Snowmelt Flood Mapping and Land Surface Short-Term Dynamics Assessment in a “Before-During-After” Scenario Based on Radar and Optical Satellite Imagery: Case Study Around the Lewisville Lake (Dallas/Fort Worth Metropolitan, Texas, USA)

The main goal of this study has been to map flood and assess land surface short-term dynamics in relation with snowy weather. The two recent snowfall events, which happened in, February 14th and 15th, of year 2021, and February 3rd and 4th, of year 2022, were chosen. A pre-analysis correlation was assumed between, the snow events, recurrency of floods, and changes in the land surface characteristics (i.e., wetness, energy, temperature), in a “Before-During-After” scenario. Active and passive microwave satellites data such as, Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral instrument (MSI) and Landsat-9 Operation Land Imager-2/Thermal Infrared Sensors-2 (OLI-2/TIRS-2), as well as cloud databased global models for water and urban layers were used. The first step of processing was thresholding of SAR image, at 0.25 cutoff, based on bimodal histogram distribution, followed by the change analysis. The following processing consisted in the images transformation, by computing the tasseled cap transformation wetness (TCTw) and the surface albedo on MSI image. In addition, the land surface temperature (LST) was modeled from OLI-2/TIRS-2 image. Then, a 5th order polynomial regression was computed, between TCTw as dependent variable and, albedo and LST as independent variables. As a first result, an area of 5.6 km2 has been mapped as recurrently flooded from the two years assessment. The other output highlighted a constant increase of wetness (TCTw), considered most influential on land surface dynamics, comparatively to energy exchange (albedo) and temperature (LST). The “After” event dependency between the three indicators was highest, with a correlation coefficient, R2 = 0.682, confirming the persistence of wetness after-snowmelt. Validation over topographic layers confirmed that, recurrently flooded areas are mostly distributed on, lowest valley depth points, farthest distances from channel network (i.e., from perennial waters), and lowest relative slope position areas. Whereas, 88.9% of the validation sampling were confirmed in the laboratory, and 86.7% of urban validation points were assessed as recurrently flooded when combining pre-/post-field-work campaign.

Comparisons of Criteria for Analyzing the Dynamical Association of Solutes in Aqueous Solutions

How to determine accurately the association states of solutes in aqueous systems is of fundamental importance in a variety of chemical, physical, and biological processes, We apply four widely used criteria to analyze the dynamic association processes of solutes, e.g., amphiphilic molecules, and to find the inappropriate selections of representative sites on solutes in these criteria may bring about appreciable influence on the estimation of dynamic association behaviors such as unrealistic packing radii and even misleading packing structures. It would be better to select dynamically representative sites on solute molecules based on the characteristic of solute associations. Our detailed discussions give a guide on how to determine an appropriate criterion to accurately analyze the association behaviors of solute molecules in aqueous solutions.

Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features

To fully exploit the rich characteristic variation laws of an integrated energy system(IES)and further improve the short-term load-forecasting accuracy,a load-forecasting method is proposed for an IES based on LSTM and dynamic similar days with multi-features.Feature expansion was performed to construct a comprehensive load day covering the load and meteorological information with coarse and fine time granularity,far and near time periods.The Gaussian mixture model(GMM)was used to divide the scene of the comprehensive load day,and gray correlation analysis was used to match the scene with the coarse time granularity characteristics of the day to be forecasted.Five typical days with the highest correlation with the day to be predicted in the scene were selected to construct a“dynamic similar day”by weighting.The key features of adjacent days and dynamic similar days were used to forecast multi-loads with fine time granularity using LSTM.Comparing the static features as input and the selection method of similar days based on non-extended single features,the effectiveness of the proposed prediction method was verified.

