摘要：A novel visible-light-responding InVO4-Cu2O-TiO2 ternary nanoheterostructure was designed on the basis of the strategy of energy gap engineering and prepared through ordinary wet chemistry methods. The as-prepared nanoheterostructure was characterized by X-ray powder diffraction（XRD）, transmission electron microscopy（TEM）, high-resolution transmission electron microscopy（HRTEM） and diffuse reflectance ultraviolet-visible spectroscopy（UV-vis/DRS）. The TEM and HRTEM images of 10%InVO4-40%Cu2O-50%TiO2 confirm the formation of nanoheterostructures resulting from contact of the nanosized TiO2, Cu2O and InVO4 in the size of 5–20 nm in diameter. The InVO4-Cu2O-TiO2 nanoheterostructure, when compared with TiO2, Cu2O, InVO4, InVO4-TiO2 and Cu2O-TiO2, shows significant enhancement in the photocatalytic performance for the degradation of methyl orange（MO） under visible-light irradiation. With a 9 W energy-saving fluorescent lamp as the visible-light source, the MO degradation rate of 10%InVO4-40%Cu2O-50%TiO2 reaches close to 90% during 5 h, and the photocatalytic efficiency is maintained at over 90% after six cycles. This may be mainly ascribed to the matched bandgap configurations of TiO2, Cu2O and InVO4, and the formations of two p-n junctions by the p-type semiconductor Cu2O with the n-type semiconductors TiO2 and InVO4, all of which favor spatial photogenerated charge carrier separation. The X-ray photoelectron spectroscopy（XPS） characterization for the used 10%InVO4-40%Cu2O-50%TiO2 reveals that only a small shakeup satellite peak appears for Cu（II） species, implying bearable photocorrosion of Cu2O. This work could provide new insight into the design and preparation of novel visible-light-responding semiconductor composites.

摘要：化工原理实验是化工类及相近学科学生的一门重要的实践课,是理论与实践密切结合、培养学生动手能力和创新能力的重要环节。文章对化工原理实验教学改革进行了探讨,提出了增设综合性、设计性、研究性等实验内容,建立仿真实验与操作实验相结合的实验教学模式以及改革考核方式等,为进一步提高实验教学质量,适应创新和应用型人才培养的需要。

摘要：施工组织设计是指导工程建设全过程活动的技术、经济和组织的综合性文件,随着自然语言处理(natural language processing,NLP)等人工智能技术的发展,针对施工组织设计文档智慧辅助审查中基础性工作:文本分类问题开展研究。为实现施工组织设计文本的自动分类,运用Word2vec词嵌入技术对文本进行向量化表示,基于双向长短时记忆网络(bi-directional long short-term memory,Bi-LSTM)捕捉文本上下文序列信息,融入Attention机制,提取文本有效信息,采用softmax激活函数分类。结果表明:Attention Bi-LSTM在房建数据集上达到0.97的准确率、召回率以及F值,整体分类效果在正确率、宏平均、加权平均上均优于其他模型。融入Attention机制的Bi-LSTM文本分类模型通过双向捕获文本的特征并利用Attention机制提取有效信息,达到了联合优化的作用,提高了模型的分类性能。

摘要：为实现长江大保护工程项目现场质量安全高效监管,在分析长江大保护工程项目工地管理特点的基础上,引入了视频AI分析、边缘计算及物联网等技术,构建了面向长江大保护工程施工过程监管的智慧工地系统。基于视频AI分析技术,解决了工地人员姿态识别、安全防护用品不规范佩戴识别、施工现场危险区域自动检测(包括基坑、井、陡坡、沟槽)等问题,提升了大范围线性工地安全监管的效率;通过物联网技术,成功实现了海量智慧工地监测感知设备便捷接入统一平台。该系统以各个施工作业面为基础,实现了工地宏观层面与微观层面相结合的多层次信息集成,提供了一种有效的管理模式,并在实践中得到了成功应用。研究成果可为智慧工地系统搭建与类似工程项目的智慧化监管提供参考。