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Timed data release refers to protecting sensitive data that can be accessed only after a pre-determined amount of time has passed. While blockchain-based solutions for timed data release provide a promising approach for decentralizing the process, designing an attack-resilient timed-release service that is resilient to malicious adversaries in a blockchain network is inherently challenging. A timed-release service on a blockchain network is inevitably exposed to the risk of post-facto attacks where adversaries may launch attacks after the data is released in the blockchain network. Existing incentive-based solutions for timed data release in Ethereum blockchains guarantee protection under the assumption of a fully rational adversarial environment in which every peer acts rationally. However, these schemes fail invariably when even a single participating peer node in the protocol starts acting maliciously and deviates from the rational behavior. In this paper, we propose a systematic solution for attack-resilient and practical blockchain-based timed data release in a mixed adversarial environment, where both malicious adversaries and rational adversaries exist. We first propose an effective uncertainty-aware reputation measure to capture the behaviors of the peer involved in timed data release activities in the network. In light of such a measure, we present the design of a basic protocol that consists of two critical ingredients, namely reputation-aware peer recruitment and verifiable enforcement protocols. The former, prior to the start of the enforcement protocols, performs peer recruitment based on the reputation measure to make the design probabilistically attack-resilient to the post-facto attacks. The latter is responsible for contractually guarding the recruited peers at runtime by transparently reporting observed adversarial behaviors. However, the basic recruitment design is only aware of the reputation of the peers and it does not consider the working time schedule of the participating peers and as a result, it results in lower attack-resilience. To enhance the attack resilience further without impacting the verifiable enforcement protocols, we propose a temporal graph-based reputation-aware peer recruitment algorithm that carefully determines the peer recruitment plan to make the service more attack-resilient. In our proposed approach, we formally capture the timed data release service as a temporal graph and we develop a novel maximal attack-resilient path-finding algorithm on the temporal graph for the participating peers. We implement a prototype of the proposed approach using Smart Contracts and deploy it on the Ethereum official test network, Rinkeby. For extensively evaluating the proposed techniques, we perform simulation experiments to validate the effectiveness of the reputation-aware timed data release protocols as well as our proposed temporal-graph-based improvements. The results demonstrate the effectiveness and strong attack resilience of the proposed mechanisms and our approach incurs only a modest gas cost. [ABSTRACT FROM AUTHOR]

With the advancement in computational approaches and experimental, simulation, and modeling tools in recent decades, a trial-and-validation method is attracting more attention in the materials community. The development of powder metallurgy Ni-based superalloys is a vivid example that relies on simulation and experiments to produce desired microstructure and properties in a tightly controlled manner. In this research, we show an integrated approach to predicting the grain size of industrial forgings starting from lab-scale cylindrical compression by employing modeling and experimental validation. (a) Cylindrical compression tests to obtain accurate flow stress data and the hot working processing window; (b) double-cone tests of laboratory scale validation; (c) sub-scale forgings for further validation under production conditions; and (d) application and validation on full-scale industrial forgings. The procedure uses modeling and simulation to predict metal flow, strain, strain rate, temperature, and the resulting grain size as a function of thermo-mechanical processing conditions. The models are calibrated with experimental data until the accuracy of the modeling predictions is at an acceptable level, which is defined as the accuracy at which the results can be used to design and evaluate industrial forgings. [ABSTRACT FROM AUTHOR]

More specifically, remote sensing of watersheds encompasses a wide range of applications that involve acquiring and utilizing hydrological, ecological, and environmental information in watersheds [[15], [17]], including watershed mapping [[18]], monitoring of underlying surface elements in the watershed [[19]], inversion of water cycle parameters [[20]], water resource assessment [[21]], acquisition of watershed ecological environment parameters [[22]], monitoring natural disasters [[23]], analyzing upstream and downstream [[24]], supporting water governance priorities [[25]], water resource development [[26]], and irrigation water management [[27]]. Data Barriers and Data Sharing Addressing data barriers and promoting data sharing is a critical perspective in remote sensing for watershed management. The advent of remote sensing has revolutionized watershed research, providing unprecedented insights into watershed dynamics and spatiotemporal patterns [[10]]. Similarly, merging remote sensing data with in situ measurements, socio-economic data, and other geospatial information can enhance the accuracy and reliability of watershed analyses. [Extracted from the article]

• A theoretical framework for determining the stability of negative stiffness metamaterials in a uniform field is established. • The concepts of directional stability and semi-directional stability are proposed. • Metamaterials with different physical feature distributions are designed and prepared to achieve directional stability. Negative stiffness metamaterials have been widely studied due to their unusual properties and potential applications in shape reconfiguration, shock isolation, and reusable energy absorption, but their stability in fields has been less studied. In this work, a theoretical framework for determining the stability of negative stiffness metamaterials in a uniform field is established. More importantly, the concepts of directional stability and semi-directional stability are proposed for the metamaterials that have different stability in different field directions; metamaterials with different physical feature distributions are designed and prepared to achieve such ability, and the finite element simulation is used for theory verification and parameter study. The proposed metamaterial has the potential to be applied as field direction detectors and field-sensitive electrical relays. [ABSTRACT FROM AUTHOR]

Accurate flow stress data are essential for the design and optimization of thermo-mechanical processes for a wide range of metallic materials. Hot compression testing is generally used to establish the dependence of flow stress on temperature, strain and strain rate. Flow stress measurements have mostly used cylindrical specimens of different sizes. While the data has been corrected for friction and adiabatic heating, the measurements assume idealized uniform deformation in the test specimen when obtaining the flow stress data from the measured load and displacement. In this paper, it is shown that there is significant non-uniform deformation in the test specimen under practical testing conditions, and a methodology is developed to correct the flow stress data. This correction is generally more significant than the correction due to friction or adiabatic heating and is necessary to obtain more accurate flow stress data. The effect of specimen geometry is also investigated, and a geometry which results in more uniform strain distribution than a cylindrical specimen is used. Measurements are conducted for a prototype powder metallurgy nickel-base superalloy at a low strain rate over a deformation temperature range. Constitutive equations are constructed with the measured flow stress data. Finite element modeling of the tests using the corrected data provides a better agreement with the measured loads. The grain microstructures vary with the test parameters and are used to correlate the flow behavior. [ABSTRACT FROM AUTHOR]

Here we develop a magnetoelastic (ME) nano-biosensor based on the competitive strategy for the detection of a carcinoembryonic antigen (CEA). Specifically, the gold-coated ME material provided a platform and the thiolated single-stranded DNA (HS-DNA) containing a half-complementary sequence towards the CEA aptamer was modified on the surface via Au-S bonding. DNA-templated silver nanoclusters (DNA-AgNCs) containing another half-complementary sequence towards the aptamer were used to amplify the signals by about 2.1 times, compared to those obtained using just the aptamer. CEA aptamers as a bio-recognition element were employed to link HS-DNA and DNA-AgNCs through DNA hybridization. The CEA aptamer preferentially combined with CEA rather than hybridized with DNA. Due to the magnetostrictive nature of the ME materials, the resonant frequency of the nano-biosensor would increase along with the release of DNA-AgNCs and CEA aptamers. The modification process was demonstrated by UV–vis spectra, x-ray photoelectron spectroscopy (XPS), Raman spectroscopy, transmission electron microscope (TEM) and an atomic force microscope (AFM). The nano-biosensor has a linear response to the logarithmic CEA concentrations ranging from 2 pg ml−1 to 6.25 ng ml−1, with a limit of detection (LOD) of 1 pg ml−1 and a sensitivity of 105.05 Hz/ng · ml−1. This study provides a low-cost, highly sensitive and wireless method for selective detection of CEA. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the most important causes for land degradation and desertification and is an important threat to land management, farming activities, water quality, and sustainable development in arid and semi-arid areas. Soil salinization is often characterized with significant spatiotemporal dynamics. The salt-affected soil is predominant in the Ebinur Lake region in the Northwestern China. However, detailed local soil salinity information is ambiguous at the best due to limited monitoring techniques. Nowadays, the availability of Multi-Spectral Instrument (MSI) onboard Sentinel-2, offers unprecedented perspectives for the monitoring and mapping of soil salinity. The use of MSI data is an innovative attempt for salinity detection in arid land. We hypothesize that field observations and MSI data and MSI data-derived spectral indices using the partial least square regression (PLSR) approach will yield fairly accurate regional salinity map. Based on electrical conductivity of 1:5 soil:water extract (EC) of 72 ground-truth measurements (out of 116 sample sites) and various spectral parameters, such as satellite band reflectance, published satellite salinity indices, red-edge indices, newly constructed two-band indices, and three-band indices from MSI data, we built a few inversion models in an attempt to produce the regional salinity maps. Different algorithms including Pearson correlation coefficient method (PCC), variable importance in projection (VIP), Gray relational analysis (GRA), and random forest (RF) were applied for variable selection. The results suggest that both the newly proposed normalized difference index (NDI) [(B12 − B7) / (B12 + B7)] and three-band index (TBI4) [(B12 − B3) / (B3 − B11)] show a better correlation with validation data and could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the remaining 44 independent ground-based measurements. The RF-PLSR model performed the best across the five models with R2 V , RMSE V , and RPD of 0.92, 7.58 dS m−1, and 2.36, respectively. The result from this model was then used to map the soil salinity over the study area. Our analyses suggest that soil salinization changes quite significantly in different seasons. Specifically, soil salinity in the dry season was higher than in the wet season, mostly in the lake area and nearby shores. We contend that the results from the study will be useful for soil salinization monitoring and land reclamation in arid or semi-arid regions outside the current study area. • The introduction of red-edge bands can enhance the sensitivities of the indices to soil salinity. • Three-band index [(B12 − B3) / (B3 − B11)] shows a best correlation (r = 0.544) with measured EC. • RF-PLSR model was proved a suitable method for soil salinity estimating and mapping. • The study shows a large variability in soil salinity in dry and wet seasons. [ABSTRACT FROM AUTHOR]

