Study Progress Analysis of Effluent Quality Prediction in Activated Sludge Process Based on CiteSpace

In this paper, CiteSpace, a bibliometrics software, was adopted to collect research papers published on the Web of Science, which are relevant to biological model and effluent quality prediction in activated sludge process in the wastewater treatment. By the way of trend map, keyword knowledge map, and co-cited knowledge map, specific visualization analysis and identification of the authors, institutions and regions were concluded. Furthermore, the topics and hotspots of water quality prediction in activated sludge process through the literature-co-citation-based cluster analysis and literature citation burst analysis were also determined, which not only reflected the historical evolution progress to a certain extent, but also provided the direction and insight of the knowledge structure of water quality prediction and activated sludge process for future research.

Manganese potential mapping in western Guangxi-southeastern Yunnan(China) via spatial analysis and modal-adaptive prospectivity modeling

While the region of western Guangxi-southeastern Yunan, China, is known and considered prospective for manganese deposits, carrying out prospectivity mapping in this region is challenging due to the diversity of geological factors, the complexity of geological process and the asymmetry of geo-information. In this work, the manganese potential mapping for further exploration targeting is implemented via spatial analysis and modal-adaptive prospectivity modeling. On the basis of targeting criteria developed by the mineral system approach, the spatial analysis is leveraged to extract the predictor variables to identify features of the geological process. Specifically, a metallogenic field analysis approach is proposed to extract metallogenic information that quantifies the regional impacts of the synsedimentary faults and sedimentary basins. In the integration of the extracted predictor variables, a modal-adaptive prospectivity model is built, which allows to adapt different data availability and geological process. The resulting prospective areas of high potential not only correspond to the areas of known manganese deposits but also provide a number of favorable targets in the region for future mineral exploration.

Bifurcation Analysis of Reduced Network Model of Coupled Gaussian Maps for Associative Memory

This paper proposes an associative memory model based on a coupled system of Gaussian maps. A one-dimensional Gaussian map describes a discrete-time dynamical system, and the coupled system of Gaussian maps can generate various phenomena including asymmetric fixed and periodic points. The Gaussian associative memory can effectively recall one of the stored patterns, which were triggered by an input pattern by associating the asymmetric two-periodic points observed in the coupled system with the binary values of output patterns. To investigate the Gaussian associative memory model, we formed its reduced model and analyzed the bifurcation structure. Pseudo-patterns were observed for the proposed model along with other conventional associative memory models, and the obtained patterns were related to the high-order or quasi-periodic points and the chaotic trajectories. In this paper, the structure of the Gaussian associative memory and its reduced models are introduced as well as the results of the bifurcation analysis are presented. Furthermore, the output sequences obtained from simulation of the recalling process are presented. We discuss the mechanism and the characteristics of the Gaussian associative memory based on the results of the analysis and the simulations conducted.

Analysis of Digital Elevation Model and LNDSAT Data Using Geographic Information System for Soil Mapping in Urban Areas

This study applies digital analysis methods of topographic data derived from digital elevation models (DEMs) and Landsat remotely sensed spectral data using GIS tools to evaluate the quality and limitations of the morphometric parameters (terrain attributes: TAs). This aims to check its suitability for digital soil mapping (DSM) and survey in urban areas at the target scale 1:50,000. This scale represents the standard scale level for compiling soil inventories within all German states. The study is conducted on an urban area of 112.68 km2 in the southwest part of the state of Berlin in Germany. These relief units are the basis for determining the soil mapping units at the scale of 1:50,000. The generated preliminary soil map was compared to soil maps made using traditional soil survey methods. For the mainly natural soils, the equivalence area is 94.91%, and for the anthropogenic soils, the equivalence area is 95.34%. The proposed methodology is adequate for preliminary mapping of soil units based on the digital derivation of TAs. Landsat scenes are spatially explicit, physical representations of environmental covariates on the land surface. The free DEM-ASTER in combination with Landsat OLI images is found to be the appropriate model to represent the terrain surface and derive the TAs for environmental modeling and fitting of derivation the relief units and their topography features. However, the 30 m spatial resolution and the fairly coarse spectral resolution of DEMs and Landsat images limit their utility for digital soil mapping at this scale in urban areas with little topographic variation.

