Grouping tree species to estimate basal area increment in temperate multispecies forests in Durango,Mexico

Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.

Heavy minerals clustering analysis in application of provenance analysis of Kong 2 Member in Kongnan area

The main task of provenance analysis is to determine the source of sediments and the position of parent rocks.Provenance analysis may find out the relationship between erosion districts and sediment zone,between the uplift and the depression in the process of basin development.The authors use the method of heavy mineral clustering analysis and estimate the provenance direction of Huanghua Depression in the Paleogene Kong 2 Member.Research shows that there were five provenance areas of Kong 2 Member in Kongnan area.They are western(Shenusi),northwestern(Cangzhou),eastern(Ganhuatun),northeastern and southeastern.The main provenance areas were northwestern and western,while the southern provenance could not be ruled out.And these areas are consistent with the known provenance areas.

Measurement method for cluster warhead's dispersion area based on binocular stereo vision technique

Cluster warhead has become the main ammunition of gun,rocket projectile and missile and it has been widely equipped in almost every country.More and more attention is paid to the damage effect of cluster warhead.The size of the dispersion area of cluster warhead is the main standard by which the damage effect of cluster warhead is estimated.The practical method of measuring the dispersion area was developed based on binocular stereo vision measurement theory.The calibration principle of the binocular stereo vision cameras was studied.The matching algorithm that relies on the gradient fields of the neighborhood of a pixel has been used to obtain the spatial information of matched points by acquiring apair of corresponding points in the left and right images of binocular cameras.The 3Dpositions of the flying path of cluster warhead were calculated.The umbrella that is similar to the dispersion track of static explosive cluster warhead was applied in the experiment to get the projection area of the umbrella on the ground.Experiment results verify the feasibility of the proposed method.

Tourism Development Strategy in Poyang Lake Eco-economic Area from the Perspective of Industrial Cluster

As a regional tourism development strategy, tourism industrial cluster can promote the core competitiveness of tourism industry in advantageous regions and the great-leap-forward development of regions with less-developed tourism industry. The conditions of forming tourism industrial cluster in Poyang Lake Eco-economic Area was analyzed, the development strategies for tourism industry from the perspective of industrial cluster was put forward.

Reconstruction of Ore-controlling Structures Resulting from Magmatic Intrusion into the Tongling Ore Cluster Area during the Yanshanian Epoch

The Tongling ore cluster area experienced intensive compression and associated shearing during the Indosinian-Yanshanian Epoch, which formed a trunk ore-controlling fold and fault system in the caprock. The magmatic intrusion in the Yanshanian Epoch induced a multi-stage unmixing of poly-phase fluids, resulting in mineralization characterized by multi-layer, wide-range, and multiform styles. The magmatic intrusion in the Tongling area not only supplied the essential ore-forming materials, but also reconstructed the ore-controlling structures according to a trend surface simulation of the following five strata boundaries： Silurian-Devonian, Devonian-Carboniferous, Carboniferous- Permian, Middle Permian-Upper Permian and Permian -Triassic. The result of this simulation shows that there exists a significant difference between the strata in the upper part and those in the lower. The lower trend surfaces are antiform whereas the upper trend surfaces are synform. In addition, superposing of the trend surfaces of adjacent bed boundaries （such as, Silurian-Devonian boundary superposed upon Devonian-Carboniferous boundary） shows that the lower trend surface always pierces the one above. Moreover, the position and orientation of the pierced parts of the different superposed trend surfaces are similar and show E-W-trending zonal distribution in accordance with the distribution of the regional E-W-trending magmatic-metallogenic belt. Based on comprehensive analysis of the mechanical properties of the strata, structural deformation mechanisms, and field phenomena, it seems that the special characteristics of the stratal trend surface resulted from jacking due to magmatic intrusion into the caprock previously controlled by an E-W-trending basement fault. Therefore, it is deduced that the major ore-controlling structures, which formed during regional horizontal compression, were reconstructed by the vertical jacking function of ore-forming magmas during the Yanshanian Epoch. During the ore-forming process, the local vertical jacking of magmas, coupled with the regional horizontal compression, optimized an extensive environment in the fluid- conduit network and accelerated the unmixing of poly-phase fluids following magmatic emplacement. Jacking also strengthened the vertical and lateral fluid-guiding structures, supplying more suitable physical conditions for multi-layer emplacement and wide-ranging transport of poly-phase fluids.

