Advanced Method for Forecasting and Warning of Severe Convective Weather and Local-scale Hazards

Hurricane Ida ferociously affected many south-eastern and eastern parts of the United States,making it one of the strongest hurricanes in recent years.Advanced forecast and warning tool has been used to track the path of the ex-Hurricane,Ida,as it left New Orleans on its way towards the northeast,accurately predicting significant supercell development above New York City on September 01,2021.This advanced method accurately detected the area with the highest possible level of convective instability with 24-h lead time and even Level 5,devised in the categorical outlooks legend of the system.Therefore,an extreme level implied a very high probability of the local-scale hazard occurring above the NYC.Cloud model output fields(updrafts and downdrafts,wind shear,near-surface convergence,the vertical component of relative vorticity)show the rapid development of a strong supercell storm with rotating updrafts and a mesocyclone.The characteristic hook-shaped echo signature visible in the reflectivity patterns indicates a signal for a highly precipitable(HP)supercell with the possibility of tornado initiation.Open boundary conditions represent a good basis for simulating a tornado that evolved from a supercell storm,initialized with initial data obtained from a real-time simulation in the period when the bow echo and tornado-like signature occurred.Тhe modeled results agree well with the observations.

Adaptive local routing strategy on a scale-free network

Due to the heterogeneity of the structure on a scale-free network, making the betweennesses of all nodes become homogeneous by reassigning the weights of nodes or edges is very difficult. In order to take advantage of the important effect of high degree nodes on the shortest path communication and preferentially deliver packets by them to increase the probability to destination, an adaptive local routing strategy on a scale-free network is proposed, in which the node adjusts the forwarding probability with the dynamical traffic load （packet queue length） and the degree distribution of neighbouring nodes. The critical queue length of a node is set to be proportional to its degree, and the node with high degree has a larger critical queue length to store and forward more packets. When the queue length of a high degree node is shorter than its critical queue length, it has a higher probability to forward packets. After higher degree nodes are saturated （whose queue lengths are longer than their critical queue lengths）, more packets will be delivered by the lower degree nodes around them. The adaptive local routing strategy increases the probability of a packet finding its destination quickly, and improves the transmission capacity on the scale-free network by reducing routing hops. The simulation results show that the transmission capacity of the adaptive local routing strategy is larger than that of three previous local routing strategies.

The impact of spatial scale on local Moran's I clustering of annual fishing effort for Dosidicus gigas offshore Peru

The spatial scale(?shing grid) of ?sheries research af fects the observed spatial patterns of?sheries resources such as catch-per-unit-ef fort(CPUE) and ?shing ef fort. We examined the scale impact of high value(HH) clusters of the annual ?shing ef fort for Dosidicus gigas of fshore Peru from 2009 to 2012.For a multi-scale analysis, the original commercial ?shery data were tessellated to twelve spatial scales from 6′ to 72′ with an interval of 6′. Under these spatial scales, D. gigas clusters were identi?ed using the Anselin Local Moran's I. Statistics including the number of points, mean CPUE, standard deviation(SD),skewness, kurtosis, area and centroid were calculated for these HH clusters. We found that the z-score of global Moran's I and the number of points for HH clusters follow a power law scaling relationship from2009 to 2012. The mean ef fort and its SD also follow a power law scaling relationship from 2009 to 2012.The skewness follows a linear scaling relationship in 2010 and 2011 but ?uctuates with spatial scale in2009 and 2012; kurtosis follows a logarithmic scale relationship in 2009, 2011 and 2012 but a linear scale relationship in 2010. Cluster area follows a power law scaling relationship in 2010 and 2012, a linear scaling relationship in 2009, and a quadratic scaling relationship in 2011. Based on the peaks of Moran's I indices and the multi-scale analysis, we conclude that the optimum scales are 12′ in 2009 ? 2011 and 6′ in 2012, while the coarsest allowable scales are 48′ in 2009, 2010 and 2012, and 60′ in 2011. Our research provides the best spatial scales for conducting spatial analysis of this pelagic species, and provides a better understanding of scaling behavior for the ?shing ef fort of D. gigas in the of fshore Peruvian waters.

