Improving the photocatalytic activity of graphitic carbon nitride by thermal treatment in a high-pressure hydrogen atmosphere

Extending visible light absorption range and suppressing the recombination of photogenerated charge carriers are always important topics in developing efficient solar-driven photocatalysts. In this study, the thermal treatment process at 400 °C in a high-pressure hydrogen atmosphere was applied to modify graphitic carbon nitride. Compared to the normal atmospheric hydrogen treatment process, this process has the merit of producing nitrogen deficient graphitic carbon nitride in high-yield. The optimal photocatalytic activity of modified graphitic carbon nitride was demonstrated by controlling the treatment duration in the hydrogen atmosphere.The changes in the crystal structure, microstructure and optical properties of carbon nitrides were investigated by several characterizations. The relationship between the photocatalytic activity and structures of graphitic carbon nitride was preliminarily established. The results obtained in this study could provide some new ways of improving the activity of graphitic carbon nitride based photocatalyst.     

PI-强LU富足半群

通过对C-L^U-富足半群的刻画,给出了PI-强L^U富足半群的结构定理。     

一类Schrödinger-Poisson系统非平凡解的存在性

利用变分方法和临界点理论,研究了一类Schrödinger-Poisson系统,其中泊松项为更一般的形式,通过给非线性项加拟临界增长和AR条件,得到了该系统非平凡解的存在性。补充和推广了以往研究Schrödinger-Poisson系统的相关结果。     

基于密度自适应深度网络的点云场景语义分割算法

由于传感器噪声干扰，点云密度不均匀，场景复杂多样以及物体之间存在遮挡现象等问题，使得三维点云场景语义分割问题的研究工作极具挑战性。针对三维点云数据采样密度不均匀以及图卷积网络深度有限的问题，提出一种密度自适应的方法。该方法通过多层感知器学习一个权重函数，利用核密度估计学习一个密度函数，对非均匀采样的点云数据进行卷积操作。同时，受深度学习在图像领域的启发，引入残差连接、空洞卷积等结构，训练更深层的点云分割网络。该算法在多个点云分割的标准数据集上取得了优秀的性能。     

乳腺X线机检定方法研究

乳腺X线机是临床医疗常用仪器,其性能直接关系到病人的生命安全。本文介绍了乳腺X线机的原理和相关参数的检定方法。     

响应式自助微课学习平台的设计

通过对目前国内外微课教学及平台建设现状进行分析研究,在建构主义学习理论指导下,依据"响应式Web设计"理念,应用现代教育技术、移动通信技术、网络技术,将基于网络的微课拓展到移动教学中,设计了"响应式自助微课学习平台",为学习者构建了一个集自助、展示、学习、检测、交流、评价为一体的学习环境。     

蛇毒神经毒素和肌肉毒素研究进展

对蛇毒中神经毒素和肌肉毒素的分类、理化性质、结构及药理学活性进行了综述。当前研究表明，蛇毒神经毒素可分为3类：突触前神经毒素、突触后神经毒素和类神经毒素；前两者主要抑制运动终板处的神经肌肉传导，造成肌肉麻痹和呼吸衰竭；而后者可以阻断平滑肌收缩和离子通道，表现出类神经毒活性。蛇毒肌肉毒素主要分为3类：响尾蛇毒素、心脏毒素和肌肉毒 PLA2；它们主要表现为破坏肌细胞膜，降解肌纤维，造成肌肉坏死。近年来，大量的蛇毒神经毒素和肌肉毒素的结构与功能得以确定，其在临床医学及研究领域中将有着重要的应用前景。随着在作用机制等方面研究的不断深入，蛇毒蛇毒毒素和肌肉毒素的用途也将越来越广泛。
     

高温作用后砂岩物理力学性质阈值温度的确定——基于机器学习算法

通过采用机器学习算法，提出了高温作用后砂岩物理力学性质变化的阈值温度的确定方法。基于Python语言，利用K-Means、SVM算法，实现了对样本数据的分类和聚类，确定了阈值温度区间，验证了二分类法的合理性和准确性.结果表明，砂岩样本的阈值温度区间为400~600 ℃，阈值温度上下砂岩的物理力学性质存在显著差异，体现为阈值温度区间以下的岩样物理力学性质较为分散，而阈值温度区间以上的岩样物理力学性质较为集中.     

Efficient Feature Extraction Using Apache Spark for Network Behavior Anomaly Detection

Extracting and analyzing network traffic feature is fundamental in the design and implementation of network behavior anomaly detection methods. The traditional network traffic feature method focuses on the statistical features of traffic volume. However, this approach is not sufficient to reflect the communication pattern features. A different approach is required to detect anomalous behaviors that do not exhibit traffic volume changes,such as low-intensity anomalous behaviors caused by Denial of Service/Distributed Denial of Service(Do S/DDo S)attacks, Internet worms and scanning, and Bot Nets. We propose an efficient traffic feature extraction architecture based on our proposed approach, which combines the benefit of traffic volume features and network communication pattern features. This method can detect low-intensity anomalous network behaviors and conventional traffic volume anomalies. We implemented our approach on Spark Streaming and validated our feature set using labelled real-world dataset collected from the Sichuan University campus network. Our results demonstrate that the traffic feature extraction approach is efficient in detecting both traffic variations and communication structure changes.Based on our evaluation of the MIT-DRAPA dataset, the same detection approach utilizes traffic volume features with detection precision of 82.3% and communication pattern features with detection precision of 89.9%. Our proposed feature set improves precision by 94%.