Blast wave characteristics of multi-layer composite charge:Theoretical analysis,numerical simulation,and experimental validation

This article investigates the characteristics of shock wave overpressure generated by multi-layer composite charge under different detonation modes.Combining dimensional analysis and the explosion mechanism of the charge,a peak overpressure prediction model for the composite charge under singlepoint detonation and simultaneous detonation was established.The effects of the charge structure and initiation method on the overpressure field characteristics were investigated in AUTODYN simulation.The accuracy of the prediction model and the reliability of the numerical simulation method were subsequently verified in a series of static explosion experiments.The results reveal that the mass of the inner charge was the key factor determining the peak overpressure of the composite charge under single-point detonation.The peak overpressure in the radial direction improved apparently with an increase in the aspect ratio of the charge.The overpressure curves in the axial direction exhibited a multi-peak phenomenon,and the secondary peak overpressure even exceeded the primary peak at distances of 30D and 40D(where D is the charge diameter).The difference in peak overpressure among azimuth angles of 0-90°gradually decreased with an increase in the propagation distance of the shock wave.The coupled effect of the detonation energy of the inner and outer charge under simultaneous detonation improved the overpressure in both radial and axial directions.The difference in peak overpressure obtained from model prediction and experimental measurements was less than 16.4%.

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.

An Emotion Analysis Method Using Multi-Channel Convolution Neural Network in Social Networks

As an interdisciplinary comprehensive subject involving multidisciplinary knowledge,emotional analysis has become a hot topic in psychology,health medicine and computer science.It has a high comprehensive and practical application value.Emotion research based on the social network is a relatively new topic in the field of psychology and medical health research.The text emotion analysis of college students also has an important research significance for the emotional state of students at a certain time or a certain period,so as to understand their normal state,abnormal state and the reason of state change from the information they wrote.In view of the fact that convolutional neural network cannot make full use of the unique emotional information in sentences,and the need to label a large number of highquality training sets for emotional analysis to improve the accuracy of the model,an emotional analysismodel using the emotional dictionary andmultichannel convolutional neural network is proposed in this paper.Firstly,the input matrix of emotion dictionary is constructed according to the emotion information,and the different feature information of sentences is combined to form different network input channels,so that the model can learn the emotion information of input sentences from various feature representations in the training process.Then,the loss function is reconstructed to realize the semi supervised learning of the network.Finally,experiments are carried on COAE 2014 and self-built data sets.The proposed model can not only extract more semantic information in emotional text,but also learn the hidden emotional information in emotional text.The experimental results show that the proposed emotion analysis model can achieve a better classification performance.Compared with the best benchmark model gram-CNN,the F1 value can be increased by 0.026 in the self-built data set,and it can be increased by 0.032 in the COAE 2014 data set.

Machine Learning Based Analysis of Real-Time Geographical of RS Spatio-Temporal Data

Flood disasters can be reliablymonitored using remote sensing photos with great spatiotemporal resolution.However,satellite revisit periods and extreme weather limit the use of high spatial resolution images.As a result,this research provides a method for combining Landsat and MODIS pictures to produce high spatiotemporal imagery for flood disaster monitoring.Using the spatial and temporal adaptive reflectance fusion model(STARFM),the spatial and temporal reflectance unmixingmodel(STRUM),and three prominent algorithms of flexible spatiotemporal data fusion(FSDAF),Landsat fusion images are created by fusing MODIS and Landsat images.Then,to extract flood information,utilize a support vector machine(SVM)to classify the fusion images.Assess the accuracy of your work.Experimental results suggest that the three spatio-temporal fusion algorithms may be used to effectively monitor floods,with FSDAF’s fusion results outperforming STARFM and STRUM in both study areas.The overall flood classification accuracy of the three STARFM,STRUM,and FSDAF algorithms in the Gwydir research region is 0.89,0.90,and 0.91,respectively,with Kappa coefficients of 0.63,0.64,and 0.67.The flood classification accuracy of the three fusion algorithms in the New Orleans research region is 0.90,0.89,and 0.91,with Kappa values of 0.77,0.76,and 0.81,respectively.The spatio-temporal fusion technique can be used to successfully monitor floods,according to this study.

变速条件下流体动压效应稳健性分析

以油膜作为中间介质,对于高速旋转机械传动系统的可靠性、稳定性的提升有显著优势。而高速轴承-转子系统在加工制造和运行过程中,不可避免地存在影响系统承载性能的不确定性因素,影响油膜形状,导致承载性能发生变化,实际工作性能偏离设计目标。以高速动压油膜为研究对象,为掌握几何参数和转速对油膜承载性能波动的影响,进行稳健优化和分析,提取油膜性能稳健优化结果的特征区域。针对传动系统的不同需求,采用计算流体力学方法求解不同转速下的动压滑动轴承油膜压力场,进而求解其主要性能指标:承载力和摩擦功耗。建立研究目标的Kriging近似模型,并在样本点临近区域选择稳健目标计算的子空间,计算目标均值和方差。利用非支配排序遗传算法(NSGA-II)求解不同目标组合的Pareto最优解集。结合自组织映射图(SOM)方法进行相关性分析,提取设计目标与几何参数、转速之间的相关性特征,分析最优特征区域中偏心率对目标稳健性的影响,最终确定稳健性好的特征区域,并选择个别结果进行仿真计算验证。结果表明:所提出的最优性分析方法能够清晰展现出稳健最优性区域在设计空间中的分布情况,便于降低几何参数和转速的波动对油膜性能的影响;所提方法能提升优化设计结果的可实现性,有效促进理论设计与工程实际的衔接。

