Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation:A case study

In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.

Research on High Altitude Remote Sensing Building Segmentation Based on Improved U-Net Algorithm

Building extraction from high resolution remote sensing image is a key technology of digital city construction[14].In order to solve the problems of low efficiency and low precision of traditional remote sensing image segmentation,an improved U-Net network structure is adopted in this paper.Firstly,in order to extract efficient building characteristic information,FPN structure was introduced to improve the ability of integrating multi-scale information in U-Net model;Secondly,to solve the problem that feature information weakens with the deepening of network depth,an efficient residual block network is introduced;Finally,In order to better distinguish the target area and background area in the image and improve the precision of building target edge detection,the cross entropy loss and Dice loss were linearly combined and weighted.Experimental results show that the algorithm can improve the image segmentation effect and improve the image accuracy by 18%.

Impact of Wavelength-Routed Network Physical Topology on Blocking Probability Using a Dynamic Traffic Growth Model

We investigate the impact of network topology on blocking probability in wavelength-routed networks using a dynamic traffic growth model. The dependence of blocking on different physical parameters is assessed.

Block Copolymer Networks Composed of Poly(ε-caprolactone) and Polyethylene with Triple Shape Memory Properties

In this contribution, we reported a novel synthesis of block copolymer networks composed of poly(ε-caprolactone)(PCL) and polyethylene(PE) via the co-hydrolysis and condensation of α,ω-ditriethoxylsilane-terminated PCL and PE telechelics. First, α,ω-dihydroxylterminated PCL and PE telechelics were synthesized via the ring-opening polymerization of ε-caprolactone and the ring-opening metathesis polymerization of cyclooctene followed by hydrogenation of polycyclooctene. Both α,ω-ditriethoxylsilane-terminated PCL and PE telechelics were obtained via in situ reaction of α,ω-dihydroxyl-terminated PCL and PE telechelics with 3-isocyanatopropyltriethoxysilane. The formation of networks was evidenced by the solubility and rheological tests. It was found that the block copolymer networks were microphase-separated. The PCL and PE blocks still preserved the crystallinity. Owing to the formation of crosslinked networks, the materials displayed shape memory properties. More importantly, the combination of PCL with PE resulted that the block copolymer networks had the triple shape memory properties, which can be triggered with the melting and crystallization of PCL and PE blocks. The results reported in this work demonstrated that triple shape memory polymers could be prepared via the formation of block copolymer networks.

A congestion bottleneck analysis model for tourist block street network

The development of the tourism industry has led to increased pressure of people flow in tourist blocks.Therefore,it is critical to ease the traffic pressure in these blocks.This paper aims to identify the bottleneck links of street networks in tourist blocks to achieve the effective prevention of congestion accidents.A logit stochastic user equilibrium model combined with spatial syntax is presented to study the travelers’route choice behavior.The nonlinear Bureau of Public Roads function is applied to calculate the time impedance of each street.A case analysis of the Chongqing Ciqikou tourist block shows that the bottleneck link has the features of high integration and a large degree of negative time impedance evolution.The research’s results are more consistent with practical circumstances because the influence of the road network topological structure on pedestrian path selection has been considered.