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Karst ecosystems have become complex social–ecological systems (SESs) as a result of the interventions of large-scale ecological restoration programs, and the ecosystem services (ESs) that provide regional well-being can, to some extent, be described as social–ecological system services (S–ESs). Understanding the relationships among multiple S–ESs and exploring their drivers are essential for effective ecological management in karst areas, especially in regions differently affected by ecological engineering programs. Taking South China Karst (SCK) as a study area, we first identified two regions as comparative boundaries, namely significant engineering impact regions (SEERs) and non-significant ecological engineering impact regions (NEERs). Then we used ES assessment models, Spearman correlation, and optimal parameter geographic detector to identify the supply capacity, trade-offs/synergies, and their drivers of six types of S–ESs in SEERs and NEERs. The findings included: (1) SEERs were predominantly concentrated in the central and southern SCK regions, accounting for 33.98% of the total SCK area, with the most concentrated distribution observed in Guizhou and Guangxi. (2) Within the entire SCK, six S–ESs maintained a relatively stable spatial distribution pattern over time, with the most pronounced increase in soil conservation and a slight decrease in water retention, and the S–ES hotspots were more concentrated within the SEERs. (3) Most S–ES pairs within SEERs were optimized synergistically, with lower trade-off intensity and higher synergy intensity compared to NEERs. (4) S–ES pairs were affected by the interactions between the natural and socio-economic factors, with land use changes playing a crucial role, and natural factors were difficult to predict but cannot be ignored. Based on the results, we propose different SES sustainable development suggestions, with a view to providing theoretical support for the optimization of SES functions and the consolidating of integrated ecological construction. [ABSTRACT FROM AUTHOR]

A small molecule disulfide unit technology platform based on dynamic thiol exchange chemistry at the cell membrane has the potential for drug delivery. However, the alteration of the CSSC dihedral angle of the disulfide unit caused by diverse substituents directly affects the effectiveness of this technology platform as well as its own chemical stability. The highly stable open-loop relaxed type disulfide unit plays a limited role in drug delivery due to its low dihedral angle. Here, we have built a novel disulfide unit starship based on the 3,4,5-trihydroxyphenyl skeleton through trigonometric bundling. The intracellular delivery results showed that the trigonometric bundling of the disulfide unit starship effectively promoted cellular uptake without any toxicity, which is far more than 100 times more active than that of equipment with a single disulfide unit in particular. Then, the significant reduction in cell uptake capacity (73–93%) using thiol erasers proves that the trigonometric bundling of the disulfide starship is an endocytosis-independent internalization mechanism via a dynamic covalent disulfide exchange mediated by thiols on the cell surface. Furthermore, analysis of the molecular dynamics simulations demonstrated that trigonometric bundling of the disulfide starship can significantly change the membrane curvature while pushing lipid molecules in multiple directions, resulting in a significant distortion in the membrane structure and excellent membrane permeation performance. In conclusion, the starship system we built fully compensates for the inefficiency deficiencies induced by poor dihedral angles. [ABSTRACT FROM AUTHOR]

Six new 2α-hydroxy ursane triterpenoids, 3α-cis-p-coumaroyloxy-2α,19α-dihydroxy-12-ursen-28-oic acid (1), 3α-trans-p-coumaroyloxy-2α,19α-dihydroxy-12-ursen-28-oic acid (2), 3α-trans-p-coumaroyloxy-2α-hydroxy-12-ursen-28-oic acid (3), 3β-trans-p-coumaroyloxy-2α-hydroxy-12,20(30)-ursadien-28-oic acid (4), 3β-trans-feruloyloxy-2α-hydroxy-12,20(30)-ursadien-28-oic acid (5), and 3α-trans-feruloyloxy-2α-hydroxy-12,20(30)-ursadien-28-oic acid (6), along with eleven known triterpenoids (7–17), were isolated from the leaves of Diospyros digyna. Their chemical structures were elucidated by comprehensive analysis of UV, IR, HRESIMS, and NMR spectra. All the isolated compounds were evaluated for their PTP1B inhibitory activity. 3β-O-trans-feruloyl-2α-hydroxy-urs-12-en-28-oic acid (13) showed the best inhibition activity with an IC50 value of 10.32 ± 1.21 μM. The molecular docking study found that the binding affinity of compound 13 for PTP1B was comparable to that of oleanolic acid (positive control). [ABSTRACT FROM AUTHOR]

