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Treating anaerobic digestion (AD) slurry as industrial wastewater not only consumes a significant amount of energy but also wastes its inherent abundant nutrients, particularly the high ammonium content. Ammonia recovery from AD slurry has attracted attention in recent years and biochar has tentatively been used to adsorb ammonia nitrogen. However, most of the previous studies used pure ammonium chloride solution to simulate ammonia-rich wastewater and ignored the influence of other components. Furthermore, how the physico-chemical properties of biochar influence the adsorption performance of ammonia in AD slurry remains unknown. Therefore, this study focused on the investigation of the adsorption behavior of different types of biochar to ammonia nitrogen in AD slurry. Biochar generated from rice straw, coconut shell, and wood shaving under pyrolysis temperatures of 300 °C, 500 °C, and 700 °C were used to adsorb ammonia from food waste (FW) AD slurry, and five different particle sizes of biochar were also tested. The results showed that biochar derived from rice straw (up to 9.44 mg/g) and coconut shell (up to 8.86 mg/g) had higher ammonium adsorption capacity than biochar derived from wood shaving (up to 5.13 mg/g). Moreover, low pyrolysis temperature resulted in high adsorption capacity, while particle size and surface area of the biochar were not the critical factors determining the adsorption capacity. The correlation results demonstrated that the H/C (aromaticity), O/C (hydrophilicity), pH, electrical conductivity (EC), and ash content influenced the adsorption significantly. Based on the kinetics model results, it seems that physical adsorption was the main adsorption mechanism, while ion exchange and reaction with function groups also contributed to the adsorption. Moreover, the lower adsorption capacity was observed in this study where the real FW AD slurry was used as compared to other studies where pure ammonium chloride solution was used, which implied that microorganisms in FW AD slurry may colonize on the surface or pores of biochar, resulting in a negative effect on the adsorption capacity of biochar. The results derived from this study provided technical support for ammonia recovery of AD slurry.

Scene classification is an active research area in the remote sensing (RS) domain. Some categories of RS scenes, such as medium residential and dense residential scenes, would contain the same type of geographical objects but have various spatial distributions among these objects. The adjacency and disjointness relationships among geographical objects are normally neglected by existing RS scene classification methods using convolutional neural networks (CNNs). In this study, a multi-output network (MopNet) combining a graph neural network (GNN) and a CNN is proposed for RS scene classification with a joint loss. In a candidate RS image for scene classification, superpixel regions are constructed through image segmentation and are represented as graph nodes, while graph edges between nodes are created according to the spatial adjacency among corresponding superpixel regions. A training strategy of a jointly learning CNN and GNN is adopted in the MopNet. Through the message propagation mechanism of MopNet, spatial and topological relationships imbedded in the edges of graphs are employed. The parameters of the CNN and GNN in MopNet are updated simultaneously with the guidance of a joint loss via the backpropagation mechanism. Experimental results on the OPTIMAL-31 and aerial image dataset (AID) datasets show that the proposed MopNet combining a graph convolutional network (GCN) or graph attention network (GAT) and ResNet50 achieves state-of-the-art accuracy. The overall accuracy obtained on OPTIMAL-31 is 96.06% and those on AID are 95.53% and 97.11% under training ratios of 20% and 50%, respectively. Spatial and topological relationships imbedded in RS images are helpful for improving the performance of scene classification.

Numerous aquaculture ponds are intensively distributed around inland natural lakes and mixed with cropland, especially in areas with high population density in Asia. Information about the distribution of aquaculture ponds is essential for monitoring the impact of human activities on inland lakes. Accurate and efficient mapping of inland aquaculture ponds using high-spatial-resolution remote-sensing images is a challenging task because aquaculture ponds are mingled with other land cover types. Considering that aquaculture ponds have intertwining regular embankments and that these salient features are prominent at different scales, a Row-wise and Column-wise Self-Attention (RCSA) mechanism that adaptively exploits the identical directional dependency among pixels is proposed. Then a fully convolutional network (FCN) combined with the RCSA mechanism (RCSANet) is proposed for large-scale extraction of aquaculture ponds from high-spatial-resolution remote-sensing imagery. In addition, a fusion strategy is implemented using a water index and the RCSANet prediction to further improve extraction quality. Experiments on high-spatial-resolution images using pansharpened multispectral and 2 m panchromatic images show that the proposed methods gain at least 2–4% overall accuracy over other state-of-the-art methods regardless of regions and achieve an overall accuracy of 85% at Lake Hong region and 83% at Lake Liangzi region in aquaculture pond extraction.

