scholarly journals BLACK ALDER DOMINATED FOREST VEGETATION IN THE WESTERN PART OF CENTRAL SLOVAKIA – SPECIES COMPOSITION AND ECOLOGY

Hacquetia ◽  
2013 ◽  
Vol 12 (2) ◽  
pp. 23-37
Author(s):  
Richard Hrivnák ◽  
Jaroslav Košťál ◽  
Michal Slezák ◽  
Anna Petrášová ◽  
Melánia Feszterová
Keyword(s):  
Species Composition ◽  
Expert Knowledge ◽  
Light Availability ◽  
Principal Component ◽  
Environmental Parameters ◽  
Soil Surface ◽  
Open Water ◽  
Forest Vegetation ◽  
Data Set ◽  
Black Alder

Abstract In some regions of Slovakia, black alder forest vegetation has not been documented appropriately yet. This paper is the first vegetation study presenting the phytosociological data and measured environmental parameters from the western part of central Slovakia. The data set was classified by using a modified TWINSPAN algorithm, which allowed us to discern floristically and ecologically distinctive plant communities. They correspond to the associations Stellario nemorum-Alnetum glutinosae Lohmeyer 1957 (riparian alder vegetation on mesic to humid sites along small brooks) and Carici acutiformis-Alnetum glutinosae Scamoni 1935 (eutrophic black alder carr forests in the colline zone) with the variants of Ligustrum vulgare and Galium palustre. The community Carici elongatae-Alnetum glutinosae Schwickerath 1933 (mesotrophic to eutrophic alder carr vegetation growing on permanently waterlogged soils), documented only with two phytosociological relevés, was distinguished following expert knowledge. A floristic and ecological pattern of these associations is presented. The major compositional gradients were interpreted based on Ellenberg’s indicator values and the values of environmental variables recorded during the field sampling in the growing season 2011. The principal component analysis revealed the importance of soil moisture, light availability, portion of open water and soil surface for species composition variability at the association level, whereas the variants of Carici acutiformis-Alnetum glutinosae were sorted along the acidity gradient.

scholarly journals Oak forest vegetation in the northern part of the Štiavnické vrchy Mts (Central Slovakia)

Hacquetia ◽  
2010 ◽  
Vol 9 (2) ◽  
pp. 221-238 ◽  
Author(s):  
Michal Slezák ◽  
Anna Petrášová
Keyword(s):  
Species Composition ◽  
Environmental Gradients ◽  
Soil Nutrient ◽  
Forest Vegetation ◽  
Environmental Data ◽  
Data Matrix ◽  
Soil Reaction ◽  
Oak Forest ◽  
Data Set ◽  
Phytosociological Relevés

Oak forest vegetation in the northern part of the Štiavnické vrchy Mts (Central Slovakia)The phytosociological research of the oak forest vegetation was carried out in the northern part of the Štiavnické vrchy Mts (Central Slovakia) using the standard Zürich-Montpellier approach. The data set consisting of 41 phytosociological relevés was obtained by the authors in two vegetation seasons in 2008 and 2009. The numerical classification and the ordination methods were applied to determine the main vegetation types and to explain the structure of the vegetation-environmental data matrix, respectively. Four associations within two classes were distinguished:Luzulo albidae-Quercetum petraeaeHilitzer 1932, typical for shallow, mineral-poor and acidic soils,Melico uniflorae-Quercetum petraeaeGergely 1962 occuring on mesic stands with skeletal and deeper soils,Poo nemoralis-Quercetum dalechampiiŠomšák et Háberová 1979 developing on moderately canopyopened stands in the submontane belt,Sorbo torminalis-QuercetumSvoboda ex Blažková 1962 growing on moderately acidic substrates in drier regions. The major environmental gradients responsible for variation in forest species composition was associated with soil nutrient and soil reaction following the Ellenberg indicator values as well as the measured environmental variables (C/N-ratio and soil acidity). Special attention was given to the discussion on species composition and site ecology.