Approximating the positions of vessels near underwater devices, such as unmanned underwater vehicles and autonomous underwater vehicles, is crucial for many underwater operations. However, long-term monitoring of vessel trajectories is challenging due to limitations in underwater communications, posing challenges for the execution of underwater exploration missions. Therefore, trajectory prediction based on AIS data is vital in the fusion of underwater detection information. However, traditional models for underwater vessel trajectory prediction typically work well for only small-scale and short-term predictions. In this paper, a novel deep learning method is proposed that leverages a look-back window to decompose the temporal and motion features of ship movement trajectories, enabling long-term vessel prediction in broader sea areas. This research introduces an innovative model structure that enables trajectory features to be simultaneously learned for a larger range of vessels and facilitates long-term prediction. Through this innovative model design, the proposed model can more accurately predict vessel trajectories, providing reliable and comprehensive forecasting results. Our proposed model outperforms the Nlinear model by a 16% improvement in short-term prediction accuracy and an approximately 8% improvement in long-term prediction accuracy. The model also outperforms the Patch model by 5% in accuracy. In summary, the proposed method can produce competitive predictions for the long-term future trajectory trends of ships in large-scale sea areas. [ABSTRACT FROM AUTHOR]

In this study, graphene quantum dots (GQDs) with a diameter of ~3 nm were successfully synthesized and incorporated into a poly(ethylene terephthalate) (PET) matrix to fabricate PET/GQDs nanocomposites. The impact of GQDs on the crystallization and thermal stability of the PET/GQDs nanocomposites was investigated. It was observed that the addition of only 0.5 wt% GQDs into the nanocomposites resulted in a significant increase in the crystallization temperature (peak temperature) of PET, from 194.3 °C to 206.0 °C during the cooling scan process. This suggested that an optimal concentration of GQDs could function as a nucleating agent and effectively enhance the crystallization temperature of PET. The isothermal crystallization method was employed to analyze the crystallization kinetics of the PET/GQDs nanocomposites, and the data showed that 0.5 wt% GQDs significantly accelerated the crystallization rate. Furthermore, the incorporation of GQDs into the PET matrix imparted photoluminescent properties to the resulting PET/GQDs nanocomposites. The PET crystals with GQDs as crystal nuclei and the crazes caused by defects played a vital role in isolating and suppressing the concentration quenching of GQDs. This effect facilitated the detection of defects in PET. [ABSTRACT FROM AUTHOR]

Bismaleimide (BMI) resin-based structural adhesives have excellent heat resistance, with important applications demonstrated in the bonding of high-temperature BMI composites. In this paper, we report an epoxy-modified BMI structural adhesive with excellent properties for bonding BMI-based CFRP. We prepared the BMI adhesive using epoxy-modified BMI as the matrix and PEK-C and core–shell polymers as synergistic tougheners. We found that the epoxy resins improve the process and bonding properties of BMI resin but slightly reduce thermal stability. PEK-C and core–shell polymers synergistically improve the toughness and bonding performances of the modified BMI adhesive system and allow the maintenance of heat resistance. The optimized BMI adhesive exhibits excellent heat resistance, with a high glass transition temperature of 208.6 °C and a high thermal degradation temperature of 425.4 °C. Most importantly, the optimized BMI adhesive exhibits satisfactory intrinsic bonding and thermal stability. It has a high shear strength of 32.0 MPa at room temperature and up to 17.9 MPa at 200 °C. The BMI adhesive-bonded composite joint has a high shear strength of 38.6 and 17.3 MPa at room temperature and 200 °C, respectively, indicating effective bonding and excellent heat resistance. [ABSTRACT FROM AUTHOR]