基于傅里叶变换红外光谱技术与多元统计分析相结合的中药葛根快速定量研究

利用傅里叶变换红外光谱技术结合多元统计分析,建立中药葛根中有效物质快速检测的质量评价体系,以期为葛根的快速、精准含量测定提供新方法,推动其质量控制,缓解当前野葛的品质危机。分别利用烘干法、HPLC法和紫外分光光度法测定葛根中的水分、葛根素、总黄酮、葛根多糖的含量,并对其中红外光谱进行聚类分析和主成分分析,后运用化学计量学方法,建立关于目标组分的快速鉴别的红外预测模型。发现葛根各产地含量测定结果表明其含量差异明显,其中陕西咸阳产地葛根素、以及总黄酮含量均最多,广西南宁葛根多糖含量最高。聚类分析显示各产地葛根中红外光谱具有相似性和差异性,可作为产地鉴别的依据。主成分分析表明前三个主成分的累计方差贡献率为88.32%,湖北恩施产地葛根样品的综合得分最高。红外定量模型结果表明水分以PLS+CONSTANT+2ndDer+SG模型最优,葛根素以SmLR+MSC+2ndDer+ND模型最优,葛根总黄酮以PLS+SNV+2ndDer+SG模型最优,葛根多糖以PLS+MSC+2ndDer+SG模型最优,其红外预测模型相关系数均大于0.8,表明其预测模型精密度高,稳定性强。结论:通过傅里叶变换光谱技术建立的葛根有效成分的定量模型可以实现对未知葛根有效成分的含量测定,且该法方便快捷,定量预测模型能够准确预测目标组分的含量,为葛根有效成分的快速、无损测定提供了新的方法依据。

上海城区动态洪水风险图应用系统及典型暴雨内涝分析

【目的】为了提高沿海超大城市对洪涝灾害的先知先觉能力,快速预测分析洪涝潮组合影响下的淹没风险时空分布,【方法】以水文-水动力暴雨洪水分析模型为核心模块,通过与外部的气象精细化降雨预报数据库、自动雨量站实时监测数据库及实时水情、闸泵运行数据库相关联,研发了上海城区动态洪水风险图应用系统。采用由雨量站点到气象格网和水力网格的降雨二级空间融合技术,实现了模型与气象预报降雨数据的有效耦合。利用数据挖掘提取和GIS空间分析技术构建了包含10项洪水风险要素要点的城市内涝预报专报自动生成方法,实现了对内涝风险的快速一站式概览。利用系统可以对城市暴雨、河道洪水和风暴潮等单一发生或遭遇组合引起的淹没分布进行快速实时预报计算,模拟和预测城市洪涝潮灾害的有关淹没特征数据和淹没动态过程。【结果】利用系统分析了2023年6月23—24日暴雨的内涝风险分布,将模型模拟的积水区域与积水监测站、灾情直报和热线灾报的积水点进行对比,结果显示在150处对比积水点中,有129处误差不超过20 cm,占86%。模型模拟的积水空间分布与实际情况基本接近。【结论】结果表明:系统满足汛期常态化、业务化运行需求,能够为城市洪涝风险的实时动态分析和防汛指挥决策提供重要工具。

一维p-Laplacian方程Neumann边值问题正解的存在性和多解性

本文利用时间映像分析法考虑了一类源自人口问题中的稳态反应扩散方程Neumann边值问题{|u'(t)|^(p-2)u'(t))'+λ(au-bu^(2)-c)=0,0<t<1,u'(0)=u'(1)=0多个正解的存在性.其中,1/λ>0为扩散系数,1<p≤2,a>0,b>0,c>0.进一步,当确定了a,b,c,1/λ的值时,本文证明了上述问题多个正解的存在性和解的精确个数,所得结果推广并改进了已有文献的相关结果.

