scholarly journals The effects of precipitation variability on the canopy cover of forage species in arid rangelands, Iran

2020 ◽  
Vol 13 (18) ◽  
Author(s):  
Hamed Joneidi ◽  
Nahid Azizi ◽  
Khaled Osati ◽  
Isa Bandak
Keyword(s):  
Linear Regression ◽  
Confidence Level ◽  
Canopy Cover ◽  
Floristic Composition ◽  
Linear Regression Models ◽  
Growing Seasons ◽  
Arid Rangelands ◽  
Annual Plant Species ◽  
The Relationship ◽  
Precipitation And Temperature

Abstract This research was conducted to monitor changes in canopy cover of typical species during a 10-year period in the part of arid rangelands, to find out the relationship between two important climate variables (precipitation and temperature) and canopy cover changes. For that reason, canopy cover percentages of six dominant perennials and all annual plant species combined were measured during a 10-year period at phenological maturity of plant in thirty 2 m × 2 m plots which were placed along two 250-m transect lines. The results demonstrated that the maximum canopy cover for water year 2006–2007 (wet year) and the minimum value for water year 2012–2013 (drought) were 15 and 5.5%, respectively. The canopy cover was modeled by linear regression in which precipitation and temperature variables were considered independent variables. April precipitation explained 65% of changes in the canopy cover percentage of Artemisia sieberi at 95% confidence level (RRMSE = 0.26 and MAE = 0.49). The best simple linear regression models for estimating canopy cover percentages of Stipa barbata and Zygophyllum eurypterum corresponded to cumulative 4-month precipitation from March to June and March precipitation respectively, representing 77% (at 99% confidence level) and 67% (at 95% confidence level) of changes correspondingly. Considering the dominance of A. sieberi, S. barbata, and Z. eurypterum in floristic composition of the study area, it can be concluded that most changes in canopy cover of the studied rangeland are predicted by variability of precipitation during growing seasons.

scholarly journals Effects of Winter Snow Cover on Spring Soil Moisture Based on Remote Sensing Data Product over Farmland in Northeast China

2020 ◽  
Vol 12 (17) ◽  
pp. 2716
Author(s):  
Shuang Liang ◽  
Xiaofeng Li ◽  
Xingming Zheng ◽  
Tao Jiang ◽  
Xiaojie Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Linear Regression ◽  
Snow Cover ◽  
Snow Depth ◽  
Northeast China ◽  
Regression Models ◽  
Linear Regression Models ◽  
Winter Snow ◽  
The Relationship

Spring soil moisture (SM) is of great importance for monitoring agricultural drought and waterlogging in farmland areas. While winter snow cover has an important impact on spring SM, relatively little research has examined the correlation between winter snow cover and spring SM in great detail. To understand the effects of snow cover on SM over farmland, the relationship between winter snow cover parameters (maximum snow depth (MSD) and average snow depth (ASD)) and spring SM in Northeast China was examined based on 30 year passive microwave snow depth (SD) and SM remote-sensing products. Linear regression models based on winter snow cover were established to predict spring SM. Moreover, 4 year SD and SM data were applied to validate the performance of the linear regression models. Additionally, the effects of meteorological factors on spring SM also were analyzed using multiparameter linear regression models. Finally, as a specific application, the best-performing model was used to predict the probability of spring drought and waterlogging in farmland in Northeast China. Our results illustrated the positive effects of winter snow cover on spring SM. The average correlation coefficient (R) of winter snow cover and spring SM was above 0.5 (significant at a 95% confidence level) over farmland. The performance of the relationship between snow cover and SM in April was better than that in May. Compared to the multiparameter linear regression models in terms of fitting coefficient, MSD can be used as an important snow parameter to predict spring drought and waterlogging probability in April. Specifically, if the relative SM threshold is 50% when spring drought occurs in April, the prediction probability of the linear regression model concerning snow cover and spring SM can reach 74%. This study improved our understanding of the effects of winter snow cover on spring SM and will be beneficial for further studies on the prediction of spring drought.

