-developed layered structure. Under the test conditions of 25 °C and 2.75–4.30 V, the first discharge specific capacity of a button cell at 0.1C(1C = 210 mA g−1) reaches 209.7 mA h g−1, the coulomb efficiency is 91.19%, and the capacity retention is 100.90% after 100 cycles at a rate of 0.2C. It should be noted that the capacity retention of the soft package battery is 88.72% after 500 cycles at a rate of 0.3C. Such excellent performance may be because, compared with polycrystalline materials, the smooth micron-sized single crystal particles effectively reduce the electrode/electrolyte interface reaction, volume change and irreversible phase change caused by charging and discharging.

The densification behavior, microstructural development, toughening and strengthening mechanisms of Al2O3 whisker-reinforced 3Y-TZP and 12Ce-TZP composites were systematically and comparatively investigated with varying whisker lengths. Compared with 3Y-TZP/Aw composites, the presence of a Ce-Al-Si-O amorphous phase, caused by the addition of Al2O3 whiskers, promoted the densification and grain growth of 12Ce-TZP/Aw composites. Crack deflection and bridging are proposed as the primary toughening mechanisms for 3Y-TZP/Aw composites, while the t-m martensitic transformation would dominate the toughening and strengthening processes of 12Ce-TZP/Aw composites. Changes in Al2O3 whisker length would vary the distributions of internal stress and amorphous phase within the ceria-stabilized ZrO2 matrix, and hence affect the toughening and strengthening results. It indicates that effective toughening and strengthening of the Al2O3 whisker-reinforced TZP composites can be achieved by taking advantage of collaborative engineering control on the reinforcement morphology and the interface chemistry/structure.

Since 1982, Landsat series of satellite sensors continuously acquired thermal infrared images of the Earth’s land surface. In this study, Landsat 5, 7, and 8 land surface temperature (LST) products...

The development of ground-based sampling strategies is vital for validating medium or coarse-resolution satellite-derived land surface temperature (LST) products. Conventional LST sampling at the satellite pixel scale has been limited to the homogeneous surfaces. In this study, an optimal sampling strategy called spatial and diurnal temperature cycle-constrained sampling (SDCS) is proposed to extend the feasibility of LST validation over heterogeneous surfaces with dramatic diurnal LST changes. SDCS integrates a priori information including land cover, diurnal LSTs data, and spatial distribution characteristics of samples to improve the representativeness of multi-temporal measurements over heterogeneous surfaces. SDCS was applied to four varied study areas using simulated satellite data and was compared with four existing methods including random sampling, systematic sampling, land cover-based stratified sampling, and conditioned Latin hypercube (CLH) sampling. Results showed that SDCS could significantly improve the representativeness of samples with a limited sample size. In homogeneous surfaces with an LST standard deviation (SD) of less than 2 K, the root mean square error (RMSE) of diurnal LSTs estimated by SDCS was less than 0.3 K when using 0.20% of the total pixels. In moderate heterogeneous surfaces (LST SD less than 5 K), 0.32% of the total pixels were required to achieve RMSE less than 0.5 K. In extremely heterogeneous surfaces (LST SD > 6 K), 0.96% of the pixels were needed to achieve the same accuracy. Further, the representativeness of the samples selected by SDCS was stable in diurnal space with uncertainties of LST bias less than 0.27 K. Moreover, the samples exhibited a dispersed spatial distribution with a nearest neighbor index of 1.27–1.54. SDCS can generalize to various regions and dates and can be employed in field campaigns for diurnal LST validation over heterogeneous surfaces.

Evapotranspiration (ET) is one of the most critical parameters in water- and energy-related domains. Two basic assumptions with respect to soil-moisture variation have been widely investigated for the retrieval of ET based on the trapezoid methods. Specifically, soil moisture within the surface and root-zone layers was assumed to vary synchronously in most of the earlier analyses. However, several recent investigations assumed that soil moisture within the upper soil layer should be dried up before the root-zone layer is stressed. To this end, the retrieval of ET under different aridity conditions over North American grasslands was investigated with the two assumptions, and the estimated ET was assessed using the flux data collected from eight AmeriFlux sites. Based on the available data from 2002 to 2018, results showed that the “asynchronous-assumed” method can obtain better ET estimates than the “synchronous-assumed” method over semiarid and subhumid areas, whereas the “synchronous-assumed” method can obtain better ET estimates in humid areas. Moreover, because of the different closure techniques used for the ET correction, no consistent conclusions could be found for the arid conditions to determine which trapezoid was better. Specifically, it was found that the cases of surface soil with zero water availability that were defined by the asynchronous-assumed trapezoid method rarely occur, even in arid areas, which indicated that the critical boundary that determines whether the root-zone layer begins to be water-stressed may need to be redefined.

Various natural protein materials have hierarchical microscale and nanoscale structures that protect animals from suffering cold weather or external threats. Herein, we contribute an effective strategy for exploring the hierarchical structure transformation mechanism by stretching a single fiber and bundle fibers. Taking advantage of controllable stretching, the disulfide bonds and peptide chains were taken apart or reconstructed by the new crosslinking bonds inside the wool fiber. If the temperature, solution concentration and stretching velocity were appropriate, the strain was more than 230% and the microstructure transformation of a single fiber underwent α→ β transition, disulfide bond breakage and macromolecule slippage occurred during the tensile process. The transformation mechanism was modeled and experimentally tested in wool bundle fibers after setting, suggesting that the stretching led to the transformation from α helix to β-pleated sheet, the breakage of disulfide bonds and the slippage of microfibrils. The availability of wool fibers provides many opportunities for bio-friendly cellular substrates and biosensor devices.