Division of hydroclimatic area over China seas—Ⅱ．Cluster analysis and fuzzy ISODATA．

In this paper, the tree cluster analysis and ISODATA of fuzzy cluster are made on the basis of the results(Chen et al, 1993) obtained by using the principal component analysis based on the hydroclimatic values over the years of the China seas,where the climatic field may be divided into three climatic zones, 9 hydroclimatic regions and 1 climatic subregion Comparison of the distribution characteristics of hydrologic seasons with those of marine fauna and flora indicates that each climatic region possesses its inherent seasonal characteristics and biota distribution, and corresponds with each other. This fact proves that the division of the above-mentioned 10 climatic regions is reliable.

An algorithm for segmentation of lung ROI by mean-shift clustering combined with multi-scale HESSIAN matrix dot filtering

A new algorithm for segmentation of suspected lung ROI(regions of interest)by mean-shift clustering and multi-scale HESSIAN matrix dot filtering was proposed.Original image was firstly filtered by multi-scale HESSIAN matrix dot filters,round suspected nodular lesions in the image were enhanced,and linear shape regions of the trachea and vascular were suppressed.Then,three types of information,such as,shape filtering value of HESSIAN matrix,gray value,and spatial location,were introduced to feature space.The kernel function of mean-shift clustering was divided into product form of three kinds of kernel functions corresponding to the three feature information.Finally,bandwidths were calculated adaptively to determine the bandwidth of each suspected area,and they were used in mean-shift clustering segmentation.Experimental results show that by the introduction of HESSIAN matrix of dot filtering information to mean-shift clustering,nodular regions can be segmented from blood vessels,trachea,or cross regions connected to the nodule,non-nodular areas can be removed from ROIs properly,and ground glass object(GGO)nodular areas can also be segmented.For the experimental data set of 127 different forms of nodules,the average accuracy of the proposed algorithm is more than 90%.

Fault Detection Based on Hierarchical Cluster Analysis in Wide Area Backup Protection System

In wide area backup protection of electric power systems, the prerequisite of protection device's accurate, fast and reliable performance is its corresponding fault type and fault location can be discriminated quickly and defined exactly. In our study, global information will be introduced into the backup protection system. By analyzing and computing real-time PMU measurements, basing on cluster analysis theory, we are using mainly hierarchical cluster analysis to search after the statistical laws of electrical quantities' marked changes. Then we carry out fast and exact detection of fault components and fault sections, and finally accomplish fault isolation. The facts show that the fault detection of fault component (fault section) can be performed successfully by hierarchical cluster analysis and calculation. The results of hierarchical cluster analysis are accurate and reliable, and the dendrograms of hierarchical cluster analysis are in intuition.

Evaluation and classification of residential greenbelt quality based on factor analysis & clustering analysis:An example of Xinxiang City,China

Five factors expressing greenbelt quality and one factor expressing quantity were adopted for evaluation of the residential greenbelt, and the AHP （Analytical Hierarchy Process） method was used to determine the value of factors. Thirty residential areas were selected as the samples. Two principal components were extracted and their expression was constructed by method of factor anlysis, therefore, quality evaluation of residential greenbelt was obtained. The accuracy of the function and implement quality classification toward the residential greenbelts in Xinxiang City were validated by clustering analysis method. The results showed that the greenbelt quality of fourteen residential areas was higher than the average level, of which eleven were newly-built residential areas. The 30 residential areas were classified into three types according to their greenbelt features and their formation by clustering analysis method. Finally rational proposal basing on aforesaid evaluating results was proposed for construction and renewal of residential greenbelt, upon which directive basis was provided for construction and renewal of residential greenbelt.