Dynamic prediction of landslide displacement using singular spectrum analysis and stack long short-term memory network

An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, this paper proposes a dynamic prediction model of landslide displacement based on singular spectrum analysis(SSA) and stack long short-term memory(SLSTM) network. The SSA is used to decompose the landslide accumulated displacement time series data into trend term and periodic term displacement subsequences. A cubic polynomial function is used to predict the trend term displacement subsequence, and the SLSTM neural network is used to predict the periodic term displacement subsequence. At the same time, the Bayesian optimization algorithm is used to determine that the SLSTM network input sequence length is 12 and the number of hidden layer nodes is 18. The SLSTM network is updated by adding predicted values to the training set to achieve dynamic displacement prediction. Finally, the accumulated landslide displacement is obtained by superimposing the predicted value of each displacement subsequence. The proposed model was verified on the Xintan landslide in Hubei Province, China. The results show that when predicting the displacement of the periodic term, the SLSTM network has higher prediction accuracy than the support vector machine(SVM) and auto regressive integrated moving average(ARIMA). The mean relative error(MRE) is reduced by 4.099% and 3.548% respectively, while the root mean square error(RMSE) is reduced by 5.830 mm and 3.854 mm respectively. It is concluded that the SLSTM network model can better simulate the dynamic characteristics of landslides.

Comments on:Perioperative von Willebrand factor dynamics are associated with liver regeneration and predict outcome after liver resection

Recentlythearticle"PerioperativevonWillebrandfactordynamics are associated with liver regeneration and predict outcome afterliver resection" was published in Hepatology[1].Prof.Starlinger et al. aimed to assess the association of von Willebrand factor (vWF) levels and clinical outcome in patients with liver cancers post-liverresection(LR).Basedonthemechanismthatplatelets accumulation in the liver may promote liver regeneration after partial LR in mice, they found the vWF-dependent pattern of platelets accumulationduringliverregenerationinpatientsaftersurgery.

Pseudo-dynamic analysis of seismic stability of reinforced slopes considering non-associated flow rule

The required reinforcement force to prevent instability and the yield acceleration of reinforced slopes are computed under seismic loading by applying the kinematic approach of limit analysis in conjunction with the pseudo-dynamic method for a wide range of soil cohesion, friction angle, dilation angle and horizontal and vertical seismic coefficients. Each parameter threatening the stability of the slope enhances the magnitude of the required reinforcement force and vice versa. Moreover, the yield acceleration increases with the increase in soil shear strength parameters but decreases with the increase in the slope angle. The comparison of the present work with some of the available solutions in the literatures shows a reasonable agreement.

STUDIES OF THE DYNAMIC BEHAVIORS OF A CLASS OF LEARNING ASSOCIATIVE NEURAL NETWORKS

This paper investigates exponential stability and trajectory bounds of motions of equilibria of a class of associative neural networks under structural variations as learning a new pattern. Some conditions for the possible maximum estimate of the domain of structural exponential stability are determined. The filtering ability of the associative neural networks contaminated by input noises is analyzed. Employing the obtained results as valuable guidelines, a systematic synthesis procedure for constructing a dynamical associative neural network that stores a given set of vectors as the stable equilibrium points as well as learns new patterns can be developed. Some new concepts defined here are expected to be the instruction for further studies of learning associative neural networks.

An oscillatory neural network and dynamical associative memory

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The IES dynamic time-scale scheduling strategy based on multiple load forecasting errors

Considering the complex coupling of multiple energies and the varying load forecasting errors for an integrated energy system(IES),this study proposes a dynamic time-scale scheduling strategy based on long short-term memory(LSTM)and multiple load forecasting errors.This strategy dynamically selects a hybrid timescale which is suitable for a variety of energies for each month.This is obtained by combining the mean absolute percentage error(MAPE)curve of the load forecasting with the error restriction requirements of the dispatcher.Based on the day-ahead scheduling plan,the output of the partial equipment is selectively adjusted at each time-scale to realize multi-energy collaborative optimization and gives full play to the comprehensive advantages of the IES.This is achieved by considering the differences in the response speed for each piece of equipment within the intra-day scheduling.This study uses the IES as an example,and it dynamically determines the time scale of the energy monthly.In addition,this investigation presents a detailed analysis of the output plan of the key equipment to demonstrate the necessity and the advantages of the strategy.