BAYESIAN IDENTIFICATION OF OUTLIERS AND CHANGE-POINTS IN MEASUREMENT ERROR MODELS

2005 ◽  
Vol 08 (04) ◽  
pp. 433-449 ◽  
Author(s):  
FERNANDO A. QUINTANA ◽  
PILAR L. IGLESIAS ◽  
HELENO BOLFARINE
Keyword(s):  
Linear Regression ◽  
Outlier Detection ◽  
Change Point ◽  
Regression Models ◽  
Point Problem ◽  
Linear Regression Models ◽  
Outlier Identification ◽  
Change Point Problem ◽  
Error In Variables ◽  
The Relationship

The problem of outlier and change-point identification has received considerable attention in traditional linear regression models from both, classical and Bayesian standpoints. In contrast, for the case of regression models with measurement errors, also known as error-in-variables models, the corresponding literature is scarce and largely focused on classical solutions for the normal case. The main object of this paper is to propose clustering algorithms for outlier detection and change-point identification in scale mixture of error-in-variables models. We propose an approach based on product partition models (PPMs) which allows one to study clustering for the models under consideration. This includes the change-point problem and outlier detection as special cases. The outlier identification problem is approached by adapting the algorithms developed by Quintana and Iglesias [32] for simple linear regression models. A special algorithm is developed for the change-point problem which can be applied in a more general setup. The methods are illustrated with two applications: (i) outlier identification in a problem involving the relationship between two methods for measuring serum kanamycin in blood samples from babies, and (ii) change-point identification in the relationship between the monthly dollar volume of sales on the Boston Stock Exchange and the combined monthly dollar volumes for the New York and American Stock Exchanges.

scholarly journals Factors associated with parasite dominance in fishes from Brazil

2016 ◽  
Vol 25 (2) ◽  
pp. 225-230
Author(s):  
Cristina Fernandes do Amarante ◽  
Wagner de Souza Tassinari ◽  
Jose Luis Luque ◽  
Maria Julia Salim Pereira
Keyword(s):  
Linear Regression ◽  
Body Length ◽  
Regression Models ◽  
Mixed Model ◽  
Multiple Linear Regression Model ◽  
Mixed Effects ◽  
The Body ◽  
Linear Regression Models ◽  
Biological Aspects ◽  
The Relationship

Abstract The present study used regression models to evaluate the existence of factors that may influence the numerical parasite dominance with an epidemiological approximation. A database including 3,746 fish specimens and their respective parasites were used to evaluate the relationship between parasite dominance and biotic characteristics inherent to the studied hosts and the parasite taxa. Multivariate, classical, and mixed effects linear regression models were fitted. The calculations were performed using R software (95% CI). In the fitting of the classical multiple linear regression model, freshwater and planktivorous fish species and body length, as well as the species of the taxa Trematoda, Monogenea, and Hirudinea, were associated with parasite dominance. However, the fitting of the mixed effects model showed that the body length of the host and the species of the taxa Nematoda, Trematoda, Monogenea, Hirudinea, and Crustacea were significantly associated with parasite dominance. Studies that consider specific biological aspects of the hosts and parasites should expand the knowledge regarding factors that influence the numerical dominance of fish in Brazil. The use of a mixed model shows, once again, the importance of the appropriate use of a model correlated with the characteristics of the data to obtain consistent results.

scholarly journals Reduced left atrial strain in magnetic resonance imaging is associated with abnormal P-waves in patients after acute myocardial infarction

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
M Wester ◽  
J Pec ◽  
C Fisser ◽  
K Debl ◽  
O Hamer ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Linear Regression ◽  
Left Atrial ◽  
Regression Models ◽  
Linear Regression Analysis ◽  
P Wave ◽  
P Value ◽  
Linear Regression Models ◽  
The Relationship

Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): ReForM-B-Program Background Abnormal P-wave terminal force in lead V1 (PTFV1) is associated with atrial remodeling. The relationship between PTFV1 and atrial function after acute myocardial injury is insufficiently understood and may be elucidated by detailed feature tracking (FT) strain analysis of cardiac magnetic resonance images (CMR). Purpose We investigated the relationship between PTFV1 and left atrial (LA) strain (measured by CMR) in a patient cohort presenting with acute myocardial infarction (MI). Methods 56 patients with acute MI underwent CMR within 3-5 days after MI. PTFV1 was measured as the product of negative P-wave amplitude and duration in lead V1 (Fig. A). A PTFV1 >4000 ms*µV was defined as abnormal. CMR cine data were retrospectively analyzed using a dedicated FT software. LA strain (ε) and strain rate (SR) for atrial reservoir ([εs]; [SRs]), conduit ([εe]; [SRe]) and booster function ([εa]; [SRa]) were measured in two long-axis views (Fig. A). Results Patients with abnormal PTFV1 had significantly reduced LA conduit function εe and SRe (Fig. B + D). There was a significant negative correlation between the extent of PTFV1 and both εe and SRe (Fig. C + E). In univariate and multivariate regression models, both PTFV1 and age predicted atrial conduit function. In contrast, multiple clinical co-factors had no significant influence on εe (Table). Interestingly, linear regression models revealed only mild dependency of PTFV1 on conventional parameters of cardiac function such as left ventricular ejection fraction (p = 0.059; R²(adj.)=0.047), and no dependency on structural parameters such as LA area (p = 0.639; R²(adj.)=0.016), or LA fractional area change (p = 0.825; R²(adj.)=0.020). Conclusion Abnormal PTFV1 was associated with reduced LA function independent from numerous clinical co-factors in patients presenting with acute myocardial infarction. Table N = 56 Linear Regression Analysis Multiple Linear Regression Analysis (R2 (adj.)=0.376, p = 0.016) Variable B 95% CI P value R2 (adj.) B 95% CI P value PTFV1 [µV*ms] -1.628 17085.298 to 27210.854 0.013 0.092 -1.315 -2.614 to -0.016 0.047 Age [y] -425.775 24985.168 to 54634.995 0.002 0.145 -610.815 -982.78 to -238.849 0.001 Body mass indes [kg/m2] -185.653 -3259.187 to 47020.775 0.671 -0.015 -506.096 -1327.357 to 315.165 0.219 Creatinine kinase [U/l] -1.571 14806.991 to 24842.272 0.121 0.027 -1.791 -3.72 to 0.138 0.067 Male sex -893.28 10701.206 to 23504.066 0.802 -0.017 4275.631 -3842.517 to 12393.78 0.292 Estimated glomerular filtration rate [ml/min/1.73m2] 88.617 -4564.177 to 21395.361 0.202 0.012 -163.981 -331.343 to 3.381 0.054 Systolic blood pressure [mmHg] -2.001 14045.786 to 22037.253 0.095 0.038 29.331 -108.243 to 166.906 0.668 nt-pro brain natriuretic peptide [pg/ml] 24.629 -4060.804 to 30920.828 0.716 -0.016 1.015 -1.778 to 3.809 0.466 Univariate and multivariate linear regression models for left atrial conduit strain Abstract Figure

scholarly journals CRANIAL MEASUREMENTS

2015 ◽  
Vol 22 (08) ◽  
pp. 1034-1038
Author(s):  
Mohammad Afzal Khan ◽  
Muhammad Naeem Chaudhry ◽  
Faris Mohammed Nour Altaf
Keyword(s):  
Linear Regression ◽  
Correlation Coefficient ◽  
Linear Regression Models ◽  
Cranial Measurements ◽  
Different Populations ◽  
The Mean ◽  
Head Height ◽  
The Relationship ◽  
The City ◽  
Gender Specific