Obtaining complete and consistent spectral images of wheat ears in the visible areas of in situ wheat canopies poses a significant challenge due to the varying growth posture of wheat. Nevertheless, detecting the presence and degree of wheat Fusarium head blight (FHB) in situ is critical for formulating measures that ensure stable grain production and supply while promoting green development in agriculture. In this study, a spectral quantitative analysis model was developed to evaluate the infection degree of FHB in an in situ wheat canopy's visible areas. To achieve this, a spectral acquisition method was used to evaluate the infection degree of FHB in a wheat canopy's visible areas. Hyperspectral images were utilized to obtain spectral data from healthy and mildly, moderately, and severely infected wheat ear canopies. The spectral data were preprocessed, and characteristic wavelengths were extracted using twelve types of spectral preprocessing methods and four types of characteristic wavelength extraction methods. Subsequently, sixty-five spectral quantitative prediction models for the infection degree of FHB in the in situ wheat canopy visible areas were established using the PLSR method, based on the original spectral data, preprocessed spectral data, original spectral characteristic wavelengths extracted data, and preprocessed spectral characteristic wavelengths extracted data. Comparative analysis of the models indicated that the MMS + CARS + PLSR model exhibited the best prediction effect and could serve as the spectral quantitative analysis model for the evaluation of the infection degree of FHB in an in situ wheat canopy's visible areas. The model extracted thirty-five characteristic wavelengths, with a modeling set coefficient of determination (R2) of 0.9490 and a root-mean-square error (RMSE) of 0.2384. The testing set of the coefficient of determination (R2) was 0.9312, with a root-mean-square error (RMSE) of 0.2588. The model can facilitate the spectral quantitative analysis of the infection degree of FHB in the in situ wheat canopy visible areas, thereby aiding in the implementation of China's targeted poverty alleviation and agricultural power strategy. [ABSTRACT FROM AUTHOR]

To provide a theoretical basis for biogenic fly-killing pesticides, in this study, we sought to examine the lethal effects of Lepista nuda mycelium-supplemented diets in Drosophila melanogaster. In doing so, we also studied the effects of Lepista nuda mycelium-supplemented diets on lifespan, antioxidant enzyme activity, peroxide content, relative transcript amounts of antioxidant enzyme genes, signaling pathways, and lifespan. Lower Lepista nuda mycelium-supplemented diets activated the antioxidant system and prolonged lifespan, while higher mycelium-supplemented diets had a significant toxic effect. After the administration of mycelium-supplemented diets for 24 h, the highest corrected mortality (41.96%) and lifespan inhibition rates (96.50%) were observed. In addition, the antioxidant enzyme activities of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px); the relative transcript amounts of the key antioxidant enzyme genes SOD, CAT, and TrxR-1; and the lifespan genes Hep and Nrf2 were found to be the lowest, while the contents of the oxidation products malondialdehyde (MDA), protein carbonylation product (PCO), and triglyceride (TG); the relative transcript amounts of the signaling pathway-related genes S6K, TOR, and Keap-1; and the lifespan gene MTH were observed to be the highest after 48 h. Higher Lepista nuda mycelium-supplemented diets significantly inhibited lifespan, acting via the initiation of oxidative stress systems. [ABSTRACT FROM AUTHOR]