以学生为中心的“电路分析”课程教学改革探究

在高等教育的本科教学改革过程中,以学生学习产出为中心的教学理念得到了广泛的认可,文章在“电路分析”课程中建立以学生为中心的教学模式,重构教学内容,优化教学流程。利用课程知识图谱作为抓手,引导学生的学习思路,培养学生主动学习的能力和方法;通过课堂多种互动教学的设计,激发学生的学习主动性;同步建立相应的教学监督体系,反馈教学质量,充分锻炼学生的自学能力。

植物生长发育动态QTL解析研究进展

准确而有效的QTL定位是克隆目的基因、开展分子育种的前提和基础。植物生长发育随外界环境因子变化而变化,其动态发育的不同时期表型是不同主效基因/QTL动态表达的结果,基于生长停滞期表型数据的传统主效基因/QTL分析只能估算QTL在多个时期的累积效应,并不能充分反映该基因位点在发育过程中的真实作用模式及效应,不能真实反映QTL在不同发育时期的动态表达模式,无法获取数量性状的动态信息。全生长周期的动态QTL分析为在分子水平上研究植物生长发育动态的遗传机制、鉴定主效基因提供了良好策略。本文总结了动态QTL分析的遗传模型、分析方法及其在植物发育数量性状定位中的研究进展,并对当前动态QTL分析存在的问题和发展趋势进行了展望,以期为植物生长发育动态QTL解析及分子标记辅助选育提供参考。

基于多源异构数据融合的造价数据与GIM模型挂接方法

针对电网工程构件的GIM模型无法包含造价分析所需要的所有信息,导致造价分析精度及效率较低的问题,提出了一种基于多源异构数据融合的造价数据与GIM模型的挂接方法。该方法建立了电网工程GIM构件属性集和造价数据库,并从中提取出构件本体来实现GIM构件到本体间的映射以及造价数据到本体间的映射关系,进而完成多源异构的造价数据与GIM模型间的挂接。仿真实验及分析结果表明,所提方法能够大幅节省造价分析人员的查询和计算时间,从而提高电网工程造价分析与管理的效率。

改进ANFIS对静压箱热误差建模研究

为了减小静压箱排气孔温度不均匀对薄膜拉伸加工时的影响,通过建立热误差模型,来分析静压箱在不同输入参数下排气孔的温场情况。采用SOM-GRA相结合的综合算法得出最优测温点,以保证输入模型的数据具有代表性,将测温点数量由20降至3。利用ANFIS模型建立静压箱的热误差模型,并通过RF算法优化ANFIS中隶属度函数数量参数,将实验验证过的数值模拟数据作为输入的训练数据。预测结果表明较原ANFIS模型、BP模型和RBF模型MAE值分别下降了22.43%、59.97%和49.87%,该优化预测模型具有更高的精度。

基于TWOA-BP的矿井冲击地压分级预测研究

为提高煤矿开采工作的安全性,准确预测煤矿冲击地压灾害发生,提出冲击地压分级预测的TWOA-BP模型。先通过灰色关联分析法(GRA)筛选冲击地压的影响因素作为TWOA-BP预测模型的输入层,最终确定8项影响因素后,采用鲸鱼算法(WOA)对BP神经网络的权值和阈值进行优化,随后利用Tent混沌映射初始化鲸鱼种群以增加种群多样性,最终解决了BP模型收敛速度慢和易陷入局部极小的问题。研究结果表明:与其他预测模型相比,TWOA-BP方法具有收敛速度快、预测精度高、操作简便等特点。

多学科计算程序联合设计平台的研发

传统计算程序在前处理、计算以及后处理这三个核心步骤均存在问题。在前处理方面对用户有较高的专业要求且直观性差;在计算过程中,用户需处理多学科计算程序联合设计验证中烦琐的数据传递;在后处理方面,需利用第三方后处理软件进行再次处理分析。本文通过多学科计算程序联合设计平台,优化了集成前处理、计算以及后处理三个核心步骤。其中,前处理实现了快速、精细化建模等功能;计算步骤通过计算设计引擎实现了计算程序运行控制、指令解析、任务调度、运行流程控制等功能;后处理实现了可提供云图等功能。最后,通过建立一个双管道模型对平台的相关功能进行验证,证明了该平台突破了系统与设备快速精细化设计建模、在线调试等关键技术,为反应堆系统性能设计方案的快速生成、评估、验证与优化提供了高效直观的技术手段。

GIS技术和数字测绘技术在矿山测量中的应用研究

为了提高矿山地质勘查的精度,减轻地质勘查人员的工作负担,本文在地质勘查技术的基础上,论述了CIS数字地质勘查技术。在此基础上,提出了利用三维GIS技术,构建三维GIS,构建地理信息系统,实现信息的数字化。通过对矿山CIS数字制图技术的研究,提出了矿山CIS数字制图技术的几点建议,提高了矿山地质测量的精度。