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The Source Area of the Yellow River is located in the northeastern part of the Qinghai-Xizang Plateau， and the meteorological stations are sparsely distributed in this basin， the study of the applicability of various precipitation data products has an important values in promoting the hydrological modeling in the basin.Based on the China Meteorological Assimilation Datasets for SWAT model Version1.1 （CMADS V1.1）， the Tropical Rainfall Measurement Mission （TRMM） precipitation datasets （3B42 Version7） and the Soil and Water Assessment Tool （SWAT） driven by these precipitation data， respectively， and the SWAT-CUP （SWAT Calibration and Uncertainty Program） and SUFI-2 （Sequential Uncertainty Fitting2） algorithm 27 sensitivity parameters were rate in simulating the variation of multi-year monthly average runoff， the simulated results were compared with the observations to evaluate the accuracy of CMADS and TRMM 3B42 precipitation data products and the applicability of SWAT model were evaluated in the Source Area of the Yellow River source area.The results show that： （1） The distribution of all three precipitation datasets showed an increasing trend from the west to the east， and TRMM 3B42 was in better agreement with the measured precipitation than CMADS data set in terms of annual and monthly variation.（2） The sensitivity analysis of the parameters showed that the sensitivity degree of SCS （Soil Conservation Service） runoff curve number， groundwater lagging coefficient， and soil evaporation compensation coefficient were stronger than that of the others.（3） The simulated runoff by using the CMADS and TRMM 3B42 precipitation datasets had better results than that by using the measured precipitation data， with the correlation coefficients R2 of 0.93， 0.92 and 0.88 for the rate period at the three hydrological stations， respectively， while the results of the TRMM 3B42 simulation were the next best， with the coefficients of correlation （R） of the rate-period and validation-period of above 0.80， and the Nash-Sutcliffe efficiency coefficient （NSE） of the simulations is above 0.50.This research demonstrates the applicability of CMADS datasets and SWAT model for runoff simulation in high-altitude areas with complex landscape types and sensitive to climate change， and provides a replacement solution for improving the hydrological models in areas where there are sparely meteorological stations.

ABSTRACTSatellite datasets have revealed significant greening and soil drying in arid Central Asia. However, the influence mechanism of vegetation and climate on soil moisture dynamics is still unclear. In this study, we investigated the spatiotemporal consistency of vegetation and soil moisture changes, examined the controlling factors of soil moisture under different vegetation indices (normalized differential vegetation index, NDVI; leaf area index, LAI), and explored the time lag and accumulation effects of vegetation greening on the soil moisture in arid Central Asia in the period of 1985 to 2020. The results showed that the co-occurrence of vegetation greening and soil drying in 30.4% (NDVI) and 19.4% (LAI) of the study area spatially. Temporally, the soil moisture increased from 1985 to 1989, but decreased from 1989 or 1990 to 2020. In contrast, vegetation activity increased continuously during 1985–2020. Precipitation accounted for the most variance of the soil moisture. The vegetation greenness demonstrated positive correlations with soil moisture, representing 20.7% (for NDVI) and 39.3% (for LAI) of vegetated areas (areas with long-term mean NDVI ≥ 0.1 and LAI > 0), while negative correlations were observed in only 5.4% (for NDVI) and 3.3% (for LAI) of vegetated areas, respectively. Soil moisture exhibited varying time lag and accumulation effects in response to different vegetation indices, precipitation, and temperature. The main temporal effects of NDVI and temperature were simultaneous changes with no time lag or accumulation effects on soil moisture. In contrast, the main temporal effects of LAI and precipitation included 1-month lag in their influence on soil moisture. Considering the inconsistencies in spatiotemporal patterns between vegetation and soil moisture, as well as the dominance and time lag effects of precipitation on soil moisture variations, we concluded that climate, especially precipitation, rather than vegetation greening, regulates soil moisture in Central Asia. These findings provide a scientific basis for vegetation restoration or regional water resource management in drylands.

Amidst swift socioeconomic progress, urbanization is intensifying, necessitating robust methodologies for environmental quality monitoring. Landscape Ecological Risk (LER) has emerged as a pivotal tool in this context. Extensive research has focused on the land utilization rates within watersheds, scrutinizing them through the lens of land use/land cover (LULC) dynamics. This study delves into the LULC attributes of the Bosten Lake Basin (BLB) across three temporal snapshots: 2000, 2010, and 2020. Grasslands and bare ground made up the vast majority area of the BLB. Selected twelve landscape pattern indices, analyzed their response curves with respect to grain size, determined the optimal grain size to be 150 m, and analysed its optimum amplitude of 10 km × 10 km on the basis of the semi-variance function, thus determined the optimal scale for analyzing the landscape pattern. Exploring scale effect facilitates a nuanced examination of the landscape patterns and land use efficacy within BLB. From 2000 to 2020, the landscape pattern showed less fragmented, less aggregated, and more spatially heterogeneous. The landscape ecological risk index model has been constructed for the investigation of the LER change characteristics of the BLB. Lowest-risk zones and lower-risk zones of LER predominate in BLB. In 2000, 2010 and 2020 the combined lower and lowest risk zones represent 62.02 %, 59.37 % and 58.44 % of the catchment area respectively. Finally, analyzed the spatial autocorrelation variation characteristics of LER. The global Moran' I index of BLB for the three periods from 2000 to 2020 exceeded 0.7. This study establishes theoretical and practical foundation for future multi-scale landscape ecological risk studies in eco-fragile regions.

Amidst the backdrop of global climate change, it is imperative to comprehend the intricate connections among surface water, vegetation, and climatic shifts within watersheds, especially in fragile, arid ecosystems. However, these relationships across various timescales remain unclear. We employed the Ensemble Empirical Mode Decomposition (EEMD) method to analyze the multifaceted dynamics of surface water and vegetation in the Bosten Lake Watershed across multiple temporal scales. This analysis has shed light on how these elements interact with climate change, revealing significant insights. From March to October, approximately 14.9–16.8% of the areas with permanent water were susceptible to receding and drying up. Both the annual and monthly values of Bosten Lake’s level and area exhibited a trend of initial decline followed by an increase, reaching their lowest point in 2013 (1,045.0 m and 906.6 km2, respectively). Approximately 7.7% of vegetated areas showed a significant increase in the Normalized Difference Vegetation Index (NDVI). NDVI volatility was observed in 23.4% of vegetated areas, primarily concentrated in the southern part of the study area and near Lake Bosten. Regarding the annual components (6 < T < 24 months), temperature, 3-month cumulative NDVI, and 3-month-leading precipitation exhibited the strongest correlation with changes in water level and surface area. For the interannual components (T≥ 24 months), NDVI, 3-month cumulative precipitation, and 3-month-leading temperature displayed the most robust correlation with alterations in water level and surface area. In both components, NDVI had a negative impact on Bosten Lake’s water level and surface area, while temperature and precipitation exerted positive effects. Through comparative analysis, this study reveals the importance of temporal periodicity in developing adaptive strategies for achieving Sustainable Development Goals in dryland watersheds. This study introduces a robust methodology for dissecting trends within scale components of lake level and surface area and links these trends to climate variations and NDVI changes across different temporal scales. The inherent correlations uncovered in this research can serve as valuable guidance for future investigations into surface water dynamics in arid regions.