A Hierarchical Clustering and Fixed-Layer Local Learning Based Support Vector Machine Algorithm for Large Scale Classification Problems

It is a challenging topic to develop an efficient algorithm for large scale classification problems in many applications of machine learning. In this paper, a hierarchical clustering and fixed-layer local learning (HCFLL) based support vector machine(SVM) algorithm is proposed to deal with this problem. Firstly, HCFLL hierarchically clusters a given dataset into a modified clustering feature tree based on the ideas of unsupervised clustering and supervised clustering. Then it locally trains SVM on each labeled subtree at a fixed-layer of the tree. The experimental results show that compared with the existing popular algorithms such as core vector machine and decision-tree support vector machine, HCFLL can significantly improve the training and testing speeds with comparable testing accuracy.

Calibration of local magnitude scale for Hindukush continental subduction zone

Hindukush is an active subduction zone where at least one earthquake occurs on daily basis.For seismic hazard studies,it is important to develop a local magnitude scale using the data of local seismic network.We have computed local magnitude scale for Hindukush earthquakes using data from local network belonging to Center for Earthquake Studies(CES)for a period of three years,i.e.2015–2017.A total of 26,365 seismic records pertaining to 2,683 earthquakes with magnitude 2.0 and greater,was used with hypocentral distance less than 600 km.Magnitude scale developed by using this data comes to be M_(L)=logA+0.929logr+0.00298r-1.84.The magnitude determined through formulated relation was compared with that of standard relation for Southern California and relation developed by the same authors for local network for Northern Punjab.It was observed that Hindukush region has high attenuation as compared to that of Southern California and Northern Punjab which implies that Hindukush is tectonically more disturbed as compared to the said regions,hence,seismically more active as well.We have calculated station correction factors for our network.Station correction factors do not show any pattern which probably owes to the geological and tectonic complexity of this structure.Standard deviation and variance of magnitude residuals for CES network determined using Hutton and Boore scale and scale developed in this study were compared,it showed that a variance reduction of 44.1%was achieved.Average of magnitude residuals for different distance ranges was almost zero which showed that our magnitude scale was stable for all distances up to 600 km.Newly developed magnitude scale will help in homogenization of earthquake catalog.It has been observed that b-value of CES catalog decreases when magnitude is calculated by using newly developed magnitude scale.

Ecological Scale Breeding Technique of Local Chickens

Developing ecological scale breeding of local chickens with natural conditions, such as forest lands, grass lands, orchards and mulberry fields, not only can improve the quality of poultry products and the production benefits of chicken breeding, but also can develop and use local chicken germplasm effectively and promote their breed protection and industrialization. From integration and application of breed selection, breeding management, nutrient regulation, grass planting and grazing, a new ecological scale breeding technique of local chickens is proposed.

Large scale classification with local diversity AdaBoost SVM algorithm

Local diversity AdaBoost support vector machine（LDAB-SVM） is proposed for large scale dataset classification problems.The training dataset is split into several blocks firstly, and some models based on these dataset blocks are built.In order to obtain a better performance, AdaBoost is used in each model building.In the boosting iteration step, the component learners which have higher diversity and accuracy are collected via the kernel parameters adjusting.Then the local models via voting method are integrated.The experimental study shows that LDAB-SVM can deal with large scale dataset efficiently without reducing the performance of the classifier.

Non-Local and Memory Character of Frictional Energy Dissipation on Atomic Scale

The traditional description of atomic-scale friction, as investigated in Friction force microscopy, in terms of mechanical stick-slip instabilities appears so successful that it obscures the actual mechanisms of frictional energy dissipation. More sophisticated theoretical approach, which takes into account damping explicitly, reveals the existence of some hidden, unexplained problems, like the universal nearly-critical damping and unexpectedly high value of the dissipation rate. In this paper, we combine analysis in the framework of nonequilibrium statistical mechanics with simple atomistic modeling to show that the hidden problems of atomic scale friction find their origin in the nontrivial character of energy dissipation that is non-local and dominated by memory effects, which have not been addressed before in the context of dry, atomic-scale friction.

