The collapse of a laser-induced cavitation bubble near a rigid boundary and its dependence on liquid (kinematic) viscosity are investigated experimentally by fiber-coupling optical beam deflection (OBD). Cavitation bubble tests are performed using a mixture of glycerin and water of various concentrations, and the viscosity ranges from 1:004 Â 10 À6 to 51:30 Â 10 À6 m 2 /s. Combining the detection principles of this detector with a widely used laser ablation model, actual liquid-jet impact forces are presented for the mentioned viscosity range. In addition, based on the model of a collapsing bubble, some characteristic parameters, such as bubble lifetime, the maximum bubble radius, and liquid-jet impact pressure, are also obtained as a function of liquid viscosity. The main conclusion is that the liquid jet is a dominant factor in cavitation damage and can be modified by liquid viscosity. A high viscosity reduces the liquid-jet impact force and cavitation erosion markedly. The mechanism of the liquid viscosity effect on cavitation erosion has also been discussed.

Significant public health emergencies greatly impact the global supply chain system of production and cause severe shortages in personal protective and medical emergency supplies. Thus, rapid manufacturing, scattered distribution, high design degrees of freedom, and the advantages of the low threshold of 3D printing can play important roles in the production of emergency supplies. In order to better realize the efficient distribution of 3D printing emergency supplies, this paper studies the relationship between supply and demand of 3D printing equipment and emergency supplies produced by 3D printing technology after public health emergencies. First, we fully consider the heterogeneity of user orders, 3D printing equipment resources, and the characteristics of diverse production objectives in the context of the emergent public health environment. The multi-objective optimization model for the production of 3D printing emergency supplies, which was evaluated by multiple manufacturers and in multiple disaster sites, can maximize time and cost benefits of the 3D printing of emergency supplies. Then, an improved non-dominated sorting genetic algorithm (NSGA-II) to solve the multi-objective optimization model is developed and compared with the traditional NSGA-II algorithm analysis. It contains more than one solution in the Pareto optimal solution set. Finally, the effectiveness of 3D printing is verified by numerical simulation, and it is found that it can solve the matching problem of supply and demand of 3D printing emergency supplies in public health emergencies.

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The increasing demand for new therapies and other clinical interventions has made researchers conduct many clinical trials. The high level of evidence generated by clinical trials makes them the main approach to evaluating new clinical interventions. The increasing amounts of data to be considered in the planning and conducting of clinical trials has led to higher costs and increased timelines of clinical trials, with low productivity. Advanced technologies including artificial intelligence, machine learning, deep learning, and the internet of things offer an opportunity to improve the efficiency and productivity of clinical trials at various stages. Although researchers have done some tangible work regarding the application of advanced technologies in clinical trials, the studies are yet to be mapped to give a general picture of the current state of research. This systematic mapping study was conducted to identify and analyze studies published on the role of advanced technologies in clinical trials. A search restricted to the period between 2010 and 2020 yielded a total of 443 articles. The analysis revealed a trend of increasing research interests in the area over the years. Recruitment and eligibility aspects were the main focus of the studies. The main research types were validation and evaluation studies. Most studies contributed methods and theories, hence there exists a gap for architecture, process, and metric contributions. In the future, more empirical studies are expected given the increasing interest to implement the AI, ML, DL, and IoT in clinical trials.

Cross-industry technology spillover perception (CTSP) refers to the degree to which the enterprise absorbs the technology spilling from outside industries. How does the enterprise’s CTSP impact its cross-industry innovation performance? To answer this question, we distinguished CTSP into CTSP-Width and CTSP-Depth and investigate their dynamical impacts on the cross-industry innovation performance from the perspective of industry development process. Furthermore, we also explored the impact of the enterprise’s knowledge flow in the innovation knowledge network on the above relationships. We conducted the hierarchical regression analysis based on patent data in UAV industry from 2006 to 2020. The results showed that (1) in the flat period of industry development, only CTSP-Width has a direct positive impact on cross-industry innovation performance. (2) In the low-speed climbing period and the high-speed growing period, the impact of CTSP-Width disappears, and the direct positive impact of CTSP-Depth appears. (3) In the flat period, both the moderator roles of knowledge contribution and knowledge profitability are not significant. In the low-speed climbing period, only knowledge profitability plays the negative moderator role, while in the high-speed growing period, both the knowledge contribution and knowledge profitability play significant negative moderator roles. This suggests that, when the industry develops into maturity, the negative moderation effect of knowledge network gradually appears, which indicates that there is a knowledge network trap.