The subset of regulatory T (Treg) cells, with its specific transcription Foxp3, is a unique cell type for the maintenance of immune homeostasis by controlling effector T (Teff) cell responses. Although it is common that a defect in Treg cells with Treg/Teff disorder causes autoimmune diseases; however, the precise mechanisms are not thoroughly revealed. Here, we report that miR-34a could attenuate human and murine Foxp3 gene expression via targeting their 3′ untranslated regions (3′ UTR). The human miR-34a, increased in peripheral blood mononuclear cells (PBMCs) and CD4+ T cells from rheumatoid arthritis (RA) or systemic lupus erythematosus (SLE) patients, displayed a positive correlation with some serum markers of inflammation including rheumatoid factor (RF), anti-streptolysin antibody (ASO), erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) as well as Th17 signature gene RORγt, but inversely correlated with the mRNA expression levels of FOXP3. In addition, murine miR-34a levels were downregulated in TGF-β-induced Treg cells but upregulated in Th17 cells induced in vitro compared to activated CD4+ T cells. It has also been demonstrated that elevated miR-34a disrupting Treg/Th17 balance in vivo contributed to the progress of pathogenesis of collagen induced arthritis (CIA) mice. Furthermore, IL-6 and TNF-α were responsible for the upregulation of miR-34a and downregulation of Foxp3, which was reverted by the addition of NF-κB/p65 inhibitor BAY11-7082, thus indicating that NF-κB/p65 inhibited Foxp3 expression in an miR-34a-dependent manner. Finally, IL-6 or TNF-α-activated p65 could bind to the miR-34a promotor and enhance its activity, resulting in upregulation of its transcription. Taken together, we show that NF-κB activated by inflammatory cytokines, such as IL-6 and TNF-α, ameliorates Foxp3 levels via regulating miR-34a expression, which provides a new mechanistic and therapeutic insight into the ongoing of autoimmune diseases. • MiR-34a expression is elevated in patients of RA and SLE and correlated with some serum markers of inflammation. • MiR-34a inhibits Foxp3 expression via targeting its 3′ UTR in Treg cells，promoting Treg/Th17 imbalance and CIA progress. • NF-κB/p65 activated by IL-6 and TNF-α induces miR-34a transcription via binding to its promoter region. [ABSTRACT FROM AUTHOR]

Mechanical metamaterials composed of curvilinear elements (MM-CEs) offer better programmability and functionality than their rectilinear counterparts. They thus hold promising potential for diverse applications, such as flexible electronics, impact protection, and deformation control. However, existing research on MM-CEs has primarily focused on specific curve shapes, neglecting the vast potential of the wider curve design space. Based on data-driven methods, this work builds a dataset with a hierarchical data structure, which transforms the infinite and difficult-to-conceptualize curve data into a finite number of potentially researchable curve sub-types. Moreover, methods are developed based on data mining to better understand the broad design space, excavate hidden connections of different curve sub-types and discover new rules for designing MM-CEs. The outcomes of this study offer novel strategies to investigate other structures and metamaterials with complex geometries as well. [ABSTRACT FROM AUTHOR]

Abstract In arid and semi-arid climatic areas, lakes are extremely essential for fragile ecological environment and regional sustainable development. Ebinur Lake is an important component of the ecological barrier of Junggar Basin, Xinjiang Uyghur Autonomous Region (XUAR), China. Due to the tremendous changes in Ebinur Lake and surrounding marshes during the last decades, Ebinur Lake becomes a representative ecological degradation region in northwestern China. The detection of the intra-annual variations of water body and its responses to changing environment are critical for regional ecological security and rehabilitation of degraded ecosystem. To extract more accurate water information using Synthetic Aperture Radar (SAR) data and further fill the gap of inter-month dynamic monitoring of Ebinur Lake, a new SAR water index (modified Sentinel 1A water index, MSWI) was proposed based on the relationship between normalized difference water index (NDWI) imageries and Sentinel 1A data. The dynamic thresholds of classification were selected using Otsu method and the results showed that the classification results were acceptable with the optimal overall accuracy of 99.94% and kappa coefficient of 0.9971, respectively. We conduct a time series analysis of surface areas of Ebinur Lake using S1A data from February 9th, 2017 to February 4th, 2018. The maximum lake surface area was 965.29 km2 in April 22nd, 2017, while the minimum value was 750.37 km2 in September 1st, 2017, and the mean area was 831.51 km2. The seasonal variations showed the stages of "sharp rising" – "significant decreasing" – "gradual stabilizing" in the study period. The water surface area was highly correlated with inflow water volume (correlation coefficient = 0.72, P < 0.001). The variation of Ebinur Lake's water surface area is crucial to monitor the resulting eco-environmental impacts under the changing environmental conditions in the arid and semi-arid areas. Graphical abstract Unlabelled Image Highlights • Proposed a new SAR water index (MSWI). • The 12-day interval spatial-temporal distributions of Ebinur Lake. • Variation of lake in arid areas and its eco-environmental impacts. • Examined the driving forces of the dynamic variations. [ABSTRACT FROM AUTHOR]

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance ( = 0.907, RMSEC = 0.425%, = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance ( = 0.888, RMSEC = 0.446%, = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area. [ABSTRACT FROM AUTHOR]

Background: Chikungunya virus (CHIKV) and Dengue virus (DENV) have similar clinical symptoms, which often induce misdiagnoses. Therefore, an antigen detection diagnostic system that can clearly identify these two viruses is desirable. Methods: In this study, we developed a novel peptide with high affinity and specificity to CHIKV, and further constructed peptide aptamer-based TRFIA assay to efficiently detect CHIKV. Peptide aptamer B2 (ITPQSSTTEAEL) and B3 (DTQGSNWI) were obtained through computer-aided design and selected as CHIKV-specific peptide aptamers based on their high binding affinity, strong hydrogen bonding, and RMSD of molecular docking. Then, a sandwich-Time-Resolved Fluoroimmunoassay (TRFIA) was successfully constructed for the detection of the interaction between peptide aptamers and viruses. Results: When using B2 as the detection element, highly specific detection of CHIKV E2 was achieved with detection limits of 8.5 ng/ml in PBS solution. Variation coefficient between inter-assay showed the disturbances received from the detection of clinical fluid specimens (including serum and urine), were also within acceptable limits. The detection limits for 10-fold dilution serum and urine were 57.8 ng/mL and 147.3 ng/mL, respectively. The fluorescent signal intensity exhibited a good linear correlation with E2 protein concentration in the range of 0-1000 ng/mL, indicating the potential for quantitative detection of E2 protein. Conclusions: These results demonstrate that the construction of peptide aptamers with high affinity and specificity provides an excellent method for rapid diagnostic element screening, and the developed peptide aptamer B2 contributed to better detection of CHIKV viral particles compared to traditional antibodies. [ABSTRACT FROM AUTHOR]