多因素引导的行人重识别数据增广方法研究

为解决行人重识别研究领域中行人标注图像获取困难的问题,提出一种多因素引导的行人数据增广方法。首先,在生成器网络中设计了一种局部多尺度引导机制,通过特征融合抑制生成图像的局部伪影;其次,提出了长距离相关性引导机制,通过外注意力引导生成图像的长距离依赖,提高生成行人图像的整体视感质量;最后,提出一种抗博弈判别网络,通过嵌入到生成对抗网络,从而构建一种三网络稳定博弈架构模型,增加生成对抗网络训练的稳定性。通过VIPeR、Market-1501、DukeMTMC-reID这3种不同规模数据集的仿真实验,结果表明该方法与目前主流方法相比,mAP与Rank-1精度上均有不同程度的提升,在小规模数据集上的提升较为显著。

不均匀积雪致大跨网架结构局部损伤分析

网架结构管球连接处局部损伤是此类结构的重要安全隐患。在风致雪漂移引起的不均匀积雪荷载作用下,局部损伤会加剧,最终导致结构局部破坏。因此很有必要分析不均匀积雪荷载作用下带伤服役网架结构局部损伤劣化情况。采用CFD数值模拟技术,以一正放四角锥网架结构为研究背景,分析了在90°风向角、12 m/s风速下,持续降雪24 h中网架结构屋面不均匀积雪分布的变化情况,并建立可表征网架结构管球连接处存在裂纹损伤的多尺度模型,分析了网架结构节点存在不同裂纹尺寸局部损伤时,不均匀积雪致网架结构局部损伤劣化程度。结果表明:网架结构风致积雪不均匀程度非常显著,而且当网架结构管球连接处存在局部裂纹损伤时,在降雪中后期,有管球连接损伤的节点大多都出现了不同程度的裂纹扩展,节点为最不利分布的穿透型裂纹时,裂纹扩展最大为15.64 mm。说明在持续特大降雪这种极端荷载作用下,带伤服役网架结构局部损伤将进一步加剧,危及结构使用安全。

基于多尺度骨架图和局部视觉上下文融合的驾驶员行为识别方法

识别非驾驶行为是提高驾驶安全性的重要手段之一。目前基于骨架序列和图像的融合识别方法具有计算量大和特征融合困难的问题。针对上述问题,本文提出一种基于多尺度骨架图和局部视觉上下文融合的驾驶员行为识别模型(skeleton-image based behavior recognition network,SIBBR-Net)。SIBBR-Net通过基于多尺度图的图卷积网络和基于局部视觉及注意力机制的卷积神经网络,充分提取运动和外观特征,较好地平衡了模型表征能力和计算量间的关系。基于手部运动的特征双向引导学习策略、自适应特征融合模块和静态特征空间上的辅助损失,使运动和外观特征间互相引导更新并实现自适应融合。最终在Drive&Act数据集进行算法测试,SIBBR-Net在动态标签和静态标签条件下的平均正确率分别为61.78%和80.42%,每秒浮点运算次数为25.92G,较最优方法降低了76.96%。

Effects of the Treatment of Carpal Tunnel Syndrome with Surgery and Injections on the Hospital Anxiety and Depression Scale (HADS)

