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The currently available atomistic simulation tools for an evolving complex interface are described, focusing on the kinetic Monte Carlo (KMC) and cellular automata (CA) methods. The different versions of the two methods are considered, stressing their relative weaknesses and strengths. Data storage considerations lead to the choice of an octree data structure, of use in both KMC and CA simulations. The octree results in a substantial reduction in memory use, simultaneously benefiting from rapid addressability and fast searching properties. Examples in anisotropic etching are used to depict the use of the methods in practice and to describe the algorithms.

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An evolutionary algorithm is presented for the automated calibration of the continuous cellular automaton for the simulation of isotropic and anisotropic wet chemical etching of silicon in as many as 31 widely different and technologically relevant etchants, including KOH, KOH+IPA, TMAH and TMAH+Triton, in various concentrations and temperatures. Based on state-of-the-art evolutionary operators, we implement a robust algorithm for the simultaneous optimization of roughly 150 microscopic removal rates based on the minimization of a cost function with four quantitative error measures, including (i) the error between simulated and experimental macroscopic etch rates for numerous surface orientations all over the unit sphere, (ii) the error due to underetching asymmetries and floor corrugation features observed in simulated silicon samples masked using a circular pattern, (iii) the error associated with departures from a step-flow-based hierarchy in the values of the microscopic removal rates, and (iv) the error associated with deviations from a step-flow-based clustering of the microscopic removal rates. For the first time, we present the calibration and successful simulation of two technologically relevant CMOS compatible etchants, namely TMAH and, especially, TMAH+Triton, providing several comparisons between simulated and experimental MEMS structures based on multi-step etching in these etchants.

Label propagation algorithm (LPA) is an extremely fast community detection method and is widely used in large scale networks. In spite of the advantages of LPA, the issue of its poor stability has not yet been well addressed. We propose a novel node influence based label propagation algorithm for community detection (NIBLPA), which improves the performance of LPA by improving the node orders of label updating and the mechanism of label choosing when more than one label is contained by the maximum number of nodes. NIBLPA can get more stable results than LPA since it avoids the complete randomness of LPA. The experimental results on both synthetic and real networks demonstrate that NIBLPA maintains the efficiency of the traditional LPA algorithm, and, at the same time, it has a superior performance to some representative methods.

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Background Nearly 85 % of lung-cancer-specific epidermal growth factor receptor (EGFR) sensitive mutations comprise a substitution at position 858 (21L858R) and deletion mutants in exon 19 (19del). The aim of this study was to assess the role of EGFR mutation subtypes in predicting the efficacy of EGFR tyrosine kinase inhibitors (EGFR TKIs) and the prognosis of patients with advanced non-small cell lung cancer (NSCLC).Method We systematically searched for eligible articles investigating the association between EGFR mutation subtypes and the efficacy of EGFR TKIs and the prognosis of patients with NSCLC. The summary risk ratio (RR) and mean difference (MD) were calculated using meta-analysis. In addition, we used variance analysis for the progression-free survival data (PFS) and used the rank sum test for the overall survival data.ResultsWe identified 22 eligible trials involving 1,082 patients. The objective response rate of the 19del mutation group was significantly higher than the 21L858R mutation group (RR 1.23; 95 % CI 1.12–1.36; P < 0.0001). The PFS (MD 3.55; 95 % CI 0.90–6.20; P = 0.009; MD 2.57; 95 % CI 0.51–4.62; P = 0.01) and overall survival (OS) (MD 10.52; 95 % CI 5.10–15.93; P = 0.0001) of the 19del mutation group were significantly longer than the 21L858R mutation group; the same results were observed in the variance analysis and rank sum test.ConclusionThe 19del mutation may be a more efficient clinical marker for predicting the response of patients with NSCLC to EGFR TKIs. Furthermore, patients with the 19del mutation have both a longer PFS and OS. The 19del mutation is also the prognostic factor for patients with NSCLC.

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