• Spectral features related to Fe and OC held potentials in estimating sediment heavy metals. • XGBoost model provided good accuracy in estimating sediment heavy metals. • The integration of CWT and RFE method was recommended for heavy metal estimation. • The samples with high Fe concentration were suitable to estimate sediment heavy metals. • A model transferability index was developed to quantitatively compare the model transferability. Soil or sediment spectral reflectance do not directly reflect information of spectrally featureless heavy metals. With mangrove sediment samples collected in five regions and four habitat types, this study aimed to explore the spectral features related to spectrally active constituents (Fe and organic carbon (OC)) in estimating low and moderate concentrations of four heavy metals (Cr, Ni, Cu and Pb). A total of 27 modeling strategies using XGBoost (extreme gradient boosting) model were compared for estimating each heavy metal: three types of spectral features (Fe, OC and heavy metal) * three spectral transformation methods (Log(1/R), first derivative (FD) and continuous wavelet transform (CWT)) * three variable selection methods (genetic algorithm (GA), successive projection algorithm (SPA) and recursive feature elimination (RFE)). The results showed that the combination of CWT and RFE was recommended for heavy metal estimation, and the ranking of model accuracy was Cu > Pb > Ni > Cr. The optimal XGBoost model was found using spectral features related to Fe for Cr (R Val 2 (determination coefficient of independent validation) = 0.42, RMSE (root mean square error) = 0.32 and RPIQ (the ratio of performance to interquartile range) = 1.53) and Cu estimation (R Val 2 = 0.87, RMSE = 0.19 and RPIQ = 2.20), spectral features related to OC for Ni estimation (R Val 2 = 0.63, RMSE = 0.26 and RPIQ = 2.38) and spectral features related to Pb itself for Pb estimation (R Val 2 = 0.82, RMSE = 0.23 and RPIQ = 2.59). Moreover, the XGBoost model transferability across different habitat types outperformed that across different regions and data ranges of Fe and OC. We conclude that the modeling strategy using spectral features related to spectrally active constituents has great potentials in quantifying low and moderate concentration of sediment heavy metals, and the samples with high Fe concentration (31.62–37.29 g kg−1) are recommended to improve estimation accuracy of low and moderate concentrations of heavy metals, which could provide sampling strategy development and early warning of heavy metal pollution in mangrove ecosystem. [ABSTRACT FROM AUTHOR]

• Proximal soil sensing is still attractive in Cr estimation. • Fractional order derivative and three-band index are useful for feature extraction. • The correlation and mechanism analysis between auxiliary attributes and chromium. • Concatenation of Proximal soil sensing and auxiliary attributes is promising. • Kriging and optimal semi-variogram function are essential in soil science. The rapid and accurate determination of soil chromium (Cr) is crucial for preventing toxic element pollution in soils and ensuring ecological security. Proximal sensing technology uses visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy, which has been demonstrated to be a viable approach for monitoring soil Cr concentrations. However, at trace levels, soil Cr is not especially spectrally active, thus limiting the practical application of using corresponding spectral data for quantifying soil Cr concentrations. In this study, we hypothesized that fused proximal sensing and soil auxiliary attributes (including organic matter (OM) and pH) could improve estimation of Cr concentrations in the soil. Additionally, the introduction of best-fit variogram models was theoretically possible to improve spatial visualization. To address these hypotheses, we collected 168 soil samples from the open coal mine area in the Eastern Junggar Basin, China. Fractional-order derivative (FOD) pretreatment and optimal band combination methods were implemented for spectral data mining and the derivation of spectral parameters, respectively. Soil Cr estimation models were calibrated with a partial least squares (PLS) approach through four designed strategies with different predictors: (I) full Vis-NIR variables, (II) effective three-band spectral indices (TBIs), (III) the effective TBIs and OM, and (IV) the effective TBIs, OM, and the pH. The results suggest that FOD could identify abundant spectral variability. Compared with full Vis-NIR variables, the effective TBIs can effectively magnify the subtle spectral signals concerning soil Cr. The optimal estimation model was determined as Strategy IV, indicating that the introduction of soil auxiliary attributes (pH and OM) can improve the estimation performance of the model; notably, the coefficient of determination (R 2) and ratio of performance to interquartile distance (RPIQ) were 0.87 and 2.68, respectively. Based on the optimal semivariance model, we used kriging interpolation to map regional soil Cr. In the study area, the soil Cr distribution features strong spatial dependence and strong associations. Our study might inspire further research on soil contamination mapping based on proximal Vis-NIR sensors. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is by using the generating function methods and some combinatorial techniques to establish some new recurrence formulae for the Apostol-Bernoulli and Apostol-Euler polynomials. It turns out that some known results in (He and Wang in Adv. Differ. Equ. 2012:209, 2012) are obtained as special cases. MSC: 11B68, 05A19. [ABSTRACT FROM AUTHOR]

Background: Masquelet membrane induction technology is one of the treatment strategies for large bone defect (LBD). However, the angiogenesis ability of induced membrane decreases with time and autologous bone grafting is associated with donor site morbidity. This study investigates if the PRP-FG-nHA/PA66 scaffold can be used as a spacer instead of PMMA to improve the angiogenesis ability of induced membrane and reduce the amount of autologous bone graft. Methods: Platelet rich plasma (PRP) was prepared and PRP-FG-nHA/PA66 scaffold was synthesized and observed. The sustained release of VEGFA and porosity of the scaffold were analyzed. We established a femur LBD model in male SD rats. 55 rats were randomly divided into four groups depending on the spacer filled in the defect area. "Defect only" group (n = 10), "PMMA" group (n = 15), "PRP-nHA/PA66" group (n = 15) and "PRP-FG-nHA/PA66" group (n = 15). At 6 weeks, the spacers were removed and the defects were grafted. The induced membrane and bone were collected and stained. The bone formation was detected by micro-CT and the callus union was scored on a three point system. Results: The PRP-FG-nHA/PA66 scaffold was porosity and could maintain a high concentration of VEGFA after 30 days of preparation. The induced membrane in PRP-FG-nHA/PA66 group was thinner than PMMA, but the vessel density was higher.The weight of autogenous bone grafted in PRP-FG-nHA/PA66 group was significantly smaller than that of PMMA group. In PRP-FG-nHA/PA66 group, the bone defect was morphologically repaired. Conclusion: The study showed that PRP-FG-nHA/PA66 scaffold can significantly reduce the amount of autologous bone graft, and can achieve similar bone defect repair effect as PMMA. Our findings provide some reference and theoretical support for the treatment of large segmental bone defects in humans. [ABSTRACT FROM AUTHOR]

The degradation of watersheds creates immense pressure on water quality, especially in arid and semiarid regions. Total suspended solids (TSS) provide essential information to water environmental quality assessments. However, the calibration of direct retrieval models requires complicated preparations and further increases uncertainties. Here, we hypothesized that a common remote sensing index (NDVI, normalized difference vegetation index) could be used to estimate TSS concentrations in water due to the effects of canopy cover. To address this hypothesis, we collected 65 water samples from the Ebinur Lake Watershed, northwest China, to investigate the potential relationships between TSS concentrations and Sentinel-2-based NDVI at various scales (100, 200, 300, 400, and 500 m). Subsequently, we established a classical measurement error (CME) model for the estimation of TSS concentrations. The results showed that TSS concentration is negatively related to the NDVI value at all buffer distances. The 300 m scale mean NDVI value showed the most effective explanation of the variations in TSS concentrations (R2 = 0.83, P-value < 0.001), which indicated that the TSS concentration can be assessed by NDVI. The CME model showed that NDVI values played an important role in the assessment of TSS concentrations in surface water. Furthermore, the results of both leave-one-out cross-validation and the accuracy measure suggested that this specific method is satisfactory. Compared with previous statistical and field monitoring results, the proposed method is promising for cost-effective monitoring of TSS concentrations in water, especially in data-poor watersheds. This specific method may provide the basis for the conservation and management of nonpoint source pollution in arid regions. • A novel method is proposed to assess TSS concentrations under data scarce conditions. • Sentintinel-2 MSI derived NDVI is an effective tool to evaluate watershed condition. • TSS concentration is negatively related with NDVI at five different spatial scales. • 300 m could be considered as the most appropriate scale for assessing TSS concentrations. [ABSTRACT FROM AUTHOR]

AMD3100 (plerixafor), a CXCR4 antagonist, has opened a variety of avenues for potential therapeutic approaches in different refractory diseases. The CXCL12/CXCR4 axis and its signaling pathways are involved in diverse disorders including HIV-1 infection, tumor development, non-Hodgkin lymphoma, multiple myeloma, WHIM Syndrome, and so on. The mechanisms of action of AMD3100 may relate to mobilizing hematopoietic stem cells, blocking infection of X4 HIV-1, increasing circulating neutrophils, lymphocytes and monocytes, reducing myeloid-derived suppressor cells, and enhancing cytotoxic T-cell infiltration in tumors. Here, we first revisit the pharmacological discovery of AMD3100. We then review monotherapy of AMD3100 and combination use of AMD3100 with other agents in various diseases. Among those, we highlight the perspective of AMD3100 as an immunomodulator to regulate immune responses particularly in the tumor microenvironment and synergize with other therapeutics. All the pre-clinical studies support the clinical testing of the monotherapy and combination therapies with AMD3100 and further development for use in humans. [ABSTRACT FROM AUTHOR]

