Precise fabrication of microscale vasculatures (MSVs) has long been an unresolved challenge in tissue engineering. Currently, light-assisted printing is the most common approach. However, this approach is often associated with an intricate fabrication process, high cost, and a requirement for specific photoresponsive materials. Here, thermoresponsive hydrogels are employed to induce volume shrinkage at 37 °C, which allows for MSV engineering without complex protocols. The thermoresponsive hydrogel consists of thermosensitive poly(N-isopropylacrylamide) and biocompatible gelatin methacrylate (GelMA). In cell culture, the thermoresponsive hydrogel exhibits an apparent volume shrinkage and effectively triggers the creation of MSVs with smaller size. The results show that a higher concentration of GelMA blocks the shrinkage, and the thermoresponsive hydrogel demonstrates different behaviors in water and air at 37 °C. The MSVs can be effectively fabricated using the sacrificial alginate fibers, and the minimum MSV diameter achieved is 50 µm. Human umbilical vein endothelial cells form endothelial monolayers in the MSVs. Osteosarcoma cells maintain high viability in the thermoresponsive hydrogel, and the in vivo experiment shows that the MSVs provide a site for the perfusion of host vessels. This technique may help in the development of a facile method for fabricating MSVs and demonstrates strong potential for clinical application in tissue regeneration.
This paper focuses on the need of the large equipment manufacturing industry to adapt collaborative operation to transform the industry to cloud manufacturing services and to solve the new problem of federal resources coordination in complete service operation. We systematically study federal resources cooperation under cloud manufacturing mode to complete a large complex project. The primary research contents are divided into four points. First, a system structure of cloud manufacturing service mode is presented. Second, a synergy logic framework from the global system perspective is designed based on generalised partial global planning. Third, a multi-level system coordination mechanism is established by integrating various methods, including the bidding game mechanism for enterprise external resources, the planning control mechanisms for enterprise internal resource and the global collaborative optimisation mechanism for enterprise global federal resources. Finally, a cloud manufacturing service platform for a typical enterprise is developed by combining theory with practice. The results can realise collaborative management in resource selection and configuration, service process planning control and service information feedback in cloud manufacturing service, as well as achieve overall synergy effect for the system. Keywords: cloud manufacturing; large equipment complete service; collaborative framework; collaborative mechanism; generalised partial global planning IntroductionIn the twenty-first century, the collaboration of cloud applications and the Internet of things (IOT) have been identified as the key technology and development trend for remodelling global manufacturing enterprises (Xu 2012). Various forms of cloud applications have emerged in each field since the concept of cloud computing was presented by Google in 2006 (Hayes 2008). Cloud computing applications in the manufacturing industry were accelerated because the manufacturing enterprise was confronted with hitherto unknown survival pressure; the rising demand for transformation caused by the financial crisis; and the rapid increase of raw materials and labour cost. Cloud manufacturing is a new concept in cloud computing, where the concept of 'software is service' has expanded to 'manufacturing is service' (Hao, Shen, and Wang 2005). Therefore, cloud manufacturing is a typical service-oriented manufacturing mode as well as a new service-oriented networked manufacturing mode that can realise agile services and green intelligent targets, solve more complex manufacturing problems and perform larger scale collaborative manufacturing. Cloud manufacturing is the transformation and development trend of the country's manufacturing industry (Editorial Department 2011). Large equipment manufacturing enterprises in China are currently upgrading from single-form product manufacturing production enterprises to modern equipment service providers who can provide comprehensive solutions for customers, enhance complete sets of equipment ability and achieve inte...
PurposeThis paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.Design/methodology/approachIn this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.FindingsWith the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.Research limitations/implicationsThe threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.Practical implicationsIt can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.Originality/valueThe classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.
The perishable nature of fresh agricultural products and their vulnerability to environmental impacts make fresh agricultural product supplies susceptible to more complex social risks and unpredictable natural risks. This study identifies 13 social and natural risk factors that could adversely affect the fresh agricultural product supply and uses ISM and MICMAC to develop a hierarchical structure of the risks to analyze the correlation of these risk factors. The results showed that these risk factors have a strong positive correlation, a reasonable risk-sharing mechanism should be established for the fresh agricultural product supply and the improvement in the supervision error correction system should be strengthened.
Three-dimensional (3D) bioprinting is an emerging research direction in bio-manufacturing, a landmark in the shift from traditional manufacturing to high-end manufacturing. It integrates manufacturing science, biomedicine, information technology, and material science. In situ bioprinting is a type of 3D bioprinting which aims to print tissues or organs directly on defective sites in the human body. Printed materials can grow and proliferate in the human body; therefore, the graft is similar to the target tissues or organs and could accurately match the defective site. This article mainly summarizes the current status of robotic applications in the medical field and reviews its research progress in in situ 3D bioprinting.
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