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Objective Intrathecal dexmedetomidine has been used in spinal anesthesia during cesarean sections. The purpose of this meta-analysis was to investigate the effect of intrathecal dexmedetomidine on the adverse reactions of spinal anesthesia during cesarean section. Methods We searched for relevant studies using PubMed, Web of Science, and the Cochrane library. After screening studies and extracting data, we performed a meta-analysis on the effect of intrathecal dexmedetomidine during cesarean section. Results A total of 278 patients from 4 studies were included in this meta-analysis. The incidence of shivering in the dexmedetomidine groups was significantly lower than that in the placebo groups during cesarean section (RR=0.40, 95% CI [0.25, 0.65], P=0.0002). Intrathecal dexmedetomidine had no effect on nausea and vomiting (RR=1.08, 95% CI [0.68, 1.71], P=0.74), bradycardia (RR=1.33, 95% CI [0.31, 5.76], P=0.70), and hypotension during cesarean section (RR=0.78, 95% CI [0.59, 1.03], P=0.08). Conclusion Intrathecal dexmedetomidine can effectively reduce the occurrence of shivering during cesarean section, but it does not affect the occurrence of nausea and vomiting, bradycardia or hypotension.

In TAKR 300 (Bob Hope) Class transport ships, the builder observed cracking of steel cloverleaf vehicle tie-down deck sockets following installation. Sockets were made from AH36 steel plate by flame cutting and cold coining, then submerged-arc welded to the shop deck. Cracks initiated from the tip of the cloverleaf pattern in >300 cases aboard several cargo vessels in various stages of construction. Consultants who analyzed the situation concluded that the problem may have been corrosion and hydrogen embrittlement. Three possible mechanisms of failure were considered: overload failure; fatigue fracture; and, environmentally-assisted cracking. Testing indicated overload failure was the cause. Remedial actions were taken to improve the fracture properties of the deck socket. A modified manufacturing process was developed involving milling and cutting instead of coining to round the comers of the flame-cut cloverleaf lobe. This new manufacturing process solved the problem.

For aircraft sheet metal parts digital cutting, this paper uses IDEF0 to structure a digital system integrated with unfolding module, nesting module, cutting path planning module and NC cutting module, then analyzes data organization and process of the system application. In the system, an algorithm for the development of a doubly curved surface onto a plane is adopted, NC cutting process-oriented nesting algorithm is presented and rules of cutter path planning is developed. Furthermore, application examples of key technologies (unfolding, nesting, path cutter planning) are demonstrated and indicate that the system can realize digital cutting for aircraft sheet metal parts quickly and precisely.

The function model of digital manufacturing system of aircraft wing integral panel is proposed using IDEF0. The digital integral panel manufacturing subsystems including process design, manufacturing model definition, tooling design and manufacturing, process information management, fabrication and inspection are described. In this system, the integral panel is formed by shot peen forming process, and the corresponding approach to solve some key manufacturing problems is described. The part manufacturing model is presented to represent the intermediate part in the fabrication process. The information flow model of the system is built. The application of the system into engineering is analyzed, and it is shown that digital manufacturing of wing integral panel can shorten the cycle, lower the cost and improve quality.

In this paper, algorithms are presented for predicting peen forming parameters of integral aircraft wing panels with complex airfoil shapes. The peen forming deformation is divided into stretching deformation and bending deformation. The stretching deformation is assumed to result from the tensile strain within the plane panel, and the bending deformation corresponds to the difference of maximum and minimum curvature of the airfoil surface. The distribution of the forming tensile strain within the panel is obtained by optimal mapping of the airfoil surface in the sense that the stretching deformation energy from the plane panel to its spatial shape is minimized. In order to fit the nonlinear relation between the peening parameters and the deforming parameters, a back-propagation (BP) artificial neural network (ANN) is modeled with input parameters of thickness, curvature, tensile strain, etc, to predict the peening parameters of coverage, air pressure and intensity. Experimental peen forming data are given to train the BP ANN. It’s verified that the predicting methods are effective.