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Carbon peaking and carbon neutrality strategies bring both opportunities and challenges to the development of green buildings. In this context, the development of high-quality green buildings requires a focus on improving carbon reduction effects throughout their entire lifespan. Based on a systematic review of the achievements and existing problems in the development of green buildings in China over the past thirty years, this article benchmarks the new trends in green building standards in developed countries abroad, dissects the new requirements for the development of green buildings in China under the new situation, and proposes a high-quality carbon reduction pathways from four aspects: theoretical research, policy systems, technical systems, and software platforms. Finally, it proposes implementation suggestions for collaborative development from four dimensions: theoretical methods for calculating carbon emissions, green finance policies, a full-process carbon reduction technology system, and carbon emission accounting tools.

Accurately evaluating the vulnerability of prefabricated components in prefabricated buildings is the basis of scientific management of its supply chain. Thus, a novel vulnerability evaluation method for the suppliers of prefabricated components in the context of prefabricated buildings was proposed in this study. First, a vulnerability evaluation index system was identified and constructed by the pressure–state–response (PSR) model. With the clear logic and strong applicability of PSR, this system, including 18 indexes, was easy to understand and exhibited strong practicability. The weights were then determined via the multiplicative analytic hierarchy process with the expansion of a probabilistic language terminology set (PLTS). PLTS could describe decision information more comprehensively, so the evaluation result is more scientific. Subsequently, the probability of the vulnerability level was superimposed in a parallel bottom-to-top method via the controlled interval and memory (CIM) models, and these were extended by the PLTS. This set handled the uncertain information in the decision-making process more efficiently and accurately, and finally determined the vulnerability distribution. Ultimately, the probability of each vulnerability level was determined by combining the weight information. The reconstruction and resettlement housing construction project of urban shanty towns in the Taohua South Road Community in Xihu District, Nanchang City, was selected as a case study. The case study proves the effectiveness of this method, which has higher resolution and is more convenient for managers to make decisions.

Tidal flats have been reported to contain many microorganisms and play a critical role in maintaining biodiversity. In surveys of filamentous fungi from tidal flat sediments in China, seven new species of Eurotiales were discovered and described. Morphological characteristics and DNA sequence analyses of combined datasets of the BenA, CaM, and RPB2 regions support their placements and recognition as new species. Aspergillus liaoningensis sp. nov. and A. plumeriae sp. nov. belong to sections Candidi and Flavipedes of subgenus Circumdati, and A. subinflatus sp. nov. is a member of section Cremei of subgenus Cremei. Penicillium danzhouense sp. nov., P. tenue sp. nov., and P. zhanjiangense sp. nov. are attributed to sections Exilicaulis and Lanata-Divaricata of subgenus Aspergilloides. Talaromyces virens sp. nov. is in section Talaromyces. Detailed descriptions and illustrations of these novel taxa are provided. Their differences from close relatives were compared and discussed.

Bisphenol S (BPS) is an environmental pollutant that can accumulate in the human body and cause harm. Puerarin (PUE) is a flavonoid with anti-inflammatory and antioxidant effects. In this study, we used 50 mg/kg/d BPS as a poison and PUE as an intervention for model mice for 42 d. BPS exposure significantly increased the levels of the impairment of the mice’s liver function, T-CHO, TG, LDL-C, ALT, and AST in the BPS group were significantly increased (p < 0.05). Additionally, BPS exposure caused inflammatory cell infiltration in the mice liver tissue and enhanced oxidative stress response, the level of MDA was significantly increased (p < 0.05). The expression of CD36 and pparγ was stimulated after BPS exposure. Moreover, the expression of cpt1a and cpt1b, which promote fatty acid oxidation, was downregulated. After PUE intervention, the levels of genes and proteins involved in lipid synthesis (PPARγ, SREBP1C, and FASN) and metabolism (Cpt1a, Cpt1b, and PPARα) in mice returned to those of the control group, or much higher than those in the BPS group. Therefore, we hypothesized that BPS causes lipid accumulation in the liver by promoting lipid synthesis and reducing lipid metabolism, whereas PUE reduces lipid synthesis and promotes lipid metabolism. Conclusively, our results imply that long-term exposure to BPS in mice affects liver lipid metabolism and that PUE intervention could maintain the liver function of mice at normal metabolic levels.

To address the issue of low positioning accuracy of mobile robots in trellis kiwifruit orchards with weak signal environments, this study investigated an outdoor integrated positioning method based on ultra-wideband (UWB), light detection and ranging (LiDAR), and odometry (ODOM). Firstly, a dynamic error correction strategy using the Kalman filter (KF) was proposed to enhance the dynamic positioning accuracy of UWB. Secondly, the particle filter algorithm (PF) was employed to fuse UWB/ODOM/LiDAR measurements, resulting in an extended Kalman filter (EKF) measurement value. Meanwhile, the odometry value served as the predicted value in the EKF. Finally, the predicted and measured values were fused through the EKF to estimate the robot’s pose. Simulation results demonstrated that the UWB/ODOM/LiDAR integrated positioning method achieved a mean lateral error of 0.076 m and a root mean square error (RMSE) of 0.098 m. Field tests revealed that compared to standalone UWB positioning, UWB-based KF positioning, and LiDAR/ODOM integrated positioning methods, the proposed approach improved the positioning accuracy by 64.8%, 13.8%, and 38.3%, respectively. Therefore, the proposed integrated positioning method exhibits promising positioning performance in trellis kiwifruit orchards with potential applicability to other orchard environments.