Porous NiTi alloys are widely applied in the field of medical implant materials due to their excellent properties. In this paper, porous NiTi alloys were prepared by non-aqueous gel-casting. The influence of solid loading on the process characteristics of slurries and the microstructure and mechanical properties of sintered samples were investigated. The viscosity and the stability of slurry significantly increased with the growth of solid loading, and the slurry had better process characteristics in the solid loading range of 40–52 vol.%. Meanwhile, the porosity and average pore diameter of the sintered NiTi alloys decreased with a rise in the solid loading, while the compressive strength increased. Porous NiTi alloys with porosities of 43.3–48.6%, average pore sizes of 53–145 µm, and compressive strengths of 87–167 MPa were fabricated by gel-casting. These properties meet the requirements of cortical bone. The results suggest that the pore structure and mechanical properties of porous NiTi products produced by gel-casting can be adjusted by controlling the solid loading. [ABSTRACT FROM AUTHOR]

Non-autoclaved aerated concrete (NAAC) is a two-phase material with a concrete matrix and air, exhibits good thermal insulation performance and shows good potential in the insulating construction industry. In this study, recycled concrete fine powder was used as an auxiliary cementing material, and the NAAC with different porosity and distribution was fabricated by the non-autoclaved method at different curing temperatures. The effect of porosity on the thermal conductivity and mechanical strength of NAAC is analyzed by experimental tests. A prediction method of thermal conductivity combining pore structure reconstruction and numerical simulation was proposed, which is established by two steps. Firstly, the pore size distributions of NAAC with different porosities were characterized by stereology image analyses. Secondly, the thermal conductivity prediction model based on the pore structure information was established by a COMSOL steady-state heat transfer module. The thermal conductivity results of COMSOL simulations were compared with the experiments and other theoretical models to verify the reliability of the model. The model was used to evaluate the effect of porosity, pore size distribution and the concrete matrix's thermal conductivity on the thermal conductivity of NAAC; these are hard to measure when only using laboratory experiments. The results show that with the increase in curing temperature, the porosity of NAAC increases, and the number and volume proportion of macropores increase. The numerical results suggest that the error between the COMSOL simulations and the experiments was less than 10% under different porosities, which is smaller than other models and has strong reliability. The prediction accuracy of this model increases with the increase in NAAC porosity. The steady thermal conductivity of NAAC is less sensitive to the distribution and dispersion of pore size in a given porosity. With the increase in porosity, the thermal conductivity of NAAC is linearly negatively correlated with that of the concrete matrix, and the correlation is close to 1. [ABSTRACT FROM AUTHOR]

Porous NiTi alloys were manufactured by integration of gel-casting and microwave sintering. The effects of sintering temperature on porosity, compressive strength, pore morphology and phase composition of sintered samples were researched. The results show that the porosity and the mean pore diameter of porous NiTi alloys decrease with increasing sintering temperature, whereas the content of the NiTi phase, the elastic modulus and compressive strength of sintered samples increase. When the gel body with the solid loading of 50 vol.% is microwave sintered at 1000 °C for 30 min, porous NiTi alloys are obtained with the porosity of 38.9%, the compressive strength of 254 MPa, elastic modulus of 4 GPa, and predominant phase of NiTi. The results suggest that the method is suitable for rapid preparation of large-size and complex-shape personalized products similar to human bones at a low cost. [ABSTRACT FROM AUTHOR]

There are abundant sea-sand resources on the earth. Traditional sea-sand concrete faced various problems relating to insufficient anticorrosion ability. In this paper, artificial seawater, sea sand, industrial waste, steel fiber, and polycarboxylate superplasticizer were used to prepare ultra-high-performance polymer cement mortar (SSUHPC). At the same time, freshwater river-sand ultra-high-performance polymer cement mortar (FRUHPC) with the same mixing ratio was prepared for comparative study. The compressive strength of SSUHPC reached 162.1 MPa, while the that of FRUHPC reached 173.3 MPa, which was slightly higher. Meanwhile, SSUHPC showed excellent anticorrosion characteristics in terms of carbonization, frost resistance and chloride resistance, and especially for sulfate resistance. The composition of SSUHPC was separated into three parts: mortar, pore and steel fiber, and the performance difference mechanisms of SSUHPC and FRUHPC were investigated by X-ray computed tomography (X-CT), mercury intrusion porosimetry (MIP), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The hydration degree of mortar in SSUHPC was higher, with higher content of CSH and CH, and its better optimized gel pore characteristics gave SSUHPC better corrosion resistance. The mechanical properties of SSUHPC were slightly poor due to the uneven dispersion of steel fibers and air pores, with an- air pore porosity of 1.52% (above 200 μm) that was twice that of FRUHPC (0.6%). In this paper, the mechanics and anticorrosion performance of ultra-high-performance polymer cement mortar prepared with seawater sea sand were comprehensively evaluated, and the mechanism of performance difference between SSUHPC and FRUHPC was revealed, conducive to the targeted improvement of sea sand concrete. [ABSTRACT FROM AUTHOR]

