Research on Cooperative Innovation Behavior of Industrial Cluster Based on Subject Adaptability

From the perspective of the interactive cooperation among subjects, this paper portrays the process of cooperative innovation in industrial cluster, in order to capture the correlated equilibrium relationship among them. Through the utilization of two key tools, evolutionary stable strategy and replicator dynamics equations, this paper considers the cost and gains of cooperative innovation and the amount of government support as well as other factors to build and analyze a classic evolutionary game model. On this basis, the subject’s own adaptability is introduced, which is regarded as the system noise in the stochastic evolutionary game model so as to analyze the impact of adaptability on the game strategy selection. The results show that, in the first place, without considering subjects’ adaptability, their cooperation in industrial clusters depends on the cost and gains of innovative cooperation, the amount of government support, and some conditions that can promote cooperation, namely, game steady state. In the second place after the introduction of subjects’ adaptability, it will affect both game theory selection process and time, which means that the process becomes more complex, presents the nonlinear characteristics, and helps them to make faster decisions in their favor, but the final steady state remains unchanged

Research on Cloud Manufacturing Service Recommendation Based on Graph Neural Network

There are an increasing number of manufacturing service resources appeared on the cloud manufacturing (CMfg) service platform recently, which leads to a serious information overloading problem to the enterprises that need these resources. To tackle this problem, a graph neural network-based recommendation method for CMfg service resources is proposed, which effectively overcomes some limitations of the traditional recommendation methods. Specifically, we first use different similarity calculation methods (e.g., Cosine similarity, Pearson correlation coefficient, etc.) to calculate the similarities between different resources based on the feature information of CMfg service resources. A resource graph dataset is accordingly established. A graph neural network is then used to perform representation learning of nodes in these graphs, obtaining the vector representations of these nodes. Finally, new links that may appear in a graph are predicted by performing dot product calculations on these nodes\u27 vector representations. And these links can be used to recommend suitable resources. Experiments mainly show that (i) the proposed method obtains better link prediction accuracy compared with that of other link prediction algorithms; (ii) when the network density used for training is relatively high, the predictive performance of the proposed method is improved significantly. Our method can shed light on how to choose suitable CMfg service resources from the CMfg service platform

An approach to syndrome differentiation in traditional chinese medicine based on neural network

Although the traditional knowledge representation based on rules is simple and explicit, it is not effective in the field of syndrome differentiation in Traditional Chinese Medicine (TCM), which involves many uncertain concepts. To represent uncertain knowledge of syndrome differentiation in TCM, two methods were presented respectively based on certainty factors and certainty intervals. Exploiting these two methods, an approach to syndrome differentiation in TCM was proposed based on neural networks to avoid some limitations of other approaches. The main advantage of the approach is that it may realize uncertain inference of syndrome differentiation in TCM, whereas it doesn't request experts to provide all possible combinations for certainty degrees of symptoms and syndromes. Rather than Back Propagation (BP) algorithm but its modification was employed to improve the capability of generalization of neural networks. First, the standard feedforward multilayer BP neural network and its modification were introduced. Next, two methods for knowledge representation, respectively based on certainty factors and certainty intervals, were presented Then, the algorithm was proposed based on neural network for the uncertain inference of syndrome differentiation in TCM. Finally, an example was demonstrated to illustrate the algorithm

Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity

Spatial crowdsourcing (SC) engages large worker pools for location-based tasks, attracting growing research interest. However, prior SC task allocation approaches exhibit limitations in computational efficiency, balanced matching, and participation incentives. To address these challenges, we propose a graph-based allocation framework optimized for massive heterogeneous spatial data. The framework first clusters similar tasks and workers separately to reduce allocation scale. Next, it constructs novel non-crossing graph structures to model balanced adjacencies between unevenly distributed tasks and workers. Based on the graphs, a bidirectional worker-task matching scheme is designed to produce allocations optimized for mutual interests. Extensive experiments on real-world datasets analyze the performance under various parameter settings