Three-node, 40 Gbit/s OTDM network experiment using electro-opticswitches

The authors demonstrate, for the first time, a three node optically time division multiplexed network with a maximum capacity of 40 Gbit/s. Electro-optical switches were used for both `drop and insert' and demultiplexing, and electronic clock recovery provided synchronisation throughout the network     

In vivo purging with high-dose cytarabine followed by high-dose chemoradiotherapy and reinfusion of unpurged bone marrow for adult acute myelogenous leukemia in first complete remission

PURPOSE: To evaluate in a prospective study the efficacy of autologous bone marrow transplantation (BMT) in adult patients with acute myelogenous leukemia (AML) in first remission, using a single course of high-dose Cytarabine (HD Ara-C) consolidation therapy as in vivo purging. PATIENTS AND METHODS: Sixty consecutive adult patients with AML in first complete remission (CR) were treated with HD Ara-C consolidation therapy as a method of in vivo purging before marrow collection. High-dose therapy consisted of fractionated total-body irradiation (FTBI) 12 Gy, intravenous etoposide 60 mg/kg, and cyclophosphamide 75 mg/kg, followed by reinfusion of cryopreserved marrow. RESULTS: Sixty patients underwent consolidation treatment with HD Ara-C with the intent to treat with autologous BMT. Sixteen patients were unable to proceed to autologous BMT (10 patients relapsed, one died of sepsis, one developed cerebellar toxicity, two had inadequate blood counts, and two refused). Forty-four patients underwent autologous BMT and have a median follow-up time of 37 months (range, 14.7 to 68.7) for patients who are alive with no relapse. The cumulative probability of disease-free survival (DFS) at 24 months in the intent-to-treat group is 49% (95% confidence interval [CI], 37% to 62%) and in those who actually underwent autologous BMT is 61% (95% CI, 46% to 74%). The probability of relapse was 44% (95% CI, 31% to 58%) and 33% (95% CI, 20% to 49%) for the intent-to-treat and autologous BMT patients, respectively. CONCLUSION: This approach offers a relatively high DFS rate to adult patients with AML in first CR. The results of this study are similar to those achieved with allogeneic BMT.     

用面向对象编程实现问题求解自动化

本文从面向对象方法基本原理和面向对象编程的技术特征出发，阐明所建应用软件系统中的对象，类及相应类层次结构和类组合结构，使系统具有很强的表现真空世界复杂系统结构的能力，系统通过消息传递在程序执行中实现对操作的调用机制，使之在面向用户问题选择和执行求解策略方面有很强的适应性，这一点对于强调人－机交互和解题协作的智能决策支持系统设计至关重要，文章给出了用Ｃ＋＋实现的模型对象系统及消息传递机制。     

高职计算机文化分专业分层次教学的实践与思考

指出了高职计算机文化课程教学面临的诸多问题,这些问题的存在严重制约了课程教学效果的提高.为此,在深入企业调研和对教学对象调查的基础上,结合各专业类别特点及其对计算机基本操作技能的不同需求,选取不同专业的班级作为研究对象,采用“横向分专业,纵向分层次”的教学方法进行了实践教学.实践结果表明,分专业分层次教学方法能有效解决高职“计算机文化”课程教学中存在的主要问题.     

Image cipher using image filtering with 3D DNA-based confusion and diffusion strategy

Neural Computing and Applications - In this paper, an image cipher is presented based on DNA sequence operations, image filtering and memrisitve chaotic system. Firstly, plain image is preprocessed...     

Exploiting 2D compressed sensing and information entropy for secure color image compression and encryption

Neural Computing and Applications - Compared to 1D compressed sensing (CS), 2D CS is more efficient for compressing the plaintext image from two directions, but security level of current 2D...     

基于D-FNN的混沌铁磁谐振系统非线性补偿控制方法

针对电网混沌铁磁谐振系统产生的混沌现象,提出基于动态模糊神经网络的混沌铁磁谐振系统非线性补偿控制方法。该方法采用动态模糊神经网络来逼近系统的非线性部分,消除了过电压的混沌现象,将系统稳定到目标位置,实现了对系统的非线性补偿控制。Matlab仿真结果表明,基于动态模糊神经网络的非线性补偿控制方法控制结果正确,响应快速。     

逆向工程点云数据采集系统设计及应用

针对目前四轴激光测量机的不足,设计了一种五轴逆向工程点云数据采集系统;在四轴的基础上增加了激光测头的旋转轴,采用基于PCI总线的运动控制系统及USB总线的数据采集系统,并植入了图像采集系统,通过二次开发技术利用Windows的应用程序编程接口函数对扫描范围进行自动识别;软件系统使用了采集路径规划及测量数据优化处理算法;在原始数据测量及曲面反求建模两个方面进行了实例应用,零件大斜度面和垂直于工作台的表面均能覆盖测量,并且所有点云数据是一次测量完成,实验结果验证了本系统的可行性与稳定性。     

Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants

Real-time and reliable measurements of the effluent quality are essential to improve operating efficiency and reduce energy consumption for the wastewater treatment process.Due to the low accuracy and unstable performance of the traditional effluent quality measurements,we propose a selective ensemble extreme learning machine modeling method to enhance the effluent quality predictions.Extreme learning machine algorithm is inserted into a selective ensemble frame as the component model since it runs much faster and provides better generalization performance than other popular learning algorithms.Ensemble extreme learning machine models overcome variations in different trials of simulations for single model.Selective ensemble based on genetic algorithm is used to further exclude some bad components from all the available ensembles in order to reduce the computation complexity and improve the generalization performance.The proposed method is verified with the data from an industrial wastewater treatment plant,located in Shenyang,China.Experimental results show that the proposed method has relatively stronger generalization and higher accuracy than partial least square,neural network partial least square,single extreme learning machine and ensemble extreme learning machine model.     

以1,3-二(4-邻羟基苯亚胺次甲基)苯氧基丙烷为载体的铁离子(Ⅲ)电极的研究

以1,3-二(4-邻羟基苯亚胺次甲基)苯氧基丙烷为中性载体制备了PVC膜Fe3+离子选择性电极。该电极对Fe3+呈现出良好的选择性和近Nernst电位响应性能。电极斜率为21mV/dec,线性范围为3.0×10-5～1.0×10-1mol/L,检出限为1.0×10-5mol/L。采用交流阻抗技术研究了电极的响应机理,并将电极作为指示电极初步用于EDTA的电位滴定。