ALBERT

Description: Sugarcane (complex hybrids of Saccharum spp., C4 plant) croplands provide cane stalk feedstock for sugar and biofuel (ethanol) production. It is critical for us to analyze the phenology and gross primary production (GPP) of sugarcane croplands, which would help us to better understand and monitor the sugarcane growing condition and the carbon cycle. In this study, we combined the data from two sugarcane EC flux tower sites in Brazil and the USA, images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and data-driven models to study the phenology and GPP of sugarcane croplands. The seasonal dynamics of climate, vegetation indices from MODIS images, and GPP from two sugarcane flux tower sites (GPPEC) reveal the temporal consistency in sugarcane phenology (crop calendar: green-up dates and harvesting dates) as estimated by the vegetation indices and GPPEC data. The Land Surface Water Index (LSWI) is shown to be useful to delineate the phenology of sugarcane croplands. The relationship between the sugarcane GPPEC and the Enhanced Vegetation Index (EVI) is stronger than the relationship between the GPPEC and the Normalized Difference Vegetation Index (NDVI). We ran the Vegetation Photosynthesis Model (VPM), which uses the light use efficiency (LUE) concept and is driven by climate data and MODIS images, to estimate the daily GPP at the two sugarcane sites (GPPVPM). The seasonal dynamics of the GPPVPM and GPPEC at the two sites agreed reasonably well with each other, which indicates that VPM is a powerful tool for estimating the GPP of sugarcane croplands in Brazil and the USA. This study clearly highlights the potential of combining eddy covariance technology, satellite-based remote sensing technology, and data-driven models for better understanding and monitoring the phenology and GPP of sugarcane croplands under different climate and management practices.

Description: Groundwater over-pumping in estuary cities leads to a series of groundwater environmental problems that seriously restricts economic development. On the basis of field investigation and long-term monitoring data analysis, a three-dimensional numerical model was built in the estuary of the Daqing River in Liaodong Bay, China. The Quaternary overburden can be generalized into five layers according to particle composition and parameters in the vertical direction. There are many scattered irrigation wells pumping in the second layer, and three water source areas mainly pumping groundwater in the fourth layer. Long-term over-pumping in multi-layered aquifers causes onshore layered seawater intrusion. The laws of layered intrusion under the layered pumping were calculated and analyzed with SEAWAT-2000, and the sensitivity was analyzed with the Sobol method. Results showed that the intrusion area had an obvious layered law. Layered pumping directly affected the layered intrusion area, as different permeability, tide and barrage further affected it. The prediction study showed that the cone of depression recovered after the pumping-limit of water source areas, and the intrusion area started to retreat in the fourth layer. At that time, the pumping quantity of irrigation wells became the main reason for the increase of the intrusion area. If the water source areas are used to bear part of the irrigation demand, so as to reduce the pressure of pumping in the second layer, the overall intrusion area can be reduced by about 0.23 km2 under the same pumping quantity.

Description: Tidal flats, which are non-vegetated land–sea transition areas, have an important ecological function in the global ecosystem. However, they have been shrinking in recent years due to natural and anthropogenic activities. Although many studies focus on tidal flats in the Yangtze River estuary (YRE) in China, how reclamation and plant invasion affect the expansion and erosion of tidal flats are still unclear. In this study, we analyzed all of the available Landsat TM/ETM+ /OLI imagery from the period 1996 to 2018 using the Google Earth Engine (GEE) cloud computing platform to obtain annual maps of coastal tidal flats of YRE at 30 m spatial resolution. We chose three sample tidal flats where severe Spartina alterniflora (S. alterniflora) invasion, reclamation, and control areas existed to explore the joint impact of plant invasion and reclamation on tidal flats. We also point out the main driving factor of tidal flat expansion of each island in YRE by multiple linear regression. Our results suggest that the tidal flats of YRE had obvious expansion from 1996 to 2018, and the speed of expansion is getting slower because of the decreasing deposits in the Yangtze River. Invasive S. alterniflora is effective at promoting silting, and tidal flats with S. alterniflora invasion expanded 2.54 times faster than the control group. Chongming and Hengsha Islands were mainly affected by sediment concentration, while Changxing and Jiuduansha Islands were affected by reclamation and S. alterniflora invasion, respectively. The results could be used to support coastal zone management and biodiversity conservation of the YRE.

