Knowledge sharing behavior based on the cluster innovation network has become the primary measure for enterprises to realize sustainable innovation. In order to promote the proactive knowledge sharing behavior among enterprises in the long term, the dynamic evolutionary process and law of knowledge sharing in the network need to be further studied. As different from the hypothesis of the rational man in the classical game theory, this paper establishes an evolutionary game model of knowledge sharing behavior in the cluster innovation network based on the evolutionary game theory, and discusses how the bounded rational enterprises can achieve the evolutionary equilibrium through continuously adaptive learning and strategy optimization, further explores the influence factors on the evolutionary trajectory. Combined with mathematical derivation and simulation analysis, the following results are obtained: over time, the dynamic evolution of knowledge sharing behavior in the cluster innovation network is influenced by initial states of the system, but can always reach the evolutionary stable equilibrium; factors such as synergy revenue have a positive impact on the evolutionary results, while factors such as opportunity interest have a negative impact on the evolutionary results; the factor of revenue distribution has a U-shape relationship with the evolutionary results, and the factor of direct revenue has no effect on the results. The results are expected to have an implication for improving the sustainable innovation development of enterprises in the cluster innovation network.

Coronary heart disease is the first killer of human health. At present, the most widely used approach of coronary heart disease diagnosis is coronary angiography, a surgery that could potentially cause some physical damage to the patients, together with some complications and adverse reactions. Furthermore, coronary angiography is expensive thus cannot be widely used in under development country. On the other hand, the heart color Doppler echocardiography report, blood biochemical indicators and personal information, such as gender, age and diabetes, can reflect the degree of heart damage in patients to some extent. This paper proposes a combined reinforcement multitask progressive time-series networks (CRMPTN) model to predict the grade of coronary heart disease through heart color Doppler echocardiography report, blood biochemical indicators and ten basic body information items about the patients. In this model, the first step is to perform deep reinforcement learning (DRL) pre-training through asynchronous advantage actor-critic (A3C). Training data is adopted to optimize the recurrent neural network (RNN) that parameterizes the stochastic policy. In the second step, soft parameter sharing module, hard parameter sharing module and progressive time-series networks are used to predict the status of coronary heart disease. The experimental results show that after DRL pre-training, the multiple tasks in the model interact with each other and learn together to achieve satisfactory results and outperform other state-of-the-art methods.

Uncertainty in demand and high online return rates tend to generate a large inventory backlog after a hot selling season. Some well-known companies such as Burberry and H&M choose to burn these backlogs, which is very unfavorable to the sustainable development of enterprises and society. The omnichannel BOPS (Buy Online and Pickup in Store) provides consumers with a new and convenient choice of purchasing channels. Considering the probability of online return, we build expected profit models for retailers before and after opening a BOPS channel based on consumer surplus and purchase ratio via an online channel, store channel, and BOPS channel. Then, we prove the existence of optimal solutions and obtain the joint optimization decision on pricing and ordering. The results show that, under certain conditions, when the proportion of online channel buyers increases, the retailers’ optimal decision before opening a BOPS channel is to reduce the price and increase the order quantity, and the retailers’ optimal decision after opening a BOPS channel is to simultaneously reduce the price and the order quantity. Whether or not a BOPS channel is opened, when the purchase proportion of store channel increases, the optimal decision of retailers is to increase the price and order quantity at the same time. Furthermore, when the online return rate, the cost of online shopping, the inconvenient cost of the store channel, and the inconvenience of the BOPS channel relative to store channel increase, the optimal decision of retailers is to simultaneously reduce the price and order quantity. Moreover, when the maximum psychological value of the product increases, the optimal decision is to reduce the order quantity while increasing the price. In addition, the opening of a BOPS channel increases optimal price, optimal order quantity, and maximum expected profit.