Salinization remains a major issue in soil degradation, for which biochar is a potential solution. In this meta-analysis, using 660 paired observations from 99 peer-reviewed articles, we evaluated biochar's effect on salt-affected soils and identified the initial soil properties, biochar properties and experimental factors influencing its efficacy. The results showed that the addition of biochar had a significant ameliorating effect on salt-affected soils. The soil electrical conductivity of saturated paste extract (ECe) was significantly reduced by 13.2%, and such effect was significant in severe salinization soil (rather than slight and moderate salinization). Meanwhile, the soil cation exchange capacity was significantly increased by 17.0%. However, the addition of biochar had no significant impact on soil pH. Model selection analyses further indicated that the level of initial salinity and the type of biochar feedstock were the most important factors regulating the response of soil ECe to biochar addition. In summary, while our study highlights the potential of biochar in ameliorating salt-affected soils, particularly in severely salinized soils, it also underscores the need for more comprehensive research in this field. Additional research is necessary to comprehensively address the significant heterogeneity of biochars, including their thorough characterization. In addition, more studies are required to explore the impact of biochar on both salt-tolerant and non-salt-tolerant plants in salt-affected soils.

Tribology is the science and engineering of interacting surfaces in relative motion [...]

The Latent Dirichlet Allocation (LDA) model is used to extract the text themes of newspaper news and construct the Chinese Economic Policy Uncertainty (EPU) Index. On this basis, based on the relevant data of Chinese A-share listed companies from 2008 to 2020, this paper empirically analyzes the impact of EPU on peer effects of firms R&D investment, and finds that EPU will aggravate the peer effects of firms R&D investment. Furthermore, the moderating effect of manager's motivation to maintain reputation on the process of EPU influencing the peer effects of firms R&D investment was tested, and the mechanism of EPU influencing the peer effects of firms R&D investment through financial frictions was verified.

Abstract Background Chronic inflammation significantly contributes to photoreceptor death in blinding retinal diseases such as age-related macular degeneration (AMD) and retinitis pigmentosa (RP). Bromodomain and extraterminal domain (BET) proteins are epigenetic readers that act as key proinflammatory factors. We recently found the first-generation BET inhibitor JQ1 alleviated sodium iodate-induced retinal degeneration by suppressing cGAS-STING innate immunity. Here, we investigated the effects and mechanism of dBET6, a proteolysis‑targeting chimera (PROTAC) small molecule that selectively degrades BET by the ubiquitin‒proteasome system, in light-induced retinal degeneration. Methods Mice were exposed to bright light to induce retinal degeneration, and the activation of cGAS-STING was determined by RNA-sequencing and molecular biology. Retinal function, morphology, photoreceptor viability and retinal inflammation were examined in the presence and absence of dBET6 treatment. Results Intraperitoneal injection of dBET6 led to the rapid degradation of BET protein in the retina without detectable toxicity. dBET6 improved retinal responsiveness and visual acuity after light damage (LD). dBET6 also repressed LD-induced retinal macrophages/microglia activation, Müller cell gliosis, photoreceptor death and retinal degeneration. Analysis of single-cell RNA-sequencing results revealed cGAS-STING components were expressed in retinal microglia. LD led to dramatic activation of the cGAS-STING pathway, whereas dBET6 suppressed LD-induced STING expression in reactive macrophages/microglia and the related inflammatory response. Conclusions This study indicates targeted degradation of BET by dBET6 exerts neuroprotective effects by inhibiting cGAS-STING in reactive retinal macrophages/microglia, and is expected to become a new strategy for treatment of retinal degeneration.

This work studied the influence of the voltage parameters on the friction and superlubricity performances of LiPF6-based ionic liquids (ILs). The results show that the voltage direction and magnitude greatly affected the friction performances of ILs and that macroscale superlubricity can be achieved with a stimulation of −0.1 V. The surface analysis and experiment results indicate that the voltage magnitude influences the coefficient of friction (COF) by determining the types of substances in the tribochemical film formed on the ball, while the voltage direction influences the COF by affecting the adsorption behavior of Li(PEG)+ ions on the ball. At −0.1 V, the cation group Li(PEG)+ adsorption film and FeOOH-containing tribochemical film contribute to friction reduction. The formation of FexOy within the tribochemical film results in an increase in friction at −0.8 V. The limited adsorption of Li(PEG)+ ions and the formation of FexOy contribute to the elevated COF at +0.1 V. This work proves that the friction performances of LiPF6-based ILs could be affected by voltage parameters. A lubrication model was proposed hoping to provide a basic understanding of the lubrication mechanisms of ILs in the electric environment.

Abstract Large-scale soil salinity surveys are time-costly and labor-intensive, and it is also more difficult to investigate historical salinity, while in arid and semi-arid regions, the investigation of the spatial and temporal characteristics of salinity can provide a scientific basis for the scientific prevention of salinity, With this objective, this study uses multi-source data combined with ensemble learning and Google Earth Engine to build a monitoring model to observe the evolution of salinization in the Werigan–Kuqa River Oasis from 1996 to 2021 and to analyze the driving factors. In this experiment, three ensemble learning models, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were established using data collected in the field for different years and some environmental variables, After the accuracy validation of the model, XGBoost had the highest accuracy of salinity prediction in this study area, with RMSE of 17.62 dS m−1, R2 of 0.73 and RPIQ of 2.45 in the test set. In this experiment, after Spearman correlation analysis of soil Electrical Conductivity (EC) with environmental variables, we found that the near-infrared band in the original band, the DEM in the topographic factor, the vegetation index based on remote sensing, and the salinity index soil EC had a strong correlation. The spatial distribution of salinization is generally characterized by good in the west and north and severe in the east and south. Non-salinization, light salinization, and moderate salinization gradually expanded southward and eastward from the interior of the western oasis over 25 years. Severe and very severe salinization gradually shifted from the northern edge of the oasis to the eastern and southeastern desert areas during the 25 years. The saline soils with the highest salinity class were distributed in most of the desert areas in the eastern part of the Werigan–Kuqa Oasis study area as well as in smaller areas in the west in 1996, shrinking in size and characterized by a discontinuous distribution by 2021. In terms of area change, the non-salinized area increased from 198.25 in 1996 to 1682.47 km2 in 2021. The area of saline soil with the highest salinization level decreased from 5708.77 in 1996 to 2246.87 km2 in 2021. overall, the overall salinization of the Werigan–Kuqa Oasis improved.