A KNN-based two-step fuzzy clustering weighted algorithm for WLAN indoor positioning

Although k-nearest neighbors （KNN） is a popular fingerprint match algorithm for its simplicity and accuracy, because it is sensitive to the circumstances, a fuzzy c-means （FCM） clustering algorithm is applied to improve it. Thus, a KNN-based two-step FCM weighted （KTFW） algorithm for indoor positioning in wireless local area networks （WLAN） is presented in this paper. In KTFW algorithm, k reference points （RPs） chosen by KNN are clustered through FCM based on received signal strength （RSS） and location coordinates. The right clusters are chosen according to rules, so three sets of RPs are formed including the set of k RPs chosen by KNN and are given different weights. RPs supposed to have better contribution to positioning accuracy are given larger weights to improve the positioning accuracy. Simulation results indicate that KTFW generally outperforms KNN and its complexity is greatly reduced through providing initial clustering centers for FCM.

Semi-Supervised Clustering Fingerprint Positioning Algorithm Based on Distance Constraints

With the rapid development of WLAN( Wireless Local Area Network) technology,an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online computation.In this paper,it proposes a novel fingerprint positioning algorithm known as semi-supervised affinity propagation clustering based on distance function constraints. We show that by employing affinity propagation techniques,it is able to use a fractional labeled data to adjust similarity matrix of signal space to cluster reference points with high accuracy. The semi-supervised APC uses a combination of machine learning,clustering analysis and fingerprinting algorithm. By collecting data and testing our algorithm in a realistic indoor WLAN environment,the experimental results indicate that the proposed algorithm can improve positioning accuracy while reduce the online localization computation,as compared with the widely used K nearest neighbor and maximum likelihood estimation algorithms.

Clustering Analysis of the Climate in Tobacco Planting Zone of Yunnan

[Objective] The research aimed to study clustering of the six climatic factors in Yunnan tobacco planting zone. [Method] 6 meteorological elements in 89 tobacco-growing counties and 12 sub-prefectures were conducted clustering analysis. According to indicator and climate characteristics of the each type, climate in tobacco planting area of Yunnan Province was divided. [Result] Climate in tobacco planting area of Yunnan Province could be divided into eight types: Jiangchuan (24 counties, belonged to northern and central subtropical climate belts), Songming (27 counties, belonged to northern subtropical and central, south, north temperate climate belts), Tengchong (3 counties, belonged to northern subtropical climate belt), Mile (12 counties, belonged to central and southern subtropical climate belts), Qiubei (11 counties, belonged to southern subtropical climate belt), Yanjin (4 counties, belonged to central subtropical humid climate belt), Yuanjiang (4 counties, belonged to southern subtropical and northern tropical climate belts), Zhenxiong (3 counties, belonged to warm temperate and northern subtropical climate belts) were eight representatives. Among 1-8 eco-zones, domestic and foreign cities where climate reached level-one similarity were respectively 3, 1, 1, 0, 1, 1, 0 and 1, up to level-two similarity, respectively 12, 15, 3, 13, 13, 1, 5 and 3. Among 8 major ecological zones, similar distance of the city reaching level-one similarity was in the range of 0.28 to 0.45, and similar degree was the highest. Variety introduction among these places would be successful. Similar distance of the city reaching level-two similarity was between 0.51 and 1.00, and similar degree was higher. Mutual variety introduction had high successful rate in these places. [Conclusion] The research provided theoretical basis for selecting new suitable tobacco variety and optimizing tobacco variety layout in different zones.

Direct measurement of the three-dimensional distribution of leaf area density and light conditions in a mature oak stand by the cube method

Although the distributions of foliage and light play major roles in various forest functions,accurate,nondestructive measurement of these distributions is difficult due to the complexity of the canopy structure.To evaluate the foliage and light distributions directly and nondestructively in a mature oak stand,we used the cube method by dividing the forest canopy into small cubes(50 cm per side)and directly measured leaf area density(LAD,the total one-sided leaf area per unit volume,i.e.,cube)and relative irradiance(RI)within each cube.The distribution of LAD and of RI was highly heterogeneous,even at the same canopy height.This heterogeneity reflected the presence of foliage clusters associated with multiple forking branches.The relationship between cumulative LAD at the canopy surface and average RI followed the Beer-Lambert law.The mean light extinction coefficient(K)was 0.32.However,K was overestimated by more than double(0.80)when calculated based on the classical method using RI at the forest floor.This overestimation was caused by the lower RI due to light absorption by nonleaf plant parts below the canopy.Our findings on the complex foliage and light distributions in canopy layers should help improve the accuracy of RI and K measurements and thus more accurate predictions of environmental responses and forest functions.

