Quantitative Description of the Germination of Littleseed Canarygrass (Phalaris minor) in Response to Temperature

Weed Science ◽  
2014 ◽  
Vol 62 (2) ◽  
pp. 250-257 ◽  
Author(s):  
Abolfazl Derakhshan ◽  
Javid Gherekhloo ◽  
Ribas A. Vidal ◽  
Rafael De Prado
Keyword(s):  
Germination Rate ◽  
Quantitative Description ◽  
Absolute Error ◽  
Optimization Method ◽  
Coefficient Of Determination ◽  
Segmented Model ◽  
Nonlinear Regression Models ◽  
Time Required ◽  
Response To Temperature ◽  
Cardinal Temperatures

Littleseed canarygrass is a troublesome grass weed in wheat fields in Iran. Predicting weed emergence dynamics can help farmers more effectively control weeds. In this work, four nonlinear regression models (beta, three-piece segmented, two-piece segmented, and modified Malo's exponential sine) were compared to describe the cardinal temperatures for the germination of littleseed canarygrass. Two replicated experiments were performed with the same temperatures. An iterative optimization method was used to calibrate the models and different statistical indices (mean absolute error [MAE], coefficient of determination [R2], intercept and slope of the regression equation of predicted vs. observed hours to germination) were applied to compare their performance. The three-piece segmented model was the best model to predict the germination rate (R2= 0.99, MAE = 0.20 d, and coefficient of variation 1.01 to 4.06%). Based on the model outputs, the base, the lower optimum, the upper optimum, and the maximum temperatures for the germination of littleseed canarygrass were estimated to be 4.69, 22.60, 29.62, and 38.13 C, respectively. The thermal time required to reach 10, 50, and 90% germination was 31.98, 39.26 and 45.55 degree-days, respectively. The cardinal temperatures depended on the model used for their estimation. Overall, the three-piece segmented model was better suited than the other models to estimate the cardinal temperatures for the germination of littleseed canarygrass.

Cardinal temperatures and thermal time required foremergence of lenti (Lens culinaris Medik)

2017 ◽  
Author(s):  
Ali reza Safahani ◽  
Behnam Kamakar ◽  
Amir Nabizadeh
Keyword(s):  
Lens Culinaris ◽  
Seedling Emergence ◽  
Information Criterion ◽  
Optimization Method ◽  
Thermal Time ◽  
Coefficient Of Determination ◽  
Nonlinear Regression Models ◽  
Maximum Temperatures ◽  
Time Required ◽  
Cardinal Temperatures

The present study was performed to compare four nonlinear regression models (segmented, beta, beta modified, and dent-like) to describe the emergence rate–temperature relationships of six lentil (Lens culinaris Medik) cultivars at field experiment with a range of sowing dates, with the aim of identifying the cardinal temperatures and physiological days (i.e., number of days under optimum temperatures) required for seedling emergence. Models and statistical indices were calibrated using an iterative optimization method and their performance was compared by root mean square error (RMSD), coefficient of determination (R2) and corrected Akaike information criterion correction (AIC). The beta model was found to be the best model for predicting the response of lentil emergence to temperature, (R2= 0.99; RMSD= 0.005; AICc= -232.97). Based on the model outputs, the base, optimum, and maximum temperatures of seedling emergence were 4.5, 22.9, and 40 °C, respectively. The Six physiological days (equivalent to a thermal time of 94 °C days) were required from sowing to emergence

scholarly journals EVALUATION OF CARDINAL TEMPERATURES AND THERMAL TIME REQUIREMENT FOR GERMINATION OF SCROPHULARIA STRIATA AND TANACETUM POLYCEPHALUM (SCHULTZ BIP. SSP. HETEROPHYLLUM)

2018 ◽  
Vol 78 ◽  
pp. 83-97
Author(s):  
Bahram Karavani ◽  
Reza Tavakkol Afshari ◽  
Nasser Majnoon Hosseini ◽  
Seyed Amir Moosavi ◽  
Hamed Akbari
Keyword(s):  
Medicinal Plants ◽  
Sesquiterpene Lactones ◽  
Germination Rate ◽  
Optimization Method ◽  
Thermal Time ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
Scrophularia Striata ◽  
Maximum Temperatures ◽  
Cardinal Temperatures

Scrophularia striata and Tanacetum polycephalum are important medicinal plants in Iran which are rich inessential oils, bitter substances, and sesquiterpene lactones. The present study was conducted to compare fournon-linear regression models (segmented, beta, beta modified and Dent-like) to describe the germination ratetemperaturerelationships of Scrophularia striata and Tanacetum polycephalum over eight and seven constanttemperatures, respectively, to find cardinal temperatures and thermal time requirements to reach differentgermination percentiles. An iterative optimization method was used to calibrate the models and differentstatistical indices including RMSE, coefficient of determination (R2), and AICc were applied to compare theirperformance. The beta model was found to be the best model to predict germination rate of Scrophulariastriata at D10, D50 and D90 (R2 = 0.96, R2 = 0.97, R2 = 0.95; RMSE = 0.005, 0.001 and 0.001, respectively).According to this model outputs, the base, optimum, and the maximum temperatures for germination wereestimated as 1.21 ± 0.39, 25.91 ± 0.33 and 46.35 ± 4.12 °C, respectively. Also the segmented model wasfound to be the best model to predict germination rate of Tanacetum polycephalum at D10, D50 and D90 (R2= 0.98, R2 = 0.98, R2 = 0.98; RMSE = 0.067, 0.59 and 0.56, respectively). According to the model outputs, thebase, optimum, and the maximum temperatures for germination were estimated as 0.44±1.15, 26.95±0.75 and38.33±0.98 oC, respectively. It seems these two medicinal plants need moderate optimum temperature for seedgermination.