Track–bridge interaction (TBI) is an increasingly essential consideration for the design and operation of railway bridges, especially for the innovative bridge structure systems that constantly spring up over the years. This paper focuses on the characteristics of additional forces in continuous welded rails (CWRs) on the 3 × 70 m integral rigid-frame bridge of the Fuzhou–Xiamen High-Speed Railway, which is a novel high-speed railway (HSR) bridge structure system in China. The differential equations of rail stress and displacement are first investigated and an integrative analysis model comprising of rail, track, bridge and piers is then established. Secondly, the characteristics of representative additional forces are illustrated and the influences of different design parameters are discussed in detail. Furthermore, suitable rail fasteners, optimal layout schemes of adjacent bridges and reasonable stiffness of piers are also studied. The results indicate that the additional expansion force accounts for the largest proportion of additional forces in integral rigid-frame bridges and that resistance reduction obviously weakens the various additional forces caused by the TBI effect, while the broken gap of the rail increases greatly. Small resistance fasteners are recommended to be applied onto this new type of HSR line as these provide reductions in additional stresses of CWRs compared to WJ-8 fasteners. The additional rail stresses after adopting an adjacent span scheme of 4 × 32 m simply supported beams are less than the corresponding stresses in other schemes. The results also show that there is a strong correlation between the minimum threshold value of the pier stiffness and the longitudinal resistance of HSR lines for the integral rigid-frame bridge. This work could serve as a valuable reference for detailed design and safety evaluation of integral rigid-frame bridges. [ABSTRACT FROM AUTHOR]

Whale vocal calls contain valuable information and abundant characteristics that are important for classification of whale sub-populations and related biological research. In this study, an effective data-driven approach based on pre-trained Convolutional Neural Networks (CNN) using multi-scale waveforms and time-frequency feature representations is developed in order to perform the classification of whale calls from a large open-source dataset recorded by sensors carried by whales. Specifically, the classification is carried out through a transfer learning approach by using pre-trained state-of-the-art CNN models in the field of computer vision. 1D raw waveforms and 2D log-mel features of the whale-call data are respectively used as the input of CNN models. For raw waveform input, windows are applied to capture multiple sketches of a whale-call clip at different time scales and stack the features from different sketches for classification. When using the log-mel features, the delta and delta-delta features are also calculated to produce a 3-channel feature representation for analysis. In the training, a 4-fold cross-validation technique is employed to reduce the overfitting effect, while the Mix-up technique is also applied to implement data augmentation in order to further improve the system performance. The results show that the proposed method can improve the accuracies by more than 20% in percentage for the classification into 16 whale pods compared with the baseline method using groups of 2D shape descriptors of spectrograms and the Fisher discriminant scores on the same dataset. Moreover, it is shown that classifications based on log-mel features have higher accuracies than those based directly on raw waveforms. The phylogeny graph is also produced to significantly illustrate the relationships among the whale sub-populations. [ABSTRACT FROM AUTHOR]