To explore the differences in the growth characteristics and population dynamics of Schizothorax wangchiachii populations in the Jinsha River (JSR) and the Yalong River (YLR), samples were collected in the upper reaches of the JSR (n = 230) from 2019 to 2020 and the middle reaches of the YLR (n = 187) from 2017 to 2018. In the JSR and YLR populations, the age range was 11 and 12 years old, respectively, and the best growth equation was the Von Bertalanffy equation. The comparative analysis of the two populations showed that the growth coefficient, initial sexual maturity age and age at first capture of the YLR population were greater than those of the JSR population. Comparing the mortality rates of the two groups, we found that the YLR population had the higher female mortality rate (0.658 years−1) and the lower male mortality rate (0.453 years−1). Our assessment of the three natural mortality rates showed that the Fcur of both male and female populations was greater than F25%, indicating that both populations were in an overexploited state. Therefore, we suggest considering the two groups as separate protection units and implementing management measures such as ecological regulation, restoration of tributary habitat and strengthening of fishing ban monitoring to protect their resources.

Understanding the genetic structure and differentiation in endangered species is of significance in detecting their phylogenetic relationships and prioritizing conservation. Here we sampled five endangered Cycas species endemic to southwest China and genotyped genetic structure and differentiation among them using the genotyping-by-sequencing (GBS) method. C. hongheensis showed high genetic diversity, but the other four species showed low genetic diversity. The genetic diversity between wild and cultivated populations was similar for C. debaoensis and C. guizhouensis, respectively. Low genetic differentiation and high gene flow were found among C. debaoensis, C. guizhouensis, and C. fairylakea, and C. hongheensis differentiated from them at ~1.74 Mya. TreeMix results showed historic migration events from C. guizhouensis to C. hongheensis, showing southward migration pathways. C. hongheensis showed increased effective population size with time, while the other four species underwent bottleneck events at ~1–5 Mya when continuous cooling events occurred. Our results indicate that the migration, differentiation, and speciation of Cycas species are associated with historical cooling events.

Kiwifruit harvesting with robotics can be troublesome due to the clustering feature. The gripper of the end effector will easily cause unstable fruit grasping, or the bending and separation action will interfere with the neighboring fruit because of an inappropriate grasping angle, which will further affect the success rate. Therefore, predicting the correct grasping angle for each fruit can guide the gripper to safely approach, grasp, bend and separate the fruit. To improve the grasping rate and harvesting success rate, this study proposed a grasping detection method for a kiwifruit harvesting robot based on the GG-CNN2. Based on the vertical downward growth characteristics of kiwifruit, the grasping configuration of the manipulator was defined. The clustered kiwifruit was mainly divided into single fruit, linear cluster, and other cluster, and the grasping dataset included depth images, color images, and grasping labels. The GG-CNN2 was improved based on focal loss to prevent the algorithm from generating the optimal grasping configuration in the background or at the edge of the fruit. The performance test of the grasping detection network and the verification test of robotic picking were carried out in orchards. The results showed that the number of parameters of GG-CNN2 was 66.7 k, the average image calculation speed was 58 ms, and the average grasping detection accuracy was 76.0%, which ensures the grasping detection can run in real time. The verification test results indicated that the manipulator combined with the position information provided by the target detection network YOLO v4 and the grasping angle provided by the grasping detection network GG-CNN2 could achieve a harvesting success rate of 88.7% and a fruit drop rate of 4.8%; the average picking time was 6.5 s. Compared with the method in which the target detection network only provides fruit position information, this method presented the advantages of harvesting rate and fruit drop rate when harvesting linear clusters, especially other cluster, and the picking time was slightly increased. Therefore, the grasping detection method proposed in this study is suitable for near-neighbor multi-kiwifruit picking, and it can improve the success rate of robotic harvesting.

To understand the characteristics of the fish community structure and biomass particle-size spectrum in the upper reaches of the Jinsha River, fish and environmental surveys were conducted in 21 segments of the upper reaches of the Jinsha River in September 2019 and June 2020. A total of 4062 fish belonging to 2 orders, 5 families, 18 genera, and 28 species were collected. Among them, Cyprinidae fish were the most abundant (14 species), accounting for 50.00%. The Shannon index and Pielou evenness index values varied from 0.402–1.770 and 0.254–0.680, respectively. The dominant species of fish were Triplophysa stenura, Schizothorax wangchiachii, and Schizopygopsis malacanthus. Redundancy analysis (RDA) was used to analyse the relationship between the fish community and environmental factors. Velocity, altitude, and dissolved oxygen were the main influencing factors of fish community structure differences in the upper reaches of the Jinsha River. The abundance/biomass curves showed that the fish communities in the upper reaches of the Jinsha River were moderately or severely disturbed. The standardized biomass particle-size spectrum of fish showed that the degree of disturbance of fish in tributaries was much lower than that in the main stream. Compared with the historical data, the fish community structure in the Jinsha River has changed significantly, with the number of exotic species increasing, and the individual fish showing miniaturization and younger ages. It is suggested that habitat conservation strategies be adopted in the upper tributaries of the Jinsha River to provide a reference for the restoration of fishery resources and the conservation of fish diversity in the Yangtze River.

