scholarly journals Understanding Determinants of Hunting Trip Productivity in an Arctic Community

2021 ◽  
Vol 5 ◽  
Author(s):  
Angus W. Naylor ◽  
Tristan Pearce ◽  
James D. Ford ◽  
David Fawcett ◽  
Peter Collings ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Tracking System ◽  
A Priori ◽  
Gps Tracking ◽  
Annual Productivity ◽  
Heating Fuel ◽  
Main Variable

We examine factors underlying hunting productivity among Inuit in Ulukhaktok, Northwest Territories, Canada. Specifically, we focus on the role of gasoline use as the main variable of interest—commonly cited as a crucial determinant of hunting participation. Over the course of 12 months, 10 hunters recorded their on-the-land activities using a GPS tracking system, participatory mapping sessions, and bi-weekly interviews. A multivariable linear regression model (MvLRM) was applied to assess whether factors such as consumables used (i.e. heating fuel, gasoline, oil, food), distances traveled, or the number of companions on a trip were associated with the mass of edible foods returned to the community. Results indicate that, despite being positively associated with hunting trip productivity when assessed through a univariable linear regression model, gasoline is not a statistically significant determinant of standalone trip yield when adjusting for other variables in a multivariable linear regression. Instead, factors relating to seasonality, number of companions, and days on the land emerged as more significant and substantive drivers of productivity while out on the land. The findings do not suggest that access to, or the availability of, gasoline does not affect whether a hunting trip commences or is planned, nor that an increase in the amount of gasoline available to a hunter might increase the frequency of trips (and therefore annual productivity). Rather, this work demonstrates that the volume of gasoline used by harvesters on standalone hunting trips represent a poor a priori predictor of the edible weight that harvesters are likely to return to the community.

scholarly journals Pengaruh pendapatan, kepercayaan dan religiusitas terhadap minat untuk membayar zakat penghasilan

2021 ◽  
Vol 1 (1) ◽  
pp. 12-24
Author(s):  
Sumadi Sumadi ◽  
Dini Priliastuti
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Sampling Technique ◽  
Quantitative Approach ◽  
Research Model ◽  
Probability Sampling

This study aims to determine the effect of income, belief, and religiosity on the interest in paying zakat income (study of residents of Makamhaji Kartasura). This study uses a quantitative approach to the population of Makamhaji Kartasura Village residents. The sampling technique uses probability sampling to analyze the data using a multiple linear regression model. The results showed that income had no significant effect on the interest in paying zakat income. In contrast, trust and religiosity have a significant effect on the interest in paying zakat income. Meanwhile, simultaneous income, belief, and religiosity have influenced the residents of Makamhaji Kartasura Village to pay zakat income. This research model contributes to explaining the role of trust and religiosity on increasing zakat income.

scholarly journals A posteriori calculation of δ<sup>18</sup>O and δD in atmospheric water vapour from ground-based near-infrared FTIR retrievals of H<sub>2</sub><sup>16</sup>O, H<sub>2</sub><sup>18</sup>O, and HD<sup>16</sup>O

2014 ◽  
Vol 7 (8) ◽  
pp. 2567-2580 ◽  
Author(s):  
N. V. Rokotyan ◽  
V. I. Zakharov ◽  
K. G. Gribanov ◽  
M. Schneider ◽  
F.-M. Bréon ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Infrared Spectra ◽  
Linear Regression Model ◽  
Near Infrared ◽  
A Priori ◽  
A Posteriori ◽  
Simple Linear Regression ◽  
Simple Linear Regression Model ◽  
Δd And Δ18o

Abstract. This paper investigates the scientific value of retrieving H218O and HDO columns in addition to H216O columns from high-resolution ground-based near-infrared spectra. We present a set of refined H216O, H218O, and HDO spectral windows. The retrieved H216O, H218O, and HDO columns are used for an a posteriori calculation of columnar δD and δ18O. We estimate the uncertainties for the so-calculated columnar δD and δ18O values. These estimations include uncertainties due to the measurement noise, errors in the a priori data, and uncertainties in spectroscopic parameters. Time series of δ18O obtained from ground-based FTIR (Fourier transform infrared) spectra are presented for the first time. For our study we use a full physics isotopic general circulation model (ECHAM5-wiso). We show that the full physics simulation of HDO and H218O can already be reasonably predicted from the H216O columns by a simple linear regression model (scatter values between full physics and linear regression simulations are 35 and 4‰ for HDO and H218O, respectively). We document that the columnar δD and δ18O values as calculated a posteriori from the retrievals of H216O, H218O, and HDO show a better agreement with the ECHAM5-wiso simulation than the δD and δ18O values as calculated from the H216O retrievals and the simple linear regression model. This suggests that the H218O and HDO column retrievals add complementary information to the H216O retrievals. However, these data have to be used carefully, because of the different vertical sensitivity of the H216O, H218O, and HDO columnar retrievals. Furthermore, we have to note that the retrievals use reanalysis humidity profiles as a priori input and the results are thus not independent of the reanalysis data.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

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.

scholarly journals Vitamin A and E Homologues Impacting the Fate of Acrylamide in Equimolar Asparagine-Glucose Model System

Antioxidants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 993
Author(s):  
Su Lee Kuek ◽  
Azmil Haizam Ahmad Tarmizi ◽  
Raznim Arni Abd Razak ◽  
Selamat Jinap ◽  
Maimunah Sanny
Keyword(s):  
Vitamin E ◽  
Linear Regression ◽  
Vitamin A ◽  
Regression Model ◽  
Linear Regression Model ◽  
Model System ◽  
Concentration Increase ◽  
Acrylamide Formation ◽  
Β Carotene

This study aims to evaluate the influence of Vitamin A and E homologues toward acrylamide in equimolar asparagine-glucose model system. Vitamin A homologue as β-carotene (BC) and five Vitamin E homologues, i.e., α-tocopherol (AT), δ-tocopherol (DT), α-tocotrienol (ATT), γ-tocotrienol (GTT), and δ-tocotrienol (DTT), were tested at different concentrations (1 and 10 µmol) and subjected to heating at 160 °C for 20 min before acrylamide quantification. At lower concentrations (1 µmol; 431, 403, 411 ppm, respectively), AT, DT, and GTT significantly increase acrylamide. Except for DT, enhancing concentration to 10 µmol (5370, 4310, 4250, 3970, and 4110 ppm, respectively) caused significant acrylamide formation. From linear regression model, acrylamide concentration demonstrated significant depreciation over concentration increase in AT (Beta = −83.0, R2 = 0.652, p ≤ 0.05) and DT (Beta = −71.6, R2 = 0.930, p ≤ 0.05). This study indicates that different Vitamin A and E homologue concentrations could determine their functionality either as antioxidants or pro-oxidants.

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