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With the rapid development of China's economy, more and more long-deep tunnels are under construction, especially in the mountainous area of western China and in karst areas [1][2], which pose a great challenge for tunnel engineering. Long-deep tunnels are always buried in complex geological conditions with high geo-stress, high water pressure, strong karst geology, and construction disturbance [3]. Additionally, it is quite difficult to make a Pol. J. Environ. Stud. Vol. 26, No. 4 (2017) AbstractWater inrush is one of the typical geological hazards of tunnel construction in karst areas. It is necessary to predict water inrush more accurately for karst tunnels. Firstly, we created a model on risk evaluation of water inrush based on the efficacy coefficient method. Then karst hydrologic and engineering geological conditions were considered in detail, and several typical factors were selected as evaluation indexes, including formation lithology, unfavorable geology, groundwater level, and so on. Moreover, the weight coefficients of the selected evaluation indices were calculated using the analytic hierarchy process method. Furthermore, the total efficacy coefficient was presented to specify the risk grade of the evaluation samples. Finally, the risk grade of water inrush for karst tunnels is divided into four levels: severe (red), high (orange), elevated (yellow), and guarded (blue). Additionally, the model of risk assessment of water inrush was applied to Jigongling tunnel along the Fanba Expressway in China. The results show that the present evaluation results agree well with the construction situation, which also agree with the relative analysis results of attribute mathematical theory. The presented work with the efficacy coefficient method is relatively simple with strong operability, which has potential for predicting water inrush in karst tunnels.

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a b s t r a c tA generalized adhesive wear analysis that takes into account the effect of interfacial adhesion on the total load was developed for three-dimensional fractal surfaces in normal contact. A wear criterion based on the critical contact area for fully-plastic deformation of the asperity contacts was used to model the removal of material from the contact interface. The fraction of fully-plastic asperity contacts, wear rate, and wear coefficient are expressed in terms of the total normal load (global interference), fractal (topography) parameters, elastic-plastic material properties, surface energy, material compatibility, and interfacial adhesion characteristics controlled by the environment of the interacting surfaces. Numerical results are presented for representative ceramic-ceramic, ceramic-metallic, and metal-metal contact systems to illustrate the dependence of asperity plastic deformation, wear rate, and wear coefficient on global interference, surface roughness, material properties, and work of adhesion (affected by the material compatibility and the environment of the contacting surfaces). The analysis yields insight into the effects of surface material properties and interfacial adhesion on the adhesive wear of rough surfaces in normal contact.

In this work we present a flexible, probabilistic and reference-free method of error correction for high throughput DNA sequencing data. The key is to exploit the high coverage of sequencing data and model short sequence outputs as independent realizations of a Hidden Markov Model (HMM). We pose the problem of error correction of reads as one of maximum likelihood sequence detection over this HMM. While time and memory considerations rule out an implementation of the optimal Baum-Welch algorithm (for parameter estimation) and the optimal Viterbi algorithm (for error correction), we propose low-complexity approximate versions of both. Specifically, we propose an approximate Viterbi and a sequential decoding based algorithm for the error correction. Our results show that when compared with Reptile, a state-of-the-art error correction method, our methods consistently achieve superior performances on both simulated and real data sets.