Background: The prevalence of carpal tunnel syndrome (CTS) and of anxiety and depression in primary care practice are high. Different studies had shown an increased prevalence of anxiety and depression in CTS patients. Nevertheless, few papers had been published studying the anxiety and depression scales in the treatment of CTS, either with corticosteroid injections (I) or with surgical decompression (S). Objective: To assess whether clinical improvement observed after the treatment of CTS either with I or with S correlates with an improvement in the punctuations of the Hospital Anxiety and Depression scales (HADS), at 3, 6 and 12-month follow-up. Methods: Randomized and open-label clinical trial, comparing I and S. Patients with symptoms suggestive of CTS (nocturnal paraesthesias) of at least 3 months duration and neurophysiological confirmation were included. Patients with clinically apparent motor impairment were excluded. The subjective evaluation of symptoms was carried out using the visual-analogue scale of pain (VAS-p). Clinical reviews were performed 3, 6 and 12 months after treatment. Each patient completed the HADS questionnaire and a VAS-p at 0, 3, 6, and 12 months. Statistical significance was established using the Student’s t test and the Mann-Whitney U test when necessary. A linear regression analysis was used to know the effect of the treatment adjusted for the initial score of both scales. Results: 65 patients were included (30 in group I and 35 in group S). There was no statistical difference between both groups in terms of age, gender distribution, disease duration, VAS-p, neurophysiological testing severity of CTS or the 8 subscales of HADS. Both groups improved significantly in relation to the baseline VAS-p values, in the reviews at 3, 6 and 12 months, with no significant differences between I and S. At 6 months, the reduction in the anxiety scale was around 3 points for both treatments (S = 3.6 and I = 3.2), without reaching significant differences. At 12 months, it was somewhat higher for those treated with I, but always around 3 points and without significant differences. The Depression scale score was slightly reduced at 6 months, and in a similar way for both groups (I = 1 and S = 1.19;p = 0.8). After 12 months, group I doubled the previous reduction, with group S experiencing a very slight change (I = 1.96 and S = 1.03;p = 0.3). When analysing the effect of group S on group I, the result was a reduction of 0.25 points for Anxiety (p = 0.7) and of 0.02 points for Depression (p = 0.9). Conclusions: Treatment of CTS with I or S results in a similar and discrete improvement in Anxiety scores on the HADS scale at 6 and 12 months. For both types of treatment, the Depression scores barely changed at 6 months, being somewhat higher in group I after 12-month follow-up. The independent effect of the S on both scales is small and not significant.

全矢融合的二元PELCD样本熵列车故障诊断

长期高速运行的服役状态会造成高速列车转向架关键部件性能蜕化甚至发生故障等情况,所导致的安全事件将造成严重的经济损失甚至人员伤亡。考虑到高速列车振动信号的特性,将部分集成的局部特征尺度分解方法拓展至二元信号处理领域,同时结合全矢谱理论对同阶分量信号进行信息融合,得到更加完备的数据特征,并对融合后的数据进行样本熵特征提取,得到列车的故障特征;采用灰狼优化算法对支持向量机进行参数寻优,通过实验对比单一故障工况、复合故障工况以及部件性能退化下的故障识别率,验证所提方法的有效性、优越性。

基于Local特征和Regional特征的图像显著性检测

提出了一种基于颜色空间的Local特征和Regional特征的自然图像显著性检测方法.该方法将图像分成8×8的子块,计算多个尺度下每一个子块的Local特征和Regional特征,并将其加权组合来确定子块的显著程度,从而得到整个图像的显著特征.此外,通过计算4个颜色通道上的色度对比度,获得显著物体的边缘.将图像的显著特征与显著物体的边缘综合后得到图像中的显著目标.实验结果显示,本文提出的方法能够快速、清晰而准确地提取出图像中的显著性目标.

比例融合与多层规模感知的人群计数方法

针对密集场景下人群图像拍摄视角或距离多变造成的多尺度特征获取不足、融合不佳和全局特征利用不充分等问题,提出一种比例融合与多层规模感知的人群计数网络。首先采用骨干网络VGG16提取人群密度初始特征;其次,设计多层规模感知模块,获得人群多尺度信息的丰富表达;再次,提出比例融合策略,根据卷积层捕获的特征权重重构多尺度信息,提取显著性人群特征;最后,采用卷积回归策略进行密度图的回归。同时,提出一种局部一致性损失函数,通过区域化密度图的方式增强生成密度图与真实密度图的相似度,提高计数性能。在多个人群数据集上的试验结果表明,所提模型优于近年人群计数的先进方法,且在车辆计数上有较好推广性。

基于局部均匀化多尺度方法的机织复合材料单轴拉伸力学行为研究

机织碳纤维增强树脂基复合材料在厚度方向存在纤维的相互交织,拥有较复杂的纤维空间结构,因此具有优异的比刚度和比强度。本文建立了平纹机织复合材料的“微观-介观-宏观”的多尺度有限元渐进损伤模型,并对介观模型中的各成分进行具有等效性能的局部均匀化简化。基于微观力学失效理论判断纤维/基体损伤起始,通过ABAQUS有限元分析平台,开发用户自定义的子程序VUMAT,对平纹机织复合材料进行拉伸力学行为和损伤机理分析。结果表明:在拉伸载荷模拟中纤维束树脂损伤最为严重,纤维失效发生在经向纤维束中,且外层损伤相较于内层更加严重。试验和数值分析的结果误差均在2%以内,表明多尺度渐进损伤模型的可靠性。