Double-arm picking robots are widely used in agricultural production for their high collaborative efficiency. While picking, area planning and collision detection between the mechanical arms is a crucial challenge for the double-arm robot, which needs to establish a collision-free path for fruit picking. In this study, we developed a double-arm cooperation method for robotic picking of kiwifruit. Firstly, the problem of dividing the picking area was simplified into a multiple traveling salesmen problem (MTSP) to be solved. The picking sequence of each robotic arm was formulated by the principle of similar picking numbers, and combined with the brainstorming optimization algorithm (BSO). Secondly, a double-arm parameter model was built to solve the forward and backward movements of the robotic arms and to figure out the joint position. The spatial mathematical relationship of the bounding boxes between the robotic arms was used to detect the collision between the two robotic arms, in order to achieve the avoidance between the robotic joints. Then, simulation software was applied to the simulation and analyzed the availability of picking area planning and collision detection. The simulation results showed that the optimized picking sequence planning using BSO was more efficient; the smooth joint trajectory during the movement of the robotic arms met the limits on the range of movement and on the angular velocity of the robotic arm joints. Finally, based on the simulation result, a double-arm collaboration platform was tested. The double-arm collaboration platform harvesting trials showed that the average picking success rate was 86.67%, and collision detection time was 3.95 ± 0.83 s per fruit. These results indicated that the proposed method could plan the operation tasks of the double-arm picking robot system, and effectively implement the collision-free picking operation.

New developments in sequencing technology and nucleotide analysis have allowed us to make great advances in reconstructing anuran phylogeny. As a clade of representative amphibians that have radiated from aquatic to arboreal habitats, our understanding of the systematic status and molecular biology of rhacophorid tree frogs is still limited. We determined two new mitogenomes for the genus Polypedates (Rhacophoridae): P. impresus and P. mutus. We conducted comparative and phylogenetic analyses using our data and seven other rhacophorid mitogenomes. The mitogenomes of the genera Polypedates, Buergeria, and Zhangixalus were almost identical, except that the ATP8 gene in Polypedates had become a non-coding region; Buergeria maintained the legacy “LTPF” tRNA gene cluster compared to the novel “TLPF” order in the other two genera; and B. buergeri and Z. dennysi had no control region (CR) duplication. The resulting phylogenetic relationship supporting the above gene rearrangement pathway suggested parallel evolution of ATP8 gene loss of function (LoF) in Polypedates and CR duplication with concerted evolution of paralogous CRs in rhacophorids. Finally, conflicting topologies in the phylograms of 185 species reflected the advantages of phylogenetic analyses using multiple loci.

Schizothorax kozlovi Nikolsky is a unique cold−water fish in the upper reaches of the Yangtze River in China and has high economic value. In our study, genetic diversity and population structure analyses were performed on seven wild populations (originating from the Jinsha River, Yalong River, and Wujiang River) in the upper reaches of the Yangtze River by genotyping by sequencing (GBS). The results indicated that a total of 303,970 single−nucleotide polymorphisms (SNPs) were identified from the seven wild populations. Lower genetic diversity was exhibited among the intrapopulations of the three tributaries, and the Wujiang River population had significant genetic differentiation when compared to the Jinsha River and Yalong River populations. Furthermore, the selected SNPs were enriched in cellular processes, environmental adaptation, signal transduction, and related metabolic processes between the Wujiang population and the other two populations. The above results indicate that the populations of S. kozlovi have different degrees of tolerance and selection pressure in response to temperature and altitude. The Wujiang intrapopulation has greater genetic diversity and differentiation than the Jinsha River and Yalong River populations, which demonstrates that the Jinsha and Yalong populations require more attention and resources for their protection. The results of this study will increase our understanding of the diversity of S. kozlovi in the upper reaches of the Yangtze River and provide a basis for the conservation and utilization of wild resources.

Alternaria is a ubiquitous fungal genus including saprobic, endophytic, and pathogenic species associated with a wide variety of substrates. It has been separated into 29 sections and seven monotypic lineages based on molecular and morphological data. Alternaria sect. Porri is the largest section, containing the majority of large-spored Alternaria species, most of which are important plant pathogens. Since 2015, of the investigations for large-spored Alternaria species in China, 13 species were found associated with Compositae plants based on morphological comparisons and phylogenetic analyses. There were eight known species and five new species (A. anhuiensis sp. nov., A. coreopsidis sp. nov., A. nanningensis sp. nov., A. neimengguensis sp. nov., and A. sulphureus sp. nov.) distributed in the four sections of Helianthiinficientes, Porri, Sonchi, and Teretispora, and one monotypic lineage (A. argyranthemi). The multi-locus sequence analyses encompassing the internal transcribed spacer region of rDNA (ITS), glyceraldehydes-3-phosphate dehydrogenase (GAPDH), Alternaria major allergen gene (Alt a 1), translation elongation factor 1-alpha (TEF1), and RNA polymerase second largest subunit (RPB2), revealed that the new species fell into sect. Porri. Morphologically, the new species were illustrated and compared with other relevant large-spored Alternaria species in the study. Furthermore, A. calendulae, A. leucanthemi, and A. tagetica were firstly detected in Brachyactis ciliate, Carthamus tinctorius, and Calendula officinalis in China, respectively.