In order to explore the application of nanomaterials in fiber concrete, in this research, the effect of nanomaterials SiO2 on the basalt fiber concrete was studied with experimental analysis methods. The variation values of mechanical properties were calculated respectively. On this basis, the influences of silicon dioxide nanometer and basalt fibers on the mechanical properties of nanosilica silicon dioxide nanometer basalt fiber concrete were studied. Different contents of silicon dioxide nanometer were added to concrete, different contents of basalt fibers were added to concrete, and both were added to concrete. The results showed that adding silicon dioxide nanometer could modify building concrete and significantly improve the mechanical properties of concrete. the increase of silicon dioxide nanometer content of nanomaterials, the strength of concrete showed a trend of first rising and then falling. When silicon dioxide nanometer content was 1.2%, the mechanical strength was the largest. As the contents of basalt fiber increase, concrete strength showed a trend of first increasing and then decreasing. When basalt fiber content was 3kg/m3, it was the optimal content and the concrete strength was the largest. And the mechanical properties of the concrete mixed with silicon dioxide nanometer and basalt fibers were significantly improved.

The growing demand for the meaningful use of electronic medical records has led to great interest in medical entities and relation extraction technologies. Most existing methods perform relation extraction based on manually labeled documents and rarely consider incorporating knowledge graphs that include rich, valuable structured knowledge, which will cause semantic ambiguities. To address this problem, we propose a knowledge-enhanced relation extraction (KERE) model. First, we extract knowledge information from the knowledge graph to generate a knowledge-guided word embedding. Then, the lexical features are considered supplementary information for semantic understanding. Experiments on real-world datasets show that the KERE model achieves important improvements in a biomedical relation extraction task.

The polypropylene (PP) composites filled with flame retardants (including aluminum hydroxide (Al(OH)3), magnesium hydroxide (Mg(OH)2), and zinc borate (ZB)) were prepared, and the composites were separately loaded with polyolefin elastomer (POE) and nanometer calcium carbonate (nano-CaCO3). The tensile properties and the fire residue morphology of the specimens after burning for these three groups of composites were measured. The results were shown that the tensile fracture strength and the Young’s modulus increased while the tensile yield strength and the tensile elongation at break ( δb) decreased with increasing flame-retardant volume fraction. At the same flame-retardant content, the δb values of the PP/Al(OH)3/Mg(OH)2/ZB/nano-CaCO3/POE composite were the highest in the three groups of composites. The fire residue in the pyrolysis zone of the specimens increased corresponding to the increased flame-retardant volume fraction and with loading the nano-CaCO3 and POE, especially at low-flame-retardant concentration. The reinforcing and the flame-retarded mechanisms of the composites were discussed.

Currently, it still remains a grand challenge to enhance energy density of micro-supercapacitors (MSCs) without sacrificing power density, cycle life and other key performance. Herein, we reported the exploration of free-standing electrodes for MSCs based on quasi-aligned SiC nanowire arrays wrapped by ultrathin graphitic carbon film (SiC@C). Such rationally designed SiC@C architecture bring an optimal specific capacitance of 78.98 mF cm−2 at a current density of 0.2 mA cm−2, which was enhanced by more than 700% in comparison to that of pure SiC counterpart (9.56 mF cm−2). The aqueous-based MSCs exhibit a high specific capacitance, fast charge-discharge rate, excellent rate capability, and long-term cycling stability (94.1% capacity retention after 10000 charge-discharge cycles). The ionic-liquid-based MSCs had an extended operating voltage up to 2.6 V with an energy density up to 2.84 μW h cm−2 (at a power density of 65.1 μW cm−2), which was a new record of SiC-based SCs, and superior to most of those of nanowire-based MSCs in other material systems ever reported.

Different from bidirectional reflectance, temperature variation takes some time with the change of illumination. However, previous thermal radiation directionality (TRD) models have less considered the influence of this temperature inertia (TI) effect. By using the concept of conversion component, this article proposed an improved geometric optical (GO) model, called MGP_TI model. This model considers the TI effect by further dividing the background component into the continuously sunlit, continuously shaded, converted from sunlit to shaded, and converted from shaded to sunlit backgrounds. Upon combining with in situ measurements and a comprehensive simulated data set of component temperatures and prescribing three levels of TI and six observation times, the TI influence on TRD modeling was comprehensively analyzed. Results indicated that: 1) the overall absolute and relative greatest influence were 0.34 °C and 6.9%, respectively, suggesting that the TI influence on the value of TRD was less significant compared with the land surface temperature (LST) retrieval accuracy and the TRD extent; 2) the TI would weaken TRD on the direction of sun motion, whereas it enhanced the TRD on the opposition direction, and the primary influence was enhancing first and then weakening during the period from 10:30 to 15:30, which were determined by the differences in conversion component fractions; and 3) the TI effect could also result in the delay of the hotspot, and the occurrence and degree of the delay were influenced by the TI strength, local solar time and temperature differences of sunlit/shaded components.

