Modeling of combustion and propulsion processes of a new concept gun using a gaseous propellant

The combustion light gas gun (CLGG) uses a low molecular weight gas as the propellant to burn, expand and propel the projectile out of the barrel with higher muzzle velocities.In order to better understand the interior ballistic process of CLGG, an multidimensional combustion and flow model for CLGG is established. It contains unsteady Reynolds-averaged Navier-Stokes (RANS) equations, the RNG k

Multi-Channel Magnetocardiogardiography System Based on Low-Tc SQUIDs in an Unshielded Environment

AbstractMagnetocardiography (MCG) using superconducting quantum interference devices (SQUIDs) is a new medical diagnostic tool measuring biomagnetic signals that are generated by the electrical activity of the human heart. This technique is completely passive, contactless, and it has an advantage in the early diagnosis of heart diseases. We developed the first unshielded four-channel MCG system based on low-Tc DC SQUIDs in China. Instead of using a costly magnetically shielded room, the environmental noise suppression was realized by using second-order gradiometers and three-axis reference magnetometer. The measured magnetic field resolution of the system is better than 1 pT, and multi-cycle human heart signals can be recorded directly. Also, with the infrared positioning system, 48 points data collection can be realized by moving the non-magnetic bed nine times

Bronchoscopic ethanol injection combined with cryotherapy is an effective treatment for benign airway stenosis caused by endotracheal intubation or tracheotomyc

The benign tracheal stenosis is a challenge in interventional pulmonary disease. Bronchoscopic ethanol injection (BEI) is always used in airway stenosis caused by malignant tracheal tumor. The efficacy and safety of BEI in benign airway stenosis has not been studied before. To compare the safety and efficacy between bronchoscopic icryotherapy and BEI combined with bronchoscopic cryotherapy in the treatment of benign tracheal stenosis. A retrospective study included 61 patients with tracheal stenosis caused by endotracheal intubation and tracheotomy from July 2010 to June 2015 was made. 33 patients received repeated bronchoscopic cryotherapy alone were in Group A, 29 patients underwent repeated cryotherapy combined with BEI were in Group B. Dyspnea index, tracheal diameter were collected before and after treatment. Efficacy and complications were compared in two groups. The changes of tracheal diameter, dyspnea index were significant before and after treatment in both groups (P < 0.05). The long-term cure rate was higher in group B than that in group A (100% vs 84.8%). The average duration for dilated airway stable was much shorter in group B than group A (166±28 days vs 278±32 days, P < 0.05). The average cryotherapy session performed in group B was significantly less than that in group A (22.1±4.7 vs 34.9±6.5, P < 0.05). Meanwhile the complications in group A were seldom, the incidence of complications related to BEI were low in group B (mild chest pain 7.1%, bleeding 3.6% and cough 10.7%). BEI combined with bronchoscopic cryotherapy is an effective minimally invasive choice for releasing the airway obstructive symptoms

Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation

Multi-modal recommendation systems, which integrate diverse types of information, have gained widespread attention in recent years. However, compared to traditional collaborative filtering-based multi-modal recommendation systems, research on multi-modal sequential recommendation is still in its nascent stages. Unlike traditional sequential recommendation models that solely rely on item identifier (ID) information and focus on network structure design, multi-modal recommendation models need to emphasize item representation learning and the fusion of heterogeneous data sources. This paper investigates the impact of item representation learning on downstream recommendation tasks and examines the disparities in information fusion at different stages. Empirical experiments are conducted to demonstrate the need to design a framework suitable for collaborative learning and fusion of diverse information. Based on this, we propose a new model-agnostic framework for multi-modal sequential recommendation tasks, called Online Distillation-enhanced Multi-modal Transformer (ODMT), to enhance feature interaction and mutual learning among multi-source input (ID, text, and image), while avoiding conflicts among different features during training, thereby improving recommendation accuracy. To be specific, we first introduce an ID-aware Multi-modal Transformer module in the item representation learning stage to facilitate information interaction among different features. Secondly, we employ an online distillation training strategy in the prediction optimization stage to make multi-source data learn from each other and improve prediction robustness. Experimental results on a video content recommendation dataset and three e-commerce recommendation datasets demonstrate the effectiveness of the proposed two modules, which is approximately 10% improvement in performance compared to baseline models.Comment: 11 pages, 7 figure