Study on Recognition Method of Similar Weather Scenes in Terminal Area

Weather is a key factor affecting the control of air traffic.Accurate recognition and classification of similar weather scenes in the terminal area is helpful for rapid decision-making in air trafficflow management.Current researches mostly use traditional machine learning methods to extract features of weather scenes,and clustering algorithms to divide similar scenes.Inspired by the excellent performance of deep learning in image recognition,this paper proposes a terminal area similar weather scene classification method based on improved deep convolution embedded clustering(IDCEC),which uses the com-bination of the encoding layer and the decoding layer to reduce the dimensionality of the weather image,retaining useful information to the greatest extent,and then uses the combination of the pre-trained encoding layer and the clustering layer to train the clustering model of the similar scenes in the terminal area.Finally,term-inal area of Guangzhou Airport is selected as the research object,the method pro-posed in this article is used to classify historical weather data in similar scenes,and the performance is compared with other state-of-the-art methods.The experi-mental results show that the proposed IDCEC method can identify similar scenes more accurately based on the spatial distribution characteristics and severity of weather;at the same time,compared with the actualflight volume in the Guangz-hou terminal area,IDCEC's recognition results of similar weather scenes are con-sistent with the recognition of experts in thefield.

An Improved Heuristic Ant-Clustering Algorithm

An improved heuristic ant-clustering algorithm(HAC)is presented in this paper.A device of ’memory bank’ is proposed,which can bring forth heuristic knowledge guiding ant to move in the bi-dimension grid space.The device lowers the randomness of ants’ moving and avoids the producing of"un-assigned data object".We have made some experiments on real data sets and synthetic data sets.The results demonstrate that HAC has superiority in misclassification error rate and runtime over the classical algorithm.

面向智能电网广域通信的可靠路由算法研究

无线传感器网络是智能电网广域通信中的基础组成单元,传统的无线传感器网络分簇路由协议虽能在一定程度上简化网络拓扑,但仍存在簇首选择不合理且系统能耗较高的问题。针对此,文中基于粒子群优化算法提出了一种改进的分簇路由协议。该协议利用粒子群算法的参数寻优特性对簇首搜寻方案加以改进,使得新方案考虑了距离、剩余能量、数据传输精度等多种因素。同时还使用粒子群自适应函数对该方案进行优化,从而提高了原算法的可靠性。在能耗实验测试中,所提算法迭代1 300次后的剩余能耗为31.2 J,在对比算法中为最优。而对路由路径的测试中,改进算法的平均运行时延为1.79 ms,平均误码率为0.212 5,在所有算法中均为最低,证明了该算法具有良好的实时性和可靠性。

