Evaluating removal of nutrients, volatile organic compounds, and nicotine by bioretention soil mixtures with biochar amendment

Urbanization results in reduced infiltration contributing to potential urban flooding and stormwater contaminants. Urban stormwater pollutants, including total suspended solids (TSS), nutrients, heavy metals, oil and grease and microbial contaminants are a concern for local water bodies. One of the most popular stormwater best managements, bioretention, has shown the capability of pollutant retention, but efficiencies are not consistent. This study introduced biochar into bioretention soil media to enhance removal of nutrients, nicotine and volatile organic compounds from synthetic stormwater runoff. Biochar is produced from the pyrolysis process by heating biomass with little to no oxygen at 500 to 900 degrees centigrade. Studies have suggested that biochar increases soil cation exchange capability (CEC), as well as provides huge surface areas. In this study, four types of bioretention soil mixtures were prepared based on the recommendations in State of Missouri, with different biochar content (0, 2%, 5%, and 10% volume percentage). Triplicate soil columns were set for each treatment to ensure statistical reliability. The biochar used in this study was purchased from a local company called Terra Char (Terra Char, 2016)

Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy

Non-negative matrix factorization (NMF) has proved effective in many clustering and classification tasks. The classic ways to measure the errors between the original and the reconstructed matrix are $l_2$ distance or Kullback-Leibler (KL) divergence. However, nonlinear cases are not properly handled when we use these error measures. As a consequence, alternative measures based on nonlinear kernels, such as correntropy, are proposed. However, the current correntropy-based NMF only targets on the low-level features without considering the intrinsic geometrical distribution of data. In this paper, we propose a new NMF algorithm that preserves local invariance by adding graph regularization into the process of max-correntropy-based matrix factorization. Meanwhile, each feature can learn corresponding kernel from the data. The experiment results of Caltech101 and Caltech256 show the benefits of such combination against other NMF algorithms for the unsupervised image clustering

Novel Ir-X thermal protection coatings designed for extreme aerodynamic heating environment

Due to the rapid evaporation of SiO2 protective layer, most Si-containing oxidation resistant coatings could not withstand a temperature above 1800℃, which is not enough for hypersonic voyage in upper atmosphere. With a higher melting point (2440℃) and lower oxygen permeability（10-20g·m-1·s-1）, iridium is supposed to be a promising coating material for ultra-high temperature applications. However, Iridium has a low emissivity ε(0.017 for 2.5-25μm) and high recombination coefficient γ(0.64 at 1200℃) of atomic oxygen, resulting in a much higher thermal response compared with the ceramic materials under the same aerodynamic environment. To solve this problem, elements such as Al, Cr, Zr etc. were selected to modify pure Ir to form Ir-X (X=Al, Cr or Zr) coating. The modification element X in Ir-X coating forms high emissivity and low recombination coeffcient oxide on Ir, which meanwhile prevents the Ir from atomic oxygen. It was found that Ir-Al, Ir-Cr, Ir-Ti, Ir-Zr, Ir-Ta and Ir-Hf diffusion coating could be prepared via pack cementation. The recombination coefficient and emissivity of as-oxidized Ir-Al were changed to 0.0089 and 0.723, respectively. Please click Additional Files below to see the full abstract

Example-based Image Recoloring in Indoor Environment

Color structure of a home scene image closely relates to the material properties of its local regions. Existing color migration methods typically fail to fully infer the correlation between the coloring of local home scene regions, leading to a local blur problem. In this paper, we propose a color migration framework for home scene images. It picks the coloring from a template image and transforms such coloring to a home scene image through a simple interaction. Our framework comprises three main parts. First, we carry out an interactive segmentation to divide an image into local regions and extract their corresponding colors. Second, we generate a matching color table by sampling the template image according to the color structure of the original home scene image. Finally, we transform colors from the matching color table to the target home scene image with the boundary transition maintained. Experimental results show that our method can effectively transform the coloring of a scene matching with the color composition of a given natural or interior scenery

Analyzing the factors influencing trust in a construction project: evidence from a Sino-German eco-park in China

Trust is regarded as a critical feature and a central mechanism in business transactions, especially in the Chinese guanxi network. In this context, the major objective of this research is to explore the key factors influencing trust in different stages of a construction project from the perspectives of owners and consultants involved in a Sino-German eco-park in China. The analytic network process (ANP) was employed to assess which factors are most closely related to trust and to establish four models to meet the objective of this study. According to the ANP results, trust is strongly influenced by factors that are associated with the mutual interests between owners and consultants. In addition, there are certain differences in the priority of the factors influencing initial trust between owners and consultants, but these gaps gradually decrease over time. The weight of guanxi also decreases over time, and the owners’ and consultants’ guanxi transforms from out-group to in-group focused

Improved RBF Network Intrusion Detection Model Based on Edge Computing with Multi-algorithm Fusion

Edge computing is difficult to deploy a complete and reliable security strategy due to its distributed computing architecture and inherent heterogeneity of equipment and limited resources. When malicious attacks occur, the loss will be immeasurable. RBF neural network has strong nonlinear representation ability and fast learning convergence speed, which is suitable for intrusion detection of edge detection industrial control network. In this paper, an improved RBF network intrusion detection model based on multi-algorithm fusion is proposed. kernel principal component analysis (KPCA) is used to extract data dimension and simplify data representation. Then subtractive clustering algorithm(SCM) and grey wolf algorithm(GWO) are used to jointly optimize RBF neural network parameters to avoid falling into local optimum, reduce the calculation of model training and improve the detection accuracy. The algorithm can better adapt to the edge computing platform with weak computing ability and bearing capacity, and realize real-time data analysis.The experimental results of BATADAL data set and Gas data set show that the accuracy of the algorithm is over 99% and the training time of larger samples is shortened by 50 times for BATADAL data set. The results show that the improved RBF network is effective in improving the convergence speed and accuracy in intrusion detection