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The visibility of retail frontages is critical for earning profits from spontaneous traffic visits to retail shops located along a street. The urban tree canopy plays a crucial role in enhancing the street-side environment, yet more is not always better when considering the placement of retail shops behind trees with big canopies. Related evidence in the literature is rarely provided, and an unclear relationship has been reported to exist between the number of shops for a specific retail type and the quantified ratio of the canopy shade in a street view. In this study, both big data crawling and deep learning were employed to unravel this relationship for retail shops in Changchun, Northeast China. The entire study area was divided into 6037 grid cells with a side length of ~0.6 km, wherein the number of shops of five retail types (food and beverage, shopping, life services, entertainment, and hotel) were quantified by computer counting their points of interest (POIs). The canopy shade was evaluated using the green view index (GVI) quantified through the ratio of canopy pixels divided by all the pixels in a street view image obtained through an online map API. A neighboring road network was categorized into four classes: class I road density mainly reduced the number of retail shops, and the road densities of classes III and IV accounted for more retail shops. The relationship between the number of retail shops and the GVI could be fitted with positive skewness curves for class II roads, where the critical peak of the GVI was estimated to be about 3.27%. The optimization scheme indicated that more retail shops should be placed along class I and II roads. In conclusion, more retail shops for food and beverage, shopping, and life services should be placed in the landscape neighboring big canopies.

A series of polycarboxylate superplasticizers (PCEs) with different side-chain densities, main chain polymerization degrees, and side-chain lengths were designed and synthesized using a novel highly active ethylene glycol mono vinyl ether polyethylene glycol as the ether monomer. The influence of polycarboxylate ether on the rheological properties, interface adsorption, and hydration characteristics in cement paste was investigated through characterization of charge density, rheological properties, adsorption behavior, and hydration heat. The results indicate that the adsorption process of PCE on cement particles is spontaneous physical adsorption, and the hydration kinetics fitting reveals that the geometric crystal growth exponent n is in the range of 1–2, with needle-like and lamellar hydration products formed. With a decrease in side-chain density, the broadening of molecular weight distribution and the increase of charge density accelerate the flow of slurry, reduces saturation adsorption, and delays cement hydration. A decrease in main chain polymerization degree results in a downward trend in molecular weight and charge density, leading to larger molecular conformations, reduced slurry flow, decreased saturation adsorption, and delayed cement hydration. As the side-chain length of PCE (molecular weight) increases, the charge density decreases, and the molecular conformation exhibits a compact structure with reduced slurry flow, decreased saturation adsorption, and delayed cement hydration. In cases of low side-chain density, short side chains, and low molecular weight, enhanced adsorption capacity and faster adsorption rates are observed, resulting in the lower viscosity and a delay in the cement hydration process.

In order to conduct an intelligent early warning assessment of stope rock burst disasters in mining areas, and effectively prevent and control them, the principal component analysis (PCA) method was embraced to perform dimensionality reduction and feature information extraction from 10 main factors that affect the occurrence of rock bursts. On this basis, six principal component elements of the influencing factors of rock bursts have been obtained as the input vectors for an extreme learning machine (ELM). In the meantime, the parameter optimization ability of the PSO algorithm was adopted, the input weight values of the ELM and the threshold values of the hidden layer were optimized, and the functions of the three models were completely combined. Therefore, an early warning model of rock bursts based on the PCA-PSO-ELM combined algorithm was creatively proposed and the risk rank of rock bursts in the Yanshitai Coal Mine was predicted and evaluated. Consequently, the research results indicated that the prediction accuracy of the PCA-PSO-ELM model improved the prediction performance and generalization ability and reached a 100% contrast with the three models, namely the BP neural network, the radial basis function, and the extreme learning machine, which presented an updated method for the early warning investigation of rock burst disasters and had favorable engineering significance.

The formation of homodimer complexes for interface stability, catalysis and regulation is intriguing. The mechanisms of homodimer complexations are even more interesting. Some homodimers form without intermediates (two-state (2S)) and others through the formation of stable intermediates (three-state (3S)). Here, we analyze 41 homodimer (25 2S and 16 3S) structures determined by X-ray crystallography to estimate structural differences between them. The analysis suggests that a combination of structural properties such as monomer length, subunit interface area, ratio of interface to interior hydrophobicity can predominately distinguish 2S and 3S homodimers. These findings are useful in the prediction of homodimer folding and binding mechanisms using structural data.

Protein dimers are either homodimers (complexation of identical monomers) or heterodimers (complexation of non-identical monomers). These dimers are common in catalysis and regulation. However, the molecular principles of protein dimer interactions are difficult to understand mainly due to the geometrical and chemical characteristics of proteins. Nonetheless, the principles of protein dimer interactions are often studied using a dataset of 3D structural complexes determined by X-ray crystallography. A number of physical and chemical properties govern protein dimer interactions. Yet, a handful of such properties are known to dominate protein dimer interfaces. Here, we discuss the differences between homodimer and heterodimer interfaces using a selected set of interface properties.

Protein subunit dimers are either homodimers (consisting of identical polypeptides) or heterodimers (consisting of different polypeptides). Protein dimers are involved in several cellular processes and an understanding of their molecular principle in complexations (subunit-subunit interaction) is essential. This is generally studied using 3D structures of homodimers and heterodimers determined by X-ray crystallography. However, the current knowledge on subunit interaction is limited due to lack of sufficient 3D dimer structures. It is our interest to study heterodimers using 3D structures to identify interaction parameters that would help in the development of a model to predict heterodimer interaction sites just from protein sequences. The efficiency of such models depends on the weighted contribution of numerous parameters characterizing heterodimer interfaces. Therefore, we studied the salient features of 111 interface parameters in 65 heterodimer structures. In this study, we applied multi-dimensional scaling for dimensionality reduction on these parameters to select the most critical ones that best characterize heterodimer interfaces. The significance of these parameters in subunit interaction is discussed.