Abstract Superlubricity has drawn substantial attention worldwide while the energy crisis is challenging human beings. Hence, numerous endeavors are bestowed to design materials for superlubricity achievement at multiple scales. Developments in graphene-family materials, such as graphene, graphene oxide, and graphene quantum dots, initiated an epoch for atomically thin solid lubricants. Nevertheless, superlubricity achieved with graphene-family materials still needs fundamental understanding for being applied in engineering in the future. In this review, the fundamental mechanisms for superlubricity that are achieved with graphene-family materials are outlined in detail, and the problems concerning graphene superlubricity and future progress in superlubricity are proposed. This review concludes the fundamental mechanisms for graphene superlubricity and offers guidance for utilizing graphene-family materials in superlubricity systems.

Abstract An oil-impregnated porous polyimide (PI) retainer is used in space rolling bearings to improve the lubrication performance, which depends on the release of lubricant from the pores, and therefore is closely related to the pore size. To study the effect of pore size, in this work, PI materials with different pore sizes were prepared by preheating the retainer tube billet during the limit pressing process, and then the friction tests were conducted with the ball-on-ring mode. The results show that the applied load deforms the pores, allowing the lubricant to be squeezed out from the pore; the centrifugal effect induced by rotation also makes the lubricant migrate out of the pore. Therefore, for the same pore sizes, the friction coefficients decrease with the increasing loads and rotation speeds. In addition, it was found that there exists an optimal pore size for the best lubrication properties of porous PI material. Furthermore, the optimal pore size should be larger for lubricants with high viscosity. The microscopic mechanism for lubricant outflow from pores is clarified by molecular dynamic simulations. The insights gained in this study can guide the preparation of oil-impregnated porous retainers under different working conditions.

Climate change has caused inland wetlands shrinkage and exacerbated problems, such as sustainable development and ecological security, for years. These issues are mainly pronounced in the inland arid area. The ecological environment’s deterioration is especially severe in the drylands of the interior. However, dryland wetland changes and their response to climate are poorly understood. This study uses the K-means algorithm in Google Earth Engine (GEE) to classify two typical dryland wetlands (Ebinur and Bosten Lakes) for rapidly and accurately detecting dryland wetland changes. Moreover, it explores the long-term spatial–temporal variation in wetland distribution. In addition, it investigates the response of various lakes to climate change in northern and southern Xinjiang using wavelet analysis. The study’s results showed that K-means clustering in the GEE platform has a high classification accuracy (Kappa > 0.8) in wetland classification, making it a feasible approach. The terminal lake wetland types, represented by the Ebinur Lake, changed significantly between 2001 and 2021. In contrast, the inflow-outflow lake wetland types, represented by the Bosten Lake, perform more consistently. Significant spatial–temporal variation is observed at Ebinur Lake, with the lake gradually shrinking and transforming into a marsh, where the largest marsh proportion degrades into non-wetland during the year. Bosten Lake experienced frequent conversions between marsh and non-wetland throughout the year. Furthermore, the responses of various dryland lakes to climate change are consistent, and a low precedes precipitation and follows evapotranspiration. However, their sensitivity to climate response varies, with the terminal lake being most affected by climate change. Mastering the dynamic changes and climate response of dryland wetlands achieves the sustainable development goals of drylands, including carbon neutrality and peak carbon dioxide emissions.

The option butterfly portfolio is the commonly option arbitrage strategy. In reality, because the distribution of the option state price density (SPD) function is not normal and unknown, so the nonparametric deep learning methods to estimate option butterfly portfolio returns are proposed. This paper constructs the single-index nonparametric option pricing model which contains multiple influencing factors and presents the nonparametric estimation form for option butterfly portfolio returns. The empirical analysis shows that the SPD function estimated by using single-index nonparametric option model can effectively calculate the option butterfly portfolio returns with the minimum option strike price interval and provide an effective reference tool for risk-averse investors with limited risk preferences.

Ecological challenges resulting from soil salinization in the Tarim River Basin (TRB), exacerbated by climate change and human activities, have emphasized the need for a quick and accurate assessment of regional ecological environmental quality (EEQ) and driving mechanisms. To address this issue, this study has developed a remote-sensing ecological index with salinity adaptability (RSEISI) for EEQ assessment in the Tarim River Basin by integrating the comprehensive salinity index (CSI) into the remote-sensing ecological index (RSEI). The RSEISI enhances the sensitivity of soil salinity and characterizes the surface features of arid regions, thus expanding the applicability. Then, we used time-series analysis methods and a geodetector to quantify the spatial temporal trends and driving factors of EEQ in the TRB from 2000 to 2022. The results show that the RSEISI with salinity adaptation effectively monitors the EEQ of the TRB. The EEQ of the TRB displayed the situation of oasis expansion, desert deterioration, and glacier melting, and the multiyear average EEQ grades were dominated by medium and poor grades in desert and saline areas, while medium, good, and excellent grades were concentrated in oasis and mountainous areas. Looking at the trend of change in conjunction with land-use types, the EEQ of the TRB showed a mild degradation trend mainly in unused land, followed by a mild improvement trend in cropland and grassland. The Hurst index indicated that the EEQ of most areas of the TRB will improve in the future. Soil type, land use, precipitation, and temperature were considered to be key factors affecting the EEQ across the TRB, and changes in the EEQ were found to be the interaction of multiple factors. This study may provide innovative concepts and methodologies, scientific and technological support for ecological management, and green development models in the northwest arid zone.

Exploring the future trends of land use/land cover (LULC) changes is significant for the sustainable development of a region. The simulation and prediction of LULC in a large-scale basin in an arid zone can help the future land management planning and rational allocation of resources in this ecologically fragile region. Using the whole Ili-Balkhash Basin as the study area, the patch-generating land use simulation (PLUS) model and a combination of PLUS and Markov predictions (PLUS–Markov) were used to simulate and predict land use in 2020 based on the assessment of the accuracy of LULC classification in the global dataset. The accuracy of simulations and predictions using the model were measured for LULC data covering different time periods. Model settings with better simulation results were selected for simulating and predicting possible future land use conditions in the basin. The future predictions for 2025 and 2030, which are based on historical land change characteristics, indicate that the overall future spatial pattern of LULC in the basin remains relatively stable in general without the influence of other external factors. Over the time scale of the future five years, the expansion of croplands and barren areas in the basin primarily stems from the loss of grasslands. Approximately 48% of the converted grassland areas are transformed into croplands, while around 40% are converted into barren areas. In the longer time scale of the future decade, the conversion of grasslands to croplands in the basin is also evident. However, the expansion phenomenon of urban and built-up lands at the expense of croplands is more significant, with approximately 774.2 km2 of croplands developing into urban and built-up lands. This work provides an effective new approach for simulating and predicting LULC in data-deficient basins at a large scale in arid regions, thereby establishing a foundation for future research on the impact of human activities on basin hydrology and related studies.