In the dwindling natural mangrove today, mangrove reforestation projects are conducted worldwide to prevent further losses. Due to monoculture and the low survival rate of artificial mangroves, it is necessary to pay attention to mapping and monitoring them dynamically. Remote sensing techniques have been widely used to map mangrove forests due to their capacity for large-scale, accurate, efficient, and repetitive monitoring. This study evaluated the capability of a 0.5-m Pléiades-1 in classifying artificial mangrove species using both pixel-based and object-based classification schemes. For comparison, three machine learning algorithms--decision tree (DT), support vector machine (SVM), and random forest (RF)--were used as the classifiers in the pixel-based and object-based classification procedure. The results showed that both the pixel-based and object-based approaches could recognize the major discriminations between the four major artificial mangrove species. However, the object-based method had a better overall accuracy than the pixel-based method on average. For pixel-based image analysis, SVM produced the highest overall accuracy (79.63%); for object-based image analysis, RF could achieve the highest overall accuracy (82.40%), and it was also the best machine learning algorithm for classifying artificial mangroves. The patches produced by object-based image analysis approaches presented a more generalized appearance and could contiguously depict mangrove species communities. When the same machine learning algorithms were compared by McNemar's test, a statistically significant difference in overall classification accuracy between the pixel-based and object-based classifications only existed in the RF algorithm. Regarding species, monoculture and dominant mangrove species Sonneratia apetala group 1 (SA1) as well as partly mixed and regular shape mangrove species Hibiscus tiliaceus (HT) could well be identified. However, for complex and easily-confused mangrove species Sonneratia apetala group 2 (SA2) and other occasionally presented mangroves species (OT), only major distributions could be extracted, with an accuracy of about two-thirds. This study demonstrated that more than 80% of artificial mangroves species distribution could be mapped. [ABSTRACT FROM AUTHOR]

A molybdenum boride (MoB) coating was prepared on molybdenum (Mo) by an in-situ chemical vapor deposition method. To develop a fuller understanding of the effect of depositional conditions on the growth of MoB coating formed on Mo substrate, this paper studied the phase composition and microstructure of the coating at varied conditions. At the same time, the relationships between the growth of the coating in thickness and depositional conditions were also investigated. The phase composition of the coating, which was prepared using 0.2 wt % boron (B), was composed of MoB andMo2B phases. The singleMoB phase was obtained using a high B content (above 0.2 wt %). The microstructure of the coating showed that MoB coating could be prepared at temperatures over 900 °C.With the increase of temperature and time, the thickness of the coating increased. The growth of the coating was also influenced by the content of NaF. With increasing content, the thickness of the coating first increased, and then decreased. In addition, the prepared coating had a high hardness (3130.85HV), and themass change of the coated samplewas only 4.92mg/cm2 after isothermal oxidation at 600 °C for 100 h. TheMoB coating could provide a good oxidation resistance forMo at 600 °C. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the physico-chemical properties of concrete made with recycled brick-concrete aggregate, which was the mixture from waste concrete and waste clay brick in a 7:3 ratio. Specifically, this paper investigated the mechanical properties, freeze-thaw resistance, and distribution of water-soluble chloride ions of concrete containing RBCA and fly ash (FA) against combined freeze-thaw and sodium chloride attack. Concrete containing RBCA replacement of natural coarse aggregate and fly ash replacement of Portland cement was subjected to 45 freeze-thaw cycles containing sodium chloride solution. It was discovered that the mechanical properties and freeze-thaw resistance to sodium chloride attack gradually decreased with increasing RBCA content. At the same time, a replacement level of 15% FA by weight resulted in significant improvements in compressive strength and resistance to combined freeze-thaw and chloride attack. Furthermore, using a replacement of 30% FA by weight markedly improved the resistance to chloride ion penetration of concrete due to the lowest water-soluble chloride content. [ABSTRACT FROM AUTHOR]