We investigated the ellipsometer-based characterization method being used to quickly evaluate the depth of the damage layer in ion-implanted 4H-SiC. This method had the advantages of low cost, convenience, and non-destructiveness. Optical absorption of n-type 4H-SiC substrate, P+ ion-implanted, laser-annealed, and conventional high-temperature annealed wafers were investigated at room temperature. Three peaks were observed in the absorption spectra collected for various samples. The degree of electrical activation after laser annealing or high-temperature annealing was evaluated qualitatively from the absorption peak intensity at 2.67 eV. The circular transmission line method (CTLM) results were consistent with the optical absorption results. However, it was found that the effective carrier concentration after laser annealing was significantly lower than that after high-temperature annealing.

Usually the planters used in agricultural machinery face two problems: The planters have to spread the soil twice using the tray seed planter, and it is difficult for them to dig the holes before planting. This study has designed a spiral digging end-effector to dig holes in parallel effectively and quickly so that the seeds can be planted in the holes easily. A spiral digging end-effector was designed with five spiral bits, a synchronous belt, a cylinder, a gear motor, and a connecting plate based on the tray size and the pumpkin seed characteristics. Before the optimization of the end-effector’s parameters, the substrate discrete element model parameters were calibrated using the method of “material funneling” applied for the EDEM discrete element model simulation. The contact parameters and model parameters (substrate–substrate static friction coefficient, substrate–substrate rolling friction coefficient, and surface energy) that have significant impacts on the substrate AOR (angle of repose) were selected by applying the Plackett–Burman Design and the path of steepest ascent method, and their respective optimal value range were determined. The optimal parameter values were obtained through the Central Composite Design response surface analysis test, and the parameters of the spiral digging end-effector were optimized combining the substrate particle simulation. The verification test results indicate that the substrate discrete element model parameters were accurate and reliable if the substrate static friction coefficient was 0.427; the substrate rolling friction coefficient was 0.039, and the surface energy was 0.228. When the cone angle is 30°, the spiral angle is 80°, and the rotational speed is 240 r/min, the section width at the hole depth of 13 mm is 23.1 mm, and the particle overflow proportion is about 2.05%. Compared with the protrusions penetrating end-effector, soil porosity is increased, and soil aeration is improved. Therefore, the spiral digging end-effector can effectively and quickly dig holes in the seedling substrate and can sow pumpkin seeds, which provides a research basis for the design and improvement of automatic pumpkin seed sowing equipment.

We investigated the low-temperature direct bonding of SiC/Si via O2 plasma activation. After optimization, a high bonding efficiency of over 90% was obtained. Surface activation was achieved via reactive ion etching (RIE) O2 plasma for 30 s without significant bombardment damage. A smooth and void-free interface was observed by transmission electron microscopy (TEM), while a significant amorphous oxide layer was also detected. By increasing the annealing temperature from 150 to 300 ∘C, the amorphous layer decreased drastically from 48 nm to 11 nm. Based on systematic experiments and analysis, the mechanism of SiC/Si low-temperature plasma-activated bonding was discussed.

Abrus cantoniensis is a Chinese herbal medicine with efficacy in clearing heat and detoxification, as well as relieving liver pain. The whole plant, except the seeds, can be used and consumed. Flavonoids have been found in modern pharmacological studies to have important biological activities, such as anti-inflammatory, antibacterial and antioxidant properties. The antibacterial and antioxidant bioactivities of the total flavonoids of Abrus cantoniensis (ATF) have been widely reported in national and international journals, but there are fewer studies on their anti-inflammatory effects. The present study focused on the optimization of the ultrasonic extraction process of ATF by response surface methodology and the study of its anti-inflammatory effects in vitro and in vivo. The results showed that the factors that had a great impact on the ATF extraction were the material-to-liquid ratio, ultrasonic extraction cycles and ethanol concentration. The best extraction process used a material-to-liquid ratio of 1:47, ultrasonic extraction cycles of 4 times, an ethanol concentration of 50%, an ultrasonic extraction time of 40 min and an ultrasonic power of 125 W. Under these conditions, the actual extraction rate of total flavonoids was 3.68%, which was not significantly different from the predicted value of 3.71%. In an in vitro anti-inflammatory assay, ATF was found to be effective in alleviating LPS (lipopolysaccharide)-induced inflammation in mouse peritoneal macrophages. In an in vivo anti-inflammatory assay, ATF was found to have a significant inhibitory effect on xylene-induced ear swelling in mice and cotton ball granuloma in mice, and the inhibitory effect was close to that of the positive control drug dexamethasone. This may provide a theoretical basis for the further development of the medicinal value of Abrus cantoniensis.