Composition of prairie plant communities on productive versus unproductive sites in wet and dry years

1995 ◽  
Vol 73 (10) ◽  
pp. 1635-1644 ◽  
Author(s):  
Jon K. Piper
Keyword(s):  
Organic Matter ◽  
Species Composition ◽  
Plant Community ◽  
Restoration Ecology ◽  
Light Availability ◽  
Soil Surface ◽  
Total N ◽  
Aboveground Production ◽  
Grass Biomass ◽  
Natural Productivity

A natural productivity gradient was used to test questions about plant species composition, diversity, and sensitivity to environmental change of prairie vegetation within the tallgrass region. From 1986 to 1992 I monitored seasonal net aboveground production and species composition at four sites with soils that differed in texture and percent organic matter, pH, and concentrations of NH4, total N, K, Ca, Mg, and SO4. Four years of the survey featured above normal precipitation and 3 were drought years. August standing crop averaged 566 ± 307, 419 ± 143, 268 ± 158, and 232 ± 148 g ∙ m−2 at the four sites. Production generally increased with soil fertility (i.e., percent organic matter, total N, and K) and precipitation. The two more productive sites featured higher percentages of grass biomass, but legumes were rare. The site with the lowest soil N supported the consistently highest legume biomass and lowest grass biomass among sites. The least productive site displayed the highest percentage of composites. Species evenness, but not richness, was inversely related to August biomass for all sites. There were significant differences in production across years, as well as in percentages of grass, legume, and composite biomass. Total plot richness ranged from 24 to 40 species sampled per year at site 2 to 51 –53 species at site 4, and tended to decline in the dry year 1989. Poor soils, although less productive overall, appear to prevent dominance by tall grasses and thereby maintain relatively more diverse spring and summer floras. Increased light availability near the soil surface probably enables the persistence of low-growing plants. Evenness, but not richness, varied among sites. The patterns of plant community composition have implications for restoration ecology as well as the design of prairielike perennial grain mixtures. Key words: diversity, evenness, plant community, prairie, soil type, variability.

Customer-Related Social Stressors

2015 ◽  
Vol 14 (4) ◽  
pp. 165-181 ◽  
Author(s):  
Sarah Dudenhöffer ◽  
Christian Dormann
Keyword(s):  
Service Providers ◽  
Principal Component ◽  
Well Being ◽  
Emotional Dissonance ◽  
Social Stressors ◽  
Data Set ◽  
Confirmatory Factor Analyses ◽  
Confirmatory Factor ◽  
Service Jobs ◽  
Single Data

Abstract. The purpose of this study was to replicate the dimensions of the customer-related social stressors (CSS) concept across service jobs, to investigate their consequences for service providers’ well-being, and to examine emotional dissonance as mediator. Data of 20 studies comprising of different service jobs (N = 4,199) were integrated into a single data set and meta-analyzed. Confirmatory factor analyses and explorative principal component analysis confirmed four CSS scales: disproportionate expectations, verbal aggression, ambiguous expectations, disliked customers. These CSS scales were associated with burnout and job satisfaction. Most of the effects were partially mediated by emotional dissonance. Further analyses revealed that differences among jobs exist with regard to the factor solution. However, associations between CSS and outcomes are mainly invariant across service jobs.

HUBUNGAN KEPADATAN BIVALVIA DENGAN PARAMETER LINGKUNGAN DI PESISIR TANJUNG PURA KABUPATEN BANGKA TENGAH

2020 ◽  
Vol 13 (2) ◽  
pp. 112-121
Author(s):  
Sudiyar . ◽  
Okto Supratman ◽  
Indra Ambalika Syari
Keyword(s):  
Negative Impact ◽  
Principal Component ◽  
Environmental Parameters ◽  
Data Retrieval ◽  
Distribution Patterns ◽  
Random System ◽  
System Method ◽  
Relationship Of ◽  
The Relationship ◽  
Anadara Granosa

The destructive fishing feared will give a negative impact on the survival of this organism. This study aims to analyze the density of bivalves, distribution patterns, and to analyze the relationship of bivalves with environmental parameters in Tanjung Pura village. This research was conducted in March 2019. The systematic random system method was used for collecting data of bivalves. The collecting Data retrieval divided into five research stasions. The results obtained 6 types of bivalves from 3 families and the total is 115 individuals. The highest bivalve density is 4.56 ind / m², and the lowest bivalves are located at station 2,1.56 ind / m²,  The pattern of bivalve distribution in the Coastal of Tanjung Pura Village is grouping. The results of principal component analysis (PCA) showed that Anadara granosa species was positively correlated with TSS r = 0.890, Dosinia contusa, Anomalocardia squamosa, Mererix meretrix, Placamen isabellina, and Tellinella spengleri were positively correlated with currents r = 0.933.

