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Herein, we explore the effect of dynamic strain aging (DSA) on dislocation evolution using nanoindentation and transmission electron microscopy (TEM). DSA is primarily manifested as serrations on the load-displacement curves at lower strain rates (10−2 and 10−3 s−1), whereas these serrations are substantially less at higher strain rate (10−1 and 100 s−1). Interestingly, despite variations in strain rates and DSA occurrence, detailed TEM observations beneath the indentations reveal no discernible differences in dislocation density and substructure. The present study indicates that although DSA can increase the gliding resistance of dislocations, it does not affect the evolution of dislocation.

Abstract Background Type 2 diabetes mellitus (T2DM) is a public health crisis that requires adequate knowledge, attitudes, and practices (KAP) by health care providers to prevent or delay the progression of the disease. This study aimed to assess the KAP regarding T2DM among primary care providers (PCPs) in Central China. Methods This multicenter cross-sectional study was conducted among 971 PCPs using self-employed KAP questionnaires. Questionnaires were designed to evaluate KAP regarding T2DM among PCPs, and was measured with SPSS software. Descriptive statistics, the Pearson correlation coefficients and multiple regression models used to analyze the data.70%, 80% and 70% of total values were considered as the cut-off point for defining good knowledge, positive attitude and correct practice. Results A total of 971 PCPs with a mean age of 44.0 ± 10.2 years were evaluated. 620 (63.9%) PCPs worked at village clinic and 605 (62.3%) PCPs have been working more than 20 years. Only 26.3% of the respondents participated in Continued Medical Education (CME) programs regarding diabetes in the past year due to Covid-19 pandemic. Overall, despite positive attitudes toward diabetes, there were substantial gaps in knowledge and practices. The PCPs scored 7.25 out of 14 points on the knowledge subscales, 7.13 out of 8 on the attitude subscales, and 4.85 out of 11 on the practice subscales. Gender, age, practice setting, professional titles, duration of practice and CME attendance were significant predictors of knowledge; Age, practice setting and duration of practice were significant predictors of attitudes; and family history of diabetes affected PCP practices. Conclusions Despite positive attitudes toward diabetes, there were substantial gaps in knowledge and practices. These findings call for action from relevant health authorities and policy makers to improve PCPs’ KAP regarding diabetes in Central China.

ABSTRACTAccurate assessments of forest biomass carbon are invaluable for managing forest resources, evaluating effects on ecological protection, and achieving goals related to climate change and sustainable development. Currently, the integration of optical and synthetic aperture radar (SAR) data has been extensively utilized in estimating forest aboveground biomass carbon (AGC), while it is limited by using single-phase remote sensing images. Time-series data, which capture the interannual dynamic growth and seasonal variations of photosynthetic phenology in forests, can sufficiently describe forest growth characteristics. However, there remains a gap in research focusing on utilizing satellite-based time-series data for AGC estimation, especially for SAR sensors. This study investigated the potential of satellite-based optical and SAR time-series data for estimating AGC. Here, we undertook nine quantitative experiments of AGC estimation from Landsat 8 and Sentinel-1 and tested several regression algorithms (including multiple linear regression (MLR), random forests (RF), artificial neural network (ANN), and extreme gradient boosting (XGBoost)) to explore the contributions of spatiotemporal features to AGC estimation. The results suggested that the XGBoost algorithm was suitable for AGC estimation with explanatory solid power and stable performance. The temporal features representing forest growth trends and periodic change characteristics (such as coefficients of continuous wavelet transform) were more valuable for AGC estimation than spatial features for both sensor types, accounting for around 40% ~50% of the variance compared to 17% ~25%. The combination of optical and SAR time-series data produced the best performance (R2 = 0.814, RMSE = 18.789 Mg C/ha, rRMSE = 26.235%), compared with when utilizing optical or SAR time-series data alone (optical: R2 of 0.657 and rRMSE of 35.317%; SAR: R2 of 0.672 and rRMSE of 34.701%). Feature importance analysis also verified that temporal features of optical vegetation indices, SWIR 1/2 bands, and SAR backscatter from VV polarization were the most critical variables for AGC estimation. Furthermore, incorporating temporal features into the modeling is illustrated to be effective in reducing saturation effects within high-biomass forests. This study demonstrated the superiority of time-series data for forest carbon estimation. While the applicability of this methodology has only been investigated in evergreen coniferous forests, it may provide a viable approach needed to make full use of increasingly better and free satellite time-series data to estimate forest AGC with high accuracy, supporting policy making of forest management and sustainable development.

Abstract Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research.

Accurate estimation of crown fire potential (CFP) can improve guidance on crown fire control and management. However, robust simulations of crown fire behavior are still challenging, limiting the accuracy of regional-scale CFP mapping. This study aims to incorporate fire spread simulation and machine learning algorithms to improve CFP mapping at a regional scale. First, we built a crown fire dataset using the fire simulations from the FARSITE model, as well as multi-source data, including fuel, weather, and topography variables. Fuel model parameters were optimized with four metaheuristic algorithms for robust fire simulations. Then, the hybrid models of CFP estimation (TBA-ML) were established by coupling with the transfer AdaBoost (TrAdaBoost) algorithm and three machine learning (ML) algorithms, i.e., Bayesian Network (BN), Random Forest (RF), and Support Vector Machine (SVM), to estimate CFP for crown fire danger assessment spatially. Results showed that the TBA-BN model performed best in estimating CFP with higher accuracy (AUC>0.9 and F1 score > 0.8) than the RF- and SVM-based CFP models. The variable importance and causal analysis showed that fuel and topography variables have major contributions to crown fire occurrence. Finally, we mapped monthly average passive and active CFP at regional scales and qualitatively demonstrated that our CFP time-series products successfully captured the dynamic change of crown fire danger. The above results suggest the potential of integrating fire spread simulation and machine learning algorithms to accurately estimate CFP to improve crown fire management.

To clarify the influence range and saturation distribution after the biogas desaturation method is applied, a three-dimensional model is established with TOUGH2 software to analyze the effect of construction parameters such as grouting volume, grouting rate, grouting depth, nitrogen source concentration, and soil porosity. After that, the sensitivity of the parameters on the influence range is determined. The grouting volume and soil porosity are the most sensitive to the lateral and vertical influence range, respectively. This study provides a basis for the engineering practice of treating liquefiable subgrade by the biogas desaturation method.

Abstract Background Regulatory T cells (Tregs) are involved in the maintenance of immune homeostasis and immune regulation. Clinical trials on the adoptive transfer of Tregs have been ongoing for > 10 years. However, many unresolved issues remain in the production of readymade Treg products and selection of patients. Hence, this study aimed to develop a method to expand off-the-shelf Tregs derived from umbilical cord blood (UCB-Tregs) in vitro without changing their phenotype and inhibitory function. In addition, the study intended to design an approach to precisely select patients who are more likely to benefit from the adoptive Treg transfer therapy. Methods UCB-Tregs were isolated and cultured in a medium containing human recombinant IL-2 and rapamycin and then multiply restimulated with human T-activator CD3/CD28 dynabeads. The phenotype and suppressive capacity of Tregs were assessed on days 18 and 42. The relationship between the suppressive function of UCB-Tregs in vitro and clinical indicators was analyzed, and the ability of the in vitro suppressive capacity to predict the in vivo therapeutic effects was evaluated. Results UCB-Tregs expanded 123-fold and 5,981-fold at 18 and 42 days, respectively. The suppressive function of UCB-Tregs on the proliferation of immune cells at 42 days was not significantly different compared with that of UCB-Tregs obtained at 18 days. The suppression rate of UCB-Tregs to PBMCs was negatively correlated with the course of diabetes. Moreover, the high-suppression group exhibited a better treatment response than the low-suppression group during the 12-month follow-up period. Conclusions Multiply restimulated UCB-Tregs expanded at a large scale without any alterations in their classical phenotypic features and inhibitory functions. The suppressive function of Tregs in vitro was negatively correlated with the disease duration. The present study revealed the possibility of predicting the in vivo therapeutic effects via the in vitro inhibition assay. Thus, these findings provided a method to obtain off-the-shelf Treg products and facilitated the selection of patients who are likely to respond to the treatment, thereby moving toward the goal of precision treatment.