International audience; Accurate estimations of daily mean land surface temperature (LST) are important for investigating the urban heat island effect, land-atmosphere energy exchanges, and global climate change. Moderate Resolution Imaging Spectroradiometer (MODIS) sensors can provide up to four instantaneous LSTs of a single day across the world. However, numerous studies, such as those on climate change and hydrology, require the input of daily mean LSTs rather than instantaneous value. In this paper, we propose a practical method to estimate the daily mean LST using instantaneous LST products derived from MODIS. Based on the in situ LST measurements collected from 235 sites distributed globally, multiple linear regressions of two to four valid instantaneous LSTs at different MODIS observations moments (at least one daytime and one nighttime observations) can provide reliable estimates of daily mean LSTs under all-weather conditions with a root mean square error (RMSE) of less than 1.60 K. In addition, the conditions of clouds would affect the estimation accuracy of daily mean LST to a certain extent. Subsequently, an algorithm is proposed to produce the most complete coverage of daily mean LSTs from instantaneous LST products derived from MODIS. Validation results with in situ measurements show that the daily mean LSTs estimated from the MOD11A1 and MYD11A1 products are similar to the daily mean of the in situ LST, with an RMSE of 2.17 K. Furthermore, the daily mean LST derived from MODIS data is successfully applied to calculate the global annual cycle parameters (ACPs) in the annual temperature cycle (ATC) model. The results of this study show that the daily mean LST can be retrieved accurately from combinations of daytime and nighttime LSTs derived from MODIS. We expect that our findings will be useful for various applications involving global LST trend analysis and climate change.

International audience; Land surface temperature (LST) and its annual or inter-annual variations play an important role in understanding global climate change, urban heat island, and the process of land-atmosphere energy exchange. Many annual temperature cycle (ATC) models [i.e., ATC with three or five parameters (ACP3 or ACP5)] have been proposed to analyze the annual variations of LST in the past decades. In this study, two year-to-year continuous and derivable models (YYCD_ACP3 and YYCD_ACP5 models) were proposed to model several years of ATCs. The fitting results of the YYCD_ACP3 model with global Aqua/MODIS daytime LSTs from 2014 to 2018 show that the YYCD_ACP3 model achieved a good performance in fitting the time-series LSTs with an overall normalized root-mean-square error (NRMSE) of 0.21, coefficient of determination (R 2) of 0.74, and refined index of agreement (d) of 0.85. In addition, the modeling results of ten representative samples covering different climatic conditions and land cover worldwide show that, except for two sites located in tropical and Antarctic, the YYCD_ACP3 model could show a good performance with R 2 greater than 0.6. Although the ACP3 model shows similar performance to the YYCD_ACP3 model, the fitting curve of the YYCD_ACP3 model is continuous and smooth for describing the interannual variations of LST. When the LSTs of 2014-2018 are fitted as a whole by using both models, the YYCD_ACP3 model shows a slightly better performance than that of the ACP3 model. The application of the YYCD_ACP3 model with the global MODIS LSTs from 2003 to 2018 indicates that the results of the YYCD_ACP3 model have the potential to reveal the interannual variations of LST. Therefore, we conclude that the YYCD models are valuable for modeling the variations of LST over several years and can be widely applied. INDEX TERMS Land surface temperature, annual temperature cycle, modeling, MODIS.

Access tunnel in the main powerhouse of Shuangjiangkou hydropower station was deep buried with high in situ stress and complex geological conditions. Microseismic monitoring technology was established to monitor microcrack evolution process inside the surrounding rock in early excavation stage. Serious falling blocks in the left spandrel of the tunnel were predicted in a timely manner by delimiting major damage areas in the tunnel. Based on comparative analysis on microseismic activity law and field failure characteristics of the access tunnel, a quantitative index was supposed between slight rockburst like falling blocks and microseismic events. Moreover, the change law of daily average apparent stress difference and b value were analyzed based on microseismic event data. In addition, a three-dimensional numerical simulation software (RFPA3D) was used to simulate the damage distribution around the tunnel, and a relationship between spatial position of tunnel damage and direction of the maximum principal stress was qualitatively analyzed. The study results showed that advance speed of the tunnel working face was an important factor affecting the state of stress redistribution in surrounding rock mass, and the change law of b values of microseismic events could be used to predict activity state inside the surrounding rock effectively, which reflected mechanical properties and stress state of surrounding rock. In particular, field falling blocks became more serious with increasing b value, and field surrounding rock was relatively stable with minor b value. A risk of surrounding rock instability was relatively high with small b values. It provided an efficient method of predicting and assessing slight rockburst like falling blocks. The study results can provide significant guidance for field construction and later construction planning.

Deepwater turbidite lobe reservoirs have massive hydrocarbon potential and represent one of the most promising exploration targets for hydrocarbon industry. Key elements of turbidite lobes internal heterogeneity include the architectural hierarchy and complex amalgamations at each hierarchical level leading to the complex distribution of shale drapes. Due to limitation of data, to build models realistically honoring the reservoir architecture provides an effective way to reduce risk and improve hydrocarbon recovery. A variety of modeling techniques on turbidite lobes exist and can be broadly grouped into pixel-based, process-based, process-oriented, surface-based, object-based and a hybrid approach of two or more of these methods. The rationale and working process of methods is reviewed, along with their pros and cons. In terms of geological realism, object-based models can capture the most realistic architectures, including the multiple hierarchy and the amalgamations at different hierarchical levels. In terms of data conditioning, pixel-based and multiple-point statistics methods could honor the input data to the best degree. In practical, different methods should be adopted depending on the goal of the project. Such a review could improve the understanding of existing modeling methods on turbidite lobes and could benefit the hydrocarbon exploration activities of such reservoirs in offshore China.

