Competition Networks as a New Insight into the Determinants of Firm Technological and Market Performance

Based on the global patents of 109 mobile phone vendors filed from 1992 to 2016 and sales data from 2002to 2016, this study formulates technological and market competition networks. We find that an increasingly large number of firms joined the market, leading to a competition network of increasingly larger scale and greater density. We also propose indicators to measure competitive intensity, which reflects the extent to which the firms are competing against each other. We examine the role of competitive intensity in mediating firms’ performance. We employ a weighted least squares regression model for the empirical analysis. The empirical results confirm the significant role of competitive intensity in determining firm performance. However, in technological and market competition networks, competitive intensity generates essentially different mediating impacts. For technological competition, competitive intensity positively mediates the impact between the leaders’ and followers’ technological performances. There is an asymmetric mediating effect that enlarges the gap between the technological performances of leading vendors and their followers. Accordingly, competitive intensity may generate different impacts on the technological performance of leaders and followers. In comparison, the competitive intensity embedded in the market competition network has a symmetrical role in mediating the mutual impact between the leaders’ and followers’ market performances, that is, it neither enlarges nor narrows the market performance gap between leaders and followers. Therefore, when creating a development strategy, firm managers need to account for their competitors’ behaviors, carefully analyze the significance of these behaviors, and adjust their strategic arrangements in a timely manner.

Co-evolution of the brand effect and competitiveness in evolving networks

The principle that ＇the brand effect is attractive＇ underlies the preferential attachment. Here we show that the brand effect is just one dimension of attractiveness. Another dimension is competitiveness. We firstly introduce a general frame- work that allows us to investigate the competitive aspect of real networks, instead of simply preferring popular nodes. Our model accurately describes the evolution of social and technological networks. The phenomenon that more competitive nodes become richer can help us to understand the evolution of many competitive systems in nature and society. In general, the paper provides an explicit analytical expression of degree distributions of the network. In particular, the model yields a nontrivial time evolution of nodes＇ properties and the scale-free behavior with exponents depending on the microscopic parameters characterizing the competition rules. Secondly, through theoretical analyses and numerical simulations, we reveal that our model has not only the universality for the homogeneous weighted network, but also the character for the heterogeneous weighted network. Thirdly, we also develop a model based on the profit-driven mechanism. It can better describe the observed phenomenon in enterprise cooperation networks. We show that the standard preferential attachment, the growing random graph, the initial attractiveness model, the fitness model, and weighted networks can all be seen as degenerate cases of our model.

New results on global exponential stability of competitive neural networks with different time scales and time-varying delays

This paper studies the global exponential stability of competitive neural networks with different time scales and time-varying delays. By using the method of the proper Lyapunov functions and inequality technique, some sufficient conditions are presented for global exponential stability of delay competitive neural networks with different time scales. These conditions obtained have important leading significance in the designs and applications of global exponential stability for competitive neural networks. Finally, an example with its simulation is provided to demonstrate the usefulness of the proposed criteria.

Competition Based Neural Networks for Assignment Problems

Competition based neural networks have been used to solve the generalized assignment problem andthe quadratic assignment problem.Both problems are very difficult and are ε approximation complete.Theneural network approach has yielded highly competitive performance and good performance for thequadratic assignment problem.These neural networks are guaranteed to produce feasible solutions.

Evolution of Industrial Competitiveness Based on Evaluation of Urban Competitiveness of Dongguan City:A Case Study of Songshan Lake High-tech Zone

Based on the theoretical system of urban competitiveness,the evaluation model of urban competitiveness from the perspective of competition and cooperation was established.According to the significance index and explanatory index,the urban competitiveness was divided into urban comprehensive competitiveness(UC),urban internal node competitiveness(NC)and external competitiveness of urban network(EC).The scores and rankings of UC,NC and EC in 2006,2010 and 2015 were obtained by conducting the comprehensive evaluation of the urban competitiveness of Dongguan City with integrated usage of the principal component analysis and Technique for Order Preference by Similarity to an Ideal Solution and the data of other 20 cities in Guangdong province,Hong Kong and Macao.The research showed that the competitiveness of Dongguan City has been greatly improved during the decade of 2006–2015,and the regional cities in Guangdong Province had significant differences,which are reflected in the two dimensions of urban internal node competitiveness(NC)and external competitiveness of urban network(EC).Based on this,this study further analyzed Dongguan’s urban competitiveness from two dimensions of NC and EC,and took Dongguan Songshan Lake High-tech Zone as an example to study how to promote Dongguan’s urban competitiveness from the industrial level.