Abstract Histamine receptors are a group of G protein-coupled receptors (GPCRs) that play important roles in various physiological and pathophysiological conditions. Antihistamines that target the histamine H1 receptor (H1R) have been widely used to relieve the symptoms of allergy and inflammation. Here, to uncover the details of the regulation of H1R by the known second-generation antihistamines, thereby providing clues for the rational design of newer antihistamines, we determine the cryo-EM structure of H1R in the apo form and bound to different antihistamines. In addition to the deep hydrophobic cavity, we identify a secondary ligand-binding site in H1R, which potentially may support the introduction of new derivative groups to generate newer antihistamines. Furthermore, these structures show that antihistamines exert inverse regulation by utilizing a shared phenyl group that inserts into the deep cavity and block the movement of the toggle switch residue W4286.48. Together, these results enrich our understanding of GPCR modulation and facilitate the structure-based design of novel antihistamines.

[Objective] During the process of EPB SC (earth pressure balanced shield construction), inevitable soil disturbances occur, causing ground deformation and subsequently impacting the surrounding built environment negatively. Therefore, conducting research on the ground deformation caused by EPB SC holds significant importance. [Method] By integrating various relevant literatures, the research is centered around the connections between each stage of the construction. The working mechanism of EPC SC is clarified, the causes of ground deformation are studied from three aspects: tunnel geometry factors, soil geological conditions, and shield tunneling parameters. The prediction methods for ground deformation caused by EPC SC are discussed, the applicability and pros and cons of each method are analyzed. Based on the causes of deformation and deformation prediction, control measures for ground deformation caused by EPB SC are listed. The shortcomings in the research of EPB SC are pointed out, and the future development direction of EPB SC is discussed. [Result & Conclusion] The causes of ground deformation induced by EPB SC are tunnel geometry factors, soil geological conditions, and shield tunneling parameters. The prediction methods for ground deformation caused by EPC SC include empirical formula method, theoretical analysis method, numerical simulation method of finite elements and finite difference, model test method, and artificial intelligence method. Based on the monitoring of ground deformation, continuously adjusting the shield parameters during construction is an effective method to control ground deformation by EPB shield.

In the current seismic analysis of high-speed railway track–bridge structures, the constitutive parameters of track lateral blocks (TLBs) are not uniform. A finite element model of the TLB’s main beam is established, which considers concrete plastic damage, interface bond between old and new concrete, and bond slip between anchorage steel bars (ASBs) and concrete. Quasi-static tests of nine TLBs with different numbers and ASB diameters at a 1:2 scale was carried out to verify the accuracy of the TLB finite element model in terms of the failure pattern, interface damage, hysteretic properties, and skeleton model. Based on the verified TLB finite element model, the influence of different TLB design parameters on its seismic performance parameters was studied. The results show that the established TLB finite element model is in good agreement with the test data in terms of failure pattern, interface damage, hysteretic properties, and skeleton model, which verifies the accuracy of the TLB finite element model. ASB number has the greatest influence on the TLB yield load, followed by ASB diameter, concrete strength, ASB strength, and ASB spacing. ASB diameter has the greatest influence on the TLB peak load, followed by ASB number, ASB strength, concrete strength, and ASB spacing. ASB diameter has the greatest influence on the TLB energy dissipation capacity, followed by ASB number, concrete strength, ASB strength, and ASB spacing. With the increase in concrete strength and ASB strength, TLB ductility decreases. When the ASB number is higher than eight or the diameter is higher than 12 mm and the corresponding interfacial steel bar ratio reaches 0.82%, the TLB ductility will decrease.

The increasing demands for better crashworthiness of vehicles require structural components with excellent energy absorption performance and anti-intrusion resistance. Here, we explored the potential of using a medium Mn steel with a 10 wt.% Mn content for hot/warm forming with the aim to produce automotive components with improved strength–ductility combination. The soaking temperature was varied from 670 to 870 °C for tuning the microstructure and the resultant mechanical properties. It is found that a duplex microstructure consisting of ferrite with a high dislocation density and retained austenite with a volume fraction of 53.5% is formed at 720 °C, leading to an exceptional strength–ductility combination (yield strength: 1016 MPa; ultimate tensile strength: 1407 MPa; total elongation: 32.7%). The energy absorbed before fracture is 39.8 GPa·%, which is four times more than that of the widely-used 22MnB5 boron steel. On basis of the experimental results, the effects of soaking temperature on austenite reverse transformation, ferrite recrystallization and martensitic transformation are discussed in depth to provide guidance for optimizing the strength–ductility combination of medium Mn steels processed by hot/warm forming.

Retrospective analysis of forest dynamics is indispensable for formulating forest management policies. However, such work is rarely performed at a regional scale in China, especially the southwest region that serves as an important carbon sink. Here, we employed annual Landsat images and LandTrendr to map stand-replacing forest disturbances and attribute them to anthropogenic, geological disaster, fire, and logging in the Sichuan province from 2001 to 2020. The results show that the Overall Accuracy (OA) of disturbance map reaches 90.5 % (±0.6 %) (95 % confidence interval) after unbiased estimation. An estimated 1.3 % (2910.0 ± 1274.1 km2) of the forest area has been disturbed as of 2020. Fire is the dominant factor that comprises 36 % of the total disturbance area, followed by geological disaster (30 %), anthropogenic (17 %), and logging (17 %). In contrast to geological disaster dropping rapidly over the last 20 years, fire increased by 58 %, while anthropogenic disturbance and logging tripled and doubled respectively. The spatial distribution of forest disturbances in Sichuan province is as expected, characterized by fire predominantly disturbing natural forests of western and southern after the implementation of the natural forest protection policy, most geological disaster occurring in northern areas adjacent to the plateau and basin, most logging occurring in southeastern planted forest areas, and most anthropogenic disturbance occurring in eastern flat basin area. Our results present a regional-scale pattern of Sichuan’s forest disturbance agents and their changes over time, which enhances our ability to accurately assess forest management, carbon dynamics, and potential disturbance risk mitigation in the region.

The purpose of this study is to expand and deepen research in the field of safety production, explore the role of enterprises as decision-making entities in safety production, analyze the role of different types of decision-making behaviors in the process of enterprise safety production, and provide certain theoretical guidance for multi-agent decision-making in safety production. By summarizing and comparing the existing dual system of supply chain safety management, this study proposes dividing supply chain safety management into the two categories of safety organization and safety research and development (safety R&D) in the context of digitalization. Differential equations are established to describe a two-level supply chain safety management problem including manufacturers and suppliers. The research results show that, compared with the Nash equilibrium, when there are strong manufacturers involved in safety management cooperation, the manufacturer's safety R&D subsidy to suppliers improves the total revenue of the supply chain. When the manufacturer's safety organization supervision efficiency is high, the Stackelberg equilibrium is lower. It is a Pareto improvement for the manufacturer to supervise the supplier's safety organization to the Nash equilibrium. In the case of dual safety cooperation in the supply chain, when there are strong manufacturers, R&D subsidies have a crowding out effect on the safety organization cooperation. Finally, based on the example analysis, relevant suggestions are put forward for supply chain safety management.

