scholarly journals Formalized classification of ephemeral wetland vegetation (Isoëto-Nanojuncetea class) in Poland (Central Europe)

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11703
Author(s):  
Zygmunt Kącki ◽  
Andrzej Łysko ◽  
Zygmunt Dajdok ◽  
Piotr Kobierski ◽  
Rafał Krawczyk ◽  
...  
Keyword(s):  
Plant Communities ◽  
Expert Knowledge ◽  
Northern Region ◽  
Data Set ◽  
Distribution Maps ◽  
Logical Operators ◽  
Ephemeral Wetland ◽  
Vegetation Database ◽  
Species Groups

Formalized classification of the class Isoëto-Nanojuncetea has not been performed in Poland. We used 69,562 relevés stored in Polish Vegetation Database. Based on the literature and expert knowledge we selected 63 diagnostic species for the Isoëto-Nanojuncetea class. Unequivocal classification was applied in this work according to Cocktail method. A set of formal definitions was established using a combination of logical operators of total cover of species in case of high-rank syntaxa while sociological species groups and cover of particular species were used for logical formulas describing class, alliances and associations. An Expert System was prepared and applied to classify the whole data set of PVD and 1,340 relevés were organized at the class level. We stratifies the data and finally we used data set of 903 relevés to prepare synoptic tables, distribution maps and descriptions of the syntaxa. Twelve associations and two plant communities were identified. Vegetation of the Isoëto-Nanojuncetea class occur in Poland’s central and southern part, with scattered stands in northern region. We described two new plant communities within Eleocharition and Radiolion alliance. The first formal classification of the Isoëto-Nanojuncetea class revealed a high diversity of ephemeral vegetation wetland found in Poland in the eastern boundary of their geographical distribution in Europe.

Spatial distribution of physico-chemical parameters for groundwater quality evaluation in a part of Satluj River Basin, India

2019 ◽  
Vol 19 (5) ◽  
pp. 1480-1490 ◽  
Author(s):  
Akshay Kumar Chaudhry ◽  
Kamal Kumar ◽  
Mohammad Afaq Alam
Keyword(s):  
Spatial Distribution ◽  
Groundwater Quality ◽  
Spatial Dependence ◽  
Groundwater Resources ◽  
Quality Parameters ◽  
Northern Region ◽  
Total Hardness ◽  
Data Set ◽  
Distribution Maps ◽  
Physico Chemical

Abstract The rising population, contamination and mismanagement of groundwater worldwide require sustainable management techniques and strategies to prevent misuse of groundwater resources especially in the semi-arid regions of the world. The aim of the present study is to assess the distribution of contaminants in groundwater at a spatial level by using a geostatistical method, namely ordinary kriging. For this, a physico-chemical parameter data set at 14 sampling locations for a period over 25 years was assessed. Three semi-variogram models, namely exponential, Gaussian and spherical, fitted well for the data set and were cross-validated using predictive statistics. Based on nugget/sill ratio, which characterizes the overall spatial dependence of water quality parameters, it was observed that, apart from nitrate, all the other parameters showed moderate to weak spatial dependence (i.e. total hardness), indicating significant influence of urbanization, fertilization and industrialization. Spatial distribution maps of all the parameters were generated. Concentration of most of the parameters reported high values in the northern region, while silicon dioxide and potassium recorded high values in the southern and central regions of the study area respectively. The study highlighted the depleting groundwater resources in various regions of the study area, indicating that the groundwater quality is in a declining state.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

Second international scientific conference «Present fundamental problems of vegetation classification» (Yalta, 15–20 September 2019)

2019 ◽  
pp. 135-142
Author(s):  
K. V. Ivanova ◽  
A. M. Lapina ◽  
V. V. Neshataev
Keyword(s):  
Plant Communities ◽  
National Archive ◽  
Botanical Garden ◽  
Vegetation Classification ◽  
Scientific Conference ◽  
Scientific Journals ◽  
Time Dynamics ◽  
The Past ◽  
Research Organizations

The 2nd international scientific conference «Fundamental problems of vegetation classification» took place at the Nikitskiy Botanical Garden (Yalta, Republic of Crimea, Russia) on 15–20 September 2019. There were 56 participants from 33 cities and 43 research organizations in Russia. The conference was mostly focused on reviewing the success in classification of the vegetation done by Russian scientists in the past three years. The reports covered various topics such as classification, description of new syntaxonomical units, geobotanical mapping for different territories and types of vegetation, studies of space-time dynamics of plant communities. The final discussion on the last day covered problems yet to be solved: establishment of the Russian Prodromus and the National archive of vegetation, complications of higher education in the profile of geobotany, and the issue of the data leakage to foreign scientific journals. In conclusion, it was announced that the 3rd conference in Nikitskiy Botanical Garden will be held in 2022.