Visual-inertial navigation systems are credited with superiority over both pure visual approaches and filtering ones. In spite of the high precision many state-of-the-art schemes have attained, yaw remains unobservable in those systems all the same. More accurate yaw estimation not only means more accurate attitude calculation but also leads to better position estimation. This paper presents a novel scheme that combines visual and inertial measurements as well as magnetic information for suppressing deviation in yaw. A novel method for initializing visual-inertial-magnetic odometers, which recovers the directions of magnetic north and gravity, the visual scalar factor, inertial measurement unit (IMU) biases etc., has been conceived, implemented, and validated. Based on non-linear optimization, a magnetometer cost function is incorporated into the overall optimization objective function as a yawing constraint among others. We have done extensive research and collected several datasets recorded in large-scale outdoor environments to certify the proposed system's viability, robustness, and performance. Cogent experiments and quantitative comparisons corroborate the merits of the proposed scheme and the desired effect of the involvement of magnetic information on the overall performance. [ABSTRACT FROM AUTHOR]

• An ME DNA-biosensor was developed to detect a common mutated DNA causing β-thalassaemia. • sDNA-AuNPs serve as the signal amplifier. • The sandwich-type structure enables label-free detection. • A low detection limit of 0.571 pM and high sensitivity of 72.7 Hz/nM were obtained. • The DNA-biosensor exhibits excellent sensitivity, selectivity and stability. β-Thalassaemia is an inherited blood disorder with serious complications. Combining the unique advantages of DNA and magnetoelastic (ME) materials, an ME DNA-biosensor was proposed that can wirelessly detect a target DNA sequence (tDNA) that is a 4-bp deletion in codon 41/42 (-TTCT) in the β-globin gene causing β-thalassaemia. The thiolated capture probe (CP) was covalently immobilized on the surface of the gold-plated ME chip and then hybridized to tDNA, and AuNPs modified with thiolated signal probe DNA (sDNA-AuNPs) serve as the signal amplifier and the direct signal indicator, enabling label-free detection. The specific hybridization process of sDNA-AuNPs to tDNA increased the surface load mass and decreased the resonance frequency of the DNA-biosensor. The resonance frequency shift of the DNA-biosensor was linear to the logarithmic concentration of tDNA in the range of 1.0 × 10−8 M to 1.0 × 10−12 M, with a detection limit (LOD) of 0.571 pM (S/N = 3) and sensitivity of 72.7 Hz/nM. The ME DNA-biosensor exhibits excellent selectivity and stability for the detection of the mutated DNA (4-bp deletion in codon 41/42) causing β-thalassaemia, suggesting this is a promising method for the clinical diagnosis of β-thalassaemia. [ABSTRACT FROM AUTHOR]

Accurate assessment of soil salinization is considered as one of the most important steps in combating global climate change, especially in arid and semi-arid regions. Multi-spectral remote sensing (RS) data including Landsat series provides the potential for frequent surveys for soil salinization at various scales and resolutions. Additionally, the recently launched Sentinel-2 satellite constellation has temporal revisiting frequency of 5 days, which has been proven to be an ideal approach to assess soil salinity. Yet, studies on detailed comparison in soil salinity tracking between Landsat-8 OLI and Sentinel-2 MSI remain limited. For this purpose, we collected a total of 64 topsoil samples in an arid desert region, the Ebinur Lake Wetland National Nature Reserve (ELWNNR) to compare the monitoring accuracy between Landsat-8 OLI and Sentinel-2 MSI. In this study, the Cubist model was trained using RS-derived covariates (spectral bands, Tasseled Cap transformation-derived wetness (TCW), and satellite salinity indices) and laboratory measured electrical conductivity of 1:5 soil:water extract (EC). The results showed that the measured soil salinity had a significant correlation with surface soil moisture (Pearson's r = 0.75). The introduction of TCW generated satisfactory estimating performance. Compared with OLI dataset, the combination of MSI dataset and Cubist model yielded overall better model performance and accuracy measures (R 2 = 0.912, RMSE = 6.462 dS m−1, NRMSE = 9.226%, RPD = 3.400 and RPIQ = 6.824, respectively). The differences between Landsat-8 OLI and Sentinel-2 MSI were distinguishable. In conclusion, MSI image with finer spatial resolution performed better than OLI. Combining RS data sets and their derived TCW within a Cubist framework yielded accurate regional salinity map. The increased temporal revisiting frequency and spectral resolution of MSI data are expected to be positive enhancements to the acquisition of high-quality soil salinity information of desert soils. Unlabelled Image • Differences between Landsat-8 OLI and Sentinel-2 MSI are distinguishable. • Satellite derived surface soil moisture is significantly correlated with soil salinity. • Cubist is a satisfactory approach for soil salinity mapping (RPIQ = 6.824). • MSI image with finer spatial resolution performs better than OLI. • We need to pay more attention to the environmental covariates. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the biggest challenges for food production worldwide and an important threat to farming activities, water quality, land degradation and sustainable development in arid and semi-arid areas. Soil salinization is mainly characterized by significant spatiotemporal dynamics. The salt-affected soils are predominant in the Ebinur Lake region in Northwestern China. However, detailed soil salinity information at local scale is ambiguous due to limited monitoring techniques. Nowadays, the availability of Multi-Spectral Instrument (MSI) onboard Sentinel-2, offers unprecedented perspectives for the monitoring and mapping soil salinity. The use of MSI data is an innovative attempt for salinity detection in arid land. We hypothesize that field observations, MSI data and MSI data-derived spectral indices using the partial least square regression (PLSR) approach will lead to an accurate regional salinity map. For this novel approach, a new assessment is required. Based on electrical conductivity (EC) of 74 ground-truth measurements and according to various spectral parameters, such as satellite band reflectance, published satellite salinity index, red-edge index, newly constructed two-band index, and three-band index from MSI data, we constructed inversion models. Different algorithms including Pearson correlation coefficient method (PCC), variable importance in projection (VIP), Gray relational analysis (GRA), and random forest (RF) were applied for variable selection. The results showed that both, the newly proposed normalized difference index (NDI) [(B12−B7)/(B12+B7)] and three-band index (TBI4) [(B12−B3)/(B3−B11)] show a better correlation with validation data and could be applied to estimate the soil salinity in the Ebinur Lake region. The established models were validated using the remaining 44 independent ground-based measurements and were then used to map the soil salinity over the study area. The RF-PLSR model performed the best across the five models with R2V, RMSEV, and RPD of 0.92, 7.58 dS m−1 and 2.36, respectively. The results suggest that soil salinization changes are significantly different for each season. Specifically, the soil salinity in the dry season was higher than in the wet season, mostly in the lake area and nearby shores. The results from this research attempt to create a more reliable soil salinity map for arid environments and could be useful to environmental planners for more precise land degradation monitoring and future land reclamation in arid or semi-arid regions. [ABSTRACT FROM AUTHOR]

Two novel coordination polymers based on Yb(III) ion as the metal ions have been successfully prepared by using two C3-symmetrical ligands via a solvothermal synthesis approach, and their chemical formulae are [Yb1.5(TAPB)1.5(DMF)]·9DMF}n (1, TAPB = 4,4′,4″-(triazine-2,4,6-triyl-tris-(benzene-4,1-diyl)) tribenzoate) and [Yb(TCBPA)·DMF]n (2, H3TCBPA = tris(4′-carboxybiphenyl)amine). Furthermore, the two complexes were applicated for the fracture nonunion treatment combined with plate let rich plasma (PRP) and n-HA/Polyamide66 (n-HA/PA66). Firstly, the Rat Femur Fracture Nonunion Model was constructed, and compounds 1 or 2 was used for indicated treatment combined with PRP and n-HA/PA66, the enzyme linked immunosorbent assay detection was used for the osteocalcin concentration measurement. The reverse transcription-polymerase chain reaction was carried out to determine alp and Runx2 relative expression level. [ABSTRACT FROM AUTHOR]

Detecting the water surface area and its temporal changes is essential in water resource management. Multispectral satellite data are applied extensively to monitor the surface water dynamics because of their repeated coverage. This letter monitors waterbody dynamics in a high temporal resolution from Sentinel-2 and Landsat 8 images by using the Google Earth Engine (GEE) platform. Water index, automatic threshold and postprocessing of noise elimination are used to extract the water pixels from the background. Taking a part of the middle reaches of the Yangtze River ( 100 k m × 100 k m ) as a case study, the surface water dynamics are estimated by 47 dates from February 2017 to November 2019. The popular JRC Global Surface Water database includes 8 qualified monthly maps for the study area in 2017 and 2018, while our results display 32 maps covering 18 months. The surface water dynamic maps obtain an overall accuracy of 92.5% (using random sample points) and 82.0% (using manual-drawing thematic map). Also, water occurrence is calculated based on the water dynamics to indicate the permanent, seasonal and flooding areas. The high temporal dynamics of surface water are meaningful for the analysis of ephemeral lake and inundation extent. [ABSTRACT FROM AUTHOR]