Development of efficient bifunctional electrocatalysts for reversible oxygen reduction (ORR) and the oxygen evolution reaction (OER) is of prime importance in rechargeable metal–air batteries. In this work, a relatively unexplored and mechanistically unclear nickel-based catalyst has been explored as an ORR and OER catalyst using density functional theory (DFT) investigations. A detailed reaction mechanism involving four-coordination and three-coordination Ni–N–C configurations on a graphene substrate have been investigated, together with the effects of OH ligand binding on Ni–N–C-gra towards ORR and the OER. Through our simulations, we identified the existence of strong influences of the N and C co-ordination environment around the Ni atoms, and the OH ligand affinity determines the overall bifunctional ability of Ni-based catalyst systems. After a detailed analysis, the four-coordination NiC4 shows excellent bifunctional catalytic potential (ηORR = 0.48 V, ηOER = 0.40 V), while NiN3, NiN2C and NiNC2 have excellent OER catalytic performance (ηOER = 0.23, 0.20 and 0.42 V). Analysis of the effects of OH ligand reveals that Ni(OH)N3, Ni(OH)NC2 and Ni(OH)C3 show efficient ORR catalytic performance (ηORR = 0.45, 0.43 and 0.42 V). The calculations in our study reveal that the co-ordination of N and C with Ni greatly affects the bifunctional activity; hence, fine tuning of the C and N contents is highly essential during the catalyst synthesis process.

Land surface temperature (LST) is an important parameter in various fields, including hydrological, meteorological, and agricultural studies. Passive microwave techniques provide a practicable method to retrieve LST under both clear and cloudy conditions. In this study, LST derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature data during nighttime in the period 2015–2016 using a physically-based algorithm was compared with Moderate Resolution Imaging Spectroradiometer (MODIS) LST product MYD11A1 over 16 study sites that represent four different land cover types, i.e., barren/sparsely vegetated, grasslands, croplands, and evergreen broadleaf forest. Compared to MODIS-derived LST, the root-mean-square error (RMSE) of AMSR2-derived LST is 6.0 K and the bias is 4.4 K over all study sites. For barren/sparsely vegetated sites, LST was overestimated by 6.7 K. To eliminate the systematic bias induced by the penetration depth effect of microwave radiation over barren/sparsely vegetated sites, a linear regression between AMSR2- and MODIS-derived LST was applied and the RMSE decreases from approximately 7.8 to 3.5 K. For the other three land cover types, the bias ranges from approximately 1.4 to 4.2 K and the RMSE ranges from approximately 2.1 to 5.9 K. The bias between AMSR2- and MODIS-derived LST is related to vegetation coverage. The value of bias increases with the decrease of normalized difference vegetation index. Furthermore, the RMSE has a strong dependency on precipitable water vapor (PWV). It presents a descending pattern of RMSE with the increase of PWV.

The influence of the size and content of multi-walled carbon nanotubes (MWCNTs) on the end pressure effects of polypropylene (PP) composite melts during capillary extrusion flow was investigated, with the test temperature varying from 190°C to 230°C and apparent shear rate ranging from 50 s−1 to 3000 s−1. The results showed that the end pressure drop increased with increasing apparent shear rates, while decreased with increasing test temperature, and the sensitivity of the end pressure drop to apparent shear rate for the composites was higher than that for the unfilled PP. The end pressure drop increased with increasing weight fraction and the specific surface area of the MWCNTs. The end pressure drop increased almost linearly with increasing the aspect ratio of the MWCNTs. Moreover, the end pressure effect mechanisms of the composite melts during a capillary extrusion flow were discussed.

Compared with classic probability or statistical theories, Bayesian inference could be a better option for real-world damage identification problems since it can simultaneously utilize both prior knowledge and current measurements of a structure. However for a complex problem, a Bayesian identification procedure should face the challenge of unknown likelihood functions and unacceptable computational expenses. Due to it, approximate Bayesian computation (ABC) is adopted and incorporated with the Metropolis Hastings sampling (MHS) algorithm and stochastic response surface (SRS). Likelihood functions are no longer required during the inference. A stable Markov chain is obtained containing qualified parameter samples, whose random responses are then fast computed by SRSs. Additionally, a nested Bayesian updating procedure with a dynamic tolerance is proposed for fast estimation convergence of posterior probability distributions. After that a probabilistic damage index is defined based on the statistical features of the estimated posterior probability distributions. Lastly, the feasibility of the proposed method has been validated using both numerical and experimental beams. Their damage was indicated by the magnitude variations of the damage index.

The influence of tension rates and filler size on the tensile properties of polypropylene (PP) composites separately reinforced with graphene nano-platelets (GNPs) were investigated at room temperature within a tension rate range from 20 to 300 mm/min using a universal materials tester. The results showed that the Young's modulus, the tensile yield strength and the tensile fracture strength of these three PP composite systems increased obviously when tension rate was lower than 100 mm/min, and then increased slightly with increasing tension rate. The tensile elongation at break of these three composite systems decreased obviously when tension rate was lower than 100 mm/min, and then decreased slightly with increasing tension rate. The morphology of the tensile specimens of the composite was observed after tensile tests. Finally, the reinforcement mechanisms of the composites under different tension rates were discussed.