Surface treatment is typically required to improve the bonding performance of carbon-fiber-reinforced composites. Herein, a wet peel ply was prepared using bismaleimide (BMI) resins as a matrix resin. The temperature–heating rate extrapolation method and rheological method were employed to study the reaction characteristics and viscosity-temperature characteristics of the matrix in the BMI wet peel ply. The curing temperatures of the BMI wet peel ply and the BMI prepreg were the same (200 °C), making this wet peel ply suitable for co-curing with the BMI prepreg. After treatment with the wet peel ply, the bonding strength of the BMI composite joint showed a mean shear strength of 35.5 MPa, which was 1.72% higher than that of the sanded composite and 17.5% higher than that of the composite treated with the dry peel ply. In addition, the BMI composite treated with the BMI wet peel ply exhibited good bonding stability with a coefficient of variation of 3.9. After damp-heat aging for 1440 h, the retention rate of shear strength at room-temperature was 82.3%. The relatively loosely woven carrier in the BMI wet peel ply increased the surface roughness of the composite, thus improving the bonding strength. [ABSTRACT FROM AUTHOR]

Mapping plucking areas of tea plantations is essential for tea plantation management and production estimation. However, on-ground survey methods are time-consuming and labor-intensive, and satellite-based remotely sensed data are not fine enough for plucking area mapping that is 0.5–1.5 m in width. Unmanned aerial vehicles (UAV) remote sensing can provide an alternative. This paper explores the potential of using UAV-derived remotely sensed data for identifying plucking areas of tea plantations. In particular, four classification models were built based on different UAV data (optical imagery, digital aerial photogrammetry, and lidar data). The results indicated that the integration of optical imagery and lidar data produced the highest overall accuracy using the random forest algorithm (94.39%), while the digital aerial photogrammetry data could be an alternative to lidar point clouds with only a ~3% accuracy loss. The plucking area of tea plantations in the Huashan Tea Garden was accurately measured for the first time with a total area of 6.41 ha, which accounts for 57.47% of the tea garden land. The most important features required for tea plantation mapping were the canopy height, variances of heights, blue band, and red band. Furthermore, a cost–benefit analysis was conducted. The novelty of this study is that it is the first specific exploration of UAV remote sensing in mapping plucking areas of tea plantations, demonstrating it to be an accurate and cost-effective method, and hence represents an advance in remote sensing of tea plantations. [ABSTRACT FROM AUTHOR]

A non-equiatomic refractory high entropy alloy (RHEA), (W35Ta35Mo15Nb15)95Ni5 with high density of 14.55 g/cm3 was fabricated by powder metallurgical process of mechanical alloying (MA) and spark plasma sintering (SPS). The mechanical alloying behavior of the metallic powders was studied systematically, and the microstructure and phase composition for both the powders and alloys were analyzed. Results show that the crystal consists of the primary solid solution and marginal oxide inclusion (Nb5.7Ni4Ta2.3O2). In addition, the maximum strength, yield strength and fracture strain are, 2562 MPa, 2128 MPa, 8.16%, respectively. [ABSTRACT FROM AUTHOR]

Using TiO2-15MgAl2O4 mixed powder as spray-feed, an electrical-heating coating was successfully fabricated by atmospheric plasma spraying technology. The phase composition, microstructure, and electrical-heating performance of the coating were characterized by XRD, SEM, and cyclic electrification tests, respectively. The coating samples were heat-treated at 350 °C in a hydrogen atmosphere. The results show that TiO2-15MgAl2O4 coating can be heated over 300 °C within 30 m at 55.1 V which preserves heat for a long time. Before heat treatment, the available heating temperature of the coating decreases significantly with electrification cycles. The resistivity of coating rises rapidly during the cyclic electricity test. After hydrogen heat treatment, the electrical-heating property of coatings is significantly enhanced. [ABSTRACT FROM AUTHOR]

