The identification of the characteristics of urban road traffic accidents is of great significance for reducing traffic accidents and the corresponding losses. In the context of big data, to accurately understand the characteristics of traffic accidents, the feature set of urban road traffic accidents is proposed, the XGBoost model is used to classify traffic accidents into minor accidents, general accidents, major accidents and serious accidents, and a GA-XGBoost feature recognition model is built. The GA-XGBoost feature recognition model is based on the genetic algorithm (GA) as a factor search algorithm and is verified by applying the big data of traffic accidents in a Chinese city from 2006 to 2016; in addition, the model is compared with the GA-RF, GA-GBDT and GA-LightGBM models. The results show that the GA-XGBoost model can accurately identify the features of the traffic accidents in 7 cities, including driving experience, illegal driving behavior, vehicle age, road intersection type, weather conditions, traffic flow and time interval. Compared with the GA-RF, GA-GBDT and GA-LightGBM models, the recognition features are more accurate, and the performance is better. INDEX TERMS Urban traffic, feature recognition, traffic accidents, XGBoost algorithm, genetic algorithm (GA).

As the offshoring becomes more widespread, business practitioners outsource their activities to overseas countries. Although there is a wealth of academic literature examining outsourcing and offshoring, there is little academic literature that addresses the current outsourcing decision most firms facing, which is where to outsource. Given multi-attribute nature of offshore location selection, this paper argues that five factors should be considered for decisions, and proposes the use of analytic hierarchy process (AHP) and PROMETHEE as aids in making offshore location selection decisions. AHP is used to analyze the structure of the location selection problem and determine weights of the criteria, and PROMETHEE method is used for final ranking, together with changing weights for a sensitivity analysis. It shows by means of an application that the hybrid method is very well suited as a decision-making tool for the offshore location selection decision. Finally, potential issues for future research are presented.

To further improve the effect of gene modules identification, combining the Newman algorithm in community detection and K-means algorithm framework, a new method of gene module identification, GCNA-Kpca algorithm, was proposed. The core idea of the algorithm was to build a gene co-expression network (GCN) based on gene expression data firstly; Then the Newman algorithm was used to initially identify gene modules based on the topology of GCN, and the number of clusters and clustering centers were determined; Finally the number of clusters and clustering centers were input into the K-means algorithm framework, and the secondary clustering was performed based on the gene expression profile to obtain the final gene modules. The algorithm took into account the role of modularity in the clustering process, and could find the optimal membership module for each gene through multiple iterations. Experimental results showed that the algorithm proposed in this paper had the best performance in error rate, biological significance and CNN classification indicators (Precision, Recall and F-score). The gene module obtained by GCNA-Kpca was used for the task of key gene identification, and these key genes had the highest prognostic significance. Moreover, GCNA-Kpca algorithm was used to identify 10 key genes in hepatocellular carcinoma (HCC): CDC20, CCNB1, EIF4A3, H2AFX, NOP56, RFC4, NOP58, AURKA, PCNA, and FEN1. According to the validation, it was reasonable to speculate that these 10 key genes could be biomarkers for HCC. And NOP56 and NOP58 are key genes for HCC that we discovered for the first time.

Background Hepatocellular carcinoma (HCC), the main type of liver cancer in human, is one of the most prevalent and deadly malignancies in the world. The present study aimed to identify hub genes and key biological pathways by integrated bioinformatics analysis. Methods A bioinformatics pipeline based on gene co-expression network (GCN) analysis was built to analyze the gene expression profile of HCC. Firstly, differentially expressed genes (DEGs) were identified and a GCN was constructed with Pearson correlation analysis. Then, the gene modules were identified with 3 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Moreover, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein protein interaction (PPI) network of the key gene module, and we identified the hub genes using nine topology analysis algorithms based on this PPI network. Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. Lastly, we explored the methylation changes of hub genes using another GEO data (GSE73003). Results Firstly, among the expression profiles, 4,130 up-regulated genes and 471 down-regulated genes were identified. Next, the multi-level algorithm which had the highest modularity divided the GCN into nine gene modules. Also, a key gene module (m1) was identified. The biological processes of GO enrichment of m1 mainly included the processes of mitosis and meiosis and the functions of catalytic and exodeoxyribonuclease activity. Besides, these genes were enriched in the cell cycle and mitotic pathway. Furthermore, we identified 11 hub genes, MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 which played key roles in HCC. The results of multiple verification methods indicated that the 11 hub genes had highly diagnostic efficiencies to distinguish tumors from normal tissues. Lastly, the methylation changes of gene CDC20, TOP2A, TK1, FEN1 in HCC samples had statistical significance (P-value < 0.05). Conclusion MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, the cell cycle and mitotic pathway might played vital roles in the progression of HCC.

Purpose Although researchers have demonstrated a keen interest in knowledge collaboration in online encyclopedias, previous studies have seldom explored the dynamic interrelations in online encyclopedias over time that involve the iteratively melding of individual cognitive system and knowledge collaboration system. Therefore, this paper aims to reveal the structure and dynamics of knowledge collaboration in online encyclopedias from a perspective of system dynamics (SD). Design/methodology/approach This paper proposes a general activity system of knowledge collaboration in online encyclopedias based on Engeström’s activity theory. According to the SD methodology proposed by Forrester, this study develops a holistic SD model by identifying interactions of knowledge collaboration factors based on behavioral theories; validating the SD model by structural tests and behavior tests involving historical data of English Wikipedia; and conducting simulation to capture the interactive dynamics of the salient factors of knowledge collaboration. Findings According to the SD methodology, this study develops and validates an SD model to explore interesting dynamic interrelations among core factors (contributors, conflicts, discussions, entries quantity and entries quality) that are neglected by previous research. The results show that there is a significant negative feedback relationship between inactive contributors and entries quality, between contributors and conflicts and between edit conflicts and entries quality. There is a complicated nonlinear feedback relationship between active contributors and entries quality, and between edit conflicts and discussions. Originality/value Different from prior empirical studies that normally investigate the unidirectional linear relationships among prominent factors of knowledge collaboration in online encyclopedias from a static perspective, this study captures a dynamic picture of their interrelations by unfolding their behavior patterns over time. The main contribution of this study is to develop a holistic SD model and to reveal and elaborate on the complex dynamics involved online encyclopedias based on activity theory.