Description: It is challenging to avoid obstacles safely and efficiently for multiple robots of different shapes in distributed and communication-free scenarios, where robots do not communicate with each other and only sense other robots’ positions and obstacles around them. Most existing multi-robot collision avoidance systems either require communication between robots or require expensive movement data of other robots, like velocities, accelerations and paths. In this paper, we propose a map-based deep reinforcement learning approach for multi-robot collision avoidance in a distributed and communication-free environment. We use the egocentric local grid map of a robot to represent the environmental information around it including its shape and observable appearances of other robots and obstacles, which can be easily generated by using multiple sensors or sensor fusion. Then we apply the distributed proximal policy optimization (DPPO) algorithm to train a convolutional neural network that directly maps three frames of egocentric local grid maps and the robot’s relative local goal positions into low-level robot control commands. Compared to other methods, the map-based approach is more robust to noisy sensor data, does not require robots’ movement data and considers sizes and shapes of related robots, which make it to be more efficient and easier to be deployed to real robots. We first train the neural network in a specified simulator of multiple mobile robots using DPPO, where a multi-stage curriculum learning strategy for multiple scenarios is used to improve the performance. Then we deploy the trained model to real robots to perform collision avoidance in their navigation without tedious parameter tuning. We evaluate the approach with multiple scenarios both in the simulator and on four differential-drive mobile robots in the real world. Both qualitative and quantitative experiments show that our approach is efficient and outperforms existing DRL-based approaches in many indicators. We also conduct ablation studies showing the positive effects of using egocentric grid maps and multi-stage curriculum learning.

Description: This investigation aims to provide theoretical and practical evidence for the efficient utilization of nitrogen (N) in paddy-upland rapeseed-rice rotation systems because a lack of previous research on such rotation systems leads to inefficient management practices. The effects of the N application rates and the N fertilizer management strategies for rapeseed and rice were examined, respectively, in relation to the photosynthetic productivity and yields of hybrid rice. The results indicated that the leaf area, Pn, with 40% as basal fertilizer, 40% as tillering fertilize, and 20% as panicle fertilizer and a reduced N rate (30 kg/ha) during the rape season, were higher than other nitrogen management strategies trialed, with conventional N rates in the rape season. The average rice grain yield (9545.15 kg/ha) over the two years with 40% as basal fertilizer, 40% as tillering fertilizer, and 20% as panicle fertilizer was higher than other N treatments with the reduced N rates during the rape season. The reduced N rate during the rapeseed season and 40% as basal fertilizer, 40% as tillering fertilizer, and 20% as panicle fertilizer management during the rice season for the rape-rice rotation system exhibited the highest rice yields. Our findings indicated that the N fertilizer management model was a high-yielding, N-saving, and environmentally friendly measure for rape–rice rotation systems in southern China.

Description: Run-off-road (ROR) accidents cause a large proportion of fatalities on roads. Exploring key factors is an effective method to reduce fatalities and improve safety sustainability. However, some limitations exist in current studies: (1) Datasets of ROR accidents have imbalance problems, in which the samples of fatal accidents (FA) are much less than non-fatal accidents (NFA). Data mining methods on such imbalanced datasets make the results biased. (2) Few studies conducted spatial analysis of ROR accidents in visualization. Therefore, this study proposes an association rule mining (ARM)-based framework to analyze ROR accidents on imbalanced datasets. A novel method is proposed to address the imbalance problem and ARM is applied to analyze accident severity. Geographic information system (GIS) is adopted for spatial analysis of ROR accidents. The proposed framework is applied to ROR accidents in Victoria, Australia. Six FA factors and seven NFA factors are identified from two-item rules. The results of three-item rules indicate factors acting interactively increase the likelihood of FA or NFA. Hot spots of ROR accidents are presented by GIS maps. Effective measures are accordingly proposed to improve road safety. Compared with traditional data-balancing methods, the proposed framework has been validated to provide more robust and reliable results on imbalanced datasets.