Aquatic vegetation in shallow freshwater lakes are severely degraded worldwide, even though they are essential for inland ecosystem services. Detailed information about the long term variability of aquatic plants can help investigate the potential driving mechanisms and help mitigate the degradation. In this paper, based on Google Earth Engine cloud-computing platform, we made use of a 33-year (1987–2019) retrospective archive of moderate resolution Landsat TM, ETM + and OLI satellite images to estimate the extent changes in aquatic vegetation in Longgan Lake from Middle Yangtze River Basin in China using the modified enhanced vegetation index, including emerged, floating-leaved and floating macrophytes. The analysis of the long term dynamics of aquatic vegetation showed that aquatic vegetation were mainly distributed in the western part of the lake, where lake bottom elevation ranged from 11 to 12 m, with average water depth of less than 1 m in spring. The vegetation area variation for the 33-year period were divided into six stages. In years with heavy precipitation, the vegetation area decreased sharply. In the following years, the area normally restored. Aquatic vegetation area had a significant negative correlation with the spring water level and summer water level. The results showed that aquatic vegetation was negatively affected when water depth exceeded 2.5 m in May and 5 m in summer. It is recommended that water depth remain close to 1 m in spring and close to 3 m in summer for aquatic vegetation growth. Our study provide quantitative evidence that water-level fluctuations drive vegetation changes in Longgan Lake, and present a basis for sustainable lake restoration and management.

Long-term information of phytoplankton bloom is critical for assessing the processes driving blooms in lakes. A three-decade survey of the phytoplankton blooms was completed for Erhai Lake from 1987 to 2016 with Landsat imagery. A modified three-band model using Landsat broad bands is developed by comparing reflectance data from Landsat imagery to two field datasets. The model is applied to the archived imagery (1987–2016) to predict chlorophyll-a (Chl-a). Predicted ln(Chl-a) and observed ln(Chl-a) measurements are significantly correlated (R2 = 0.70; RMSE = 0.13 ug/L). Bloom maps are generated by identifying Landsat pixels that have Chl-a concentrations larger than 20 ug/L as bloom area. Bloom extent and magnitude are estimated. Our study reveals that algal blooms first occurred in 1996 with a bloom area of 150 km2. Bloom occurred frequently from 2002 to 2016, with extreme blooms in 2003, 2013 and 2016. Algal blooms were mostly distributed in the northern and southern part of the lake. The proposed method uses one model for all Landsat images for Erhai Lake and can predict past blooms and extend the record to early years when field data is not available. The bloom extent and magnitude produced in this study can be used as the basis for the understanding of the processes that control the bloom outbreak.

Abstract As a drug delivery system with wide application potential, serum albumin drug‐carrying nanoparticles have attracted extensive attention in the field of drug delivery in recent years. This review aims to summarize the research progress of serum albumin drug‐loaded nanoparticles in the field of drug delivery. Firstly, the excellent properties of serum albumin as a drug carrier were introduced, several methods of preparing serum albumin nanoparticles were discussed in depth, and their advantages and disadvantages were compared. Then, the design of the nanoparticle albumin drug carrier is discussed, including the control of the size of the nanoparticle, the surface properties and the amount of drug loading, so as to achieve better drug delivery effect. Finally, a series of therapeutic approaches using serum albumin nanoparticles as drug carriers, such as anti‐cancer drug delivery and drug delivery across the blood‐brain barrier, are systematically summarized. In summary, the research of serum albumin drug‐carrying nanoparticles has brought new possibilities and opportunities to the field of drug delivery, and inspired more innovative research and practical applications.

Within-class spectral variation and between-class spectral confusion in remotely sensed imagery degrades the performance of builtup area detection when using planar texture, shape, and spectral features. Terrain slope and building height are often used to optimize the results, but extracted from auxiliary data (e.g. LIDAR data, DSM). Moreover, the auxiliary data must be acquired around the same time as image acquisition. Otherwise, built-up area detection accuracy is affected. Stereo imagery incorporates both planar and height information unlike single remotely sensed images. Stereo imagery acquired by many satellites (e.g. Worldview-4, Pleiades-HR, ALOS-PRISM, and ZY-3) can be used as data source of identifying built-up areas. A new method of identifying highaccuracy built-up areas from stereo imagery is achieved by using a combination of planar and height features. The digital surface model (DSM) and digital orthophoto map (DOM) are first generated from stereo images. Then, height values of above-ground objects (e.g. buildings) are calculated from the DSM, and used to obtain raw built-up areas. Other raw built-up areas are obtained from the DOM using Pantex and Gabor, respectively. Final high-accuracy built-up area results are achieved from these raw built-up areas using the decision level fusion. Experimental results show that accurate built-up areas can be achieved from stereo imagery. The height information used in the proposed method is derived from stereo imagery itself, with no need to require auxiliary height data (e.g. LIDAR data). The proposed method is suitable for spaceborne and airborne stereo pairs and triplets. [ABSTRACT FROM AUTHOR]

An analytical model for isochronal phase transformation kinetics has attracted much attention for its advantages and importance. However, simple but exact analytical formulae of the isochronal transformation are unavailable because of the so-called temperature integral. Here an improved analytical model for isochronal transformation kinetics has been established by considering various nucleation and growth modes on using an improved treatment for the temperature integral. The powerful model-analysis method was also adopted to investigate the validity and effectiveness of the general and the improved isochronal transformation analytical model by applying them to two crystallization processes of amorphous alloys: a wide temperature interval one and a narrow one. The results showed that the improved model can provide a much better description for the narrow one while the improved and general models perform equally well in the wide one. [ABSTRACT FROM AUTHOR]