Effect of the Silicic Acid Polymerization Degree on the Coagulating Property of Polyalumium Silicate Chloride

Polyaluminum silicate is a new type of inorganic macromolecular flocculants, which is developed as a complex product in recent years on the base of polyalumium flocculant and polysilicic acid flocculant. Sodium silicate is diluted to the concentration of 3.0% SiO 2 with distilled water. The solution was adjusted to pH 4.5 with sulphate acid and kept at quiescency for different time to get polysilicic acid with different polymerization degrees. Using polysilicic acid prepared and aluminium chloride as materials, a series of polyaluminium silicate chlorides(PASC) with different basicities(B), Al/Si molar ratios and polymerization degree of silicic acid are prepared by copolymerization technique. The Al-Ferron timed complex colorimetric method is applied to characterize species distribution of PASC and to study its influence factors. The interaction between hydrolyzed aluminum species and polysilicic acid with different polymerization degrees in PASC is discussed.The flocculant prepared is applied to the treatment of commercial concrete wastewater. The turbidity removal of commercial concrete wastewater is up to 97% when a PASC, prepared with polysilicic acid of 145 min polymerization degree.     

DYNAMIC ANALYSIS OF SPINDLE

In this paper, taking a D1201 spindle as an example, we discuss the dynamic analysis of spindle by using the theory of the complex model, we also analyse the factors which effect the dynamic characterics of spindle and give results which arc reference value to improving the structure of spindle,     

Optimization of Energy Dissipation Braces in Frame Structures

In this paper, the complex method of the nonlinear programming is used to optimize the parameters of energy dissipation bracing in energy dissipation braced frame. This method is realized with computer program and proved to be efficient. A 6-story reinforced concrete frame is used as a demonstrative example.     

直墙拱形巷道围岩应力场分析

为得出直墙拱形巷道围岩应力分布规律,应用复变函数弹性理论推导了直墙拱形巷道围岩应力分布的解析表达式。对直墙拱形巷道边界的围岩应力和巷道水平线方向的围岩应力分布规律进行分析,并考虑直墙拱形巷道断面高宽比和侧压系数对其影响规律。研究表明：在不同巷道断面高宽比、侧压系数下,直墙拱形巷道围岩应力集中区域主要集中在直墙底部底角处、拱形顶板中点附近和底板中部3个位置。不同巷道断面高宽比下,直墙拱形巷道沿水平线的应力分布规律基本相同。侧压系数大于1时,采用巷道断面高宽比小于1较有利于巷道稳定;侧压系数小于等于1时,采用巷道断面高宽比大于1较有利于巷道稳定。     

Relationship of Conservation of Historic Buildings and Urban Construction

The conservation of historic buildings and their environment is an unavoidable problem in urban construction. In this paper, the authors give a few new thoughts about the problem. First, the historic buildings are important part of the core power in city competition.Second,the conservation work should be combined with their development. Third, a classified and multilevel system of protection and reuse of old buildings should be set up.     

基于YOLOv5s-ESTC的肉苁蓉检测

为了解决因梭梭和红柳等宿主遮挡、样本分布密集、样本大小不均衡等造成人工种植肉苁蓉检测精度低以及模型参数量过大难以向嵌入式设备移植等问题,该研究提出一种基于改进YOLOv5s的人工种植肉苁蓉轻量化检测方法。首先,将YOLOv5s的主干网络替换为EfficientNetv2网络,以减少模型参数量和计算复杂度进而实现模型轻量化,便于后期将模型向嵌入式设备部署;其次,为了增强模型对小目标肉苁蓉特征信息的提取能力,将C3模块与Swin Transformer Block进行整合,得到C3STR模块,将其引入主干网络,并将其输出的特征图与Neck网络中的特征图进行融合;最后,在检测头前端与颈项加强网络之间添加CA注意力机制,以弱化背景信息、聚焦目标特征,提高网络检测性能。试验结果表明,该模型对于肉苁蓉的检测精度和准确率分别为89.8%和92.3%,模型的参数量和计算量分别为5.69×10⁶ MB和6.8 GB,权重文件大小为11.9 MB,单幅图像推理时间为8.9 ms,能够实现实时性检测。同其他主流模型相比,改进后的模型的检测精度分别比SSD、Faster R-CNN、YO...     

氮素形态对小麦幼苗叶绿体色素蛋白复合体含量及希尔反应活性的影响

采用水培方法研究了不同氮素形态对小麦幼苗叶绿体色素、色素蛋白复合体及希尔反应活性的影响.结果表明,50%硝态氮 50%铵态氮处理时,电泳条带中31～43kD范围内的多肽组分及高分量区属于PSⅠ的多肽组分含量最高,两种氮素形态单独施用时最低;叶片的光吸收能力、叶绿素含量及叶绿体蛋白含量均在氮素形态配比相等时达到最大,两种氮素形态单独施用时最低;希尔反应活性则随着铵态氮比例降低而降低.     

基于改进MobileNet-V2的田间农作物叶片病害识别

农作物病害是造成粮食产量下降的重要因素,利用智能化手段准确地识别农作物病害有利于病害的及时防治,该研究基于改进的MobileNet-V2识别复杂背景下的农作物病害,对未来覆盖各种作物的智能化病害识别工作具有重要意义。首先创建含有11类病害叶片及4类健康叶片的农作物数据集,采用数据增强操作构造不同的识别场景。其次在原始模型MobileNet-V2的基础上,嵌入轻量型的坐标注意力机制,建立通道注意力与位置信息的依赖关系。然后对网络中不同尺寸的特征图采取上采样融合操作,构建兼具网络高、低层信息的新特征图。此外,采用分组卷积并删除模型中不必要的分类层,减少模型参数量。试验结果表明：改进模型的参数量为2.30 MB,改进模型的识别准确率在背景复杂的农作物叶片病害数据集中达到了92.20%,较改进前提高了2.91个百分点。相比EfficientNet-b0、ResNet-50、ShuffleNet-V2等经典卷积神经网络架构,改进模型不仅达到了更高的识别准确率,还具有更平稳的收敛过程以及更少的参数。该研究改进的模型较好地平衡了模型的复杂度和识别准确率,为深度学习模型移植至田间移动病害检测设备提供了思路。     

基于改进YOLOv8n的复杂环境下柑橘识别

针对复杂环境下柑橘果实大量重叠、枝叶遮挡且现有模型参数量大、计算复杂度高等问题,提出了一种基于改进YOLOv8n的柑橘识别模型YOLOv8-MEIN。首先,该研究设计了ME卷积模块并使用它改进YOLOv8n的C2f模块。其次,为了弥补CIoU损失函数在检测任务中泛化性弱和收敛速度慢的问题,使用Inner-CIoU损失函数加速边界框回归,提高模型检测性能。最后,在自建数据集上进行模型试验对比,试验结果表明,YOLOv8-MEIN模型交并比阈值为0.5的平均精度均值mAP_0.5值为96.9%,召回率为91.7%,交并比阈值为0.5~0.95的平均精度均值mAP_0.5～0.95值为85.8%,模型大小为5.8MB,参数量为2.87M。与原模型YOLOv8n相比,mAP_0.5值、召回率、mAP_0.5～0.95值分别提高了0.4、1.0、0.6个百分点,模型大小和参数量相比于原模型分别降低了3.3%和4.3%,为柑橘的自动化采摘提供技术参考。