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

Agreement and reliability of magnetic, angular rate, and gravity (MARG) sensors to assess multiple body segment’s external loads during off-road running

2021 ◽  
pp. 175433712199698
Author(s):  
Daniel Rojas-Valverde ◽  
José Pino-Ortega ◽  
Rafael Timón ◽  
Randall Gutiérrez-Vargas ◽  
Braulio Sánchez-Ureña ◽  
...  
Keyword(s):  
Vastus Lateralis ◽  
Angular Rate ◽  
Wearable Sensors ◽  
Intraclass Correlation ◽  
Principal Component ◽  
Test Reliability ◽  
Data Set ◽  
Body Segments ◽  
External Loads ◽  
Good Agreement

The extensive use of wearable sensors in sport medicine, exercise medicine, and health has increased the interest in their study. That is why it is necessary to test these technologies’ efficiency, effectiveness, agreement, and reliability in different settings. Consequently, the purpose of this article was to analyze the magnetic, angular rate, and gravity (MARG) sensor’s test-retest agreement and reliability when assessing multiple body segments’ external loads during off-road running. A total of 18 off-road runners (38.78 ± 10.38 years, 73.24 ± 12.6 kg, 172.17 ± 9.48 cm) ran two laps (1st and 2nd Lap) of a 12 km circuit wearing six MARG sensors. The sensors were attached to six different body segments: left (MPLeft) and right (MPRight) malleolus peroneus, left (VLLeft) and right (VLRight) vastus lateralis, lumbar (L1-L3), and thorax (T2-T4) using a special neoprene suit. After a principal component analysis (PCA) was performed, the total data set variance of all body segments was represented by 44.08%–70.64% for the 1st PCA factor considering two variables, Player LoadRT and Impacts, on L1-L3, respectively. These two variables were chosen among three total accelerometry-based external load indicators (ABELIs) to perform the agreement and reliability tests due to their relevance based on PCAs for each body segment. There were no significant differences between laps in the Player LoadRT or Impacts ( p > 0.05, trivial). The intraclass correlation and lineal correlation showed a substantial to almost perfect over-time test consistency assessed via reliability in both Player LoadRT and Impacts. Bias and t-test assessments showed good agreement between Laps. It can be concluded that MARGs sensors offer significant test re-test reliability and good agreement when assessing off-road kinematics in the six different body segments.

scholarly journals Effect of Speech-to-Noise Ratio and Luminance on a Range of Current and Potential Pupil Response Measures to Assess Listening Effort

2021 ◽  
Vol 25 ◽  
pp. 233121652110093
Author(s):  
Patrycja Książek ◽  
Adriana A. Zekveld ◽  
Dorothea Wendt ◽  
Lorenz Fiedler ◽  
Thomas Lunner ◽  
...  
Keyword(s):  
Time Course ◽  
Principal Component ◽  
Growth Curve Analysis ◽  
Listening Effort ◽  
Data Set ◽  
Holistic View ◽  
Hearing Research ◽  
Speech In Noise ◽  
Noise Test ◽  
Noise Ratio

In hearing research, pupillometry is an established method of studying listening effort. The focus of this study was to evaluate several pupil measures extracted from the Task-Evoked Pupil Responses (TEPRs) in speech-in-noise test. A range of analysis approaches was applied to extract these pupil measures, namely (a) pupil peak dilation (PPD); (b) mean pupil dilation (MPD); (c) index of pupillary activity; (d) growth curve analysis (GCA); and (e) principal component analysis (PCA). The effect of signal-to-noise ratio (SNR; Data Set A: –20 dB, –10 dB, +5 dB SNR) and luminance (Data Set B: 0.1 cd/m2, 360 cd/m2) on the TEPRs were investigated. Data Sets A and B were recorded during a speech-in-noise test and included TEPRs from 33 and 27 normal-hearing native Dutch speakers, respectively. The main results were as follows: (a) A significant effect of SNR was revealed for all pupil measures extracted in the time domain (PPD, MPD, GCA, PCA); (b) Two time series analysis approaches (GCA, PCA) provided modeled temporal profiles of TEPRs (GCA); and time windows spanning subtasks performed in a speech-in-noise test (PCA); and (c) All pupil measures revealed a significant effect of luminance. In conclusion, multiple pupil measures showed similar effects of SNR, suggesting that effort may be reflected in multiple aspects of TEPR. Moreover, a direct analysis of the pupil time course seems to provide a more holistic view of TEPRs, yet further research is needed to understand and interpret its measures. Further research is also required to find pupil measures less sensitive to changes in luminance.

scholarly journals Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

2020 ◽  
Vol 17 (10) ◽  
pp. 3730 ◽  
Author(s):  
Sina Shaffiee Haghshenas ◽  
Behrouz Pirouz ◽  
Sami Shaffiee Haghshenas ◽  
Behzad Pirouz ◽  
Patrizia Piro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Infectious Disease ◽  
Relative Humidity ◽  
Cost Function ◽  
Selection Process ◽  
Environmental Parameters ◽  
Pso Algorithm ◽  
Data Set ◽  
De Algorithm

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters—such as daily average temperature, relative humidity, wind speed—and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

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