Human body exhibits regular age, sex and race dependent proportions amongstits various segments relative to its height. Knowledge of the cranial morphometry is importantfrom clinical and forensic view point. The stature of a person being genetically predeterminedis an inherent characteristic, the estimation of which is considered to be important assessmentin identification of human remains. Norms of regression formulae for calculation of height arerequired for different populations. Objectives: To document norms for cranial dimensions andpresent linear regression formulae for stature prediction in adult male and female populationof Southern Punjab. Place and duration of study: The study was conducted at the MultanMedical and Dental College, Multan and took about fourteen months to complete. Materialand methods: The study was conducted on 672 adult individuals (430 males and 242 females)from in and around the city of Multan in Punjab. Measurements of the head including maximumcranial length (glabella-inion length), maximum cranial breadth (maximum bi-parietal diameter)and maximum auricular head height were taken. Results were expressed as mean ± SD.Height was measured in standing anatomical position. Correlation coefficient of Pearsonwas used to find the relationship between various cranial dimensions using which the linearregression formulae to predict the stature were derived. Results: The mean height of the studypopulation was found to be significantly different between genders. The males appeared tobe considerably taller than females. The mean cranial length, cranial breadth and auricularhead height the measurements were larger significantly in the males as compared to females.Pearson’s correlation coefficient between stature and cranial measurements was found to behighly positive for both sexes. Linear regression formulae to predict the stature from the cranialdimensions were derived. Conclusion: The study is conducted to document norms for cranialdimensions and it presented gender specific linear regression models for stature prediction inadult South Punjab population.

scholarly journals A study on factors related to readership of scientific articles

2017 ◽  
Vol 3 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Tony Xu ◽  
Shayan Khalili ◽  
Cynthia Deng
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Scientific Research ◽  
The Other ◽  
Linear Regression Models ◽  
Research Papers ◽  
Scatter Plots ◽  
The Subject ◽  
Moderate Effect ◽  
The Relationship

This paper analyzes the relationship between the number of Twitter and Mendeley readers with the article’s subject, publisher, journal, and title length. It also looks at which country has the greatest number of readers to see if researchers can garner more visibility by publishing an article relevant to issues in those countries. The purpose of this report is to help researchers improve the visibility and impact value of their research. The data was gathered from 550,000 scientific research papers published between January 1st and July 1st of 2016. Python’s built-in JSON library was used to extract the number of Twitter and Mendeley readers, as well as the article count for each factor. The correlation between readers per article and each factor was then visualized using bubble graphs, linear regression models, and scatter plots. This paper concludes that the length of the title is the strongest factor affecting readership. In particular, titles with lengths between 51 and 90 characters have the greatest number of readers. Moreover, articles relevant to issues in countries with a higher GDP have the highest overall readership. On the other hand, the publisher and the journal did not have a significant effect on readership, while the subject of the article had a moderate effect on readership.

scholarly journals Comparison of Simple and Segmented Linear Regression Models on the Effect of Sea Depth toward the Sea Temperature

2021 ◽  
Vol 1 (2) ◽  
pp. 68-75
Author(s):  
Muhammad Bayu Nirwana ◽  
Dewi Wulandari
Keyword(s):  
Linear Regression ◽  
Linear Regression Model ◽  
Change Point ◽  
Regression Models ◽  
Linear Regression Models ◽  
Likelihood Estimator ◽  
Sea Temperature ◽  
Independent Variables ◽  
Modeling Data ◽  
The Relationship

The linear regression model is employed when it is identified a linear relationship between the dependent and independent variables. In some cases, the relationship between the two variables does not generate a linear line, that is, there is a change point at a certain point. Therefore, themaximum likelihood estimator for the linear regression does not produce an accurate model. The objective of this study is to presents the performance of simple linear and segmented linear regression models in which there are breakpoints in the data. The modeling is performed onthe data of depth and sea temperature. The model results display that the segmented linear regression is better in modeling data which contain changing points than the classical one.Received September 1, 2021Revised November 2, 2021Accepted November 11, 2021

Electoral Participation, Disagreement, and Diversity in Social Networks: A Matter of Intimacy?

