Quantification of Effects of Natural Geographical Factors and Land-scape Patterns on Non-point Source Pollution in Watershed Based on Geodetector:Burhatong River Basin,Northeast China as An Example

Changes in natural geographic features and landscape patterns directly influence the hydrology and non-point source pollution processes in the watershed;however,to slow down non-point source pollution,it is necessary to distinguish their effects.But the non-point source pollution process is interactional as a result of multiple factors,and the collinearity between multiple independent variables limits our ability of reason diagnosis.Thus,taking the Burhatong River Basin,Northeast China as an example,the methods of hydrological simulation,geographic detectors,and redundancy analysis have been combined to determine the impact of natural geographic features and landscape patterns on non-point source pollution in the watershed.The Soil&Water Assessment Tool(SWAT)has been adopted to simulate the spatial and temporal distribution characteristics of total nitrogen and total phosphorus in the watershed.The results show that the proportions of agricultural land and forest area and the location-weighted landscape contrast index(LWLI)are the main indicators influencing the rivers total nitrogen and total phosphorus.The interaction of these indicators with natural geographic features and landscape configuration indicators also significantly influences the changes in total nitrogen(TN)and total phosphorus(TP).Natural geographical features and landscape patterns have different comprehensive effects on non-point source pollution in the dry and wet seasons.TN and TP loads are affected mainly by the change in landscape pattern,especially in the wet season.Although the ecological restoration program has improved forest coverage,the purification effect of increased forest coverage on the water quality in the watershed may be offset by the negative impact of increased forest fragmentation.The high concentration and complexity of farmland patches increase the risk of non-point source pollution spread to a certain extent.

Research Progress of the Sampling Theorem Associated with the Fractional Fourier Transform

Sampling is a bridge between continuous-time and discrete-time signals,which is import-ant to digital signal processing.The fractional Fourier transform(FrFT)that serves as a generaliz-ation of the FT can characterize signals in multiple fractional Fourier domains,and therefore can provide new perspectives for signal sampling and reconstruction.In this paper,we review recent de-velopments of the sampling theorem associated with the FrFT,including signal reconstruction and fractional spectral analysis of uniform sampling,nonuniform samplings due to various factors,and sub-Nyquist sampling,where bandlimited signals in the fractional Fourier domain are mainly taken into consideration.Moreover,we provide several future research topics of the sampling theorem as-sociated with the FrFT.

A DYNAMIC MATHEMATICAL MODEL STUDY OFTHE PLATE-FIN HUMID HEAT EXCHANGER

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Spatio-temporal Evolution of the Agricultural Eco-efficiency Network and Its Multidimensional Proximity Analysis in China

As a traditional agricultural country,China has always prioritized agricultural development,and has increasingly focused on green and sustainable agricultural development.Based on the inter-provincial panel data for China from 1997 to 2019,this study divided these data into five periods according to the Five-Year Plan(FYP)of China,measured the agricultural eco-efficiency(AEE)values using the Super-SBM model,and then determined the spatial association network of the inter-provincial AEE of China using the improved gravity model.Finally,social network analysis(SNA)was used to further analyze the evolution process of AEE,and we de-veloped a framework of how multidimensional proximity,which includes geographical,economic,technological,cognitive,and institutional proximity,made an influence on the formation of AEE spatial relation network.The findings indicated that:1)in 1997−2019,the AEE in China was present in some spatial and temporal differences characteristics at the provincial scale,and we specifically found that national macro-regulation and policy incentives played a positive role in the long-term development of AEE.2)The spatial correlation of AEE development among provincial regions were becoming closer and exhibits obvious spatial correlation and spillover effects.The evolution of the AEE network has clearly observable trends of hierarchization and aggregation,and the complexity of the correlation network continues to increase and exhibits spatial clustering characteristics that are dense in the east and sparse in the west.The network structure has changed from monocentric radiation to a multicentric network,and network nodes select the more advantageous nodes with which to connect.3)Finally,the geographical proximity had a significant negative effect;the economic,technological,and institutional proximities were all observed to contribute to the AEE network formation,and cognitive proximity did not significantly influence this network formation.