Ultraviolet (UV) networks are widely applied in complex electromagnetic environments. Designing an efficient multi-node medium access control (MAC) protocol for these networks is important. In this study, we proposed an enhanced clustering time division multiple access (TDMA) MAC protocol based on clustering and learning automata (LA). Subsequently, the effects of the network topology, class of service, and number of cluster nodes on the network performance under the proposed protocol were analyzed. Then, the protocol was compared with the TDMA protocol and clustering system. Results revealed that it obtained a better network performance, proving its suitability for multi-node UV networking.

Abstract Wildfire occurrence is attributed to the interaction of multiple factors including weather, fuel, topography, and human activities. Among them, weather variables, particularly the temporal characteristics of weather variables in a given period, are paramount in predicting the probability of wildfire occurrence. However, rainfall has a large influence on the temporal characteristics of weather variables if they are derived from a fixed period, introducing additional uncertainties in wildfire probability modeling. To solve the problem, this study employed the weather variables in continuous nonprecipitation days as the “dynamic-step” weather variables with which to improve wildfire probability modeling. Multisource data on weather, fuel, topography, infrastructure, and derived variables were used to model wildfire probability based on two machine learning methods—random forest (RF) and extreme gradient boosting (XGBoost). The results indicate that the accuracy of the wildfire probability models was improved by adding dynamic-step weather variables into the models. The variable importance analysis also verified the top contribution of these dynamic-step weather variables, indicating the effectiveness of the consideration of dynamic-step weather variables in wildfire probability modeling.

Live fuel moisture content (LFMC) is a crucial variable affecting the ignition potential of shrublands. Different remote sensing-based models (either empirical or physical) have been adopted to estimate LFMC in shrublands but with mixed success potentially owing to differences in vegetation cover (closed vs. open shrublands). This study aimed to evaluate and discuss LFMC estimation in open and closed shrublands using different remote sensing approaches. For each case, three broadly used radiative transfer models (RTMs) (PROSAILH, PROGeoSail, and PROACRM), and two empirical models were selected and compared. The empirical models were calibrated by a stepwise regression approach using a spectral index (SI) and its normalized form (SImax-min). Results showed that both RTMs and empirical models performed well in retrieving LFMC of closed shrublands (RTMs: R2 = 0.60–0.66, RMSE = 14.96–18.51%, bias = −5.99–4.36%, and empirical models: R2 = 0.69–0.72, RMSE = 10.67–11.30%, bias = 0.18–0.35%). However, all RTMs failed to retrieve LFMC for open shrublands (R2 = 0.01–0.09, RMSE = 45.21–48.66%, bias = 9.76–14.75%) potentially due to the high heterogeneity of vegetation in this vegetation type. In contrast, the SImax-min-based model outperformed the RTMs for the open shrublands LFMC estimation (R2 = 0.44, RMSE = 32.32%, bias = −0.34%) but saturated at high LFMC values (> 120%). In conclusion, PROACRM and the empirical model using SImax-min as an explanatory variable are recommended to model closed and open shrublands LFMC, respectively. This study gives insights into developing effective models for improving shrubland LFMC estimation by considering various fractions of covers of shrublands that were not previously considered.

Corneal regeneration has always been a challenge due to its sophisticated structure and undesirable keratocyte-fibroblast transformation. Herein, we propose 3D printing of a biomimetic epithelium/stroma bilayer implant for corneal regeneration. Gelatin methacrylate (GelMA) and long-chain poly(ethylene glycol) diacrylate (PEGDA) are blended to form a two-component ink, which can be printed to different mechanically robust programmed PEGDA-GelMA objects by Digital Light Processing (DLP) printing technology, due to the toughening effect of crystalline crosslinks from long-chain PEGDA on GelMA hydrogel after photo-initiated copolymerization. The printed PEGDA-GelMA hydrogels support cell adhesion, proliferation, migration, meanwhile demonstrating a high light transmittance, and an appropriate swelling degree, nutrient permeation and degradation rate. A bi-layer dome-shaped corneal scaffold consisting of rabbit corneal epithelial cells (rCECs)-laden epithelia layer and rabbit adipose-derived mesenchymal stem cells (rASCs)-laden orthogonally aligned fibrous stroma layer can be printed out with a high fidelity and robustly surgical handling ability. This bi-layer cells-laden corneal scaffold is applied in a rabbit keratoplasty model. The post-operative outcome reveals efficient sealing of corneal defects, re-epithelialization and stromal regeneration. The concerted effects of microstructure of 3D printed corneal scaffold and precisely located cells in epithelia and stroma layer provide an optimal topographical and biological microenvironment for corneal regeneration.

Breeding success is an important factor determining fecundity with nest predation being the main factor limiting avian breeding success. Understanding of nest predation and its influencing factors are highly significant to explore the dynamics of bird populations and developing appropriate conservation strategies. In two breeding seasons of the year 2020 and 2021, natural nests of the red junglefowl (Gallus gallus jabouillei) were systematically searched and monitored using infrared camera, in two nature reserves (Datian and Bangxi) of tropical Hainan island, China. Results showed that breeding season of the red junglefowl is mainly from March to July, with April being the breeding peak. The clutch size was 5.15 ± 1.28 (n = 13), and nesting success of natural nests was 31.2%, with nest predation accounting for 45.4% of nest failure. Artificial nest experiments showed that predation rates of artificial nests were 25% (Datian, 2020), 6.67% (Datian, 2021), and 0% (Bangxi, 2020). Rodents, reptiles, and coucals are the main nest predators of red junglefowls, while activities of Hainan Eld’s deers (Panolia siamensis) may interfere with the reproduction of red junglefowls. We suggest that the conservation management policies should consider the impacts on junglefowls’ breeding success when reconstructing the suitable habitat of the Hainan Eld’s deer.

Development of a biostable and biosafe vitreous substitute is highly desirable, but remains a grand challenge. Herein, we propose a novel strategy for constructing a readily administered vitreous substitute based on a thiol-acrylate clickable polyzwitterion macromonomer. A biocompatible multivinyl polycarboxybetaine (PCB-OAA) macromonomer is designed and synthesized, and mixed with dithiothreitol (DTT) via a Michael addition reaction to form a hydrogel in vitreous cavity. This resultant PCB-OAA hydrogel exhibits controllable gelation time, super anti-fouling ability against proteins and cells, excellent biocompatibility, and approximate key parameters to human vitreous body including equilibrium water content, density, optical properties, modulus. Remarkably, outperforming clinically used silicone oil in biocompatibility, this rapidly formed hydrogel in the vitreous cavity of rabbit eyes remains stable in vitreous cavity, showing an appealing ability to prevent significantly inflammatory response, fibrosis and complications such as raised intraocular pressure (IOP), and cataract formation. This zwitterionic polymer hydrogel holds great potential as a vitreous substitute.