Video satellite imagery has become a hot research topic in Earth observation due to its ability to capture dynamic information. However, its high temporal resolution comes at the expense of spatial resolution. In recent years, deep learning (DL) based super-resolution (SR) methods have played an essential role to improve the spatial resolution of video satellite images. Instead of fully considering the degradation process, most existing DL-based methods attempt to learn the relationship between low-resolution (LR) satellite video frames and their corresponding high-resolution (HR) ones. In this paper, we propose model-based deep neural networks for video satellite imagery SR (VSSR). The VSSR is composed of three main modules: degradation estimation module, intermediate image generation module, and multi-frame feature fusion module. First, the blur kernel and noise level of LR video frames are flexibly estimated by the degradation estimation module. Second, an intermediate image generation module is proposed to iteratively solve two optimal subproblems and the outputs of this module are intermediate SR frames. Third, a three-dimensional (3D) feature fusion subnetwork is leveraged to fuse the features from multiple video frames. Different from previous video satellite SR methods, the proposed VSSR is a multi-frame-based method that can merge the advantages of both learning-based and model-based methods. Experiments on real-world Jilin-1 and OVS-1 video satellite images have been conducted and the SR results demonstrate that the proposed VSSR achieves superior visual effects and quantitative performance compared with the state-of-the-art methods.

Single-molecule techniques such as electron tunneling and atomic force microscopy have attracted growing interests in protein sequencing. For these methods, it is critical to refine and stabilize the protein sample to a “suitable mode” before applying a high-fidelity measurement. Here, we show that a planar heterostructure comprising boronic graphene (BC3) and nitrogenized graphene (C3N) sandwiched stripe (BC3/C3N/BC3) is capable of the effective stretching and confinement of three types of intrinsically disordered proteins (IDPs), including amyloid-β (1–42), polyglutamine (Q42), and α-Synuclein (61–95). Our molecular dynamics simulations demonstrate that the protein molecules interact more strongly with the C3N stripe than the BC3 one, which leads to their capture, elongation, and confinement along the center C3N stripe of the heterostructure. The conformational fluctuations of IDPs are substantially reduced after being stretched. This design may serve as a platform for single-molecule protein analysis with reduced thermal noise.

TGF-β receptors play important roles in mediating TGF-β signals during gonadal development. To identify the functions of TGF-β receptors, including the type I receptor (activin receptor-like kinase 5, ALK5) and type II receptor (bone morphogenetic protein receptor 2, BMPR2), during the gonadal development of S. prenanti, the full-length cDNA sequences of ALK5 and BMPR2 were isolated and characterized. Their expression patterns in developing gonads and in the gonads of exogenous estradiol (E2) -fed fish were analyzed. The cDNAs of ALK5 and BMPR2 were 1925 bp and 3704 bp in length and encoded 501 and 1070 amino acid residues, respectively. ALK5 and BMPR2 were mostly expressed in gonads, particularly in cortical alveoli stage ovaries and mid-spermatogenic stage testes; however, the overall level of BMPR2 mRNA was higher than that of ALK5 during gonadal development. Furthermore, immunohistochemical signals of ALK5 and BMPR2 were mostly detected at chromatin nucleolar oocytes and perinuclear oocytes in ovaries and at spermatocytes and spermatogonia in testes. Exogenous E2 induces the gonadal expression of ALK5 and BMPR2, and BMPR2 is more responsive to E2 than ALK5. These results suggest that ALK5 and BMPR2 might play a potentially vital role in both folliculogenesis and spermatogenesis in S. prenanti.

The study aimed at determining the effect of different zinc (Zn) supplementation forms on Zn accumulation, activities of Zn-containing enzymes, gene expression of metallothionein (MT), and Zn transporters in piglets. Eighteen piglets were randomly divided into three groups: (a) a basal diet supplemented with 150 mg/kg Zn from Zn methionine (Zn-Met) in the feed (Zn-Met group), (b) a basal diet supplemented with 150 mg/kg Zn from Zn sulfate (ZnSO4) in the feed (ZnSO4, feed group), and (c) a basal diet supplemented with the same dose of Zn as in ZnSO4,feed group but in water (ZnSO4, water group). The results showed that Zn-Met added in feed and ZnSO4 dissolved in drinking water significantly improved (p < 0.05) the Zn concentration in liver and jejunum and the apparent digestibility of Zn in comparison with the ZnSO4 added in feed. In addition, dietary Zn supplementation as Zn-Met significantly increased (p < 0.05) the activity of alkaline phosphatase (AKP) in the jejunum of piglets in comparison with the ZnSO4, feed group. Furthermore, the Zn-Met and ZnSO4, water groups showed an improved total superoxide dismutase activity (T-SOD) in the ileum as compared to the ZnSO4, feed group. Meanwhile, the qPCR and western blot results showed that Zn-Met and ZnSO4 dissolved in drinking water increased the expression of MT in the jejunum in comparison with the ZnSO4 added in the piglets’ feed. However, different Zn supplementation forms had no effect on the mRNA expressions of Zip4 and ZnT1 transporters. In conclusion, Zn-Met added in feed and ZnSO4 dissolved in drinking water had higher bioavailability in piglets.

In the preparation of CuCr alloy using the self-propagating high-temperature synthesis (SHS)-metallurgy method, the dissolution of alumina in molten slag has an important influence in two key steps: aluminum thermal reduction and slag refining. In the present work, the dissolution behavior of Al2O3 into molten SHS-metallurgical slags was investigated by employing the rotating cylinder method and static dissolution method. It is concluded that the increase of MgO, CaF2, CaO, and Na3AlF6 contents can increase the dissolution rate of alumina in SHS-metallurgical slag, and the order of influence is from strong to weak. Both temperature and rotating speed can increase the dissolution rate, and the rate-limiting step is the diffusion of alumina in the boundary layer, with the solid alumina first reacting with lime to form two intermediate phases, CaO·2Al2O3 and CaO·6Al2O3, and finally dissolving into the slag in the form of an aluminum polymer.