Prediction of vertical PM2.5 concentrations alongside an elevated expressway by using the neural network hybrid model and generalized additive model

A study on vertical variation of PM2.5 concentrations was carried out in this paper. Field measurements were conducted at eight different floor heights outside a building alongside a typical elevated expressway in downtown Shanghai, China. Results show that PM2.5 concentration decreases significantly with the increase of height from the 3rd to 7th floor or the 8th to 15th floor, and increases suddenly from the 7th to 8th floor which is the same height as the elevated expressway. A non-parametric test indicates that the data of PM2.5 concentration is statistically different under the 7th floor and above the 8th floor at the 5% significance level. To investigate the relationships between PM2.5 concentration and influencing factors, the Pearson correlation analysis was performed and the results indicate that both traffic and meteorological factors have crucial impacts on the variation of PM2.5 concentration, but there is a rather large variation in correlation coefficients under the 7th floor and above the 8th floor. Furthermore, the back propagation neural network based on principal component analysis （PCA-BPNN）, as well as generalized additive model （GAM）, was applied to predict the vertical PM2.5 concentration and examined with the field measurement dataset. Experimental results indicated that both models can obtain accurate predictions, while PCA-BPNN model provides more reliable and accurate predictions as it can reduce the complexity and eliminate data co-linearity. These findings reveal the vertical distribution of PM2.5 concentration and the potential of the proposed model to be applicable to predict the vertical trends of air pollution in similar situations.

From advance exploration to real time steering of TBMs:A review on pertinent research in the Collaborative Research Center.“Interaction Modeling in Mechanized Tunneling”

This paper reports on planning and construction related results from research performed at the Collaborative Research Center“Interaction Modeling in Mechanized Tunneling”at Ruhr-University Bochum,Germany.Research covers a broad spectrum of topics relevant for mechanized tunneling in soft soil conditions.This includes inverse numerical methods for advance exploration and models for the characterization of the in situ ground conditions,the interaction of the face support and the tail gap grouting with the porous soil,multi-scale models for the design offiber reinforced segmental linings with enhanced robustness,computational methods for the numer-ical simulation of the tunnel advancement,the soil excavation and the material transport in the pressure chamber,logistics processes and risk analysis in urban tunneling.Targeted towards the continuous support of the construction process,a concept for real-time steering support of tunnel boring machines in conjunction with model update procedures and methods of uncertainty quantification is addressed.

Spatial and temporal assessment of the initial pattern of phytoplankton population in a newly built coastal reservoir

For decades, the main threat to the water security of a metropolis, such as the city of Shanghai, has been the rapidly growing demand for water and at the same time, the decrease in water quality, including eutrophica- tion. Therefore Shanghai shifted the preferred freshwater source to the Yangtze Estuary and constructed the Qingcaosha Reservoir, which is subject to less eutrophic water from the Yangtze River. To assess the population of phytoplankton for the first time in the newly built reservoir, this study improved an integrated method to assess the phytoplankton pattern in large-water-area reservoirs and lakes, using partial triadic analysis and Geographic Information Systems. Monthly sampling and monitoring from 10 stations in the reservoir from July 2010 to December 2011 were conducted. The study examined the common pattern of the phytoplankton population structure and determined the differences in the specific composition of the phytoplankton community during the transition period of the reservoir. The results suggest that in all but three sampling stations in the upper parts of Qingcaosha Reservoir, there was a strong common compromise in 2011. The two most important periods occurred from late summer to autumn and from winter to early spring. The former was characterized by the dominance of cyanobac- teria, whereas the latter was characterized by the dominance of both chlorophyta and diatoms. Cyanobac- teria （Microcystis spp. as the main genus） were the monopolistic dominant species in the summer after reservoir operation. The statistical analysis also indicated the necessity for regular monitoring to focus on the stations in the lower parts of the reservoir and on several limited species.

A new 2D graphical representation of protein sequence and its application

Graphical representation is a very efficient tool for visual analysis of protein sequences. In this paper, a novel 2D graphical representation scheme is proposed on the basis of a newly introduced concept, named characteristic model of the protein sequences. After obtaining the 2D graphics of protein sequences, two numerical characterizations of them is designed as descriptors to analyze the nine DN5 protein sequences, simulation and analysis results show that, comparing with existing methods, our method is not only visible, intuitional, and simple, but also has no circuit or degeneracy, and even more important, since the storage space required by our method is constant and has nothing to do with the length of protein sequences, then it can keep excellent visual inspection for long protein sequences.