In this paper, a novel and wireless magnetoelastic (ME) immunosensor for human serum albumin (HSA) detection is presented. Gold nanoparticles layer on the sensor surface can enhance its biocompatibility, stability and sensitivity. The anti-HSA IgG, as a capture probe, was immobilized on the gold-plated ME sensor surface to specifically recognize the target HSA. The binding complex accumulated on the immunosensor surface leads to a decrease in the immunosensor's resonance frequency, corresponding to the HSA concentrations. The atomic force microscopy, the Raman spectrum and the X-ray electron spectroscopy were used to confirm that the antibody immobilization was successful. Furthermore, to enhance its sensitivity, the working concentration of the antibody was optimized to be 25 µg ml−1. The experimental results demonstrated that the immunosensor exhibited a linear response to the logarithm of HSA concentrations ranging from 0.01 to 100 µg ml−1, with the sensitivity of 9.3 Hz/μg ml−1 and the detection limit of 0.01 µg ml−1, which is significantly lower than the minimum diagnosis limit of urine microalbumin. [ABSTRACT FROM AUTHOR]

[Display omitted] • The growth of exotic mangrove was significantly faster than that of native species. • The morphological patterns can be used to indicate the hotspots of mangroves. • Sonneratia species was found in the forest gap and forest edge of native mangroves. • The spread of Sonneratia species is fast in bare tidal flat and tidal creek zones. • Invasive potentials of Sonneratia mangrove in Shenzhen Bay should not be ignored. Mangroves are essential forest communities in tropical and subtropical coastal zones, providing unique ecological functions and significant social and economic value. Accurate and efficient monitoring of mangroves is crucial for their protection and management. The impact of exotic species on native species is still debated, especially for mangroves, the spatiotemporal dynamics of introduced species and native mangrove species need to be closely monitored. This study aimed to investigate how exotic mangrove species affects the spatial dynamics of mangroves in Shenzhen Bay, China. Yearly Landsat images from 2000 to 2011 and Chinese high spatial resolution images during 2012–2022 were obtained and the pixel-based and multiscale object-based methods were used to obtain mangrove distribution. The results showed that: (1) Overall, mangroves in the study area increased from 281.51 ha to 526.43 ha from 2000 to 2022, with different spatiotemporal patterns in Shenzhen (+5.14%) and Hong Kong (+2.38%); (2) rapid spread of Sonneratia species was one of the main contributions to the growth of mangrove hotspots, and growth hotspots were mainly concentrated in the estuary delta; and (3) Sonneratia mangrove species was dominant in interspecific competition, not only invading the habitat of local mangroves, but also preventing the expansion of native mangrove growth. The results provide a reference for fine-scale and accurate mangrove mapping with high spatial and temporal resolutions, and highlight the need to pay more attentions to the spread and invasive potential of exotic mangrove species in the study area, as well as cooperation in adjacent reserves. [ABSTRACT FROM AUTHOR]

Aerosols are complex compounds that greatly affect the global radiation balance and climate system and even human health; in addition, aerosols are currently a large source of uncertainty in the numerical simulation process. The arid and semi-arid areas have fragile ecosystems with abundant dust but lack related high-accuracy aerosol data. To solve these problems, we use the bagging trees ensemble model, based on 1 km aerosol optical depth (AOD) data and multiple environmental covariates, to produce a monthly advanced-performance, full-coverage, and high-resolution (250 m) AOD product (named FEC AOD, fusing environmental covariates AOD) covering the arid and semi-arid areas. Then, based on the FEC AOD products, we analyzed the spatiotemporal AOD pattern and further discussed the interpretation of environmental covariates to AOD. The results show that the bagging trees ensemble model has a good performance, with its verification R2 values always remaining at 0.90 and the R2 being 0.79 for FEC AOD compared with AERONET AOD product. The high-AOD areas are located in the Taklimakan Desert and on the Loess Plateau, and the low-AOD areas are concentrated in southern Qinghai province. The higher the AOD, the stronger the interannual variability. Interestingly, the AOD reflected a dramatic decrease on the Loess Plateau and an evident increase in the south-eastern Taklimakan Desert, while the southern Qinghai province AODs showed almost no significant change between 2000 and 2019. The annual variation characteristics show that the AOD was largest in spring (0.267±0.200) and smallest in autumn (0.147±0.089); the annual AOD variation pattern showed different features, with two peaks in March and August over Gansu province but only one peak in April in other provinces/autonomous regions. Farmlands and construction lands have high AOD levels compared to other land cover types. Meteorological factors demonstrate the maximum interpretation ability of the AODs on all set temporal scales, followed by the terrain factors, while surface properties have the smallest explanatory abilities; the corresponding average contributions are 77.1 %, 59.1 %, and 50.4 %, respectively. The capability of the environmental covariates to explain the AOD varies seasonally in the following sequence: winter (86.6 %) > autumn (80.8 %) > spring (79.9 %) > summer (72.5 %). In this research, we provide a pathbreaking high spatial resolution (250 m) and long time series (2000–2019) FEC AOD dataset covering arid and semi-arid regions to support atmospheric and related studies in northwest China; the full dataset is available at 10.5281/zenodo.5727119 (Chen et al., 2021b). [ABSTRACT FROM AUTHOR]

Evaluation of the spatial distribution of urban emergency shelters can effectively identify defects in the current distribution of urban emergency shelters and weaknesses in the overall evacuation service capacity of the city and provide reference for improving the level of urban emergency shelters and evacuation and disaster relief capacity. At present, evaluation of the spatial distribution of urban emergency shelters is mainly carried out on three aspects: effectiveness, accessibility, and safety. However, there are problems, such as individual evaluation scales and incomplete indicator systems, unreasonable allocation of indicator weights, and ignoring the influence of fuzzy incompatibility between different indicator attributes on the evaluation results. In this paper, we start from two scales, the individual emergency shelter and the regional groups of emergency shelters. Based on the five criteria of effectiveness, accessibility, safety, suitability, and fairness, the evaluation indicator system of the spatial distribution of urban emergency shelters was constructed. It was combined with AHP, CRITIC, the optimal weight coefficient solution method based on the maximum deviation sum of squares theory, and fuzzy optimization theory to construct a multi-indicator evaluation model. Further, the spatial distribution condition of the existing emergency shelter in Shanghai was evaluated. The results show that: among the existing ninety-one emergency shelters in Shanghai, there are nine places with unreasonable spatial distribution; nineteen places are comparatively unreasonable. From the scale of regional groups, there is one district (Pudong New District) with unreasonable spatial distribution: its relative superiority value is far lower than other districts, and there are three districts that are comparatively unreasonable. Further, the evaluation scores of the spatial distribution reasonableness of emergency shelters in each region of Shanghai show a high–low–middle distribution from the downtown area of Shanghai outward. The evaluation indicator system and evaluation method used in this paper can effectively reflect the deficiencies in the spatial distribution of urban emergency shelters, thus providing a reference for the relevant departments to improve and plan emergency shelters. [ABSTRACT FROM AUTHOR]

[Display omitted] • For population modelling, remote sensing nighttime light data and nighttime LBS data were utilized. • Population maps of the Tibetan Plateau at the city level were modelled by geographically weighted regression. • The crowd exposure frequency characterized by nighttime LBS data contributed significantly to population modelling. • The model predicted more accurate population maps than three mainstream international population datasets. • The operational mechanism of the new demographic model was revealed. Population mapping is one of the fundamental materials for regional sustainability studies. Most scholars applied remote sensing data with excessive indicators to fit the population distribution. Nevertheless, over-complex models were lack of accuracy. This paper proposed a population model in the Qinghai-Tibet Plateau as a study area; the model has consisted of Human Activity Extent and Crowd Exposure Frequency. Performed remote sensing land cover data, nighttime light data, and LBS geographic big data as candidate indicators for exploratory regression experiments eventually developed an optimal population model assembled by nighttime LBS data and nighttime light data. The model fits significantly better at the city level (R2values of 0.9922) and reduces the error compared with other studies and publicly available datasets (%RMSE values of 6.83%). For the first time, the model proposes that Crowd Exposure Frequency based on nighttime LBS data can provide effective population simulation in the global, nighttime light data gain compensation for it. Nighttime light data plays a dominant role in densely populated areas; at the same time, nighttime LBS revised its overestimation. They modified each other to make the model accuracy significantly elevated. The modelling framework allows dynamic and low-cost population estimates of ecologically vulnerable areas and thus serves sustainable regional development. [ABSTRACT FROM AUTHOR]