基于卷积胶囊编码器和多尺度局部特征共现的图像分割网络

U-Net在图像分割领域取得了巨大成功,然而卷积和下采样操作导致部分位置信息丢失,全局和长距离的语义交互信息难以被学习,并且缺乏整合全局和局部信息的能力。为了提取丰富的局部细节和全局上下文信息,提出了一个基于卷积胶囊编码器和局部共现的医学图像分割网络MLFCNet(network based on convolution capsule encoder and multi-scale local feature co-occurrence)。在U-Net基础上引入胶囊网络模块,学习目标位置信息、局部与全局的关系。同时利用提出的注意力机制保留网络池化层丢弃的信息,并且设计了新的多尺度特征融合方法,从而捕捉全局信息并抑制背景噪声。此外,提出了一种新的多尺度局部特征共现算法,局部特征之间的关系能够被更好地学习。在两个公共数据集上与九种方法进行了比较,相比于性能第二的模型,该方法的mIoU在肝脏医学图像中提升了4.7%,Dice系数提升了1.7%。在肝脏医学图像和人像数据集上的实验结果表明,在相同的实验条件下,提出的网络优于U-Net和其他主流的图像分割网络。

联合多尺度块匹配的非局部均值去噪算法

针对非局部均值(Non-Local Means,NLM)图像去噪算法易产生伪影与平滑细节的问题,提出一种联合多尺度图像块匹配的像素相似性测度,提高NLM算法去噪性能。首先,研究与分析了加权欧氏距离与欧氏距离两种相似性度量以及图像块尺寸设置对NLM算法的影响。其次,通过引入图像特征信息并利用K-means聚类方法将图像划分为平坦区域和包含边缘与纹理的结构区域,对每个类别中的像素点,联合两种尺度图像块匹配计算像素的平滑权重。最后,优化了算法的滤波参数。实验结果表明,提出的算法在噪声去除与细节保持方面明显优于经典的NLM算法,相比其他改进的NLM算法也有优势。

全国—本地尺度下东北三省城市创新网络演化特征与创新能力研究

在网络科学不断发展,创新过程存在跨区域、多尺度、动态性特点的背景下,创新网络及其演化已成为创新经济地理学关注的重点领域。本文将2004-2018年城市间合作专利信息作为原始数据,来表征东北三省城市创新网络,借助Gephi、Ucinet等分析工具,运用复杂网络等研究方法,基于全国—本地尺度分析其格局和结构的演化特征,测度城市创新网络资本,评价城市创新能力,进而对二者关系进行探讨。结果表明:全国—本地尺度下的东北三省城市创新网络的演化特征存在一定的相似性,以“哈长沈大”为网络核心的格局逐渐清晰,网络成长性明显但创新合作的紧密程度和效率均不可观。全国—本地尺度下东北三省城市创新网络演化特征同样存在差异性,全国尺度的网络增速小于本地尺度且存在“小世界”特性,创新资源的跨区域流动不畅通。东北三省城市创新网络资本与城市创新能力水平相关性增强,城市创新能力格局与城市网络地位分布较为一致,城市位于网络的地位以及获取异质性知识与技术的能力更为重要。

轨迹预测中局部自注意力时序编码网络

针对传统编码器应用于轨迹预测时,难以捕捉短暂停车或急转弯等局部时间尺度下的轨迹变化(简称局部变化)从而影响预测准确性的问题,提出一种局部自注意力时序编码架构(Loc-SelfAttention)。该算法充分利用小尺度卷积核的优越局部感知能力,敏锐地捕捉和提取局部变化的特征,并利用自注意力机制,根据局部变化对于未来轨迹分布的影响程度动态赋予提取的局部特征注意力权重,从而过滤噪声和杂点,筛选出有效的局部特征,提高轨迹预测准确性。实验结果表明:Loc-SelfAttention算法在Argoverse运动预测基准上与HOME轨迹预测算法相比,最小平均位移误差降低3.7%,最小最终位移误差降低3.1%,失误率降低4.8%,在自动驾驶和智能交通管理等领域具有一定应用前景。