ObjectiveIn this study, we aimed to observe the effects of ultrasound-guided fascia iliaca compartment block (FICB) combined with hypobaric spinal anesthesia on post-operative pain and cognitive function in elderly patients with high-risk hip replacement.MethodsA total of 84 elderly patients—aged 65–85 years, with American Society of Anesthesiologists physical status III–IV, and scheduled for hip arthroplasty between September 2021 and May 2022—were selected. One or more organs with moderate to severe impairment were included in all patients. The patients were randomly divided into a hypobaric spinal anesthesia group (group C, control group) and an ultrasound-guided FICB combined with hypobaric spinal anesthesia group (group E, experimental group). Group C was given 3.5 mL of 0.32% ropivacaine hypobaric spinal anesthesia, and group E received ultrasound-guided FICB combined with 3.5 mL of 0.32% ropivacaine hypobaric spinal anesthesia. The patients were compared using the visual analog scale (VAS) for pain, Harris hip function score, and simple Mini-Mental State Examination (MMSE) scale. Blood β-amyloid (Aβ) and neuronal microtubule-associated protein (tau) levels were measured. We compared intraoperative conditions and post-operative complications between the two groups to assess the effects of FICB combined with hypobaric spinal anesthesia on post-operative pain and cognitive function in elderly patients with high-risk hip replacement.ResultsAt 1 and 3 days after the operation, patients in group C had significantly higher VAS and lower MMES scores than those in group E. The differences were statistically significant at 1 (P < 0.01) and 3 (P < 0.05) days after the operation. At 1 day after operation, the Harris score of patients in group C was significantly lower than that of patients in group E (P < 0.05). The Aβ and tau levels of patients in group C were significantly higher than those of patients in group E at 1 day after the operation (P < 0.01). The Aβ levels of patients in group C were significantly higher than those of patients in group E at 3 days after the operation (P < 0.05). The intraoperative conditions and post-operative complication rates did not differ significantly between the two groups. At 1 day before and 5 days after the operation, there was no difference in any of the indicators.ConclusionBy lowering pain and managing Aβ and tau protein concentrations, FICB can successfully lower the incidence of early post-operative cognitive impairment in elderly patients with high-risk hip replacement.Clinical trial registrationwww.chictr.org.cn, identifier: ChiCTR2100051162.

Foliage fuel is the most flammable component in crown fires. Spatiotemporal dynamics of foliage fuel load (FFL) are important for fire managers to assess fire risk. Here, we integrated optical data from the Landsat 8 Operational Land Imager (OLI) with synthetic aperture radar (SAR) data from Sentinel-1 to estimate FFL. We first reconstructed seamless time series from the Landsat 8 and Sentinel-1 imagery by accounting for unequal time intervals between image observations and outliers. We then extracted temporal features that are proxies of the intra- and inter-annual dynamics from these time series. In addition, we derived spatial features from the imagery that quantify spatial context and therefore used varying window sizes. The random forest regression was implemented to assess the importance of the spatiotemporal features, reduce errors, and derive robust FFL estimates. The satellite estimates were validated against 96 field measurements from Pinus yunnanensis forests in the Liangshan Yi Autonomous Prefecture, Sichuan Province, China. Both the spatiotemporal features of SAR and optical data importantly contributed to FFL estimation. When only optical data was used, the model achieved a R2 of 0.75 (relative Root Mean Squared Error (rRMSE) = 25.3 %), while when only SAR data was used the R2 was 0.76 (rRMSE = 25.6 %). However, when optical and SAR data were combined, the R2 increased to 0.81 (rRMSE = 23.2 %). We also found that temporal features were more important predictors of FFL than features that captured spatial context. We demonstrated our FFL mapping method by a case study in the Chinese Sichuan Province, in relation to the occurrence of a fire. Our method needs additional validation over different tree species and forest types, yet has potential for mapping forest fuel loads and fire risk.

The upcoming BIOMASS mission will provide P-band repeat-pass PolInSAR data from space for the improved mapping of global biomass. PolInSAR technique has been widely validated with the potential to invert forest height and estimate forest aboveground biomass (AGB). However, the robustness of PolInSAR-based AGB estimation across different sites still lacks full evaluation, especially for those with a varied forest type, heterogeneity (varied growth ratio between cover and height), and topographic relief. In this study, we concentrated on backscatter decomposition and forest height inversion, and developed a robust AGB estimation method that can be applied to different sites. Two dense and closed tropical forest sites (Paracou and Nouragues) and one open and heterogeneous boreal forest site (Krycklan) were selected as the study areas, and the corresponding airborne PolInSAR, LiDAR, and ground measured AGB data were used for validation and analysis. Results show that ground backscatter has the strongest correlation with AGB in boreal forests, but this correlation cannot be transferred to the tropical forests. Only canopy volume backscatter is almost free from topographic influence, and its relationship with AGB across three sites can be formulated using one exponential equation, producing the best estimation accuracy, with R2 of 0.79 and RMSE of 61.5 tons/ha (relative RMSE of 20.0 %). Multi-baseline PolInSAR retrieved forest height with little bias in spite of the presence of temporal decorrelation. One power equation can be used to correlate PolInSAR forest height with AGB across three different sites, and LOO (leave-one-out) validation shows the R2 of 0.85 and RMSE of 51.8 tons/ha (relative RMSE of 16.9 %). However, the RVoG-inverted PolInSAR FH was found to mainly represent the top forest height for open and heterogeneous forests, which means PolInSAR FH (forest height) lacks consideration for forest horizontal structure (e.g. forest density). In contrast, volume backscatter better captured forest density, and the proposed AGB model that combines PolInSAR FH and volume backscatter further improved the AGB estimation accuracy, especially for open forests: the plot-scale validation from all three sites shows R2 was improved from 0.79 (volume backscatter) and 0.85 (PolInSAR FH) to 0.89, and RMSE decreased from 61.5 and 51.8 to 45.2 (relative RMSE of 14.7 %) tons/ha; for region-scale validation, R2 was improved from 0.77 and 0.83 to 0.89, and RMSE decreased from 64.2 (relative RMSE of 39.0 %) and 54.5 (34.5 %) to 48.1 (29.4 %) tons/ha.

It is very important to unseal the closed coal mine fire zone safely. Although the fire source disappears in the closed fire zone, it is uncertain whether there are still have high-temperature ignition points, and there is a risk of fire or even explosion again after unsealing. To ensure that the closed fire zone can be unsealed successfully at one time, the study puts forward the method of oxygen augmentation observation to understand the combustion state of the closed fire zone in underground coal mine from the perspective of gas analysis technology. When the mine fire is detected to be extinguished, the method is to inject 8–12% oxygen into the fire area, and the fire area shall be monitored for 2 weeks to determine whether the fire area is completely extinguished. The accuracy and practicality of the method of oxygen augmentation observation are verified by comparing and analyzing the difference results generated by two oxygen augmentation observations in closed zone of Yuwu Coal Mine. In this paper, the use conditions of unsealing in fire zone, detailed methods and steps in the whole process of unsealing and emergency rescue plan are given. The research results can provide valuable reference for one-time safe unsealing of fire zone during the process of disaster relief.

It is significant to predict the gas emission in the mine gob accurately for gas drainage to prevent accidents and ensure safe production. The change of atmospheric pressure makes the gas emission in the mine gob presents a “breathing phenomenon”, which causes the gas concentration in the return airway of the working face fluctuates even if there are no other disturbances. The gas emission of the return airway in mine working face is affected by many factors, so it is difficult to analyze the influences of atmospheric pressure fluctuations on the gas emission of mine gob. In order to reduce interference factors, by continuously monitoring the gas concentration in the return airway of a does not work mine, in the meantime, simultaneously monitoring and analyzing the atmospheric pressure on the ground, this paper studies the influence rules of atmospheric pressure on gas emission in goaf. And a new multi-parameter mathematical model for gas emission in gob is developed, the Particle Swarm Optimization algorithm (PSO) is used to invert the optimal parameters, and the model is verified by random monitoring data. The results show that the model can accurately indicate the influence of atmospheric pressure on gas emission in the gob. The faster the ground atmospheric pressure decreases, the higher the gas emission rate of the gob is, and the faster the ground atmospheric pressure increases, the lower the gas emission rate is. In addition, the mathematical model can also invert the effective permeability and effective porosity of the gob, which provides a new idea for the prediction of gas emission from goaf and the evolution characteristics of permeability in goaf.