血清中的免疫炎症生物标志物与慢性湿疹病人的疾病严重程度相关性研究

目的探讨慢性湿疹病人血清免疫炎症生物标志物与疾病严重程度的相关性。方法将2021年9月至2022年8月于武汉市中医医院接受治疗的100例慢性湿疹病人纳入研究,包括局限性湿疹69例、泛发性湿疹31例;根据疾病严重程度分为轻度组37例、中度组34例和重度组29例;将同期该院40例健康体检者纳入对照组。采用酶联免疫吸附测定检测血清免疫炎症生物标志物白细胞介素(IL)-2、IL-4、IL-5、IL-6、γ干扰素(IFN-γ)、IL-10、IL-17、IL-18、IL-23、IL-33、肿瘤坏死因子α(TNF-α)水平;采用流式细胞仪检测外周血分化抗原(CD)4+、CD8+水平;采用Pearson相关分析慢性湿疹病人血清免疫炎症生物标志物与湿疹面积及严重度指数(EASI)评分的相关性。结果对照组、轻度组、中度组、重度组慢性湿疹病人血清IL-2[(10.51±2.10)μg/L比(12.94±2.26)μg/L比(15.03±2.34)μg/L比(17.94±2.56)μg/L]、IL-4、IL-5、IL-6、IL-10、IL-17、IL-18、IL-23、IL-33、TNF-α、IFN-γ、CD8+依次升高,CD4+[(43.24±6.79)%比(37.04±4.25)%比(29.12±2.78)%比(25.62±2.35)%]依次降低(P<0.05)。局限性与泛发性慢性湿疹病人血清IL-2、IL-4、IL-5、IL-6、IL-10、IL-17、IL-18、IL-23、IL-33、TNF-α、IFN-γ、CD4+、CD8+差异无统计学意义(P>0.05)。慢性湿疹病人EASI评分与血清IL-2、IL-4、IL-5、IL-6、IL-10、IL-17、IL-18、IL-23、IL-33、TNF-α、IFN-γ、CD8+呈正相关,与CD4+呈负相关(P<0.05)。结论血清免疫炎症生物标志物与慢性湿疹病人病情严重程度存在显著相关性,可考虑将各指标作为病情评估的相关辅助指标,在临床中推广应用。

宁波都市区森林城市群建设现状、发展指标与总体布局——基于中欧合作研究的城市群森林建设规划

区域是生态环境的共同体,需要协同在城市群尺度推进森林城市群建设。基于对宁波都市区森林城市群建设现状的分析,结合上位规划要求及国内外经验,构建包括7项一级指标(生态资源总量、生态网络、生态保护修复、生物多样性、生态文化、生态福利、区域协同)、24项二级指标的宁波都市区森林城市群建设指标体系;同时,立足宁波都市区现状与发展目标,形成都市区“两山三湾一岛链”,中心区“两屏一区、多廊多点”,生态建设核心区“绿心、绿隔、绿核、绿廊、绿网、绿园”的森林城市群分层布局。研究结果不仅对宁波都市区森林城市群的落地建设具有指导作用,也对我国其他地区森林城市群建设具有重要的参考价值。

宁波都市区城市群的生态环境问题与森林应对策略

2021年中欧双方联合开展了中欧绿色城镇化的城市森林应对关键技术研究项目,我国宁波都市区是长三角区域内的重要发展极核之一,其城市群建设也是该研究项目的典型案例。文章在总结宁波都市区“小城镇点状发展-大都市主导发展-城镇化高速发展”的发展历程的基础上,从区域尺度综合分析城市组团之间生态空间、森林质量与景观风貌、外来入侵植物、河流水系生态廊道、城乡居民生态福祉、平原村庄风貌等9个方面的生态环境问题,最后提出群域区生态共建共享共治、中心区生态空间查漏补缺、核心区生态服务能力提升和绿水青山变金山银山挖潜4方面的森林应对策略,以推进长三角区域生态协同、助力美丽杭州湾建设、构建宁波都市区生态安全共同体。

一种基于证据多视角的模糊C-means聚类算法

针对传统多视角聚类算法难以准确识别噪声和有效划分类间重叠区域样本的问题,提出一种基于证据多视角的模糊C均值(evidential multi-view fuzzy C-means,EMVFCM)聚类算法。首先,在证据推理框架下,研究一种改进的模糊C-means多视角聚类算法,通过优化改进的目标函数获得待测样本属于单类和噪声的信任值,从而识别出噪声数据。然后,由于重叠区域的样本不能被准确地划分类别,所以将其划分到相对应的复合类,这不仅能够表征数据样本类别的不精确性,还能降低错误分类的风险。最后,通过人工数据集和UCI数据集验证本文算法的性能并与相关算法对比。实验结果表明,本文算法较传统多视角聚类算法能更有效地处理数据中的噪声和重叠样本难以准确划分的问题。