Comprehensive Analysis of the Molecular Mechanism for Gastric Cancer Based on Competitive Endogenous RNA Network

Objective: To explore the regulatory mechanism of competitive endogenous RNAs(ce RNA) in gastric cancer(GC) and to predict the prognosis of GC. Materials and Methods: Expression profiles of long noncoding RNAs(lnc RNAs), micro RNAs(mi RNAs), and m RNAs were obtained from The Cancer Genome Atlas platform. Differentially expressed RNAs(DERNAs) were screened to construct a lnc RNA-mi RNA-m RNA ce RNA network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed on the ce RNA network-related differentially expressed m RNAs(DEm RNAs). Next, the DERNAs were subjected to Cox regression and survival analyses to identify crucial prognostic factors for patients with GC. Results: We detected 1029 differentially expressed lnc RNAs, 104 differentially expressed mi RNAs, and 1659 DEm RNAs in patients with GC. Next, we performed bioinformatic analysis to construct the lnc RNA-mi RNA-m RNA ce RNA network, which included 10 mi RNAs, 65 lnc RNAs, and 10 m RNAs. Subsequently, Kaplan Meier(K-M) analysis showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group, and the area under the curve value of the receiver operating characteristic curve revealed that the polygenic model had good predictive ability. The results indicated that ADAMTS9-AS1, ATAD2, and CADM2 might be potential therapeutic targets and prognostic biomarkers for GC. Conclusions: Our study has implications for predicting prognosis and monitoring surveillance of GC and provides a new theoretical and experimental basis for the clinical prognosis of GC.

ON GLOBAL ROBUST STABILITY FOR COMPETITIVE NEURAL NETWORKS WITH TIME DELAYS AND DIFFERENT TIME SCALES

In this paper, using the theory of topological degree and Liapunov functional methods, the authors study the competitive neural networks with time delays and different time scales and present some criteria of global robust stability for this neural network model.

Global Exponential Convergence of Neutral Type Competitive Neural Networks with D Operator and Mixed Delay

The models of competitive neural network(CNN)was in recent past proposed to describe the dynamics of cortical cognitive maps with unsupervised synaptic modifications,where there are two types of memories:Long-term memories(LTM)and short-term memories(STM),LTM presents unsupervised and slow synaptic modifications and STM characterize the fast neural activity.This paper is concerned with a class of neutral type CNN’s with mixed delay and D operator.By employing the appropriate differential inequality theory,some sufficient conditions are given to ensure that all solutions of the model converge exponentially to zero vector.Finally,an illustrative example is also given at the end of this paper to show the effectiveness of the proposed results.

Ethanol mediated As(Ⅲ) adsorption onto Zn-loaded pinecone biochar：Experimental investigation,modeling,and optimization using hybrid artificial neural network-genetic algorithm approach

Organic matters（OMs） and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol（EtO H）-mediated As（Ⅲ） adsorption onto Zn-loaded pinecone（PC） biochar through batch experiments conducted under Box–Behnken design. The effect of EtO H on As（Ⅲ） adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtO H and pH on As（Ⅲ） adsorption,whereas neural network revealed the stronger influence of Et OH（64.5%） followed by pH（20.75%）and As（Ⅲ） concentration（14.75%） on the adsorption phenomena. Besides, the interaction among process variables indicated that Et OH enhances As（Ⅲ） adsorption over a pH range of2 to 7, possibly due to facilitation of ligand–metal（Zn） binding complexation mechanism.Eventually, hybrid response surface model–genetic algorithm（RSM–GA） approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As（Ⅲ）（10.47 μg/g） is facilitated at 30.22 mg C/L of Et OH with initial As（Ⅲ） concentration of 196.77 μg/L at pH 5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As（Ⅲ） species in the presence of OM.