Abstract Background Genome-wide association studies have reported more than 100 risk loci for rheumatoid arthritis (RA). These loci are shown to be enriched in immune cell-specific enhancers, but the analysis so far has excluded stromal cells, such as synovial fibroblasts (FLS), despite their crucial involvement in the pathogenesis of RA. Here we integrate DNA architecture, 3D chromatin interactions, DNA accessibility, and gene expression in FLS, B cells, and T cells with genetic fine mapping of RA loci. Results We identify putative causal variants, enhancers, genes, and cell types for 30–60% of RA loci and demonstrate that FLS account for up to 24% of RA heritability. TNF stimulation of FLS alters the organization of topologically associating domains, chromatin state, and the expression of putative causal genes such as TNFAIP3 and IFNAR1. Several putative causal genes constitute RA-relevant functional networks in FLS with roles in cellular proliferation and activation. Finally, we demonstrate that risk variants can have joint-specific effects on target gene expression in RA FLS, which may contribute to the development of the characteristic pattern of joint involvement in RA. Conclusion Overall, our research provides the first direct evidence for a causal role of FLS in the genetic susceptibility for RA accounting for up to a quarter of RA heritability.

Soil salinization has hampered the achievement of sustainable development goals (SDGs) in many countries worldwide. Several countries have recently launched hyperspectral remote sensing satellites, opening new avenues for accurate soil-salinity monitoring. Among them, Gaofen-5 (GF-5) from China has a high comprehensive performance, including a spectral resolution of 5 nm, 330 bands, and signal-to-noise ratio of 700. However, the potential of GF-5 for estimating soil salinity is not well understood. In this study, we proposed a strategy that includes bootstrap methods, fractional order derivative (FOD) techniques and decision-level fusion models to exploit the soil salinity diagnostic information and reduce estimation uncertainty in the Ebinur Lake oasis in northwestern China. The results showed that the GF-5 data were suitable for assessing soil salinity. The FOD technique enhanced the correlation between soil salinity and spectra, identified more diagnostic bands, improved the accuracy of soil salinity estimation, and reduced model uncertainty. The low-order FOD outperformed the high-order FOD. The spectra processed by the 0.9 order derivative were the most correlated with soil salinity (r = −0.76). The model driven by the 0.8 order derivative produced the optimal estimated model (R2 = 0.95, root mean square error (RMSE) = 3.20 dS m−1 and a ratio of performance to interquartile distance (RPIQ) = 5.96). The model driven by the 0.8 order derivative had less uncertainty than the models based on the original and integer-order derivative (first- and second- derivatives) spectra. This study provides a reference for estimating soil salinity from GF-5 data using the proposed framework with low uncertainty and high accuracy. GF-5 data have great potential for assessing environmental problems and facilitating further SDGs.

Water scarcity has emerged as a major impediment to the long-term development of inland arid region basins. Increased human activity has produced land degradation in arid areas and water stress in recent years. The objective of this research was to expose patterns in land cover changes in inland arid basin and the mechanisms driving them, as well as to optimize the socioeconomic and ecological water consumption structure of water resources. Therefore, this research used Google Earth Engine to map land cover year by year from 2000 to 2020, simulated future land cover changes and driving force analysis using a Patch-generating land use simulation model, and finally established the relationship between water consumption of different systems and different land cover types using linear equations, and scenario simulation method were used to estimates the ecological and agricultural water consumption in 2035. The results showed (1) Over the previous 20 years, there has been an increase in the amount of saline, agricultural land, and natural vegetation (grassland, scrub, and forest), as well as a considerable drop around permanent glaciers and snow. (2) Under the Markov-2035 scenario, saline land decreases to 32.92 km2, natural vegetation decreases to 381.89 km2, arable land shrinks to 135.69 km2, and the permanent glacier shrinks to 30.03 km2. Ecological water consumption would be 0.127 × 108 m3, while agricultural water consumption would be 0.394 × 108 m3. (3) Elevation influenced the variety of all land cover types. Saline land was more sensitive to temperature, railroad proximity, and pH; agricultural land was more sensitive to population density and gross regional product; and natural vegetation was more sensitive to soil organic matter, railroad proximity, and temperature.

Abstract Quantiles of random variable are crucial quantities that give more delicate information about distribution than mean and median and so on. We establish Jensen’s inequality for q-quantile ( q ≥ 0.5 $q\geq 0.5$ ) of a random variable, which includes as a special case Merkle (Stat. Probab. Lett. 71(3):277–281, 2005) where Jensen’s inequality about median (i.e. q = 0.5 $q= 0.5$ ) was given. We also refine this inequality in the case where q < 0.5 $q

Abstract Chronic myeloid leukemia (CML) is characterized by the accumulation of malignant and immature white blood cells which spread to the peripheral blood and other tissues/organs. Despite the fact that current tyrosine kinase inhibitors (TKIs) are capable of achieving the complete remission by reducing the tumor burden, severe adverse effects often occur in CML patients treated with TKIs. The differentiation therapy exhibits therapeutic potential to improve cure rates in leukemia, as evidenced by the striking success of all-trans-retinoic acid in acute promyelocytic leukemia treatment. However, there is still a lack of efficient differentiation therapy strategy in CML. Here we showed that MPL, which encodes the thrombopoietin receptor driving the development of hematopoietic stem/progenitor cells, decreased along with the progression of CML. We first elucidated that MPL signaling blockade impeded the megakaryocytic differentiation and contributed to the progression of CML. While allogeneic human umbilical cord-derived mesenchymal stem cells (UC-MSCs) treatment efficiently promoted megakaryocytic lineage differentiation of CML cells through restoring the MPL expression and activating MPL signaling. UC-MSCs in combination with eltrombopag, a non-peptide MPL agonist, further activated JAK/STAT and MAPK signaling pathways through MPL and exerted a synergetic effect on enhancing CML cell differentiation. The established combinational treatment not only markedly reduced the CML burden but also significantly eliminated CML cells in a xenograft CML model. We provided a new molecular insight of thrombopoietin (TPO) and MPL signaling in MSCs-mediated megakaryocytic differentiation of CML cells. Furthermore, a novel anti-CML treatment regimen that uses the combination of UC-MSCs and eltrombopag shows therapeutic potential to overcome the differentiation blockade in CML.