Representative elementary volume (REV) is a significant parameter in analyzing the size effect and the continuous media theory of natural jointed rock masses. Generalized RVEs, which include the REV of jointed rock masses (RREV) and the REV of mechanical parameters of jointed rock masses (PREV), were introduced and investigated based on the anisotropy of natural jointed rock masses. A two-dimensional model was developed based on the available geology, material heterogeneity and Monte Carlo simulation of joint network. The size effect and anisotropy of the elastic modulus and the uniaxial compressive strength (UCS) of jointed rock masses were investigated, and then, the PREV was determined. Numerical results showed that the PREVs for both the elastic modulus and UCS exhibited strong anisotropy. The RREV dimension was determined as 14 × 14 m based on the PREV of different rotational directions. The equivalent mechanical parameters of the elastic modulus and UCS were determined for different rotational directions. It is suggested that the anisotropy of the PREV should be considered when the RREV and corresponding equivalent mechanical parameters are assessed.

Limited light harvesting and charge collection are recognized as grand challenges for the exploration of highly efficient TiO2 photoanodes. To overcome these intrinsic shortcomings, we reported the...

The melt spinning flow behaviour of a high-density polyethylene (HDPE) blended with a low-density polyethylene (LDPE) was studied using a melt spinning technique in temperature ranging from 160 to ...

Influence of triaxial stress states on failure behaviors and safety factor of deep tunnels, involving the intermediate principal stress and the ratio of the vertical stress to the horizontal stress, is investigated using a three-dimensional numerical model and the strength reduction method (SRM). The numerical model is able to consider the heterogeneity of rock masses described by the Weibull distribution based on the statistical damage theory. The strength reduction method is applied to implement the progressive failure modelling of the surrounding rocks, and the tunnel stability under different stress conditions is obtained. The results show that the failure behaviors of the surrounding rocks are complex under the effect of triaxial stress states. Both the lateral pressure coefficient and axial stress have significant influence on the tunnel failure patterns. When the direction of the intermediate principal stress is parallel to the tunnel axis, the safety factor of the tunnels increases with the increasing of the intermediate principal stress. When the direction of the intermediate principal stress is perpendicular to the tunnel axis, the safety factor increases in the beginning and then decreases as the intermediate principal stress increases gradually until reaching the maximal principal stress. When the direction of the maximum principal stress is parallel to the tunnel axis, the zonal disintegration is observed because of the large axial stress. Overall, the numerical model is successfully adopted to model the fracture patterns and evaluate the tunnel stability. The simulated results have contributed to reveal the nonlinear failure behaviors of deep tunnels and understand their safety condition under complex triaxial stress states.

Rock bolting has been widely utilized for reinforcing underground openings, such as tunnels and mining. To ease the complexity of numerically simulating of the bolted rock mass, a novel method for equivalent anchoring simulates the reinforcement effect of the bolts by considering the anisotropic characteristics of the layered rock mass. The jointed material model in ABAQUS is improved by incorporating the anisotropy through the programming language FORTRAN. Based on the improved jointed material model, the equivalent anchoring parameters of the bolted layered rock masses are studied through a series of numerical tests. The numerical outcomes reveal that the improved jointed material model can be used to simulate the anisotropic features of the layered rock mass by comparing the classic and analytic solutions. The degree to which the supporting parameters affect the equivalent reinforcement performance are different in anisotropic rock masses, and the sensitivity of their influence is bolt length > bolt spacing > bolt diameter. Furthermore, an equivalent anchoring model for bolted rock masses (the formula of the improved parameter between the ratio of bolt length to tunnel diameter and the ratio of bolt diameter to bolt spacing) is proposed through numerical results analysis by the multiple linear regression method. An application case shows the field monitoring agreed well with the predicted vertical displacement of the tunnel demonstrating the feasibility of the proposed simulation method for the bolted rock mass. The results show the proposed method in this study can be conveniently utilized for efficient simulation of the mechanical behavior of bolted rock masses.

A series of algorithms for satellite retrievals of sun-induced chlorophyll fluorescence (SIF) have been developed and applied to different sensors. However, research on SIF retrieval using hyperspectral data is performed in narrow spectral windows, assuming that SIF remains constant. In this paper, based on the singular vector decomposition (SVD) technique, we present an approach for retrieving SIF, which can be applied to remotely sensed data with ultra-high spectral resolution and in a broad spectral window without assuming that the SIF remains constant. The idea is to combine the first singular vector, the pivotal information of the non-fluorescence spectrum, with the low-frequency contribution of the atmosphere, plus a linear combination of the remaining singular vectors to express the non-fluorescence spectrum. Subject to instrument settings, the retrieval was performed within a spectral window of approximately 7 nm that contained only Fraunhofer lines. In our retrieval, hyperspectral data of the O2-A band from the first Chinese carbon dioxide observation satellite (TanSat) was used. The Bayesian Information Criterion (BIC) was introduced to self-adaptively determine the number of free parameters and reduce retrieval noise. SIF retrievals were compared with TanSat SIF and OCO-2 SIF. The results showed good consistency and rationality. A sensitivity analysis was also conducted to verify the performance of this approach. To summarize, the approach would provide more possibilities for retrieving SIF from hyperspectral data.

International audience; Numerical simulation of random rough surface finds wide applications in scientific disciplines, e.g., radar remote sensing of terrain and sea. In scattering simulation of rough surface, not only energy conservation must be ensured, but also, perhaps equally important, the surface inherent properties must be preserved. However, the proper choice of surface and grid sizes that are statistically representative poses a problematic issue. This study applied the entropy measure to determine such parameter settings by examining the relative error of sample entropy associated with roughness parameters and by noticing the fact that a rough surface with certain roughness parameters, including power spectrum density function, must have unique sample entropy. It is found that if the two criteria are met, proper choice of surface length and grid size is attainable to warrant minimum uncertainties of rough surfaces and maximum information content for different roughness spectra density functions under different correlation lengths. The feasibility and superiority of the proposed entropy-based method are validated in terms of minimum error of roughness parameters and also the energy conservation in bistatic scattering coefficients of rough surfaces generated using obtained simulation parameters.