Alpine vegetation of the Ivanovskiy, Prokhodnoy and Rossypnoy Ridges (Western Altai)

2015 ◽  
pp. 96-124
Author(s):  
E. G. Zibzeev ◽  
T. A. Nedovesova
Keyword(s):  
Plant Communities ◽  
Wide Spectrum ◽  
Climate Forcing ◽  
High Mountain ◽  
Air Mass ◽  
Rich Diversity ◽  
Mountain Systems ◽  
Mountain Vegetation ◽  
Coenotic Diversity

The mountain systems are characterized by diverse ecological conditions (climate, geomorphological, soil, etc.). The wide spectrum of environmental conditions entails a rich diversity of plant communities growing on the small territory and determines the different flora and vegetation geneses. The uniqueness of floristic and coenotic diversities of the high-mountain vegetation of the south of Western Altai (Ivanovskiy, Prokhodnoi, and Rossypnoi Ranges) are associated with the effect of two climate-forcing factors such as the westerly humid air mass and dry warm airflow from the inner Kazakhstan regions. The paper summarizes the data on coenotic diversity (Zibzeev, 2010, 2012) and gives a syntaxonomic analysis of the high-mountain vege­tation in the Ivanovskii, Prokhodnoi, and Rossypnoi Ranges (Western Altai, Kazakhstan). The classification of plant communities was carried out using the Braun-Blanquet approach (Westhoff, van der Maarel, 1973). The relevés records were stored in the TURBOVEG database and classified by ­TWINSPAN (Hill 1979).

Classification of woody-shrub vegetation of the forest-steppe complex of Privolzhskaya Upland

2009 ◽  
pp. 27-53
Author(s):  
A. Yu. Kudryavtsev
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Dominant Species ◽  
Large Scale ◽  
Nature Reserve ◽  
Vegetation Mapping ◽  
Vegetation Classification ◽  
Forest Steppe ◽  
Mapping Data

Diversity of plant communities in the nature reserve “Privolzhskaya Forest-Steppe”, Ostrovtsovsky area, is analyzed on the basis of the large-scale vegetation mapping data from 2000. The plant community classi­fication based on the Russian ecologic-phytocoenotic approach is carried out. 12 plant formations and 21 associations are distinguished according to dominant species and a combination of ecologic-phytocoenotic groups of species. A list of vegetation classification units as well as the characteristics of theshrub and woody communities are given in this paper.

Plant communities on rock outcrops in the northwest part of the Black Sea Caucasian coast

2009 ◽  
pp. 3-14
Author(s):  
V. B. Golub ◽  
N. A. Grechushkina ◽  
A. N. Sorokin ◽  
L. F. Nikolaychuk
Keyword(s):  
Plant Communities ◽  
Black Sea ◽  
Soil Cover ◽  
Geographical Location ◽  
The Black Sea ◽  
Rock Outcrops ◽  
Exact Test ◽  
Main Factors ◽  
Phi Coefficient

The classification of petrophytic vegetation of coastal steeps was proposed for the Northwest Cauca­sian coast of the Black Sea using the Braun-Blanquet approach. The main factors that influence the deve­lopment of vegetation in question are abrasion and denudation sea coast processes. The coastal steeps in study area are formed by carbonate flysch. The plant communities occur on rocky slopes with poorly deve­loped soil cover, fine stone chips as well as rock crevices. Nine associations and four communities without syntaxonomic rank were documented in the table and described with respect to their phyto­socio­logical affinities, ecology, and geographical location. Diagnostic species of syntaxa were established using phi-coefficient calculations of fidelity and Fisher’s exact test. In addition, the results of relevé ordination were given using the algorithm of non-metric multi­dimensional scaling (NMS) that is embedded in PC-ORD 5.0 software package.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

Discovery of New Genera Challenges the Subtribal Classification of Tok-Tok Beetles (Coleoptera: Tenebrionidae: Sepidiini)

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Olivia M Gearner ◽  
Marcin J Kamiński ◽  
Kojun Kanda ◽  
Kali Swichtenberg ◽  
Aaron D Smith
Keyword(s):  
Dna Sequences ◽  
Species Group ◽  
Phylogenetic Placement ◽  
Darkling Beetles ◽  
Species Groups ◽  
Taxonomic Groups ◽  
Inference Methods ◽  
A New Species ◽  
Coleoptera Tenebrionidae