2018 ◽  
Vol 47 (7) ◽  
pp. 1056-1078 ◽  
Author(s):  
Riccardo Ladini ◽  
Moreno Mancosu ◽  
Cristiano Vezzoni
Keyword(s):  
Social Networks ◽  
Linear Regression ◽  
Regression Models ◽  
Electoral Participation ◽  
Linear Regression Models ◽  
Election Study ◽  
Significant Evidence ◽  
Political Views ◽  
National Election Study ◽  
The Relationship

General consensus concerning the nature of the relationship between political disagreement and turnout has not yet been reached: while several studies have demonstrated the demobilizing effect of disagreement, others have found no significant evidence for this. Recently, scholars have argued that diversity — a situation in which some people are in agreement with ego and some are not — can boost electoral participation. The present article argues that the insights of previous studies are the result of two differentiated effects that depend on the level of intimacy of the discussants to which one is exposed. By employing a set of linear regression models on Italian National Election Study 2013 pre-electoral survey ( N > 8,000), we show that mixed political views among friends boost electoral participation. For what concerns relatives, the likelihood to vote decreases linearly with increasing disagreement.

Effect of Pasture Availability on Bait Uptake by Feral Pigs in Australia's Semi-Arid Rangelands.

1996 ◽  
Vol 23 (4) ◽  
pp. 421 ◽  
Author(s):  
D Choquenot ◽  
B Lukins
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
New South ◽  
Linear Regression Analysis ◽  
Feral Pigs ◽  
South Wales ◽  
Arid Rangelands ◽  
The Relationship ◽  
North Western ◽  
Semi Arid

Factors that influence bait uptake by feral pigs will determine the efficacy of poisoning and trapping programmes for the control of pigs and have the potential to introduce bias to indices of pig abundance requiring bait consumption. In this study, the influence of pasture availability on uptake of bait trails consisting of soaked wheat by pigs in the semi-arid rangelands of north-western New South Wales was investigated. Percentage uptake of bait trails, pig density and pasture biomass were estimated for six sites along the Paroo River on six occasions: two each when pasture biomass was relatively high, moderate and low. The influence of pasture biomass on the relationship between percentage uptake of bait trails and pig density was examined by linear regression analysis. The analysis demonstrated that increasing pasture biomass significantly reduced the density of pigs corresponding to a given percentage uptake of bait trails, suggesting that, as pasture biomass increased, fewer pigs consumed bait trails andlor the number of bait trails each pig consumed declined. Assuming the former, the effect of increasing pasture biomass on the relationship between percentage uptake of bait trails and pig density indicated that, for every increase in pasture biomass of 100 kg ha-1, the percentage of pigs consuming bait declined by about 10%. The implications of these results for pig control and bias associated with indices of pig abundance requiring bait consumption are discussed.

scholarly journals Utilization of Air Permeability in Predicting the Thermal Conductivity

2004 ◽  
Vol os-13 (2) ◽  
pp. 1558925004os-13
Author(s):  
Pamela Banks-Lee ◽  
Massoud Mohammadi ◽  
Parviz Ghadimi
Keyword(s):  
Thermal Conductivity ◽  
Linear Regression ◽  
Pore Size ◽  
Effective Thermal Conductivity ◽  
Regression Models ◽  
Temperature Drop ◽  
Air Permeability ◽  
Linear Regression Models ◽  
Fabric Weight ◽  
The Relationship

General linear regression models were used to determine the relationship between thermal conductivity and specific air permeability of 48 heterogeneous, needlepunched, nonwoven samples that were made from ceramic and glass webs. Parameters analyzed included number of needle barbs, fabric weight, thickness, and porosity. Other factors considered were fabric layering structure, temperature drop across the fabric and specific air permeability. Of the linear regression models examined, three models were found to be significant at greater than 95% confidence. These models had r2 values of greater than 97%. Factors that proved to be greater than 95% significant in predicting the effective thermal conductivity of the samples tested were fabric thickness and weight, fabric porosity, fabric mean pore size, and specific air permeability.

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