Deep learning methods have been successfully applied for multispectral and hyperspectral images classification due to their ability to extract hierarchical abstract features. However, the performance of these methods relies heavily on large-scale training samples. In this paper, we propose a three-dimensional spatial-adaptive Siamese residual network (3D-SaSiResNet) that requires fewer samples and still enhances the performance. The proposed method consists of two main steps: construction of 3D spatial-adaptive patches and Siamese residual network for multiband images classification. In the first step, the spectral dimension of the original multiband images is reduced by a stacked autoencoder and superpixels of each band are obtained by the simple linear iterative clustering (SLIC) method. Superpixels of the original multiband image can be finally generated by majority voting. Subsequently, the 3D spatial-adaptive patch of each pixel is extracted from the original multiband image by reference to the previously generated superpixels. In the second step, a Siamese network composed of two 3D residual networks is designed to extract discriminative features for classification and we train the 3D-SaSiResNet by pairwise inputting the training samples into the networks. The testing samples are then fed into the trained 3D-SaSiResNet and the learned features of the testing samples are classified by the nearest neighbor classifier. Experimental results on three multiband image datasets show the feasibility of the proposed method in enhancing classification performance even with limited training samples.

Sentinel-2 data is of great utility for a wide range of remote sensing applications due to its free access and fine spatial-temporal coverage. However, restricted by the hardware, only four bands of Sentinel-2 images are provided at 10 m resolution, while others are recorded at reduced resolution (i.e., 20 m or 60 m). In this paper, we propose a parallel residual network for Sentinel-2 sharpening termed SPRNet, to obtain the complete data at 10 m resolution. The proposed network aims to learn the mapping between the low-resolution (LR) bands and ideal high-resolution (HR) bands by three steps, including parallel spatial residual learning, spatial feature fusing and spectral feature mapping. First, rather than using the single branch network, the parallel residual learning structure is proposed to extract the spatial features from different resolution bands separately. Second, the spatial feature fusing is aimed to fully fuse the extracted features from each branch and produce the residual image with spatial information. Third, to keep spectral fidelity, the spectral feature mapping is utilized to directly propagate the spectral characteristics of LR bands to target HR bands. Without using extra training data, the proposed network is trained with the lower scale data synthesized from the observed Sentinel-2 data and applied to the original ones. The data at 10 m spatial resolution can be finally obtained by feeding the original 10 m, 20 m and 60 m bands to the trained SPRNet. Extensive experiments conducted on two datasets indicate that the proposed SPRNet obtains good results in the spatial fidelity and the spectral preservation. Compared with the competing approaches, the SPRNet increases the SRE by at least 1.538 dB on 20 m bands and 3.188 dB on 60 m bands while reduces the SAM by at least 0.282 on 20 m bands and 0.162 on 60 m bands.

Recent studies have shown that deep learning methods provide useful tools for wetland classification. However, it is difficult to perform species-level classification with limited labeled samples. In this paper, we propose a semi-supervised method for wetland species classification by using a new efficient generative adversarial network (GAN) and Jilin-1 satellite image. The main contributions of this paper are twofold. First, the proposed method, namely ShuffleGAN, requires only a small number of labeled samples. ShuffleGAN is composed of two neural networks (i.e., generator and discriminator), which perform an adversarial game in the training phase and ShuffleNet units are added in both generator and discriminator to obtain speed-accuracy tradeoff. Second, ShuffleGAN can perform species-level wetland classification. In addition to distinguishing the wetland areas from non-wetlands, different tree species located in the wetland are also identified, thus providing a more detailed distribution of the wetland land-covers. Experiments are conducted on the Haizhu Lake wetland data acquired by the Jilin-1 satellite. Compared with existing GAN, the improvement in overall accuracy (OA) of the proposed ShuffleGAN is more than 2%. This work can not only deepen the application of deep learning in wetland classification but also promote the study of fine classification of wetland land-covers.

Deep learning (DL)-based paradigms have recently made many advances in image pansharpening. However, most of the existing methods directly downscale the multispectral (MSI) and panchromatic (PAN) images with default blur kernel to construct the training set, which will lead to the deteriorative results when the real image does not obey this degradation. In this paper, a deep self-learning (DSL) network is proposed for adaptive image pansharpening. First, rather than using the fixed blur kernel, a point spread function (PSF) estimation algorithm is proposed to obtain the blur kernel of the MSI. Second, an edge-detection-based pixel-to-pixel image registration method is designed to recover the local misalignments between MSI and PAN. Third, the original data is downscaled by the estimated PSF and the pansharpening network is trained in the down-sampled domain. The high-resolution result can be finally predicted by the trained DSL network using the original MSI and PAN. Extensive experiments on three images collected by different satellites prove the superiority of our DSL technique, compared with some state-of-the-art approaches.