Height is a key factor in monitoring the growth status and rate of crops. Compared with large-scale satellite remote sensing images and high-cost LiDAR point cloud, the point cloud generated by the Structure from Motion (SfM) algorithm based on UAV images can quickly estimate crop height in the target area at a lower cost. However, crop leaves gradually start to cover the ground from the beginning of the stem elongation stage, making more and more ground points below the canopy disappear in the data. The terrain undulations and outliers will seriously affect the height estimation accuracy. This paper proposed a ground point fitting method to estimate the height of winter wheat based on the UAV SfM point cloud. A canopy slice filter was designed to reduce the interference of middle canopy points and outliers. Random Sample Consensus (RANSAC) was applied to obtain the ground points from the valid filtered point cloud. Then, the missing ground points were fitted according to the known ground points. Furthermore, we achieved crop height monitoring at the stem elongation stage with an R2 of 0.90. The relative root mean squared error (RRMSE) of height estimation was 5.9%, and the relative mean absolute error (RMAE) was 4.6% at the stem elongation stage. This paper proposed the canopy slice filter and fitting missing ground points. It was concluded that the canopy slice filter successfully optimized the extraction of ground points and removed outliers. Fitting the missing ground points simulated the terrain undulations effectively and improved the accuracy.

Rice false smut (RFS) is a late-onset fungal disease that primarily affects rice panicle in recent years. Severe RFS can decrease the yield by 20–30% and severely affect rice quality. This research used hyperspectral remote sensing data from unmanned aerial vehicles (UAV). On the basis of genetic algorithm combined with partial least squares to select the feature bands, this paper creates a new method to use the Pearson correlation coefficient method and Instability Index between Classes (ISIC) method to further select characteristic bands, which further eliminated 27.78% of the feature bands when the model monitoring accuracy was improved overall. The prediction accuracy of the Gradient Boosting Decision Tree model and Random Forest model was the best, which were 85.62% and 84.10%, respectively, and the monitoring accuracy was improved by 2.22% and 2.4% compared with that before optimization. Then, based on the UAV hyperspectral data and the combination of characteristic bands selected by the three band optimization methods, the sensitive band ranges of rice false smut monitoring were determined, which were 698–800 nm and 974–997 nm. This paper provides an effective method of selecting characteristic bands of hyperspectral data and a method of monitoring crop diseases’ using unmanned aerial vehicles.

Soil moisture is vital for the crop growth and directly affects the crop yield. The conventional synthetic aperture radar (SAR) based soil moisture monitoring is often influenced by vegetation cover and surface roughness. The machine-learning methods are not constrained by physical parameters and have high nonlinear fitting capabilities. In this study, machine-learning methods were applied to estimate soil moisture over winter wheat fields during its growing season. RADARSAT-2 data with quad polarizations and 240 sample plots in the study area were acquired and collected, respectively. In addition to the four linear polarization channels, polarimetric decomposition parameters were extracted to expand the SAR feature space. Three advanced machine-learning models were selected and compared, which were support vector regression, random forests (RF), and gradient boosting regression tree. To improve the performances of the models, three feature-selection methods were compared, which were based on Pearson correlation, support vector machine recursive feature elimination, and RF, respectively. The coefficient of determination (R2) and root-mean-square error (RMSE) were used to compare and assess the performances of those models. The results revealed that polarimetric decomposition parameters were effective in estimating soil moisture, and RF model obtained the highest prediction accuracy (training set: RMSE = 2.44 vol.% and R2 = 0.94; and validation set: RMSE = 4.03 vol.%, and R2 = 0.79). This study finally concluded that using polarimetric decomposition parameters combined with machine-learning and feature-selection methods could effectively estimate soil moisture at a high accuracy, which helps monitor soil moisture across the agricultural field during its growing season.

Soil moisture (Mv) estimation and monitoring over agricultural areas using Synthetic Aperture Radar (SAR) are often affected by vegetation cover during the growing season. Volume scattering and vegetation attenuation can complicate the received SAR backscatter signal when microwave interacts with the vegetation canopy. To address the existing problems, this article employed the model-based polarimetric decomposition method considering the two-way attenuation to remove the volume scattering and vegetation attenuation. A deorientation process of SAR data was applied to remove the influence of randomly distributed target orientation angles before the polarimetric decomposition. To parameterize the two-way attenuation, Radar Vegetation Index derived from the SAR intensity images was adopted. The Dubois model was used to describe backscattering from the underlying bare soil. Since the soil roughness parameters are difficult to measure under vegetation cover, the optimum surface roughness method was used to parameterize the Dubois model. This soil moisture retrieval algorithm was applied to the polarimetric multitemporal RADARSAT-2 SAR data over soybean fields. The validation indicates the root-mean-square error of 9.2 vol.% and 8.2 vol.% at HH and VV polarization, respectively, over the entire soybean growing period, suggesting that the proposed method is capable of reducing the effect of vegetation cover for soil moisture monitoring over the soybean field.

Foliage fuel load (FFL) is a critical factor affecting crown fire intensity and rate of spread. Satellite observations provide the potential for monitoring FFL dynamics across large areas. Previous studies commonly used empirical methods to estimate FFL, which potentially lacks reproducibility. This study applied Landsat 7 ETM+ and 8 OLI data for FFL retrieval using radiative transfer model (RTM) and machine learning method. To this end, the GeoSail, SAIL, and PROSPECT RTMs were first coupled together to model the near-realistic scenario of a two-layered forest structure. Second, available ecological information was applied to constrain the coupled RTM modeling phases in order to decrease the probability of generating unrealistic simulations. Third, the coupled RTMs were linked to three machine learning models—random forest, support vector machine, and multilayer perceptron—as well as the traditional lookup table. Finally, the performance of each method was validated by FFL measurements from Southwest China and Sweden. The resulting multilayer perceptron (R2 = 0.77, RMSE = 0.13, and rRMSE = 0.43) outperformed the other three methods. The evaluation of the applicability of the FFL estimates was conducted in a southwest China forest where two occurred in 2014 and 2020. The FFL dynamics from 2013 through 2020 showed that the fire was likely to occur when the FFL accumulated to a critical point (around 27 × 106 kg), highlighting the relevance of remote sensing derived FFL estimates for understanding potential fire occurrence.

Object detection is challenging in high spatial resolution (HSR) remote sensing images that have a complex background and irregular object locations. To minimize manual annotation cost in supervised learning methods and achieve advanced detection performance, we proposed a point-based weakly supervised learning method to address the object detection challenge in HSR remote sensing images. In the study, point labels are introduced to guide candidate bounding box mining and generate pseudobounding boxes for objects. Then, pseudobounding boxes are applied to train the detection model. A progressive candidate bounding box mining strategy is proposed to refine object detection. Experiments are conducted on a comprehensive HSR dataset which contains four categories. Results indicate the proposed method achieves competitive performance compared to YOLOv5 which is trained on manual bounding box annotations. In comparison to the state-of-the-art weakly supervised learning method, our method outperforms WSDDN method with 0.62 mean average precision score.