Disk additions to an RAID-6 storage system can increase the I/O parallelism and expand the storage capacity simultaneously. To regain load balance among all disks including old and new, RAID-6 scaling requires moving certain data blocks onto newly added disks. Existing approaches to RAID-6 scaling, restricted by preserving a round-robin data distribution, require migrating all the data, which results in an expensive cost for RAID-6 scaling. In this paper, we propose RS6—a new approach to accelerating RDP RAID-6 scaling by reducing disk I/Os and XOR operations. First, RS6 minimizes the number of data blocks to be moved while maintaining a uniform data distribution across all data disks. Second, RS6 piggybacks parity updates during data migration to reduce the cost of maintaining consistent parities. Third, RS6 selects parameters of data migration so as to reduce disk I/Os for parity updates. Our mathematical analysis indicates that RS6 provides uniform data distribution, minimal data migration, and fast data addressing. We also conducted extensive simulation experiments to quantitatively characterize the properties of RS6. The results show that, compared with existing “moving-everything” Round-Robin approaches, RS6 reduces the number of blocks to be moved by 60.0%–88.9%, and saves the migration time by 40.27%–69.88%. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hyperspectral data were used to detect Chromium(Cr) pollution. • A data enhancement method combined with a deep learning method showed the optimal accuracy. • Our method is expected to provide large-scale accurate, real-time monitoring and guidance in soil pollution. In China, over 10% of cultivated land is polluted by heavy metals, which can affect crop growth, food safety and human health. Therefore, how to effectively and quickly detect soil heavy metal pollution has become a critical issue. This study provides a novel data preprocessing method that can extract vital information from soil hyperspectra and uses different classification algorithms to detect levels of heavy metal contamination in soil. In this experiment, 160 soil samples from the Eastern Junggar Coalfield in Xinjiang were employed for verification, including 143 noncontaminated samples and 17 contaminated soil samples. Because the concentration of chromium in the soil exists in trace amounts, combined with the fact that spectral characteristics are easily influenced by other types of impurity in the soil, the evaluation of chromium concentrations in the soil through hyperspectral analysis is not satisfactory. To avoid this phenomenon, the pretreatment method of this experiment includes a combination of second derivative and data enhancement (DA) approaches. Then, support vector machine (SVM), k-nearest neighbour (KNN) and deep neural network (DNN) algorithms are used to create the discriminant models. The accuracies of the DA-SVM, DA-KNN and DA-DNN models were 95.61%, 95.62% and 96.25%, respectively. The results of this experiment demonstrate that soil hyperspectral technology combined with deep learning can be used to instantly monitor soil chromium pollution levels on a large scale. This research can be used for the management of polluted areas and agricultural insurance applications. [ABSTRACT FROM AUTHOR]

Both antitumor and protumor property of mesenchymal stem cells (MSCs) have been demonstrated. We hypothesize that this contradiction is due to the heterogeneity of MSC subsets and that extracellular vesicles (EVs) from distinct MSC subsets can transfer the corresponding antitumor activities. Here we evaluated the antitumor activities of two subsets of adipose-derived mesenchymal stem cells (ADSCs) and ADSC-derived EVs (ADSC-EVs) in immunocompetent syngeneic mouse models of breast cancer. We identified CD90high and CD90low ADSC subsets and demonstrated that CD90high ADSCs could be converted into CD90low ADSCs by stimulation with LPS. CD90low ADSCs and its derived EVs significantly inhibited tumor growth in tumor-bearing mice. Benefit of tumor control were associated with decreased tumor cell proliferation and migration, and enhanced tumor cell apoptosis mediated by ADSC-EVs. Antioncogenic miRNA-16-5p loaded CD90low ADSC-EVs further significantly enhanced antitumor activities. Taken together, this study represents the first attempt to apply our newly identified antitumor ADSCs and its derived EVs in preclinical treatment of breast cancer. This study also provides the evidence that EVs can serve as a novel and effective therapeutics or drug delivery vesicle. This new therapeutic approach could be potentially applicable to breast cancer and many other types of cancer. [ABSTRACT FROM AUTHOR]

The aerosol optical depth (AOD) represents the light attenuation by aerosols and is an important threat to urban air quality, production activities, human health, and sustainable urban development in arid and semiarid regions. To some extent, the AOD reflects the extent of regional air pollution and is often characterized by significant spatiotemporal dynamics. However, detailed local AOD information is ambiguous at best due to limited monitoring techniques. Currently, the availability of abundant satellite data and constantly updated AOD extraction algorithms offer unprecedented perspectives for high-resolution AOD extraction and long-time series analysis. This study, based on the long-term sequence MOD09A1 data from 2010 to 2018 and lookup table generation, uses the improved deep blue algorithm (DB) to conduct fine-resolution (500 m) AOD (at 550 nm wavelength) remote sensing (RS) estimation on Landsat TM/OLI data from the Urumqi region, analyzes the spatiotemporal AOD variation characteristics in Urumqi and combines gray relational analysis (GRA) and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to analyze AOD influence factors and simulate pollutant propagation trajectories in representative periods. The results demonstrate that the improved DB algorithm has a high inversion accuracy for continuous AOD inversion at a high spatial resolution in urban areas. The spatial AOD distribution in Urumqi declines from urban to suburban areas, and higher AODs are concentrated in cities and along roads. Among these areas, Xinshi District has the highest AOD, and Urumqi County has the lowest AOD. The seasonal AOD variation characteristics are distinct, and the AOD order is spring (0.411) > summer (0.285) > autumn (0.203), with the largest variation in spring. The average AOD in Urumqi is 0.187, and the interannual variation generally shows an upward trend. However, from 2010 to 2018, AOD first declined gradually and then declined significantly. Thereafter, AOD reached its lowest value in 2015 (0.076), followed by a significant AOD increase, reaching a peak in 2016 (0.354). This shows that coal to natural gas (NG) project implementation in Urumqi promoted the improvement of Urumqi's atmospheric environment. According to GRA, the temperature has the largest impact on the AOD in Urumqi (0.699). Combined with the HYSPLIT model, it was found that the aerosols observed over Urumqi were associated with long-range transport from Central Asia, and these aerosols can affect the entire northern part of China through long-distance transport. [ABSTRACT FROM AUTHOR]

• FODs were used to process and analyze soil Vis-NIR spectra. • The correlation coefficient with SOMC was best at 1.05- to 1.45-order. • MNDI was used to emphasize the SOMC information. • FOD-MNDI showed the best estimation accuracy, with an RPIQ reaching 3.40. Visible-near-infrared (Vis-NIR) spectroscopy makes it possible to estimate soil organic matter content (SOMC). Spectral pretreatment techniques have important significance in the quantitative analysis of SOMC. A total of 150 soil samples collected in northwestern China were used as data sets for calibration and validation. The SOMC values and reflectance spectra were measured in the laboratory. Fractional-order derivatives (FODs) (intervals of 0.05, range of 0–2) were used for soil spectral pretreatment, and a new three-band index (modified normalized difference index, MNDI) was constructed based on the band-optimization algorithm and the existing two-band exponential form (normalized difference index, NDI). Partial least square-support vector machine (PLS-SVM) models were calibrated using spectral parameters selected based on a single dimension (FOD), two-dimensional index (NDI) and three-dimensional index (MNDI) and subsequently applied to estimate SOMC. Three model evaluation parameters, namely, the coefficient of determination (R2), root mean squared error (RMSE), and ratio of performance to interquartile range (RPIQ), were used to evaluate the estimation accuracy of the models. The results showed that with increased derivative order, the spectral strength gradually decreased, but the spectral detail increased. Furthermore, the correlation between FOD spectra and SOMC was significantly enhanced in the visible region, with the most obvious effect in the 1.05- to 1.45-order range. The PLS-SVM modeling results showed that the sensitivity and estimation accuracy of SOMC increased with increasing spectral synergy (i.e., 1D (FOD) < 2D (NDI) < 3D (MNDI)). Among the models, MNDI exhibited the best model performance, yielding a validation R2 and RPIQ of 0.846 and 3.396, respectively. The combination of FOD and MNDI could weaken the soil noise information and improve the prediction accuracy of SOMC. Furthermore, the three-dimensional index has strong application potential for estimating other biochemical parameters of soil using Vis-NIR spectroscopy. [ABSTRACT FROM AUTHOR]