Description: Petroleum depletion and climate change have inspired research on bio-based polymers and CO2 capture. Tung-oil-based polyols were applied to partially replace polyether-type polyols from petroleum for sustainable polyurethane. Tung-oil-based polyurethane (TBPU), was prepared via a two-step polycondensation, that is, bulk prepolymerization and chain extension reaction. The graphene oxide (GO) was prepared via Hummer’s method. Then, TBPU was composited with the GO at different ratios to form a TBPU/GO hybrid film. The GO/TBPU films were characterized by fourier transform infrared spectroscopy (FTIR), differential scanning calorimeter (DSC), thermal gravimetric analysis (TGA) and scanning electron microscope (SEM), followed by the measurement of mechanical properties and gas permeability. The results showed that the addition of tung-oil-based polyols enhanced the glass transition temperature and thermal stability of TBPU. The mechanical properties of the hybrid film were significantly improved, and the tensile strength and elongation at break were twice as high as those of the bulk TBPU film. When the GO content was higher than 2.0%, a brittle fracture appeared in the cross section of hybrid film. The increase of GO content in hybrid films improved the selectivity of CO2/N2 separation. When the GO content was higher than 0.35%, the resulting GO agglomeration constrained the gas separation and permeation properties.

Description: Near-infrared fluorescence probes (NIFPs) have been widely used in immunoassay, bio-imaging and medical diagnosis. We review the basic principles of near-infrared fluorescence and near-infrared detection technology, and summarize structures, properties and characteristics of NIFPs (i.e., cyanines, xanthenes fluorescent dyes, phthalocyanines, porphyrin derivates, single-walled carbon nanotubes (SWCNTs), quantum dots and rare earth compounds). We next analyze applications of NIFPs in immunoassays, and prospect the application potential of lateral flow assay (LFA) in rapid detection of pathogens. At present, our team intends to establish a new platform that has highly sensitive NIFPs combined with portable and simple immunochromatographic test strips (ICTSs) for rapid detection of food-borne viruses. This will provide technical support for rapid detection on the port.

Description: Toxoplasma gondii is a leading cause of foodborne illness and consumption of undercooked pig meat is a major risk factor for acquiring toxoplasmosis, which causes a substantial burden on society. Here, we used isobaric tags for relative and absolute quantification (iTRAQ) labelling coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify cellular proteins and pathways altered during T. gondii infection in pigs. We also used parallel reaction monitoring-based LC-MS/MS to verify the levels of protein expression of infected spleens and mesenteric lymph nodes (MLNs). At 6 days post-infection (dpi), 156, 391, 170, 292, and 200 differentially expressed proteins (DEPs) were detected in the brain, liver, lung, MLNs and spleen, respectively. At 18 dpi, 339, 351, 483, 388, and 303 DEPs were detected in the brain, liver, lung, MLNs and spleen, respectively. Although proteins involved in immune responses were upregulated in all infected tissues, protein expression signature in infected livers was dominated by downregulation of the metabolic processes. By weighted gene co-expression network analysis, we could further show that all proteins were clustered into 25 co-expression modules and that the pink module significantly correlated with the infection status. We also identified 163 potential anti-T. gondii proteins (PATPs) and provided evidence that two PATPs (HSP70.2 and PDIA3) can reduce T. gondii burden in porcine macrophages in vitro. This comprehensive proteomics analysis reveals new facets in the pathogenesis of T. gondii infection and identifies key proteins that may contribute to the pig’s defense against this infection.