Optical data records have been widely used for monitoring forest aboveground biomass (AGB) dynamics given their freely available long-term archives and high temporal and spatial resolution. However, most of the methods utilized only the spatial information of reflectance (e.g. annual mean reflectance) to estimate AGB, lack of consideration for dynamic characteristics of reflectance. These methods are susceptible to reflectance saturation and temporal noise and may cause uncertainties in their estimation. In this study, we exploited dynamic time-series information of reflectance to enhance the accuracy and robustness of AGB estimation. More specifically, we utilized the continuous change detection and classification (CCDC) algorithm for temporal modeling of Landsat reflectance, followed by estimation of forest AGB with a random forest (RF) algorithm using the temporal information extracted by the CCDC model as input. The AGB estimation method was developed using in situ AGB measurements in Australia (10304 plots, range: [0.05, 936] tons/ha, mean: 153 tons/ha) and Landsat 5/7/8 data. Plot-scale results show that the overall reflectance derived by the CCDC model outperformed the annual mean reflectance when used in RF to estimate AGB because the overall reflectance is more robust to noisy or missing data. Introducing the CCDC model parameters into the RF model further improved estimation accuracy. The use of overall reflectance plus temporal parameters in AGB estimation increased R2 to 0.60 and reduced RMSE to 72.9 tons/ha, compared to using annual mean reflectance (R2 of 0.45 and RMSE of 85.7 tons/ha). This improvement may be attributed to CCDC model better capturing temporal changes in reflectance as the solar angle changes between seasons. Those changes relate to forest canopy coverage, width, and height, which are linked to AGB. Regional analysis for a 19,000-km2 area in Western Australia showed that this approach could characterize different types of forest AGB change over a 30 years period (1990–2019), including both gradual and sudden AGB losses and gains from, e.g., forest disturbance and management activities. This AGB estimation method has the potential to mitigate noisy or missing data and could be applied to large-scale forest biomass monitoring using the rich Landsat data archive.

Abstract Background A growing body of evidence supports neutrophils as having an active role in the development of diabetic kidney disease (DKD). However, the clinical relevance of neutrophils and DKD in autoimmune diabetes remains unknown. This study aimed to investigate the relationship between circulating neutrophils and DKD in autoimmune diabetes. Methods Patients with type 1 diabetes (T1D, n = 226) and latent autoimmune diabetes in adults (LADA, n = 79) were enrolled and stratified according to the urinary albumin to creatinine ratio (ACR). Circulating levels of white blood cells (WBCs), including neutrophils, were measured in a central laboratory, and the neutrophil-to-lymphocyte ratio (NLR) was calculated. The risk factors associated with DKD were analysed by logistic regression. Results In T1D and LADA patients, the peripheral neutrophil counts increased in parallel with DKD advancement. The neutrophil counts in the patients with macroalbuminuria were significantly higher than those in the patients with normoalbuminuria for each type of diabetes. Furthermore, neutrophil counts positively correlated with ACR in T1D. In addition, neutrophils were independently associated with DKD in T1D in the logistic regression analysis, when various well-known risk factors, including age, gender, disease duration, hypertension, dyslipidemia and smoking status, were adjusted. Conclusions Neutrophil counts are closely associated with DKD in patients with autoimmune diabetes, suggesting that neutrophil-mediated inflammation may be involved in the pathogenesis of DKD in patients with autoimmune diabetes.

Abstract Background At present, the distinctness, uniformity, and stability (DUS) testing of flue-cured tobacco (Nicotiana tabacum L.) depends on field morphological identification, which is problematic in that it is labor intensive, time-consuming, and susceptible to environmental impacts. In order to improve the efficiency and accuracy of tobacco DUS testing, the development of a molecular marker-based method for genetic diversity identification is urgently needed. Results In total, 91 simple sequence repeats (SSR) markers with clear and polymorphic amplification bands were obtained with polymorphism information content, Nei index, and Shannon information index values of 0.3603, 0.4040, and 0.7228, respectively. Clustering analysis showed that the 33 study varieties, which are standard varieties for flue-cured tobacco DUS testing, could all be distinguished from one another. Further analysis showed that a minimum of 25 markers were required to identify the genetic diversity of these varieties. Following the principle of two markers per linkage group, 48 pairs of SSR markers were selected. Correlation analysis showed that the genetic relationships revealed by the 48 SSR markers were consistent with those found using the 91 SSR markers. Conclusions The genetic fingerprints of the 33 standard varieties of flue-cured tobacco were constructed using 48 SSR markers, and an SSR marker-based identification technique for new tobacco varieties was developed. This study provides a reliable technological approach for determining the novelty of new tobacco varieties and offers a solid technical basis for the accreditation and protection of new tobacco varieties.

As one of the important coastal cities in China, Tianjin has been urbanized dramatically over the past 40 years, and the urbanization rate has been up to 83.15% by 2018. In this study, we used the continuous change detection and classification algorithm to comprehensively understand the urban expansion processes in Tianjin based on the Landsat time series from 1985 to 2018 with 30-m resolution. Specially, we applied the c-factor approach with the Ross Thick-LiSparse-R model to correct the bidirectional reflectance distribution function (BRDF) effect for each Landsat image and calculated a spatial line density feature for improving the change detection and the classification. Based on the study in Tianjin, we found that BRDF correction can substantially improve the change detection (9.00% higher overall accuracy) and classification (1.08% higher overall accuracy); and the line density is also beneficial to classification (0.48% higher overall accuracy), especially for impervious surface (1.70% less commission errors and 1.49 % less omission errors). By analyzing the imperious surface change processes, we observed that Tianjin has undergone rapid urban expansion in the past decades, and the urban area was mainly transformed from cropland around the central area before 2005 and later from the coast.

The influence of morphology of the Ti2Cu phase on biological corrosion and antibacterial properties of Ti-Cu alloy was investigated by micro-galvanic corrosion. Elongated “micro-galvanic cells” were formed between lamellar Ti2Cu phase (L-Ti2Cu) and α-Ti matrix due to the different Volta potentials. The corrosion rate (Vcorr) of L-Ti2Cu was twice that of granular Ti2Cu phase (G-Ti2Cu) because lamellar Ti2Cu phase had more galvanic interface. The release of Cu2+ ions in L-Ti2Cu was 55 % higher than G-Ti2Cu, reaching 99.5 % of the antibacterial rate in L-Ti2Cu and providing a great potential in clinical application for dental implants.

Lactic acid bacteria (LAB) are industrially important bacteria that are widely used in the fermented food industry, especially in the manufacture of yogurt. The fermentation characteristics of LAB are an important factor affecting the physicochemical properties of yogurts. Here, different ratios of L. delbrueckii subsp. bulgaricus IMAU20312 and S. thermophilus IMAU80809 were compared with a commercial starter JD (control) for their effects on viable cell counts, pH values, titratable acidity (TA), viscosity and water holding capacity (WHC) of milk during fermentation. Sensory evaluation and flavour profiles were also determined at the end of fermentation. All samples had a viable cell count above 5.59 × 107 CFU/mL at the end of fermentation, and a significant increase in TA and decrease in pH were observed. Viscosity, WHC and the sensory evaluation results of one treatment ratio (A3) were closer to the commercial starter control than the others. A total of 63 volatile flavour compounds and 10 odour-active (OAVs) compounds were detected in all treatment ratios and the control according to the results from solid-phase micro-extraction–gas chromatography–mass spectrometry (SPME–GC–MS). Principal components analysis (PCA) also indicated that the flavour characteristics of the A3 treatment ratio were closer to the control. These results help us understand how the fermentation characteristics of yogurts are affected by the ratio of L. delbrueckii subsp. bulgaricus to S. thermophilus in starter cultures; this is useful for the development of value-added fermented dairy products.