Soil moisture content (SMC) is a basic condition for crop growth, and a key parameter for crop yield prediction and drought monitoring. An advantage of large-scale synchronous observation using remote sensing technology is that it provides the possibility of dynamic monitoring of soil moisture content in a large area. This study aimed to explore the feasibility of accurately estimating soil moisture content at a regional scale by combining ground hyper-spectral data with multispectral remote sensing (Sentinel-2) data. The results showed that the different mathematical transformations increased the correlation between soil spectral reflectance and SMC to varying degrees. Hyper-spectral optimized index normalized difference index (NDI) ((B769~797 – B848~881/B769~797 + B848~881); (B842 – B740/B842 + B740)) derived from the transformed reflectance (the first-order derivate of reciprocal-logarithm (Log (1/R))′, second-order derivate of reciprocal-logarithm (Log (1/R)) ′′) showed significant correlation (correlation coefficient (r) = 0.61; r = 0.47) with SMC, and the correlation coefficient values higher than difference index (DI) and ratio index (RI). From the performance of 12 prediction models which were taken optimized indices as independent variables, the central wavelength reflectance model (Log (1/R))′′ and the average wavelength reflectance model ((Log (1/R)) ′ presented higher validation coefficients (coefficient of determination (R2) = 0.61, root mean square error (RMSE) = 4.09%, residual prediction deviation (RPD) = 1.82; R2 = 0.69, RMSE = 3.48%, RPD = 1.91) compared with other models. When verifying the accuracy, the model yields R2 values of 0.619 and 0.701. These results indicated that the two-band hyper-spectral optimized indices (NDI) as an optimal indicator for quickly and accurately soil moisture content estimation. Combining the ground hyper-spectral data and satellite remote sensing image regional scale soil moisture content prediction provides a scientific reference for land-space integrated soil moisture content remote sensing monitoring. [ABSTRACT FROM AUTHOR]

• A spatio-temporal prediction framework was developed to map SOC. • The inter-decadal maps of China SOC were updated to the 2010s. • Effects of climatic factors for SOC variability are location- and scale- specific. • The soils in China function as carbon sinks at national scale from 1980s to 2010s. Spatiotemporal variability in soil organic carbon (SOC) plays key roles in ecosystem stability and terrestrial carbon cycling. However, the inter-decadal characteristics of SOC variability and its climate drivers in China from the 1980s to 2010s remain unclear. In this study, a modeling framework for combining spatiotemporal prediction models was proposed. Within this model, soil samples from each of the two periods were combined and calibrated to obtain three models, and their prediction outputs were selectively combined to obtain SOC maps for the different inter-decadal periods. A total of 8473 soil samples from three periods (1980s, 2000s and 2010s) were used for model validation. The results suggested that the SOC prediction accuracy in the top 0–20 cm of soil (SOC 020) was better than that of the first meter of soil (SOC 0100), and the predicted spatial patterns shared characteristics with findings from previous studies. Predictions for the 1990s exhibited relatively high uncertainty, especially in the Tibetan Plateau, Tien Shan, and Northeast China. Both individual and coupled climatic factors exerted scale- and location-specific effects on the SOC. Coupled climatic factors accounted for more than 65% of the SOC variability in both soil layers. The regional SOC in North and Northeast China exhibited inter-decadal decline. Soil in China functioned as carbon sinks from the 1980s to 2010s at a national scale, with SOC 020 increasing from 30.40 Pg to 33.97 Pg and SOC 0100 from 79.29 Pg to 82.26 Pg. Overall, our results provide reliable baseline data for optimizing carbon cycle models of terrestrial ecosystems, and to guide future carbon sequestration policies. [ABSTRACT FROM AUTHOR]

• The SOC in two depths were mapped at a resolution of 1 km and five-year interval during 1980–2100. • Three climate scenarios of SSP 1–1.9, SSP2-4.5 and SSP5-8.5 in CMIP6 were considered. • The top 20 cm and 100 cm SOC of China both acted as carbon sinks during 1980–2020. • The SOC will fluctuate significantly in the future under different climate scenarios. • Under the SSP2-4.5 scenario, SOC presented a larger likelihood of being a carbon source in most region. Revealing historical changes in soil organic carbon (SOC) and exploring its future status are important for safeguarding soil health and food security, giving full play to the service function of soil ecosystems, and coping with climate change. However, there is still a gap in our understanding of the SOC stocks in China and their spatial patterns in response to future climate change. Therefore, we attempted to fill this knowledge gap using a large amount of soil observation data, digital soil mapping technology, and global circulation models from Coupled Model Inter-comparison Project phase 6 (CMIP6). In this study, a random forest model was selected to construct the spatial relationship between SOC in the top 0–20 cm (SOC020) and 0–100 cm (SOC0100) of the soil and 21 environmental factors. Spatiotemporal changes in SOC from 1980 to 2100 were revealed at a resolution of 1 km and five-year interval for three climate scenarios in CMIP6. The cross-validation results indicated acceptable predictions for both depths of SOC; however, relatively large prediction uncertainties were observed for SOC0100 in the Tibetan Plateau and northeastern China. The mean values of SOC020 and SOC0100 over the last four decades were 35.77 and 84.62 Tg, respectively, and showed carbon sinks at the national scale, with accumulation rates of 0.05 Pg yr-1 and 0.036 Pg y-1 for the two depths. Compared with historical values (1980–2020), SOC will fluctuate significantly in the future under different climate scenarios. Among them, SOC showed a slow increasing trend at both depths under the SSP1-1.9 low emission scenario, while SOC presented a decreasing trend under the SSP2-4.5 and SSP5-8.5 medium–high emission scenarios. In particular, in most of China under the SSP2-4.5 scenario, SOC0100 presented a larger likelihood of being a carbon source. This study provides a reference for exploring the response of soil carbon pools to climate change and evaluating the effectiveness of land management and ecological protection. [ABSTRACT FROM AUTHOR]

• A framework was developed and evaluated to estimate daily FAPAR from instantaneous FAPAR. • Daily FAPAR can be estimated from MODIS instantaneous FAPARs. • MODIS instantaneous FAPAR underestimates daily value. • Daily FAPAR is potential to improve GPP estimation. The daily Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is crucial for vegetation dynamics monitoring and terrestrial carbon modeling. However, current FAPAR products only provide instantaneous values at the satellite transit times and a large data gap exists between instantaneous and daily FAPAR. To fill this gap, this study aimed to estimate daily FAPAR from instantaneous values retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) data. A framework combining method to estimate instantaneous FAPAR (instantaneous FAPAR method) and method to calculate daily FAPAR (instantaneous-to-daily FAPAR method) was proposed. Instantaneous FAPAR method for estimating intra-daily FAPAR from reduced observations was evaluated. Three instantaneous-to-daily FAPAR methods (ratio, simple average and solar zenith angle weighted average methods) were compared to estimate the daily FAPAR from continuous instantaneous values. The daily FAPAR was finally estimated from MODIS instantaneous values. The estimation and validation were performed at seven forest sites distributed in North America and Western Europe. The results show that the instantaneous FAPAR method can accurately simulate the intra-daily instantaneous FAPAR (RMSE ∼ 0.01, bias ∼ 0). The three instantaneous-to-daily FAPAR estimation methods showed similar performances (R2 ∼1, RMSE ∼ 0.01, and bias ∼ 0). The in situ instantaneous FAPAR values at the MODIS acquisition times underestimated the daily value during the non-peak growing season (FAPAR < 0.9) and the underestimation is also affected by the diffuse PAR radiation. MODIS instantaneous FAPAR underestimates in situ daily FAPAR over the year. The daily FAPAR estimates from in situ instantaneous values at MODIS acquisition times or the MODIS instantaneous FAPAR data reduce the gap between instantaneous and daily values. Validated by in situ data, the MODIS instantaneous FAPAR products should be further optimized to improve the accuracy of daily FAPAR estimation. This study lays the foundation for estimating the daily FAPAR from instantaneous values to improve GPP estimation. [ABSTRACT FROM AUTHOR]

• GMM can distinguish similar spectral features in vegetation. • After spectral feature partition, the difference modeling is superior to whole-sample modeling. • UAV hyperspectral can indirectly reveal salinization patterns by inversion of saline vegetation SPAD. The chlorophyll content of saline vegetation can indirectly reflect salinization. Rapid and non-destructive capture of chlorophyll content of saline vegetation at a regional scale is essential for saline soil improvement and sustainability of saline agriculture. However, traditional soil–plant analyzer development (SPAD) monitoring based on SPAD-502 is carried out at the leaf scale, which does not allow rapid access to SPAD information for the whole region. In this study, we proposed that post-hyperspectral classification based on spectral differences that could contribute to enhanced estimation of SPAD in saline vegetation. To test this proposal, we partitioned the hyperspectral images using a Gaussian mixture model. Then, estimation models based on spectral partition and full-sample SPAD were developed using in situ observation data and a random forest model, respectively. Finally, the unmanned aerial vehicles (UAV) hyperspectral images were used as the input data source to digitally map the SPAD of saline vegetation in the region, using the prediction model. The results indicated that there were significant intensity and shape differences in the spectral reflectance characteristics of saline vegetation under different clusters. The SPAD prediction model, based on spectral feature partition, performed significantly better than the full sample. The SPAD maps of saline vegetation before and after clustering displayed similar spatial distribution models, but the prediction uncertainty of the models, based on spectral feature partition, was relatively low. Our results confirm the effectiveness and stability of UAV hyperspectral and spectral partition-based modeling in developing SPAD spatial distribution estimation models for saline vegetation. [ABSTRACT FROM AUTHOR]