Recent booming advances in luminescent gold nanoclusters (AuNCs), have prompted the development of novel fluorescent sensors. The luminescent AuNCs possess unique and intriguing physical and chemical properties including responsive photoluminescence and peroxide-like activity, providing abundant potentials for sensing strategy design. As of now, a wide variety of chem-/bio-sensors based on AuNCs have been developed and reviewed according to varied analytes. In this review, from a different point of view, we follow the route of how those sensors realize their functions and focus on the actual roles AuNCs play, in order to hierarchically and logically display the recent progress in the sensing applications of AuNCs. This review not only opens new windows to understand the development of sensors based on AuNCs but can also inspire broader and deeper utilization of luminescent nanomaterials.

Investigating mangrove species composition is a basic and important topic in wetland management and conservation. This study aims to explore the potential of close-range hyperspectral imaging with a snapshot hyperspectral sensor for identifying mangrove species under field conditions. Specifically, we assessed the data pre-processing and transformation, waveband selection and machine-learning techniques to develop an optimal classification scheme for eight mangrove species in Qi’ao Island of Zhuhai, Guangdong, China. After data pre-processing and transformation, five spectral datasets, which included the reflectance spectra R and its first-order derivative d(R), the logarithm of the reflectance spectra log(R) and its first-order derivative d[log(R)], and hyperspectral vegetation indices (VIs), were used as the input data for each classifier. Consequently, three waveband selection methods, including the stepwise discriminant analysis (SDA), correlation-based feature selection (CFS), and successive projections algorithm (SPA) were used to reduce dimensionality and select the effective wavebands for identifying mangrove species. Furthermore, we evaluated the performance of mangrove species classification using four classifiers, including linear discriminant analysis (LDA), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). Application of the four considered classifiers on the reflectance spectra of all wavebands yielded overall classification accuracies of the eight mangrove species higher than 80%, with SVM having the highest accuracy of 93.54% (Kappa = 0.9256). Using the selected wavebands derived from SPA, the accuracy of SVM reached 93.13% (Kappa = 0.9208). The addition of hyperspectral VIs and d[log(R)] spectral datasets further improves the accuracies to 93.54% (Kappa = 0.9253) and 96.46% (Kappa = 0.9591), respectively. These results suggest that it is highly effective to apply field close-range snapshot hyperspectral images and machine-learning classifiers to classify mangrove species.

Existing hyperspectral sensors usually produce high-spectral-resolution but low-spatial-resolution images, and super-resolution has yielded impressive results in improving the resolution of the hyperspectral images (HSIs). However, most of the super-resolution methods require multiple observations of the same scene and improve the spatial resolution without fully considering the spectral information. In this paper, we propose an HSI super-resolution method inspired by the deep Laplacian pyramid network (LPN). First, the spatial resolution is enhanced by an LPN, which can exploit the knowledge from natural images without using any auxiliary observations. The LPN progressively reconstructs the high-spatial-resolution images in a coarse-to-fine fashion by using multiple pyramid levels. Second, spectral characteristics between the low- and high-resolution HSIs are studied by the non-negative dictionary learning (NDL), which is proposed to learn the common dictionary with non-negative constraints. The super-resolution results can finally be obtained by multiplying the learned dictionary and its corresponding sparse codes. Experimental results on three hyperspectral datasets demonstrate the feasibility of the proposed method in enhancing the spatial resolution of the HSI with preserving the spectral information simultaneously.

Active learning (AL) has been shown to be very effective in hyperspectral image (HSI) classification. It significantly improves the performance by selecting a small quantity of the most informative training samples to reduce the complexity of classification. Multiview AL (MVAL) can make the comprehensive analysis of both object characterization and sampling selection in AL by using various features of multiple views. However, the original MVAL cannot effectively exploit the spectral-spatial information by respecting the three-dimensional (3D) nature of the HSI and the query selection strategy in the MVAL is only based on the disagreement of multiple views. In this paper, we propose a 3D-Gabor inspired MVAL method for spectral-spatial HSI classification, which consists of two main steps. First, in the view generation step, we adopt a 3D-Gabor filter to generate multiple cubes with limited bands and utilize the feature assessment strategies to select cubes for constructing views. Second, in the sampling selection step, a novel method is proposed by using both internal and external uncertainty estimation (IEUE) of views. Specifically, we use the distributions of posterior probability to learn the “internal uncertainty” of each independent view, and adopt the inconsistencies between views to estimate the “external uncertainty”. Classification accuracies of the proposed method for the four benchmark HSI datasets can be as high as 99.57%, 99.93%, 99.02%, 98.82%, respectively, demonstrating the improved performance as compared with other state-of-the-art methods.

Multitask learning (MTL) has recently provided significant performance improvements in supervised classification of hyperspectral images (HSIs) by incorporating shared information across multiple tasks. However, the original MTL cannot effectively exploit both local and global structures of the HSI and the class label information is not fully used. Moreover, although the mathematical morphology (MM) has attracted considerable interest in feature extraction of HSI, it remains a challenging issue to sufficiently utilize multiple morphological profiles obtained by various structuring elements (SEs). In this paper, we propose a joint sparse and low-rank MTL method with Laplacian-like regularization (termed as sllMTL) for hyperspectral classification by utilizing the three-dimensional morphological profiles (3D-MPs) features. The main steps of the proposed method are twofold. First, the 3D-MPs are extracted by the 3D-opening and 3D-closing operators. Different SEs are adopted to result in multiple 3D-MPs. Second, sllMTL is proposed for hyperspectral classification by taking the 3D-MPs as features of different tasks. In the sllMTL, joint sparse and low-rank structures are exploited to capture the task specificity and relatedness, respectively. Laplacian-like regularization is also added to make full use of the label information of training samples. Experiments on three datasets demonstrate the OA of the proposed method is at least about 2% higher than other state-of-the-art methods with very limited training samples.