Abstract Sepidiini is a speciose tribe of desert-inhabiting darkling beetles, which contains a number of poorly defined taxonomic groups and is in need of revision at all taxonomic levels. In this study, two previously unrecognized lineages were discovered, based on morphological traits, among the extremely speciose genera Psammodes Kirby, 1819 (164 species and subspecies) and Ocnodes Fåhraeus, 1870 (144 species and subspecies), namely the Psammodes spinosus species-group and Ocnodes humeralis species-group. In order to test their phylogenetic placement, a phylogeny of the tribe was reconstructed based on analyses of DNA sequences from six nonoverlapping genetic loci (CAD, wg, COI JP, COI BC, COII, and 28S) using Bayesian and maximum likelihood inference methods. The aforementioned, morphologically defined, species-groups were recovered as distinct and well-supported lineages within Molurina + Phanerotomeina and are interpreted as independent genera, respectively, Tibiocnodes Gearner & Kamiński gen. nov. and Tuberocnodes Gearner & Kamiński gen. nov. A new species, Tuberocnodes synhimboides Gearner & Kamiński sp. nov., is also described. Furthermore, as the recovered phylogenetic placement of Tibiocnodes and Tuberocnodes undermines the monophyly of Molurina and Phanerotomeina, an analysis of the available diagnostic characters for those subtribes is also performed. As a consequence, Phanerotomeina is considered as a synonym of the newly redefined Molurina sens. nov. Finally, spectrograms of vibrations produced by substrate tapping of two Molurina species, Toktokkus vialis (Burchell, 1822) and T. synhimboides, are presented.

scholarly journals ResCaps: an improved capsule network and its application in ultrasonic image classification of thyroid papillary carcinoma

2021 ◽  
Author(s):  
Xiongzhi Ai ◽  
Jiawei Zhuang ◽  
Yonghua Wang ◽  
Pin Wan ◽  
Yu Fu
Keyword(s):  
Papillary Carcinoma ◽  
Network Model ◽  
Reliability And Validity ◽  
Thyroid Papillary Carcinoma ◽  
Data Set ◽  
Ultrasonic Image ◽  
First Choice ◽  
Thyroid Papillary ◽  
Ultrasonic Images

AbstractUltrasonic image examination is the first choice for the diagnosis of thyroid papillary carcinoma. However, there are some problems in the ultrasonic image of thyroid papillary carcinoma, such as poor definition, tissue overlap and low resolution, which make the ultrasonic image difficult to be diagnosed. Capsule network (CapsNet) can effectively address tissue overlap and other problems. This paper investigates a new network model based on capsule network, which is named as ResCaps network. ResCaps network uses residual modules and enhances the abstract expression of the model. The experimental results reveal that the characteristic classification accuracy of ResCaps3 network model for self-made data set of thyroid papillary carcinoma was $$81.06\%$$ 81.06 % . Furthermore, Fashion-MNIST data set is also tested to show the reliability and validity of ResCaps network model. Notably, the ResCaps network model not only improves the accuracy of CapsNet significantly, but also provides an effective method for the classification of lesion characteristics of thyroid papillary carcinoma ultrasonic images.

A comparison of methodological approaches to the subfamilial classification of the Naididae (Oligochaeta)

1987 ◽  
Vol 65 (3) ◽  
pp. 691-707 ◽  
Author(s):  
A. F. L. Nemec ◽  
R. O. Brinkhurst
Keyword(s):  
Data Matrix ◽  
Jaccard Coefficient ◽  
Data Set ◽  
Parsimony Method ◽  
Fit Statistics ◽  
Reconstruction Methods ◽  
Linkage Methods ◽  
The Family ◽  
First Time

A data matrix of 23 generic or subgeneric taxa versus 24 characters and a shorter matrix of 15 characters were analyzed by means of ordination, cluster analyses, parsimony, and compatibility methods (the last two of which are phylogenetic tree reconstruction methods) and the results were compared inter alia and with traditional methods. Various measures of fit for evaluating the parsimony methods were employed. There were few compatible characters in the data set, and much homoplasy, but most analyses separated a group based on Stylaria from the rest of the family, which could then be separated into four groups, recognized here for the first time as tribes (Naidini, Derini, Pristinini, and Chaetogastrini). There was less consistency of results within these groups. Modern methods produced results that do not conflict with traditional groupings. The Jaccard coefficient minimizes the significance of symplesiomorphy and complete linkage avoids chaining effects and corresponds to actual similarities, unlike single or average linkage methods, respectively. Ordination complements cluster analysis. The Wagner parsimony method was superior to the less flexible Camin–Sokal approach and produced better measure of fit statistics. All of the aforementioned methods contain areas susceptible to subjective decisions but, nevertheless, they lead to a complete disclosure of both the methods used and the assumptions made, and facilitate objective hypothesis testing rather than the presentation of conflicting phylogenies based on the different, undisclosed premises of manual approaches.

Sign in / Sign up

Export Citation Format

Share Document