[Display omitted] • We updated information on soil salinity: soil salinization exacerbated overall. • Spectral information and environmental covariates were necessary but still needed to filter. • The model with environmental covariates improved on performance for soil salinity. • The GBRT model performance was the best among machine learning models. Soil salinization is the main source of global soil degradation. It has impeded progress towards sustainable development goals (SDGs) by threatening 20% of irrigated areas. However, in many data-poor regions, accurate soil salinization information is unavailable. Thus, an updated soil salinity map with high accuracy and resolution is urgently needed to help local governments conduct precise management. In this study, a bootstrap hybrid machine learning framework was developed combine Sentinel-2 data and environmental covariates. The Boruta algorithm was applied to input the spectral information and environmental variables to identify the primary factors influencing soil salinity. By averaging 100 model iterations within a bootstrap framework, the soil salinity mapping outcomes were compared from four machine learning methods (bagging, classification and regression tree, random forest, and gradient boosting regression tree (GBRT)). The results showed that the models driven by spectral information and environmental covariates (strategy II) explained 68∼88% of the variability in soil salinity. The model accuracy of strategy II was improved by 5%-8% over that of the models driven only by spectral information (strategy I). The GBRT yielded the most appropriate averaging performance of the four machine learning approaches within strategy II, with an R2 of 0.88, a root mean square error (RMSE) of 6.33 dS m−1, a ratio of performance to interquartile distance (RPIQ) of 4.66 and model stability (ROB) of 0.44. The bootstrap averaging method was relatively stable and had high accuracy potential. The distribution of soil salinity in the Ebinur Lake region was the result of a combination of natural and human activity influences. The proposed approach provides a soil salinity mapping strategy with a fine resolution (10 m) and high accuracy in data-poor places. It may also aid in the restoration of biodiversity, the decrease in land degradation, and the avoidance of future food output reductions in the future. [ABSTRACT FROM AUTHOR]

[Display omitted] • Single and composite heavy metal pollution indices of mangrove sediment were explored. • Four types of spectral transformation methods were compared. • XGBoost and physicochemical properties were integrated to estimate pollution indices. • Composite pollution index was more accurately estimated than the other indices. • Fe and organic carbon significantly affected estimation accuracy of pollution indices. Single and composite pollution indices (SPI and CPI) of heavy metals are calculated with background heavy metal concentrations. They are efficient evaluation indicators of sediment pollution levels and could be directly compared among different regions and habitat types, however, they are rarely investigated in mangrove sediment with visible and near-infrared spectroscopy (VNIRS). With mangrove sediment samples collected from five regions and four habitat types, this study aimed to develop estimation models of heavy metal (Cr, Ni, Cu, Zn and Pb) pollution indices using XGBoost (extreme gradient boosting) method, and further to explore the effect of physicochemical properties (Fe, organic carbon (OC), clay and salinity) on the estimation accuracy. Kruskal-Walls test showed that regions and habitat types were significant factors affecting the six pollution indices. Compared with first derivative, second derivative and continuum removal reflectance, standard normal variate (SNV) reflectance was more accurate for estimating SPIs and CPI using XGBoost model. The XGBoost models with sensitive wavelengths of SNV reflectance outperformed that with whole spectrum (410 bands) in estimating the six pollution indices, and the ranking of estimation accuracy was CPI > SPI Cu > SPI Zn > SPI Pb > SPI Ni > SPI Cr. Compared with the XGBoost model with sensitive wavelengths alone, the model with additional physicochemical properties could increase R 2 and RPD (the ratio of standard deviation to RMSE of validation in the test subset) values by 0.795–7.282% and 1.199–8.780%, and decrease RMSE values by 4.101–26.866% in estimating SPI Cr , SPI Ni and SPI Cu. Moreover, Fe and OC had greater effect on the estimation accuracy than clay and salinity. We conclude the integration of XGBoost and physicochemical properties holds potentials for accurately assessing heavy metal pollution indices in mangrove sediment with VNIRS, which can provide methodological basis for evaluating environment quality and ecological risk in mangrove ecosystem at the plot or landscape scale. [ABSTRACT FROM AUTHOR]

Since hyperspectral satellite images (HSIs) usually hold low spatial resolution, improving the spatial resolution of hyperspectral imaging (HSI) is an effective solution to explore its potential for remote sensing applications, such as land cover mapping over urban and coastal areas. The fusion of HSIs with high spatial resolution multispectral images (MSIs) and panchromatic (PAN) images could be a solution. To address the challenging work of fusing HSIs, MSIs and PAN images, a novel easy-to-implement stepwise fusion approach was proposed in this study. The fusion of HSIs and MSIs was decomposed into a set of simple image fusion tasks through spectral grouping strategy. HSI, MSI and PAN images were fused step by step using existing image fusion algorithms. According to different fusion order, two strategies ((HSI+MSI)+PAN and HSI+(MSI+PAN)) were proposed. Using simulated and real Gaofen-5 (GF-5) HSI, MSI and PAN images from the Gaofen-1 (GF-1) PMS sensor as experimental data, we compared the proposed stepwise fusion strategies with the traditional fusion strategy (HSI+PAN), and compared the performances of six fusion algorithms under three fusion strategies. We comprehensively evaluated the fused results through three aspects: spectral fidelity, spatial fidelity and computation efficiency evaluation. The results showed that (1) the spectral fidelity of the fused images obtained by stepwise fusion strategies was better than that of the traditional strategy; (2) the proposed stepwise strategies performed better or comparable spatial fidelity than traditional strategy; (3) the stepwise strategy did not significantly increase the time complexity compared to the traditional strategy; and (4) we also provide suggestions for selecting image fusion algorithms using the proposed strategy. The study provided us with a reference for the selection of fusion strategies and algorithms in different application scenarios, and also provided an easy-to-implement solution and useful references for fusing HSI, MSI and PAN images. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales. [ABSTRACT FROM AUTHOR]

• BEMD was applied to high-resolution SOM map in northwestern China. • Scale- and region-specific variations in SOM were unraveled. • The variance of SOM was partitioned by climate, topography and land surface. • Main factors that influencing SOM were different in different scales. Soil organic matter (SOM) significantly affects soil quality, food security, and the global carbon cycle. Variations in the SOM content are controlled by many environmental factors and differ depending on the scale and region. The Mountain–Oasis–Desert System (MODS) in arid areas is limited by climate conditions and water and soil resources. Therefore, the effects of environmental factors on the SOM content may differ. This study aimed to characterize spatial patterns of the SOM content in a MODS in northwestern China and reveal the SOM variations at different scales and regions. First, we employed the random forest model to predict the SOM content and the uncertainty at the nodes of a 90 m grid. We then extracted scale- and region-dependent variations in the SOM content using bivariate empirical mode decomposition (BEMD). Data analysis, geostatistics, and a two-dimensional fast Fourier transform were applied to analyze the SOM pattern and its correlations with environmental covariates at different scales. The results suggest that the variations in the SOM content were mainly concentrated at the large scale. The correlations between environmental factors and the SOM were scale- and region-specific, indicating complex interrelationships among topography, land use, salinization, soil erosion, and fertilization. At the intermediate-large spatial scale, topography characteristics and surface features were the major factors controlling the SOM content, whereas the climate had a larger effect on SOM than other factors at the large scale. Based on our results, BEMD has great potential to reveal SOM variations in the scale–region domain. BEMD can be used in environmental modeling in the future to gain insights into the variations in soil properties depending on environmental factors and anthropogenic activities. [ABSTRACT FROM AUTHOR]

• Successful application of the ESTARFM to study the dynamics of playas surface. • NDVI and SI are sensitive to precipitation events. • Precipitation events affect the spatiotemporal distribution of surface salinity. • Salinity spectral indices decrease after precipitation events. Playas are desert landscapes unique to arid regions that respond quickly to climate change. Changes in surface salinity in Praia are directly linked to water security and regional ecological and economic health. This study aimed to explore the sensitivity of remotely sensed spectral indices to precipitation events. An ESTARFM was utilized to fuse daily (less than 10% cloudiness) MODIS and Landsat 8 imagery from April to September 2019 in the Ebinur Lake Wetland Reserve. Salinity was tested in the laboratory after soil collections in May (spring) and August (summer). The significantly correlated normalized difference vegetation and salinity indices were compared to the precipitation data and found to fluctuate in response to precipitation. The normalized difference vegetation and salinity indices decreased following precipitation events and increased during precipitation intervals, and the precipitation amount and evaporation intensities may have influenced the magnitudes of the decrease and increase. Following precipitation, the playa surface produces new puddles and a new spatial distribution pattern of soil salinity. This study determined that the spatiotemporal fusion technique is an effective method for observing the dynamics of playa surfaces, and precipitation and evaporation affect the spatial distribution of salt on the playa surface; however, the monitoring period should be short when utilizing remote sensing to monitor playa salinity. [ABSTRACT FROM AUTHOR]