Mangroves are one of the most important coastal wetland ecosystems, and the compositions and distributions of mangrove species are essential for conservation and restoration efforts. Many studies have explored this topic using remote sensing images that were obtained by satellite-borne and airborne sensors, which are known to be efficient for monitoring the mangrove ecosystem. With improvements in carrier platforms and sensor technology, unmanned aerial vehicles (UAVs) with high-resolution hyperspectral images in both spectral and spatial domains have been used to monitor crops, forests, and other landscapes of interest. This study aims to classify mangrove species on Qi’ao Island using object-based image analysis techniques based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (UHD 185) onboard a UAV platform. First, the image objects were obtained by segmenting the UAV hyperspectral image and the UAV-derived digital surface model (DSM) data. Second, spectral features, textural features, and vegetation indices (VIs) were extracted from the UAV hyperspectral image, and the UAV-derived DSM data were used to extract height information. Third, the classification and regression tree (CART) method was used to selection bands, and the correlation-based feature selection (CFS) algorithm was employed for feature reduction. Finally, the objects were classified into different mangrove species and other land covers based on their spectral and spatial characteristic differences. The classification results showed that when considering the three features (spectral features, textural features, and hyperspectral VIs), the overall classification accuracies of the two classifiers used in this paper, i.e., k-nearest neighbor (KNN) and support vector machine (SVM), were 76.12% (Kappa = 0.73) and 82.39% (Kappa = 0.801), respectively. After incorporating tree height into the classification features, the accuracy of species classification increased, and the overall classification accuracies of KNN and SVM reached 82.09% (Kappa = 0.797) and 88.66% (Kappa = 0.871), respectively. It is clear that SVM outperformed KNN for mangrove species classification. These results also suggest that height information is effective for discriminating mangrove species with similar spectral signatures, but different heights. In addition, the classification accuracy and performance of SVM can be further improved by feature reduction. The overall results provided evidence for the effectiveness and potential of UAV hyperspectral data for mangrove species identification.

Classification of hyperspectral image (HSI) is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning) have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs). Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF) is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator) trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method , especially with a limited number of labeled samples.

To accurately estimate leaf area index (LAI) in mangrove areas, the selection of appropriate models and predictor variables is critical. However, there is a major challenge in quantifying and mapping LAI using multi-spectral sensors due to the saturation effects of traditional vegetation indices (VIs) for mangrove forests. WorldView-2 (WV2) imagery has proven to be effective to estimate LAI of grasslands and forests, but the sensitivity of its vegetation indices (VIs) has been uncertain for mangrove forests. Furthermore, the single model may exhibit certain randomness and instability in model calibration and estimation accuracy. Therefore, this study aims to explore the sensitivity of WV2 VIs for estimating mangrove LAI by comparing artificial neural network regression (ANNR), support vector regression (SVR) and random forest regression (RFR). The results suggest that the RFR algorithm yields the best results (RMSE = 0.45, 14.55% of the average LAI), followed by ANNR (RMSE = 0.49, 16.04% of the average LAI), and then SVR (RMSE = 0.51, 16.56% of the average LAI) algorithms using 5-fold cross validation (CV) using all VIs. Quantification of the variable importance shows that the VIs derived from the red-edge band consistently remain the most important contributor to LAI estimation. When the red-edge band-derived VIs are removed from the models, estimation accuracies measured in relative RMSE (RMSEr) decrease by 3.79%, 2.70% and 4.47% for ANNR, SVR and RFR models respectively. VIs derived from red-edge band also yield better accuracy compared with other traditional bands of WV2, such as near-infrared-1 and near-infrared-2 band. Furthermore, the estimated LAI values vary significantly across different mangrove species. The study demonstrates the utility of VIs of WV2 imagery and the selected machine-learning algorithms in developing LAI models in mangrove forests. The results indicate that the red-edge band of WV2 imagery can help alleviate the saturation problem and improve the accuracy of LAI estimation in a mangrove area.

Recently, sparse representation has yielded successful results in hyperspectral image (HSI) classification. In the sparse representation-based classifiers (SRCs), a more discriminative representation that preserves the spectral-spatial information can be exploited by treating the HSI as a whole entity. Based on this observation, a tensor block-sparsity based representation method is proposed for spectral-spatial classification of HSI in this paper. Unlike traditional vector/matrix-based SRCs, the proposed method consists of tensor block-sparsity based dictionary learning and class-dependent block sparse representation. By naturally regarding the HSI cube as a third-order tensor, small local patches centered at the training samples are extracted from the HSI to maintain the structural information. All the patches are then partitioned into a number of groups, on which a dictionary learning model is constructed with a tensor block-sparsity constraint. A test sample is also expressed as a small local patch and the block sparse representation is then performed in a class-wise manner to take advantage of the class label information. Finally, the category of the test sample is determined by using the minimal residual. Experimental results of two real-world HSIs show that our proposed method greatly improves the classification performance of SRC.