PM2.5, which refers to fine particles with an equivalent aerodynamic diameter of less than or equal to 2.5 µm, can not only affect air quality but also endanger public health. Nevertheless, the spatial distribution of PM2.5 is not well understood in data-poor regions where monitoring stations are scarce. Therefore, we constructed a random forest (RF) model and a bagging algorithm model based on ground-monitored PM2.5 data, aerosol optical depth (AOD) and meteorological data, and auxiliary geographical variables to accurately estimate the spatial distribution of PM2.5 concentrations in Xinjiang during 2015–2020 at a resolution of 1 km. Through 10-fold cross-validation (CV), the RF model and bagging algorithm model were verified and compared. The results showed the following: (1) The RF model achieved better model performance and thus can be used to estimate the PM2.5 concentration at a relatively high resolution. (2) The PM2.5 concentrations were high in southern Xinjiang and low in northern Xinjiang. The high values were concentrated mainly in the Tarim Basin, while most areas of northern Xinjiang maintained low PM2.5 levels year-round. (3) The PM2.5 values in Xinjiang showed significant seasonality, with the seasonally averaged concentrations decreasing as follows: winter (71.95 µg m−3) > spring (64.76 µg m−3) > autumn (46.01 µg m−3) > summer (43.40 µg m−3). Our model provides a way to monitor air quality in data-scarce places, thereby advancing efforts to achieve sustainable development in the future.

Although genome-wide association studies have identified genetic variation contributing to disease risk, assigning causal genes is challenging. Here, the authors generate ATAC-seq, Hi-C, Capture Hi-C and RNA-seq data in stimulated CD4+ T cells to identify functional enhancers and demonstrate interactions of expression quantitative trait loci with target genes in rheumatoid arthritis.

As the system becomes more and more complex, we are usually in the state of indeterminacy. In the real world, the states of uncertainty and randomness are the two most common types of indeterminacy. An uncertain random graph is applied to describe a graph model with uncertainty and randomness simultaneously. This paper mainly focuses on the connectivity of two vertices in an uncertain random graph. Firstly, a local connectivity index is proposed to unveil the chance measure that two special vertices are connected in an uncertain random graph. Furthermore, a method for calculating the local connectivity index is formulated. In addition, some simplified forms of the method are developed, and an algorithm is designed to obtain the local connectivity index. Finally, the information relevant to the relationship between the local connectivity index and the connectivity index is discussed.

This paper aims to explore several ways to construct a scientific and comprehensive early warning system (EWS) for local government debt risk in China. In order to achieve this goal, this paper studies the local government debt risk from multiple perspectives, i.e., individual risk, contagion risk, static risk and dynamic risk. Firstly, taking China’s 30 provinces over the period of 2010~ 2018 as a sample, this paper establishes early warning indicators for individual risk of local government debt, and uses the network model to establish early warning indicators for contagion risk of local government debt. Then, this paper applies the criteria importance though intercrieria correlation (CRITIC) method and coefficient of variation method to obtain the proxy variable Ⅰ, which combines the above two risks. Secondly, based on the proxy variable Ⅰ, both the Markov-switching autoregressive (MS-AR) model and coefficient of variation method are used to obtain the proxy variable Ⅱ, which comprehensively considers the individual risk, contagion risk, static risk and dynamic risk of local government debt. Finally, machine learning algorithms are adopted to generalize the EWS designed in this paper. The results show that: (1) From different perspectives of local government debt risk, the list of provinces that require early warning is different; (2) The support vector machines can well generalize our EWS.

One reason for soil degradation is salinization in inland dryland, which poses a substantial threat to arable land productivity. Remote-sensing technology provides a rapid and accurate assessment for soil salinity monitoring, but there is a lack of high-resolution remote-sensing spatial salinity estimations. The PlanetScope satellite array provides high-precision mapping for land surface monitoring through its 3-m spatial resolution and near-daily revisiting frequency. This study’s use of the PlanetScope satellite array is a new attempt to estimate soil salinity in inland drylands. We hypothesized that field observations, PlanetScope data, and spectral indices derived from the PlanetScope data using the partial least-squares regression (PLSR) method would produce reasonably accurate regional salinity maps based on 84 ground-truth soil salinity data and various spectral parameters, like satellite band reflectance, and published satellite salinity indices. The results showed that using the newly constructed red-edge salinity and yellow band salinity indices, we were able to develop several inversion models to produce regional salinity maps. Different algorithms, including Boruta feature preference, Random Forest algorithm (RF), and Extreme Gradient Boosting algorithm (XGBoost), were applied for variable selection. The newly constructed yellow salinity indices (YRNDSI and YRNDVI) had the best Pearson correlations of 0.78 and −0.78. We also found that the proportions of the newly constructed yellow and red-edge bands accounted for a large proportion of the essential strategies of the three algorithms, with Boruta feature preference at 80%, RF at 80%, and XGBoost at 60%, indicating that these two band indices contributed more to the soil salinity estimation results. The best PLSR model estimation for different strategies is the XGBoost-PLSR model with coefficient of determination (R2), root mean square error (RMSE), and ratio of performance to deviation (RPD) values of 0.832, 12.050, and 2.442, respectively. These results suggest that PlanetScope data has the potential to significantly advance the field of soil salinity research by providing a wealth of fine-scale salinity information.

The digital economy can change the proportions and types of production factors, gradually replace traditional backward production factors, reconstruct the division of labor and cooperation system, and improve productivity, which is an important basis for balanced and sufficient development. This paper measures the comprehensive level of the digital economy and balanced and sufficient development, by using Chinese provincial panel data from 2013 to 2021, and uses the panel fixed effect model, mediation effect model, and spatial econometric model to examine the digital economy’s effect on balanced and sufficient development as well as the digital economy’s mechanism. The results show that the digital economy has significantly promoted balanced and sufficient development, though there are obvious regional heterogeneity and spatial spillover effects, and the relevant conclusions are still valid after an endogenous treatment and a robustness test. The total factor productivity is an important mechanism for the digital economy to affect balanced and sufficient development.