High-speed machining (HSM) has been studied for several decades and has potential application in various industries, including the automobile and aerospace industries. However, the underlying mechanisms of HSM have not been formally reviewed thus far. This article focuses on the solid mechanics framework of adiabatic shear band (ASB) onset and material metallurgical microstructural evolutions in HSM. The ASB onset is described using partial differential systems. Several factors in HSM were considered in the systems, and the ASB onset conditions were obtained by solving these systems or applying the perturbation method to the systems. With increasing machining speed, an ASB can be depressed and further eliminated by shock pressure. The damage observed in HSM exhibits common features. Equiaxed fine grains produced by dynamic recrystallization widely cause damage to ductile materials, and amorphization is the common microstructural evolution in brittle materials. Based on previous studies, potential mechanisms for the phenomena in HSM are proposed. These include the thickness variation of the white layer of ductile materials. These proposed mechanisms would be beneficial to deeply understanding the various phenomena in HSM.

P-band PolInSAR technique has been validated to be a useful tool for forest height inversion. Aiming at the residual ground scattering contribution and temporal decorrelation in repeat-pass interferometry that affect the inversion accuracy, we proposed a dual-baseline method based on the RVoG + VTD model. This method is firstly applied to E-SAR P-band data acquired over the Krycklan Catchment, Sweden with mixed forests, and the influence of ground scattering contribution and temporal decorrelation related to vertical wavenumber kz and height hv are theoretically and experimentally explored by comparing the traditional three-stage inversion method, the fixed extinction method and the proposed method. The experimental results show that the traditional three-stage inversion method has a overall serious overestimation for the whole study area, resulting in a R2 of ca. 0.4 and RMSE of ca. 7 m. Compared with the three-stage inversion method, the fixed extinction method improves the overestimation in the near range but has little effect in the far range. After introducing the RVoG + VTD model, both the overestimation in the near and far range are improved. In general, the study area is affected by both the influence of ground scattering contribution and temporal decorrelation. The overestimation in the near range (with kz larger than ca. 0.1 rad/m) is mainly caused by ground scattering contribution while for the far range (with kz smaller than ca. 0.06 rad/m) temporal decorrelation is the dominated reason. The simulation experiment further reveals the relationship between the influence of ground scattering contribution, temporal decorrelation and kz. Simulative results also show that ground scattering contribution has a greater influence for a large kz while temporal decorrelation generally brings overestimation, and has a greater impact for a small kz inversely. The proposed method simultaneously reduces the effects of ground scattering contribution and temporal decorrelation, and produces the highest accuracy with R2 reaching to ca. 0.6 and RMSE reducing to ca. 3.48 m, further demonstrating its validity for forest height inversion.

Gait phase classification is important for rehabilitation training in patients with lower extremity motor dysfunction. Classification accuracy of the gait phase also directly affects the effect and rehabilitation training cycle. In this article, a multiple information (multi-information) fusion method for gait phase classification in lower limb rehabilitation exoskeleton is proposed to improve the classification accuracy. The advantage of this method is that a multi-information acquisition system is constructed, and a variety of information directly related to gait movement is synchronously collected. Multi-information includes the surface electromyography (sEMG) signals of the human lower limb during the gait movement, the angle information of the knee joints, and the plantar pressure information. The acquired multi-information is processed and input into a modified convolutional neural network (CNN) model to classify the gait phase. The experiment of gait phase classification with multi-information is carried out under different speed conditions, and the experiment is analyzed to obtain higher accuracy. At the same time, the gait phase classification results of multi-information and single information are compared. The experimental results verify the effectiveness of the multi-information fusion method. In addition, the delay time of each sensor and model classification time is measured, which shows that the system has tremendous real-time performance.

Forest fuel load is the key factor for fire risk assessment, firefighting, and carbon emissions estimation. Remote sensing technology has distinct advantages in fuel load estimation due to its sensitivity to biomass and adequate spatiotemporal observations for large scales. Many related works applied empirical methods with individual satellite observation data to estimate fuel load, which is highly conditioned on local data and limited by saturation problems. Here, we combined optical data (i.e., Landsat 7 ETM+) and spaceborne Synthetic Aperture Radar (SAR) data (i.e., ALOS PALSAR) in a proposed semi-empirical retrieval model to estimate above-ground live forest fuel loads (FLAGL). Specifically, optical data was introduced into water cloud model (WCM) to compensate for vegetation coverage information. For comparison, we also evaluated the performance of single spaceborne L-band SAR data (i.e., ALOS PALSAR) in fuel load estimation with common WCM. The above two comparison experiments were both validated by field measurements (i.e., BioSAR-2008) and leave-one-out cross-validation (LOOCV) method. WCM with single SAR data could achieve reasonable performance (R2 = 0.64 or higher and RMSEr = 35.3% or lower) but occurred an underestimation problem especially in dense forests. The proposed method performed better with R2 increased by 0.05–0.13 and RMSEr decreased by 5.8–12.9%. We also found that the underestimation problem (i.e., saturation problem) was alleviated even when vegetation coverage reached 65% or the total FLAGL reached about 183 Tons/ha. We demonstrated our FLAGL estimation method by validation in an open-access dataset in Sweden.

Fuel moisture content (FMC) of live vegetation is a crucial wildfire risk and spread rate driver. This study presents the first daily FMC product at a global scale and 500 m pixel resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and radiative transfer models (RTMs) inversion techniques. Firstly, multi-source information parameterized the PROSPECT-5 (leaf level), 4SAIL (grass and shrub canopy level) and GeoSail (tree canopy level) RTMs to generate three look-up tables (LUTs). Each LUT contained the most realistic model inputs range and combination, and the corresponding simulated spectra. Secondly, for each date and location of interest, a global landcover map classified fuels into three classes: grassland, shrubland and forest. For each fuel class, the best LUT-based inversion strategy based on spectral information, cost function, percentage of solutions, and central tendency determined the optimal model for the global FMC product. Finally, 3,034 FMC measurements from 120 worldwide sites validated the statistically significant results (R2 = 0.62, RMSE = 34.57%, p

The spatial pattern and temporal variation in paddy rice areas captured by remote sensing imagery provide an effective way of performing crop management and developing suitable agricultural policies. However, fragmented and scattered rice paddies due to undulating and varied topography, and the availability and quality of remote sensing images (e.g., frequent cloud coverage) pose significant challenges to accurate long-term rice mapping, especially for traditional pixel and phenological methods in subtropical monsoon regions. This study proposed a superpixel and deep-learning-based time series method to analyze Landsat time series data for paddy rice classification in complex landscape regions. First, a superpixel segmentation map was generated using a dynamic-time-warping-based simple non-iterative clustering algorithm with preprocessed spectral indices (SIs) time series data. Second, the SI images were overlaid onto the superpixel map to construct mean SIs time series for each superpixel. Third, a multivariate long short-term memory full convolution neural network (MLSTM-FCN) classifier was employed to learn time series features of rice paddies to produce accurate paddy rice maps. The method was evaluated using Landsat imagery from 2000 to 2020 in Cengong County, Guizhou Province, China. Results indicate that the superpixel MLSTM-FCN achieved a high performance with an overall accuracy varying from 0.9547 to 0.9721, which presents an 0.17–1.23% improvement compared to the random forest method. This study showed that combining spectral, spatial, and temporal features with deep learning methods can generate accurate paddy rice maps in complex landscape regions.