To estimate mangrove biomass at finer resolution, such as at an individual tree or clump level, there is a crucial need for elaborate management of mangrove forest in a local area. However, there are few studies estimating mangrove biomass at finer resolution partly due to the limitation of remote sensing data. Using WorldView-2 imagery, unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data, and field survey datasets, we proposed a novel method for the estimation of mangrove aboveground biomass (AGB) at individual tree level, i.e., individual tree-based inference method. The performance of the individual tree-based inference method was compared with the grid-based random forest model method, which directly links the field samples with the UAV LiDAR metrics. We discussed the feasibility of the individual tree-based inference method and the influence of diameter at breast height (DBH) on individual segmentation accuracy. The results indicated that (1) The overall classification accuracy of six mangrove species at individual tree level was 86.08%. (2) The position and number matching accuracies of individual tree segmentation were 87.43% and 51.11%, respectively. The number matching accuracy of individual tree segmentation was relatively satisfying within 8 cm ≤ DBH ≤ 30 cm. (3) The individual tree-based inference method produced lower accuracy than the grid-based RF model method with R2 of 0.49 vs. 0.67 and RMSE of 48.42 Mg ha−1 vs. 38.95 Mg ha−1. However, the individual tree-based inference method can show more detail of spatial distribution of mangrove AGB. The resultant AGB maps of this method are more beneficial to the fine and differentiated management of mangrove forests. [ABSTRACT FROM AUTHOR]

Hainan Island is the second-largest island in China and has the most species-diverse mangrove forests in the country. To date, the height and aboveground ground biomass (AGB) of the mangrove forests on Hainan Island are unknown, partly as a result of the challenges faced during extensive field sampling in mangrove habitats (intertidal mudflats inundated by periodic seawater). Therefore, this study used a low-cost UAV-LiDAR (light detection and ranging sensor mounted on an unmanned aerial vehicle) system as a sampling tool and Sentinel-2 imagery as auxiliary data to estimate and map the mangrove height and AGB on Hainan Island. Hainan Island has 3697.02 hectares of mangrove forests with an average patch area of approximately 1 ha. The results show that the mangroves on whole Hainan Island have an average height of 6.99 m, a total AGB of 474,199.31 Mg and an AGB density of 128.27 Mg ha−1. The AGB hot spots are located in Qinglan Harbor and the south of Dongzhai Harbor. The proposed height model LiDAR-S2 performed well with an R2 of 0.67 and an RMSE (root mean square error) of 1.90 m; the proposed AGB model G~LiDAR~S2 performed better (an R2 of 0.62 and an RMSE of 50.36 Mg ha−1) than the traditional AGB model G~S2 that directly related ground plots and Sentinel-2 data. The results also indicate that the LiDAR metrics describing the canopy's thickness and its top and bottom characteristics are the most important variables for mangrove AGB estimation. For the Sentinel-2 indices, the red-edge and shortwave infrared features, especially the red-edge 1 and shortwave infrared Band 11 features, play the most important roles in estimating mangrove AGB and height. In conclusion, this paper presents the first mangrove height and AGB maps of Hainan Island and demonstrates the feasibility of using UAV-LiDAR as a sampling tool for mangrove forests. [ABSTRACT FROM AUTHOR]

The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate relationship. However, traditional road-network matching algorithms, which are mainly central processing unit (CPU)-based approaches, may have performance bottleneck problems when facing big data. We developed a particle-swarm optimization (PSO)-based parallel road-network matching method on graphics-processing unit (GPU). Based on the characteristics of the two main stages (similarity computation and matching-relationship identification), data-partition and task-partition strategies were utilized, respectively, to fully use GPU threads. Experiments were conducted on datasets with 14 different scales. Results indicate that the parallel PSO-based matching algorithm (PSOM) could correctly identify most matching relationships with an average accuracy of 84.44%, which was at the same level as the accuracy of a benchmark—the probability-relaxation-matching (PRM) method. The PSOM approach significantly reduced the road-network matching time in dealing with large amounts of data in comparison with the PRM method. This paper provides a common parallel algorithm framework for road-network matching algorithms and contributes to integration and update of large-scale road-networks. [ABSTRACT FROM AUTHOR]

Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species. [ABSTRACT FROM AUTHOR]