Although understanding the carbon and water cycles of dryland ecosystems in terms of water use efficiency (WUE) is important, WUE and its driving mechanisms are less understood in Central Asia. This study calculated Central Asian WUE for 2001–2021 based on the Google Earth Engine (GEE) platform and analyzed its spatial and temporal variability using temporal information entropy. The importance of atmospheric factors, hydrological factors, and biological factors in driving WUE in Central Asia was also explored using a geographic detector. The results show the following: (1) the average WUE in Central Asia from 2001–2021 is 2.584–3.607 gCkg−1H2O, with weak inter-annual variability and significant intra-annual variability and spatial distribution changes; (2) atmospheric and hydrological factors are strong drivers, with land surface temperature (LST) being the strongest driver of WUE, explaining 54.8% of variation; (3) the interaction of the driving factors can enhance the driving effect by more than 60% for the interaction between most atmospheric factors and vegetation factors, of which the effect of the interaction of temperature (TEM) with vegetation cover (FVC) is the greatest, explaining 68.1% of the change in WUE. Furthermore, the interaction of driving factors with very low explanatory power (e.g., water pressure (VAP), aerosol optical depth over land (AOD), and groundwater (GWS)) has a significant enhancement effect. Vegetation is an important link in driving WUE, and it is important to understand the mechanisms of WUE change to guide ecological restoration projects.

When oil droplets impact a solid surface for oil-air lubrication, they may spread out to produce a thin oil layer on the surface, which serves as a significant source of lubrication. A test rig was constructed in this research to observe the spreading behavior of oil droplets impacting surfaces from both frontal and lateral views. From the frontal view, laser-induced fluorescence techniques are used to measure the thickness of the oil layer quantitatively during the spreading of oil droplets. While the lateral view can observe the shape evolution of the droplets. Oil droplet spreading patterns on the sheet with dry surfaces and with different thin liquid film thicknesses were studied, and the effect of viscosity and the thickness of the thin liquid film on spreading radius and spreading thickness is considered. The experimental findings demonstrate that the maximum spreading factor, the spreading central layer thickness, and the apparentness of retraction all increase as viscosity increases. The retraction is obviously impacted by thin liquid films, and the retraction weakens as the thin liquid film thickness increases.

Graphene-family materials have been investigated by researchers as promising additives for various lubrication systems due to their unique physical-chemical properties. It has been proven that graphene-family materials can lead to enhanced lubrication and wear-resistance performance, which have potential to reduce the energy losses and carbon emissions, and the wear of machines for industrial applications. Experimental, theoretical, and simulation studies have been performed to investigate the tribological behaviors of graphene-family materials as additives. The tribological properties of graphene-family materials, including graphene, reduced graphene oxide, functionalized graphene, and the combination of graphene-family materials and other materials as additives, and the fundamental mechanism are systematically reviewed and concluded. The authors also discuss the potential engineering applications of graphene-family materials as lubricating additives, and the unsolved issues and optimistic outlooks in the near future.

Neuroinflammation plays a crucial role in neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), and anti-inflammation has been considered as a potential therapeutic strategy. Achyranthes bidentate polypeptide fraction k (ABPPk) was shown to protect neurons from death and suppress microglia and astrocyte activation in PD model mice. However, how ABPPk regulates neuroinflammation to exert a neuroprotective role remains unclear. Toxic Aβ oligomers (AβOs) can trigger inflammatory response and play an important role in the pathogenesis of AD. In the present study, for the first time, we investigated the effects and underlying mechanisms of ABPPk on neuroinflammation in AβOs-induced models of AD. In vitro, ABPPk pretreatment dose-dependently inhibited AβOs-induced pro-inflammatory cytokines mRNA levels in BV2 and primary microglia. ABPPk pretreatment also reduced the neurotoxicity of BV2 microglia-conditioned media on primary hippocampal neurons. Furthermore, ABPPk down-regulated the AβOs-induced phosphorylation of IκBα and NF-κB p65 as well as the expression of NLRP3 in BV2 microglia. In vivo, ABPPk pre-administration significantly improved locomotor activity, alleviated memory deficits, and rescued neuronal degeneration and loss in the hippocampus of AβOs-injected mice. ABPPk inhibited the activation of microglia in hippocampal CA3 region and suppressed the activation of NF-κB as well as the expression of NLRP3, cleaved caspase-1, and ASC in the brain after AβOs injection. ABPPk hindered the release of pro-inflammatory cytokines and promoted the release of anti-inflammatory cytokines in the brain. Notably, the polarization experiment on BV2 microglia demonstrated that ABPPk inhibited M1-phenotype polarization and promoted M2-phenotype polarization by activating the LPS- or AβOs-impaired autophagy in microglia. Taken together, our observations indicate that ABPPk can restore the autophagy of microglia damaged by AβOs, thereby promoting M2-phenotype polarization and inhibiting M1-phenotype polarization, thus playing a role in regulating neuroinflammation and alleviating neurotoxicity.

Abstract This paper focuses on effects of the wind flow velocity on the air flow and the air pollution dispersion in a street canyon with Skytrain. The governing equations of air pollutants and air flow in this study area are the convection–diffusion equations of species concentration and the Reynolds-averaged Navier–Stokes (RANS) equations of compressible turbulent flow, respectively. Finite element method is utilized for the solution of the problem. To investigate the impact of the air flow on the pattern of air pollution dispersion, three speeds of inlet wind in three different blowing directions are chosen. The results illustrate that our model can depict the airflows and dispersion patterns for different wind conditions.

Background: Obesity leads to the chronic inflammation in the whole body and triggers the macrophage polarization to the pro-inflammatory phenotype. Targeting macrophage polarization provides a promising therapeutic strategy for obesity-related metabolic disorders and inflammation. Here, we show that SO1989, a derivative of natural occurring compound oleanolic acid, restores the balance between M1-polarized and M2-polarized macrophages in high fat diets (HFD)-induced obese mice resulting in the improvement of adipose inflammation and the metabolic dysfunctions. Methods: Histological analysis, magnetic cell sorting and FACS, in vitro cell model of adipose inflammation, Western blotting, HFD mice model. Findings: SO1989 exhibits similar or even stronger activity in inhibiting inflammation and M1 polarization of macrophages both in vitro and in vivo compared to its analogue CDDO-Me, previously known as a powerful anti-inflammation chemical small molecule. In addition, SO1989 can significantly increase the level of fatty acid oxidation in macrophages which can efficiently facilitate M2 polarization of macrophages. Unlike CDDO-Me, SO1989 shows less adverse effects on obese mice. Interpretation: Taken all together, our findings identify SO1989 as a modulator in macrophage polarization and a safer potential leading compound for pro-resolution of inflammation treatment in metabolic disorders. Fund: Supported by grants from the National Key Research and Development Plan (2017YFA0506000, 2017YFA0205400) and National Natural Science Foundation of China (81673439) and Natural Science Fund project in Jiangsu Province (BK20161408). Keywords: Inflammation, Macrophage polarization, SO1989, Fatty acid oxidation, Adverse effects

Sliding electrical contacts need to be lubricated by conductive lubricants to perform low energy dissipation, high reliability, and long service life. This work studied the thermal stability, anti-corrosion capacity, and conductive, and tribological behaviors of several solid additives in multiply alkylated cyclopentanes (MACs), including carbon nanotubes (CNTs), multilayer graphene (MG), and silver microparticles. The results showed that all the additives possessed favorable thermal stability and corrosion resistance; in particular, CNTs and MG exhibited lower and more stable electrical contact resistance (ECR) and better lubricity abilities than Ag microparticles. Moreover, based on the characterization of the worn surfaces and the film thickness calculation, the favorable conductive and tribological properties of CNTs and MG were related to the high conductivity and specific structure of the additives and the good chemical inertness of MACs.