International audience; Net surface shortwave radiation (NSSR) is a key parameter that drives the surface material exchange and energy balance. Herein, we propose an improved artificial neuron network (ANN) with parameterized (ANN-P) method to first calculate the albedo at the top of atmosphere (TOA) by considering the surface non-Lambertian effect. Subsequently, the NSSR is estimated based on the relationship between TOA broadband albedo and the Earth's surface-absorbed shortwave radiation using a parameterized method under clear sky. The modeling process is implemented with Chinese Gaofen-5 (GF-5) visible/near-infrared channels data simulated via MODTRAN. For comparison, a previously reported lookup table (LUT) with parameterized (LUT-P) method and an ANN method are also employed. The performances of all these methods are evaluated. In terms of model simulation part, the root-mean-square errors (RMSEs) are 15.01 (17.07), 10.04 (13.67), and 20.39 (29.99) W/m 2 for land, water, and snow/ice surfaces, respectively, for the ANN-P (versus LUT-P) method. Their mean bias errors (MBEs) are within 0.9 W/m 2. With respect to the direct ANN method, Manuscript it shows the highest accuracy yet relatively large deviation for water surface. Additionally, the sensitivity analysis of water vapor content (WVC) confirms that the ANN-P method is more stable than the LUT-P and ANN methods and is, thereby, recommended for clear-sky NSSR estimation. Finally, the ground validations indicate that the mean RMSEs (MBEs) for the LUT-P, ANN-P, and ANN methods are 49.33 (−3.01), 47.55 (1.75), and 104.24 (−75.72) W/m 2 , respectively. Index Terms-Artificial neuron network (ANN), Chinese Gaofen-5 (GF-5), net surface shortwave radiation (NSSR), top of atmosphere (TOA) broadband albedo.

International audience; Evapotranspiration (ET) is a primary mechanism for water and heat transfer between land and the atmosphere. One approach to estimate ET is from instantaneous remotely sensed data. The constant evaporative fraction (EF) method is then usually used to estimate integrated daily fluxes, which are typically underestimated values. Here we present a theoretical improvement to the conventional EF. The improved EF is shown to be robust and superior to the conventional approach, and it significantly reduces the underestimation bias.

International audience; The linear spectral emissivity constraint (LSEC) method has been proposed to separate temperature and emissivity in hyperspectral thermal infrared data with an assumption that land surface emissivity (LSE) can be described by an equal interval piecewise linear function. This paper combines a pre-estimate shape method with the LSEC method to provide an initial-shape estimation of LSE which will create a new piecewise scheme for land surface temperature (LST) and LSE separation. This new scheme is designated as the pre-estimate shape (PES)-LSEC method. Comparisons with the LSEC method using simulated data sets show that the PES-LSEC method has better performance in terms of accuracy for both LSE and LST. With an at-ground error of 0.5 K, the root-mean-square errors (RMSEs) of LST and LSE are 0.07 K and 0.0045, respectively, and with the scale factor of moisture profile 0.8 and 1.2, the RMSEs of LST are 1.11 K and 1.14 K, respectively. The RMSEs of LSE in each channel are mostly below 0.02 and 0.04, respectively, which are better than for the LSEC method. In situ experimental data are adopted to validate our method: The results show that RMSE of LST is 0.9 K and the mean value of LSE accuracy is 0.01. The PES-LSEC method with fewer segments achieves better accuracy than that of LSEC and preserves most of the crest and trough information of emissivity.

Zirconium-iron metal–organic frameworks (Zr/Fe-MOFs) and Zr/Fe-MOF/graphene oxide (GO) composites were prepared via solvothermal synthesis using ferrous sulfate heptahydrate, zirconium acetate, and 1,3,5-benzenetricarboxylic acid. The MOFs and composites were measured using scanning electron microscopy (SEM), infrared spectrometry (IR), and thermogravimetric analysis (TGA). In this study, we explored the ability of Zr/Fe-MOFs and Zr/Fe-MOF/GO composites to adsorb tetracycline hydrochloride from an aqueous solution. Additionally, we optimized the adsorption performance by varying the ratio of MOFs and MOF composites to tetracycline hydrochloride solution, the concentration of tetracycline hydrochloride solution, and the pH of the solution. The results were investigated and fit to both pseudo-first-order and pseudo-second-order kinetic models. The results of the Freundlich and Langmuir isotherm models indicate that Zr/Fe-MOFs and Zr/Fe-MOF/GO composites have heterogeneous adsorption surfaces and that tetracycline hydrochloride is adsorbed over Zr/Fe-MOFs and Zr/Fe-MOF/GO by multilayer adsorption. Overall, our findings indicate that Zr/Fe-MOFs and Zr/Fe-MOF/GO composites can effectively treat wastewater, providing an inexpensive alternative to other methods.

In recent years, the global energy environment has become increasingly severe, and the problems such as global warming, soaring carbon emissions, and excessive use of petrochemical energy have attracted increasing attention from all walks of life. The construction industry, which accounts for about 30% of the total energy consumption, needs to effectively manage and use renewable energy. The application of building information model (BIM) in the evaluation of retrofitting schemes of existing buildings is one of the main research issues in the field of building digital twins at present. The method of this research is to use 3D laser scanning technology to efficiently create the building energy model (BEM) of existing buildings and to identify and evaluate the feasibility of existing building retrofitting schemes. The purpose of this research is to evaluate the retrofitting scheme of existing buildings based on the concept of nearly zero-energy buildings (nZEBs), aiming to improve the energy efficiency of existing buildings and use clean energy to satisfy building energy demand. According to the case study in this paper, an nZEBs solution suitable for the building can reduce building energy costs by 14.1%, increase solar photovoltaic power generation by 24.13%, and reduce carbon dioxide emissions by 4306.0 kg CO2eq/a.