A survey: which features are required for dynamic visual simultaneous localization and mapping?

In recent years,simultaneous localization and mapping in dynamic environments(dynamic SLAM)has attracted significant attention from both academia and industry.Some pioneering work on this technique has expanded the potential of robotic applications.Compared to standard SLAM under the static world assumption,dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly.Therefore,dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments.Additionally,to meet the demands of some high-level tasks,dynamic SLAM can be integrated with multiple object tracking.This article presents a survey on dynamic SLAM from the perspective of feature choices.A discussion of the advantages and disadvantages of different visual features is provided in this article.

Study on Relation Between Dynamic Pattern of Regional Vertical Strain Rate and Several Strong Earthquakes such as Lijiang(M_s7.0)and Menyuan(M_s6.4)Earthquakes

Making use of the method of obtaining regional vertical strain rate from regional preciseleveling data and gaining dynamic pattern combining with deformation data on spanningfaults, the regional vertical strain dynamic evolution characteristics of several moderatelystrong earthquakes such as Lijiang (M_s 7.0) and Menyuan (M_s 6.4) earthquakes occurredin crustal deformation monitoring areas located in the western Yunnan and Qilianshan-Hexiregion. Based on the above-mentioned facts, by studying the time-space nonhomogeneity andstrain energy accumulation status, some criteria for judging the medium. and short-termstrong seismic risk regions according to the regional vertical strain rate dynamic informationare proposed.

突出煤层掘进工作面瓦斯涌出相关性及通风安全分析与应用

掌握掘进工作面瓦斯涌出的动态规律对于工作面通风安全和煤与瓦斯突出防治具有重要意义。为此,选取大平煤矿21下部煤柱面的3条煤巷掘进工作面为研究对象,采用ANSYS模拟分析了工作面风流和瓦斯分布规律,在距离掘进面1、3 m断面上瓦斯浓度分布较为紊乱;在距离掘进面10 m的位置瓦斯和空气是混合后均匀流动的,即为加设瓦斯传感器采集掘进动态瓦斯涌出量的最佳位置。研究了工作面瓦斯涌出与掘进作业工序、作业班次、巷道长度和煤壁暴露时间因素的关联特征,掘进工作面瓦斯涌出主要来源于煤壁,且出现2个瓦斯涌出量峰值时间点:在落煤替顶作业工序时和二次波峰为中间落煤清煤工序时。通过建模模拟和主控因素分析,准确掌握了瓦斯动态涌出的具体地点和时间,从通风监控、掘进工序和抽采部署方面,提出了针对性的优化方案,对于工作面煤与瓦斯突出危险性预测、瓦斯抽采达标评判和瓦斯事故风险感知等安全管理工作具有重要的参考价值。

基于时频融合特征的肺动脉高压心音分类模型

先心病相关肺动脉高压是一种严重的心血管疾病,致死率高,对其进行早期筛查与识别对于治愈尤为重要。目前临床是通过右心导管术确诊,此为有创检查,不便于在大规模筛查中采用,研究一种无创便捷的识别方法迫在眉睫。文中建立了一种时频融合的心音分类模型。首先对心音信号进行预处理,然后使用融合滤波器组对信号进行转换并求取动态时频特征,最后将得到的融合特征参数输入表格式先验数据拟合网络(TabPFN)中进行分类识别。实验结果表明,该算法在正常、CHD-PAH和CHD中的平均准确率、精确率、灵敏度、特异度和F1分别为92.21%,92.15%,92.15%,96.11%,92.14%。对于先心病相关肺动脉高压的早期筛查与识别具有重要意义。