This work studied the lubrication performances of polyalkylene glycols, which are insulating oils, at the steel interface under external electric fields. The results show that external electric fields greatly affect the lubrication performances of polyalkylene glycols, and there is an optimal voltage (−1.0 V) for the improvement in friction reduction performance. The surface analysis and experiment results indicate that the polyalkylene glycol adsorption film and the reduction in the amount of FexOy and FeOOH in the tribochemical film contribute to improved friction performance under the negative voltage condition. This work proves that the lubrication performances of insulating oils can be affected by external electric fields as well. A lubrication model was proposed, hoping to provide a basic understanding of the lubrication mechanisms of ether-bond-containing insulating oils in the electric environment.

Abstract In thin-film lubrication (TFL), generally, the viscosity of the lubricant and its coefficient of friction (CoF) increase. Finding a method to reduce the CoF in TFL is a significant challenge for tribologists. In the present work, we report a robust superlubricity attained by using polyalkylene glycols (PAGs, polar molecules) and poly-α-olefins (PAOs, nonpolar molecules) as lubricants on steel/steel friction pairs that have been pre-treated by wearing-in with polyethylene glycol aqueous solution (PEG(aq)). A steady superlubricity state with a CoF of 0.0045 for PAG100 and 0.006 for PAO6 could be maintained for at least 1 h. Various affecting factors, including the sliding velocity, normal load, and viscosity of the lubricants, were investigated. Element analysis proved that composite tribochemical layers were deposited on the worn region after the treatment with PEG(aq). These layers were formed by the tribochemical reactions between PEG and steel and composed of various substances including oxides, iron oxides, FeOOH, and Fe(OH)3, which contributed to the superlubricity. In addition to the tribochemical layers, ordered layers and a fluid layer were formed by the PAGs and PAOs during the superlubricity periods. All the three types of layers contributed to the superlubricity, indicating that it was attained in the TFL regime. Accordingly, a mechanism was proposed for the superlubricity of the PAGs and PAOs in the TFL regime in this work. This study will increase the scientific understanding of the superlubricity in the TFL regime and reveal, in the future, the potential for designing superlubricity systems on steel surfaces for industrial applications.

How do investors require a distribution of the wealth among multiple risky assets while facing the risk of the uncontrollable payment for random liabilities? To cope with this problem, firstly, this paper explores the approach of asset-liability management under the state-dependent risk aversion with only risky assets, which has been considered under a continuous-time Markov regime-switching setting. Next, based on this realistic modelling, an extended Hamilton-Jacob-Bellman (HJB) system has been necessarily established for solving the optimization problem of asset-liability management. It has been derived closed-form analytical expressions applied in the time-inconsistent investment with optimal control theory to see that happens to the optimal value of the function. Ultimately, numerical examples presented with comparisons of the analytical results under different market conditions are exposed to analyse numerically the developed mean variance asset liability management strategy. We find that our proposed model can explain the financial phenomena more effectively and accurately.

Aerosol optical depth (AOD), which represents the optical attenuation, poses a major threat to the production activity, air quality, human health and regional sustainable development of arid and semi-arid areas. To some degree, AOD shows areal air pollution level and possesses obvious spatio-temporal characteristics. However, long-time sequences and detailed AOD information can not be provided due to currently limited monitoring technology. In this paper, a daily AOD product, MODIS-based Multi-angle Implementation of Atmospheric Correction (MAIAC), is deployed to analyze the spatio-temporal characteristics in Xinjiang Uygur Autonomous Region from 2000 to 2019. In addition, the importance of influencing factors for AOD is calculated through Random Forest (RF) Model and the propagation trajectories of pollutants are simulated through Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Model. Spatio distribution of AOD presents a tendency that AOD value in northern Xinjiang is low while the value in southern Xinjiang is high. Regions with high AOD values are mainly concentrated in Tarim Basin. AOD in southern Xinjiang is the highest, followed by that in eastern Xinjiang and AOD value in northern Xinjiang is the lowest. Seasonal variation of AOD is significant: Spring (0.309) > summer (0.200) > autumn (0.161) > winter (0.158). Average AOD value in Xinjiang is 0.196. AOD appears wavy from 2000 to 2014 with its low inflection point (0.157) appearing in 2005, and then increases, reaching its peak in 2014 (0.223). The obvious downward tendency after 2014 shows that the use of coal to natural gas (NG) conversion project improves the conditions of local environment. According to RF Model, NG contributes most to AOD. HYSPLIT Model reveals that aerosol in southern Xinjiang is related to the short-distant carriage of dust aerosol from the Taklimakan Desert. Aerosol there can affect Inner Mongolia through long-distant transport. Blocked by the Tianshan Mountains, fine dust particles can not cross the Tianshan Mountains to become a factor contributing to AOD in northern Xinjiang.

Intellectual property pledge financing is effective in alleviating the financing problems of scientific and technological enterprises to a certain extent. However, compared with traditional loans, intellectual property pledge financing is at greater risk with an eye to the particularity of intellectual property pledges. Therefore, it is very important to evaluate the risk of intellectual property pledge financing. This article first outlines the information entropy method, functionalizing indicator data and weight data, and applies the combination of the weight function and indicator function in dynamic evaluation function, to get the final evaluation result. Second, taking four high-tech listed companies on the growth enterprises market (GEM) as example, an evaluation indicator system has been constructed with 16 relevant financial indicators from 2015 to 2019. Then, based on the dynamic function method, we can construct the evaluation function to describe the change trend of the financial indicators of enterprises with a comparison of the advantages and disadvantages of the financial indicators of enterprises. Finally, this article has made a comprehensively comparative analysis on the dynamic evaluation, and the financial risk of intellectual property pledge financing of high-tech enterprises has been done with the combination of the dynamic evaluation curve with the optimal time weight determined by the maximum entropy method.

