核桃雄花中总多酚提取工艺的研究

采用单因素和正交试验研究核桃雄花中多酚类物质的最佳提取工艺条件。通过对4种不同溶剂(无水乙醇、甲醇、丙酮和乙酸乙酯)在相同试验条件下的浸提效果比较发现,甲醇是核桃雄花中总酚类物质的最佳提取试剂。然后分别采用常规水浴浸提法和超声波辅助浸提法对甲醇体分数、浸提温度、浸提时间和料液比等因素进行研究,在确定最佳单因素水平的基础上,再利用正交试验优化以甲醇为浸提剂的提取工艺条件,试验得出超声波辅助浸提法具有用时短、总多酚提取量高的优点,其最佳工艺条件为:料液比1∶30(g/m L)、甲醇溶液体积分数30%、温度70℃、浸提时间10 min。在此条件下,核桃雄花中的总多酚含量可达6.56%,明显高于其他条件下的。     

DCS分布式冷却系统在通信基站的应用研究

针对基站柜式空调耗电量大的问题,创造性地使用基站分布式冷却的方式,达到精确送风,节能高效的目的.在应用实践中检验了分布式冷却系统实际节能量和应用效果,总结出分布式冷却系统应用场景和范围,为更大范围推广应用DCS分布式冷却系统提供实践依据.     

Isolated hepatic tuberculosis mimicking liver tumors in a dialysis patient

Cases of isolated hepatic tuberculosis (TB) are rare. The diagnosis is often delayed or missed because of nonspecific symptoms and laboratory findings. Besides, the disease is extremely rare even in a country where TB is an alarming public health problem. This report demonstrates the difficulty in correctly diagnosing local hepatic TB. We report the case of a 62‐year‐old male patient with end‐stage renal disease treated with hemodialysis, who developed 2 months of abdominal distension and general anorexia, with hyperechoic hepatic lesions on ultrasound. Computed tomography suspected multiple liver tumors. The liver biopsy finally led to the diagnosis of TB of the liver without other involvements. We conclude that isolated hepatic TB is one of the rare forms of extrapulmonary TB in dialysis patients. A greater awareness of this rare clinical entity may prevent needless surgical interventions.     

Wearing prediction of stellite alloys based on opposite degree algorithm

In order to predict the wearing of stellite alloys,the related methods of rare metals data processing were discussed. The method of opposite degree(OD) algorithm was put forward to predict the wearing of stellite alloys.OD algorithm is based on prior numerical data, posterior numerical data and the opposite degree between numerical forecast data. To compare the performance of predicted results based on different algorithms, the back propagation(BP) and radial basis function(RBF) neural network methods were introduced. Predicted results show that the relative error of OD algorithm is smaller than those of BP and RBF neural network methods. OD algorithm is an effective method to predict the wearing of stellite alloys and it can be applied in practice.