In this study, urban stream sediment samples were collected in the Suzhou Industrial Park (SIP), one of the earliest national demonstration eco-industrial parks of China. PAHs were analyzed in these sediments, and concentrations of total PAHs were 180–81,000 ng g−1 (5700 ± 14,000 ng g−1). Medium molecular weight (4- ring) PAHs were predominant (42 ± 12%), followed by high molecular weight (5- and 6- ring) PAHs (31 ± 10%). No correlation was found between concentrations of PAHs and land uses of SIP in this study. Diagnostic ratios and a positive matrix factorization (PMF) model indicated that coal/biomass combustion might be the primary PAH source (61%), followed by non-combustion sources (21%) and vehicular emission (18%). According to the spatial analysis, PAHs in the sediments of SIP might be mainly associated with the coal/biomass combustion in the northeast industrial zone. Residential & commercial activities seem not to be the major causes of PAH contamination. Total PAH toxic equivalent concentrations, effect range low/effect range median values, and mean effects range–median quotient all showed that PAHs were present at a low toxicity risk level in most regions of the SIP. However, vigilance is required at some sampling sites with extremely high PAH concentrations or high mean effects range–median quotient.

Soil moisture content (SMC) is a significant factor affecting crop growth and development. However, SMC estimation, based on synthetic aperture radar (SAR), is influenced by a variety of surface parameters, such as vegetation cover and surface roughness. As a result, determining the SMC across agricultural areas (e.g., wheat fields) remotely (i.e., without ground measurement) is difficult to achieve. In this study, a model-based polarization decomposition method was used to decompose the original SAR signal into different scattering components that represented different scattering mechanisms. The different volume scattering models were applied, and then the results were compared in order to remove the scattering contribution from vegetation canopy, and extract the surface scattering components related to the soil moisture. Finally, by combining extensively used surface scattering models (e.g., CIEM and Dubois), and a method of roughness parameters optimization, a lookup table was developed to estimate the soil moisture during the wheat growth period. When CIEM is applied, the R2 and RMSE of the SMC are 0.534, 5.62 vol.%, and for the Dubois model, 0.634, 5.16 vol.%, respectively, which indicates that this approach provides good estimation performance for measuring soil moisture during the wheat growing season.

Detailed spatial information on the presence and properties of woody vegetation serves many purposes, including carbon accounting, environmental reporting and land management. Here, we investigated whether machine learning can be used to combine multiple spatial observations and training data to estimate woody vegetation canopy cover fraction (‘cover’), vegetation height (‘height’) and woody above-ground biomass dry matter (‘biomass’) at 25-m resolution across the Australian continent, where possible on an annual basis. We trained a Random Forest algorithm on cover and height estimates derived from airborne LiDAR over 11 regions and inventory-based biomass estimates for many thousands of plots across Australia. As predictors, we used annual geomedian Landsat surface reflectance, ALOS/PALSAR L-band radar backscatter mosaics, spatial vegetation structure data derived primarily from ICESat/GLAS satellite altimetry, and spatial climate data. Cross-validation experiments were undertaken to optimize the selection of predictors and the configuration of the algorithm. The resulting estimation errors were 0.07 for cover, 3.4 m for height, and 80 t dry matter ha-1 for biomass. A large fraction (89–94 %) of the observed variance was explained in each case. Priorities for future research include validation of the LiDAR-derived cover training data and the use of new satellite vegetation height data from the GEDI mission. Annual cover mapping for 2000–2018 provided detailed insight in woody vegetation dynamics. Continentally, woody vegetation change was primarily driven by water availability and its effect on bushfire and mortality, particularly in the drier interior. Changes in woody vegetation made a substantial contribution to Australia’s total carbon emissions since 2000. Whether these ecosystems will recover biomass in future remains to be seen, given the persistent pressures of climate change and land use.

In this contribution, we investigate the influence of three heat treatments, including intercritical annealing (IA), quenching and partitioning (Q&P), and combination of IA and Q&P (IA-Q&P), on microstructure and mechanical properties of the medium Mn steel. The steel treated by the IA process has the largest volume fraction of austenite, which is responsible for the longest elongation and largest energy absorption with the operation of both transformation-induced plasticity (TRIP) effect and twinning-induced plasticity (TWIP) effects. The medium Mn steel produced by IA-Q&P treatment has a mixture microstructure with retained austenite, ferrite, and lath martensite. The existence of martensite in the IA-Q&P steel makes the higher yield strength than that of the IA steel. The better uniform elongation of the IA-Q&P steel than that of the Q&P steel is ascribed to the larger volume fraction of retained austenite and higher mechanical stability of austenite. The Q&P steel has the highest yield stress due to its martensite matrix. It is expected that the tensile properties of medium Mn steel can be tuned by different thermal processes (IA, Q&P, and IA-Q&P) to facilitate its broad automotive applications.

Synthetic Aperture Radar (SAR) texture has been demonstrated to have the potential to improve forest biomass estimation using backscatter. However, forests are 3D objects with a vertical structure. The strong penetration of SAR signals means that each pixel contains the contributions of all the scatterers inside the forest canopy, especially for the P-band. Consequently, the traditional texture derived from SAR images is affected by forest vertical heterogeneity, although the influence on texture-based biomass estimation has not yet been explicitly explored. To separate and explore the influence of forest vertical heterogeneity, we introduced the SAR tomography technique into the traditional texture analysis, aiming to explore whether TomoSAR could improve the performance of texture-based aboveground biomass (AGB) estimation and whether texture plus tomographic backscatter could further improve the TomoSAR-based AGB estimation. Based on the P-band TomoSAR dataset from TropiSAR 2009 at two different sites, the results show that ground backscatter variance dominated the texture features of the original SAR image and reduced the biomass estimation accuracy. The texture from upper vegetation layers presented a stronger correlation with forest biomass. Texture successfully improved tomographic backscatter-based biomass estimation, and the texture from upper vegetation layers made AGB models much more transferable between different sites. In addition, the correlation between texture indices varied greatly among different tomographic heights. The texture from the 10 to 30 m layers was able to provide more independent information than the other layers and the original images, which helped to improve the backscatter-based AGB estimation.

The effect of the combination of vanillin and chitosan (VC) coating on the microbiota composition and shelf-life of turbot (Scophthalmus maximus) filets during a 15-day storage period at 4 ± 1°day was investigated in this study. The control and coated fish samples were analyzed periodically for sensory and chemical attributes [total volatile basic nitrogen (TVB-N), thiobarbituric acid reactive substances (TBARS), and pH] and the presence of dominant spoilage microbiota. The results suggested that the sensory and the chemical quality of turbot filets effectively improved after treatment with vanillin (final concentration 2 mg/ml) combined with 1% chitosan, and the shelf-life was prolonged for 6 to 7 days compared with the control group. Furthermore, high-throughput sequencing showed that Proteobacteria (52.2%) and Firmicutes (29.8%) were the dominant bacteria at the phylum level in fresh turbot filets, while Pseudomonadaceae (40.2%) and Lactobacillaceae (39.4%) were the dominant bacteria at the family level in deteriorated turbot filets. However, after VC treatment, the relative abundance of Pseudomonadaceae and Lactobacillaceae decreased significantly due to the growth inhibition of potential bacteria, specifically spoilage bacteria, along with the rich bacterial diversity at the end of storage. Therefore, our data indicated that VC treatment might be effective in decreasing bacteria-induced quality deterioration and in extending the shelf-life of refrigerated turbot filets.