It has been found that the surface of implied volatility has appeared in financial market embrace volatility “Smile” and volatility “Smirk” through the long-term observation. Compared to the conventional Black-Scholes option pricing models, it has been proved to provide more accurate results by stochastic volatility model in terms of the implied volatility, while the classic stochastic volatility model fails to capture the term structure phenomenon of volatility “Smirk.” More attempts have been made to correct for American put option price with incorporating a fast-scale stochastic volatility and a slow-scale stochastic volatility in this paper. Given that the combination in the process of multiscale volatility may lead to a high-dimensional differential equation, an asymptotic approximation method is employed to reduce the dimension in this paper. The numerical results of finite difference show that the multiscale volatility model can offer accurate explanations of the behavior of American put option price.

In this study, graphene oxide (GO) nanoflakes and lithium salt (LiPF6) were utilized as lubrication additives in ether bond−containing dihydric alcohol aqueous solutions (DA(aq)) to improve lubrication performances. The apparent friction reduction and superlubricity were realized at the Si3N4/sapphire interface. The conditions and laws for superlubricity realization have been concluded. The underlying mechanism was the synergy effect of GO and LiPF6. It was proven that a GO adsorption layer was formed at the interface, which caused the shearing interface to transfer from solid asperities to GO interlayers (weak interlayer interactions), resulting in friction reduction and superlubricity realization. In addition to the GO adsorption layer, a boundary layer containing phosphates and fluorides was formed by tribochemical reactions of LiPF6 and was conducive to low friction. Additionally, a fluid layer contributed to friction reduction as well. This work proved that GO−family materials are promising for friction reduction, and provided new insights into realizing liquid superlubricity at macroscale by combining GO with other materials.

Friction and wear usually lead to huge energy loss and failure of machine pairs, which usually causes great economic losses. Researchers have made great efforts to reduce energy dissipation and enhance durability through advanced lubrication technologies. Single-layer coatings have been applied in many sectors of engineering, but the performance of single-layer coatings still has many limitations. One solution to overcome these limitations is to use a multilayer coating that combines different components with varied physical and chemical properties. In addition, multilayer coating with alternating layers only containing two components can lead to improved performance compared to a coating with only two different layers. This paper systematically reviews the design concept and properties of different types of multilayer coatings, including transition-metal nitride coatings, diamond-like carbon-based coatings, and other multilayer coatings. The inherent functional mechanisms of the multilayer structures are also detailed and discussed.

In order to model the emergency facility location-allocation problem with uncertain parameters, an uncertain multi-objective model is developed within the framework of uncertainty theory. The proposed model minimizes time penalty cost, distribution cost and carbon dioxide emissions. The equivalents of the model are discussed via operational laws of uncertainty distribution. By employing the goal attainment technique, a series of Pareto-optimal solutions are generated that can be used for decision-making. Finally, several numerical experiments are presented to verify the validity of the proposed model and to illustrate decision-making strategy.

Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective.

We consider the following nonlinear problem u″x+λux+fx,u=0, x∈S1, (P) where S1 is a unit circle, λ∈ℝ is a parameter, and fx,u is a nonlinearity. By using the variational method, we obtain the existence of a positive solution to (P) for all λ∈ℝ. This phenomenon is different from the corresponding boundary value problem.

In order to tackle the problem of how investors in financial markets allocate wealth to stochastic interest rate governed by a nested stochastic differential equations (SDEs), this paper employs the Nash equilibrium theory of the subgame perfect equilibrium strategy and propose an extended Hamilton-Jacobi-Bellman (HJB) equation to analyses the optimal control over the financial system involving stochastic interest rate and state-dependent risk aversion (SDRA) mean-variance utility. By solving the corresponding nonlinear partial differential equations (PDEs) deduced from the extended HJB equation, the analytical solutions of the optimal investment strategies under time inconsistency are derived. Finally, the numerical examples provided are used to analyze how stochastic (short-term) interest rates and risk aversion affect the optimal control strategies to illustrate the validity of our results.

Controlling and managing surface source pollution depends on the rapid monitoring of total nitrogen in water. However, the complex factors affecting water quality (plant shading and suspended matter in water) make direct estimation extremely challenging. Considering the spectral response mechanisms of emergent plants, we coupled discrete wavelet transform (DWT) and fractional order discretization (FOD) techniques with three machine learning models (random forest (RF), bagging algorithm (bagging), and eXtreme Gradient Boosting (XGBoost)) to mine this potential spectral information. A total of 567 models were developed, and airborne hyperspectral data processed with various DWT scales and FOD techniques were compared. The effective information in the hyperspectral reflectance data were better emphasized after DWT processing. After DWT processing the original spectrum (OR), its sensitivity to TN in water was maximally improved by 0.22, and the correlation between FOD and TN in water was optimally increased by 0.57. The transformed spectral information enhanced the TN model accuracy, especially for FOD after DWT. For RF, 82% of the model R2 values improved by 0.02~0.72 compared to the model using FOD spectra; 78.8% of the bagging values improved by 0.01~0.53 and 65.0% of the XGBoost values improved by 0.01~0.64. The XGBoost model with DWT coupled with grey relation analysis (GRA) yielded the best estimation accuracy, with the highest precision of R2 = 0.91 for L6. In conclusion, appropriately scaled DWT analysis can substantially improve the accuracy of extracting TN from UAV hyperspectral images. These outcomes may facilitate the further development of accurate water quality monitoring in sophisticated global waters from drone or satellite hyperspectral data.

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.

Superlubricity is generally classified as solid superlubricity and liquid superlubricity according to the lubricants involved at the interfaces, and is a popular research topic in tribology, which is closely linked to energy dissipation. Significant advancements in both experimental studies and theoretical analysis have been made regarding superlubricity in the past two decades. Compared with solid superlubricity, liquid superlubricity has many advantages; e.g., it is more easily achieved at the macroscale and less sensitive to the surface smoothness and atmospheric conditions. In the present study, the advancements in liquid superlubricity at the macroscale are reviewed, and the corresponding mechanisms for various types of liquid lubricants are discussed. This investigation is important for engineering traditional mechanical lubricating systems. Finally, the issues regarding the liquid superlubricity mechanism and the future development of liquid superlubricity are addressed.

In financial markets, there exists long-observed feature of the implied volatility surface such as volatility smile and skew. Stochastic volatility models are commonly used to model this financial phenomenon more accurately compared with the conventional Black-Scholes pricing models. However, one factor stochastic volatility model is not good enough to capture the term structure phenomenon of volatility smirk. In our paper, we extend the Heston model to be a hybrid option pricing model driven by multiscale stochastic volatility and jump diffusion process. In our model the correlation effects have been taken into consideration. For the reason that the combination of multiscale volatility processes and jump diffusion process results in a high dimensional differential equation (PIDE), an efficient finite element method is proposed and the integral term arising from the jump term is absorbed to simplify the problem. The numerical results show an efficient explanation for volatility smirks when we incorporate jumps into both the stock process and the volatility process.