Links between terrain characteristics and forage patterns of elephants (Loxodonta africana) in northern Botswana

2002 ◽  
Vol 18 (6) ◽  
pp. 835-844 ◽  
Author(s):  
Christian Nellemann ◽  
Stein R. Moe ◽  
Lucas P. Rutina
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Vegetation Change ◽  
Vegetation Type ◽  
Multiple Linear Regression Model ◽  
Stand Density ◽  
Tree Height ◽  
Rugged Terrain ◽  
Stem Breakage ◽  
Terrain Index

Spatial vegetation utilization of elephants was investigated within mixed woodland savanna along the Chobe River in northern Botswana in the dry season of 1998. Using multiple linear regression, accumulated stem breakage by elephants was predicted by a terrain index, distance to water, stand density, number of trees > 4 m tall, tree height, density of Combretum apiculatum, C. elaeagnoides, C. mossambicense and the density of other (accumulated) tree species. Within mixed woodland at 2–7 km distance from the river fine-grained terrain ruggedness was the most important factor contributing to 55% of observed differences in use by elephants, while distance to water and the density of C. apiculatum contributed an additional 20% and 4%, respectively to the multiple linear regression model. Stem breakage was, on average, almost twice as high in rugged terrain compared with flat terrain at similar distance to water within the same vegetation type. Rugged terrain had 2–3-fold higher proportion of plots with very high Combretum shrub densities. These results suggest that the terrain index may be useful in management, predicting the areas most sensitive to vegetation change in a woodland system with increasing elephant densities.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

Predictive Factors for Hamstring Autograft Diameter in Anterior Cruciate Ligament Reconstruction

2019 ◽  
Author(s):  
Willem M.P. Heijboer ◽  
Mathijs A.M. Suijkerbuijk ◽  
Belle L. van Meer ◽  
Eric W.P. Bakker ◽  
Duncan E. Meuffels
Keyword(s):  
Anterior Cruciate Ligament ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Acl Reconstruction ◽  
Cruciate Ligament ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Gracilis Tendon ◽  
Thigh Circumference ◽  
Anterior Cruciate

AbstractMultiple studies found hamstring tendon (HT) autograft diameter to be a risk factor for anterior cruciate ligament (ACL) reconstruction failure. This study aimed to determine which preoperative measurements are associated with HT autograft diameter in ACL reconstruction by directly comparing patient characteristics and cross-sectional area (CSA) measurement of the semitendinosus and gracilis tendon on magnetic resonance imaging (MRI). Fifty-three patients with a primary ACL reconstruction with a four-stranded HT autograft were included in this study. Preoperatively we recorded length, weight, thigh circumference, gender, age, preinjury Tegner activity score, and CSA of the semitendinosus and gracilis tendon on MRI. Total CSA on MRI, weight, height, gender, and thigh circumference were all significantly correlated with HT autograft diameter (p < 0.05). A multiple linear regression model with CSA measurement of the HTs on MRI, weight, and height showed the most explained variance of HT autograft diameter (adjusted R 2 = 44%). A regression equation was derived for an estimation of the expected intraoperative HT autograft diameter: 1.2508 + 0.0400 × total CSA (mm2) + 0.0100 × weight (kg) + 0.0296 × length (cm). The Bland and Altman analysis indicated a 95% limit of agreement of ± 1.14 mm and an error correlation of r = 0.47. Smaller CSA of the semitendinosus and gracilis tendon on MRI, shorter stature, lower weight, smaller thigh circumference, and female gender are associated with a smaller four-stranded HT autograft diameter in ACL reconstruction. Multiple linear regression analysis indicated that the combination of MRI CSA measurement, weight, and height is the strongest predictor.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

Musculoskeletal symptoms, psychosocial factors and work ability in Brazilian labor justice workers

Work ◽  
2021 ◽  
pp. 1-10
Author(s):  
Fauzi El Kadri Filho ◽  
Tha&ıs Moreira São-João ◽  
Neusa Maria Costa Alexandre ◽  
Sergio Roberto de Lucca ◽  
Cristiane Helena Gallasch ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Psychosocial Factors ◽  
Work Ability ◽  
Multiple Linear Regression Model ◽  
Female Gender ◽  
Musculoskeletal Symptoms ◽  
Judicial Process ◽  
Cross Sectional ◽  
Important Relationship

BACKGROUND: The implementation of the electronic judicial process (PJe) in recent years is associated with an increase in workload and stricter control through productivity targets in the Brazilian labor judiciary. OBJECTIVES: To evaluate the relations between musculoskeletal symptoms, psychosocial factors and work ability in civil servants of a labor justice body in the context of the PJe. METHODS: A cross-sectional exploratory study with a quantitative approach involving 449 workers. Sociodemographic, occupational and related data were collected through questionnaires validated in the Brazilian context. Data analysis was conducted by descriptive and inferential statistics: Mann-Whitney test, Spearman’s correlation coefficient and multiple linear regression. RESULTS: There was a correlation between musculoskeletal symptoms and psychosocial factors (p <  0.05), as well as between both musculoskeletal symptoms and psychosocial factors with reduced work ability (p <  0.05). The multiple linear regression model pointed to the female gender and the dimensions “demands”, “control” and “peer support” as related to the musculoskeletal symptoms. CONCLUSION: We highlight the importance of a broader approach, involving psychosocial factors in preventive actions related to musculoskeletal disorders considering the important relationship with work ability.

On the System of Multiple Linear Regression of Higher Education Tuition in China

2011 ◽  
Vol 211-212 ◽  
pp. 752-755
Author(s):  
Wei Wang ◽  
De Quan Liu ◽  
Jian Liu
Keyword(s):  
Higher Education ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multiple Linear Regression Model ◽  
The State ◽  
State Funding ◽  
Reasonable Assumption ◽  
Training Costs ◽  
Higher Education In China ◽  
Education In China

Based on the fairness and benefits of the opportunity to accept higher education, tuition should be paid. The average cost of higher education and the ability to pay tuition which is determined by the average income are two fundamental basics for making the standard of tuition. In order to research the standard of tuition, the paper has focused on two issues: What is the acceptable range of tuition for higher education in China? How much is the reasonable tuition for higher education in China? Then we have established a multiple linear regression model on the basis of the reasonable assumption. Using the Eviews Software, we got that the range of average tuition was [4674.4, 7516.1]. Then we made a sensitivity analysis on the state funding and got the conclusion, tuition of colleges and universities all over our country were reasonable, and that the average tuition was negatively correlated to the state funding to some extent. Through the conclusion, we obtained that the state funding accounted for 25% in training costs of students. When the state funding rose by 250 yuan, the average tuition would decrease by 238 yuan.

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