一种空中目标关联关系挖掘分析方法

快速准确挖掘分析雷达历史数据中蕴含的目标关联关系,可以为空中目标活动规律研究、目标识别判性等提供辅助,充分发挥历史数据作用。根据目标时空特性,设计了一种新的空中目标关联关系挖掘分析方法。设计了一种时间匹配方法,生成时间关联数据集。设计了一种空间关联度量,生成空域关联数据集。运用DTW及生成树算法,匹配并搜索具有关联关系的空中目标,生成关联关系集合。经数字仿真验证,该方法可有效挖掘雷达数据历史信息,寻找目标之间的关联规律。

基于分布式缓存的科技文献信息动态检索系统设计

为便于科研人员研究科技发展前沿动态,设计基于分布式缓存的科技文献信息动态检索系统,为用户匹配最佳检索结果。用户层将用户检索需求传输至处理层数据采集模块,该模块采集数据信息后经网络层通信模块将信息传输至数据层科技文献信息数据库中存储;数据层分布式存储模块采用分布式缓存方法将以往的检索记录信息分别存储至热数据库及冷数据库中;利用网络层传输科技文献信息数据库内的检索需求信息至处理层检索处理模块,通过科技文献信息动态检索方法处理接收到的检索信息,将处理后信息与数据层分布式存储模块内的关联数据进行排序重组,获取检索结果,并通过用户层展示检索结果。结果表明,该系统可有效且准确地为用户检索其所需的科技文献信息,检索MAP值为0.95以上,系统具备较快的读写速度与较强的数据传输能力。

致密油藏注伴生气驱油可行性分析及效果评价

致密油藏在开发过程中注水困难,亟需探索补充地层能量的新方式,同时油藏伴生气难以规模性开发。为研究油藏注气可行性,在室内开展原油相态特征、注气驱混相机理及注气驱长岩心实验研究,分析了M区块原油的相态特征、注入气与原油的互溶特征、注气混相机理以及长岩心注气驱油动态关键指标变化规律,结合现场应用情况,对其应用效果进行分析。结果表明,地层压力16 MPa下,原油溶解伴生气体积膨胀了1.161倍,黏度降低了20.5%;地层温度65℃下,注伴生气驱替长岩心原油采收率比衰竭开采提高23.15%;油藏开发中后期注入伴生气可有效促进油水界面下移,射孔段剩余油增多,目的层段由高含水变为低含水。经X井现场应用,剩余油饱和度由注气前的28.62%升高至42.17%。注伴生气可有效提高油藏采收率,可为同类油藏制定开发方案提供重要实验数据支撑和理论指导。

基于动态贝叶斯网络的邻近下穿隧道深基坑施工风险分析

为了减小邻近既有下穿隧道深基坑施工风险及灾害损失,科学预防施工安全事故,提出了一种基于动态贝叶斯网络(DBN)的深基坑施工风险分析模型。首先,运用BWM(best worst method)确定准则的权重;其次,基于关联规则挖掘风险因素间的相互关系,并以此构建DBN结构模型;最后,以新建厦门北站地下一层社会连廊深基坑工程为例,对提出的方法进行有效性和适用性检验。结果表明:基坑围护结构的安全度在静态被评为“较高”和“极高”的概率分别为34.6%和36.1%,且此结果随着输入风险证据发生动态变化,运用反向推理也能迅速找出围护桩渗水风险;提出的模型能明确邻近既有下穿隧道深基坑施工风险传递过程中的关键风险点,并能进行动态风险预测以及事故后致因诊断,从而实现邻近既有下穿隧道深基坑施工风险的动态管控。所提出的优化DBN模型对工前风险评估、先验分析和风险诊断有较好的适用性和较高的准确性,可为邻近既有隧道深基坑施工过程中的安全管控提供有效的决策支持,大幅提高风险控制效率。