Fine spatial resolution land surface temperature (LST) data derived from a thermal infrared remote sensing image are essential to the study of land surface energy, water and carbon cycles. As an alternative and effective way to obtain fine spatial resolution LST, a large number of LST downscaling methods have been proposed in recent decades to enhance coarse resolution LST to fine resolution. However, the drawbacks of the random selection of scaling factors and the establishment of statistical regression relationships are obvious. In this context, a general and physical LST downscaling method based on surface energy balance (DTsEB) is proposed in this study. Moderate Resolution Imaging Spectroradiometer (MODIS) LST data at 990 m spatial resolution were downscaled to 90 m by using this new proposed SEB-based LST downscaling method in this study. Compared with the concurrent 90 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST data, the downscaled results have a mean absolute error (MAE) of 1.37 K and a root mean square error (RMSE) of 1.84 K.

The tradeoff between spatial and temporal resolution in satellite observations substantially restrains the potential applications of Land Surface Temperature (LST) products. So far, many spatiotemporal fusion models have been developed to address the issue and a unified comparison in LST data fusion is still required. In this paper, four popular spatiotemporal fusion algorithms including Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Unmixing-based data fusion method, Flexible Spatiotemporal Data Fusion (FSDAF) and Spatio-Temporal Integrated Temperature Fusion Model (STITFM) were adopted to generate high spatial resolution LST using polar-orbiting and geostationary satellite data. The predicted LST was evaluated by the actual LST product and the result indicates that the overall accuracy of FSDAF is satisfied (about 2.87K) and the FSDAF algorithm is recommended to generate LSTs at high spatial and temporal resolution in heterogeneous area.

By taking energy awareness and efficiency into consideration, this research applies multiple linear regression (MLR) models and artificial neural networks (ANNs) for predicting the real-time heating energy demand and natural gas consumption in the drying chamber of an automotive painting line. With the estimation of the correlation and randomness during the painting process, a total of six fundamental variables that integrate energy, product and process data are considered in order to cover the impact factors of the natural gas consumption. The independent variable value, used to calibrate and evaluate the model, is obtained from our constructed energy consumption monitoring system. In the prediction cases, the ANN-based model, which offers a well performance, provides great precision in the determination of the natural gas demand with an R2 coefficient and other error measurements of over 90%. Based on the MLR model, the process temperature and the corresponding variation are found to be two decisive factors for the natural gas consumption. It is foreseen that the ANN model, which can effectively perform input/output mapping, is reliable and powerful for predicting the natural gas consumption and estimating the energy demand. Being a general data-driven method, our method is convenient for application to other heating process lines and even other energy-consuming plants.

The high-density polyethylene (HDPE) conductive composites filled separately with carbon fibers (CFs), ethylene-vinyl acetate copolymer (EVA), and three kinds of carbon blacks (CBs) (including diff...

The melt strength and drawability of a low-density polyethylene (LDPE), a high-density polyethylene (HDPE) and HDPE/LDPE blends during extension flow were studied using a Rheotens test method under die extrusion temperature ranging from 160 to 200 °C and die extrusion rates varying from 9 to 36 mm/s. The results showed that the melt strength of the HDPE, the LDPE and the blends decreased with increasing die extrusion temperature while it increased with increasing die extrusion rate. The drawability of the HDPE, the LDPE and the blend melts could be improved through increasing die extrusion temperature and reducing die extrusion ratio. The dependence of the melt strength of the HDPE, the LDPE and the blend melts on the die extrusion temperature approximately obeyed the Arrhenius equation.

Developing a precise and reproducible bandgap tuning method that enables tailored design of materials is of crucial importance for optoelectronic devices. Towards this end, we report a sphere diameter engineering (SDE) technique to manipulate the bandgap of two-dimensional (2D) materials. A one-to-one correspondence with an ideal linear working curve is established between the bandgap of MoS2 and the sphere diameter in a continuous range as large as 360 meV. Fully uniform bandgap tuning of all the as-grown MoS2 crystals is realized due to the isotropic characteristic of the sphere. More intriguingly, both a decrease and an increase of the bandgap can be achieved by constructing a positive or negative curvature. By fusing individual spheres in the melted state, post-synthesis bandgap adjustment of the supported 2D materials can be realized. This SDE technique, showing good precision, uniformity and reproducibility with high efficiency, may further accelerate the potential applications of 2D materials. The bandgap of 2D molybdenum disulfide can be tuned uniformly, reproducibly and precisely over a large range by using a glass sphere support and changing the diameter of the spheres.

Realgar and (-)-Epigallocatechin-3-gallate (EGCG) are natural medicines that inhibit cancer cell growth, resulting in inhibition of formation and development of tumors. The anticancer effects of realgar and EGCG were greatly improved following formulation as nanoparticles. EGCG has received increased attention as a drug carrier. The aim of this study was to prepare a new nanomedicine, (EGCG-RNPs), in which encapsulated nano-realgar. EGCG-RNPs were prepared by coprecipitation and characterized by transmission electron microscopy (TEM), differential scanning calorimetry (DSC), particle size and zeta potential, X-ray diffraction, Fourier transform infrared spectroscopy (FTIR) and in vitro release. Furthermore, we evaluated the antiproliferative effects of EGCG-RNPs on HL-60 cells in vitro, antitumor effect by intratumoral injection of EGCG-RNPs into solid tumors derived from APL HL-60 cells in vivo. Possible mechanisms were evaluated using uptake and efflux experiments in HL-60 cells. The results showed that the average particle size and zeta potentials of EGCG-RNPs was 200.3 ± 1.23 nm and −46.8 ± 1.31 mV. Controlled release of EGCG-RNPs was sustained and continued up to 72 h in vitro. Compared with nano-realgar and EGCG + RNPs (EGCG and nano-realgar physical mixing), EGCG-RNPs significantly inhibited growth of HL-60 cells. In a solid tumor model, EGCG-RNPs decreased tumor volumes, with an inhibitory rate of 60.18% at a dose of 70 mg · kg−1. The mechanisms of antitumor improvement may correlate with the increased uptake of realgar and prolonged the retention time of realgar in HL-60 cells due to EGCG as a carrier. EGCG-RNPs could enhance anticancer therapeutic efficacy for acute promyelocytic leukemia.

Recently, heteroatom co-doped carbon material catalysts have shown extraordinary oxygen reduction reaction (ORR) catalytic activity, but a lack of understanding of the specific mechanism of the ORR has hindered their further development. In this study, we studied the geometric structure, stability, electronic properties, catalytic sites and detailed ORR pathways of P, N co-doped graphene (PN-Gra) by density functional theory (DFT) calculations, trying to reveal the effect of N and P co-doped graphene on the catalytic activity of the ORR. Our calculations indicate that the PN-Gra structure remains stable at high temperature (1000 K) and P, N atoms with their adjacent four carbon atoms constitute the active region to adsorb O2 more efficiently than single phosphorus or nitrogen doped catalysts. Combining energy barrier and free energy diagram analysis, the most favorable pathway is O2* → O* + O* → OH* + O* → 2OH* → OH* + H2O → 2H2O, which is thermodynamically favorable and the associated energy barrier is low, with an overpotential of 0.96 V. After comprehensive analysis and comparison, P, N co-doping does improve the catalytic ability due to the synergistic effect compared to single doping. We believe that this study provides researchers a way to understand the reaction mechanism and ways to improve the catalytic properties of carbon-based materials.

Herein, a nonequilibrium molecular-dynamics simulation in an NVE ensemble was performed to investigate the spontaneous dissociation of methane-hydrate when it came in contact with liquid water. The nonequilibrium in the interface region is linked to the dissociation process of the hydrate near the interface according to the Onsager's hypothesis. The simulated thickness of the interface was found to be close to the acoustic phonon mean path of methane hydrate and agreed with the reference value. The normalized heat flow autocorrelation function was introduced to study fluctuation–dissipation in terms of the thickness and moving velocity of the interface and the Stefan number. This helped to clearly identify three distinct hydrate-decomposition regimes dominated by sensible heat, latent heat and an intrinsically unstable lattice framework. It was found that the fluctuation–dissipation theory could express the nonequilibrium nature in the front two stages before the threshold was reached, and the dissociation rate increased in the latter stage; this was different from the case of thermal-driven dissociation. The Stefan number decreased rapidly with dissociation in the initial stage and then fluctuated in the intermediate stage; this was analogous to the fluctuation characteristics of the heat flow autocorrelation function. The Stefan number effect shows that thermal dissipation drives the hydrate dissociation and correlates fluctuation to the nonequilibrium nature. It was also found that a small Stefan number was enough to break up the residual hydrate soon after the threshold was achieved. The transient interfacial thermal resistance of the interfacial region was obtained as a typical value in the range of 10−7–10−9 m2 K W−1. This justifies that fluctuation–dissipation exists in the nonequilibrium process of hydrate dissociation either in terms of heat flux, as observed in this study, or the diffusion of guest molecules, as reported in other studies.

Welding spatter seriously affects the surface quality of the product. Aiming at the automatic detection problem of spatter on welding plate surface, an in-situ detection algorithm of welding spatter based on machine vision is designed. In the extraction process of the welding spatter, the two-dimensional Fourier transform is adopted to obtain the frequency and phase information of image, and the elliptical high-pass filter is introduced to filter the low-frequency signal. The experimental results show that the proposed algorithm has higher extraction rate and extraction accuracy rate of welding spatter than the threshold method, the rectangular high-pass filter and the Canny operator, and it has the characteristics of high efficiency, high precision, and good robustness.

To avoid the defects caused by the hydrogen evolution and improve the corrosion and wear properties of the electroplated films in the traditional aqueous bath electrodeposition, a supercritical carbon dioxide (Sc-CO2) emulsion was proposed to electrodeposite ternary nanocrystalline Co−Ni−P alloy films. Microstructure, corrosive and tribological properties of the Co−Ni−P films were investigated and compared with the ones electroplated by conventional method. The results show that the Co−Ni−P films produced with Sc-CO2 assisted electrodeposition exhibit a more compact microstructure. The preferred orientation plane of hcp (110) for the Co−Ni−P films produced in conventional aqueous bath is changed to be hcp (100) for the one prepared in emulsified Sc-CO2 bath. The microhardness, corrosion resistance and tribological properties of the Co−Ni−P films are substantially improved with the assistance of Sc-CO2 in the electrodeposition bath.