多重关联下中国省际政府债务风险传染研究

经济发展压力下的地方政府财政缺口和资金需求与日俱增,债务规模不断扩大,随之而来的债务风险问题引起了社会各界的广泛关注。利用2010-2020年中国省级面板数据,基于地理、财政和经济社会关联构建具有不同空间权重矩阵凸组合的动态空间面板模型,在多重关联视角下探究了中国省际政府债务风险的空间传染效应及其效应分解,进一步分析了债务风险传染效应的影响因素及其经济后果。通过构建多个空间权重矩阵的凸组合,可以更全面和准确地刻画地方政府之间可能存在的多重空间互动关系,并在统一研究框架下评估不同风险传染网络的相对重要性。研究发现:中国省际政府债务风险在基于地理、财政和经济社会关联的三类风险传染网络中均存在显著的空间传染效应,且基于财政关联的风险传染网络最为重要;基于最优加权空间权重矩阵进行效应分解,发现多数情况下长期效应明显大于短期效应,直接效应比间接效应更为显著,各省份所接收和输出的债务风险存在显著的非对称性。在此基础上,进一步研究发现,经济越繁荣、地方政府间引资竞争越激烈,中国省际政府债务风险传染效应越弱;其他省份的债务风险会对本省经济发展产生显著的抑制作用。最后,从多角度对防范化解地方政府债务风险和实施科学有效的管控政策提供了针对性建议。

基于网络有向关系的节点重要性排序方法

针对因网络多重分流导致的节点重要性特征分布混乱、差异性较大的问题,提出一种基于网络有向关系的节点重要性排序方法。建立多重分流复杂网络结构的动态模型,根据节点的线性变化关系,动态计算节点在网络流域中的实时状态值。根据节点的实时状态值,通过效率矩阵求得预设节点与任意点间重要性贡献参数,其可以反映两个节点间的关联程度,根据关联指向大小实施重要性排序。实验结果表明,所提方法可以精准排序全局重要度值在0.021~0.085范围内的节点,可以在1s内完成重要性较低的节点排序。

星斗山天然次生林优势种的生态位和种间联结动态

[目的]常绿落叶阔叶混交林是我国特有的天然林类型,作为亚热带山地的主要植被类型。探讨典型亚热带常绿落叶阔叶混交林优势种的生态位和种间联结动态变化,阐明短期群落发展中优势种的适应能力、种间关系和变化规律,为亚热带地区生物资源保护、恢复和森林管理提供理论依据。[方法]本研究以湖北星斗山国家级自然保护区天然次生林为研究对象,结合2013年和2018年的两次固定样地(48个)调查监测数据,对比分析重要值排名前10名的优势种(重要值大于2%)的生态位宽度、生态位重叠指数、方差比率法、χ^(2)检验、联结系数、Jaccard指数和Spearman秩相关系数的动态变化。[结果](1)2013年和2018年物种组成变化较小,物种株数、科属种数减少;重要值排名前10名的优势种仅1名物种发生变动。(2)两次调查结果的生态位重叠指数均以0.2~0.5之间为主,种对数随群落发展而增加,大部分物种对生境需求具有一定相似性、互补性。随着群落发展,多数优势种的生态位重叠程度逐渐降低、生态位宽度逐渐变窄、利用资源能力逐渐减弱。(3)5年间,重要值排序与生态位宽度排序并不完全相同,群落资源未被完全充分利用。水榆花楸和翅柃一直占据优势地位,与其他优势种的生态位重叠指数呈现稳定或增加趋势。(4)两次调查中优势种的总体联结性均为显著正关联,χ^(2)检验、联结系数、Jaccard指数和Spearman秩相关系数的结果基本一致:正关联种对数大于负关联种对数,极显著或显著正关联种对数比极显著或显著负关联种对数多,不显著关联种对数也占有重要比例。随着群落发展,总体联结性的显著正关联逐渐增强,正相关对数和正负比值逐渐增加,负相关对数逐渐减少。[结论]湖北星斗山国家级自然保护区天然次生林处于自然演替的早期至中期阶段,种间联结松散、不紧密,水榆花楸和翅柃竞争力强,长蕊杜鹃、多脉青冈和包果柯常绿树种对环境资源利用能力不断提高。