scholarly journals Effect of temperature on the physical changes and drying kinetics in plum (Prunus domestica L.) Pozegaca variety

2011 ◽  
Vol 17 (3) ◽  
pp. 283-289 ◽  
Author(s):  
Milovan Zivkovic ◽  
Sveto Rakic ◽  
Radojka Maletic ◽  
Dragan Povrenovic ◽  
Milos Nikolic ◽  
...  
Keyword(s):  
Effective Diffusivity ◽  
Absolute Error ◽  
Drying Kinetics ◽  
Effect Of Temperature ◽  
Coefficient Of Determination ◽  
Maximum Absolute Error ◽  
Nonlinear Regression Models ◽  
Regression Error ◽  
Physical Changes ◽  
Model Correlation

In this study, drying kinetics of autochthonous variety Pozegaca plum was examined in a laboratory dryer at three temperatures. The whole plum fruits, together with the kernels were subjected to the drying process. The effect of drying has been examined at temperatures 55, 60 and 75?C, with a constant air velocity of 1.1 m s-1. The corresponding experimental results were tested using six nonlinear regression models. Coefficient of determination (R2), standard regression error (SSE), model correlation coeficient (Vy), as well as the maximum absolute error (?Y) showed that logaritmic model was in good agreement with the experimental data obtained. During drying of plums, the effective diffusivity was found to be between 5.6?10-9 for 55?C and 8.9?10-9 m2 s-1 at 75?C, respectively. The physical characteristics of the fresh (lenght 39.64 mm and width 29.15 mm) and dried (lenght 37.52 mm and width 22.85 mm) plum fruit were determined. Finally, by chemical analysis, the content of micro-and macro41 elements (Fe, Mn, Cu, B and N, F, K, Ca, Mg, S) in the skin and flesh of the dried product, prunes, has been established.

scholarly journals Cardinal temperatures for germination of Salvia leriifolia Benth

2015 ◽  
Vol 61 (1) ◽  
pp. 5-18 ◽  
Author(s):  
Majid Dashti ◽  
Mohammad Kafi ◽  
Hossein Tavakkoli ◽  
Mahdi Mirza
Keyword(s):  
Regression Models ◽  
Germination Rate ◽  
Germination Percentage ◽  
Randomized Design ◽  
Germination Process ◽  
Mean Germination Time ◽  
Completely Randomized Design ◽  
Response To Temperature ◽  
Germination Traits ◽  
Cardinal Temperatures

Summary The focus of this study is based on the examination of the germination traits and the development of thermal models of the medicinal plant Salvia leriifolia Benth. A laboratory experiment was carried out at constant temperatures ranging from 0 to 35°C, at 5°C intervals in a completely randomized design with eight replications. To describe the germination rate response to temperature, three regression models, namely Intersected-Lines (ISL), Quadratic Polynomial (QPN) and Five-Parameters Beta (FPB) were used. The highest Germination Percentage (GP) (92.8%) occurred in 15°C, but GP in the range of 10-25°C was not significant (p≤0.05). The germination process stopped at 0°C and at above 30°C. The results indicated that the highest Germination Rate (GR), the lowest Mean Germination Time (MGT) and also times to 50% germination (D50) were obtained at 20°C. Seeds did not reach to their 50% germination level in temperatures higher than 25°C. The FPB model had the best realistic estimation for cardinal temperatures. Based on models estimation, Base (Tb), Optimum (To) and Ceiling (Tc) temperatures were in the ranges of (1-1.9°C), (18.1-20.8°C) and (34.5-38.7°C), respectively.

scholarly journals An interpretable prediction method for university student academic crisis warning

2021 ◽  
Author(s):  
Zhai Mingyu ◽  
Wang Sutong ◽  
Wang Yanzhang ◽  
Wang Dujuan
Keyword(s):  
Academic Achievement ◽  
Visual Analysis ◽  
Prediction Method ◽  
Absolute Error ◽  
University Student ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Achievement Prediction ◽  
Academic Achievement Prediction ◽  
Education Administrators

AbstractData-driven techniques improve the quality of talent training comprehensively for university by discovering potential academic problems and proposing solutions. We propose an interpretable prediction method for university student academic crisis warning, which consists of K-prototype-based student portrait construction and Catboost–SHAP-based academic achievement prediction. The academic crisis warning experiment is carried out on desensitization multi-source student data of a university. The experimental results show that the proposed method has significant advantages over common machine learning algorithms. In terms of achievement prediction, mean square error (MSE) reaches 24.976, mean absolute error (MAE) reaches 3.551, coefficient of determination ($$R^{2}$$ R 2 ) reaches 80.3%. The student portrait and Catboost–SHAP method are used for visual analysis of the academic achievement factors, which provide intuitive decision support and guidance assistance for education administrators.

scholarly journals A Multimethod Analysis for Average Annual Precipitation Mapping in the Khorasan Razavi Province (Northeastern Iran)

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 592
Author(s):  
Mehdi Aalijahan ◽  
Azra Khosravichenar
Keyword(s):  
Regional Planning ◽  
Interpolation Method ◽  
Absolute Error ◽  
Rain Gauge ◽  
Annual Precipitation ◽  
Coefficient Of Determination ◽  
Distance Weighting ◽  
Average Annual Precipitation ◽  
Inverse Distance

The spatial distribution of precipitation is one of the most important climatic variables used in geographic and environmental studies. However, when there is a lack of full coverage of meteorological stations, precipitation estimations are necessary to interpolate precipitation for larger areas. The purpose of this research was to find the best interpolation method for precipitation mapping in the partly densely populated Khorasan Razavi province of northeastern Iran. To achieve this, we compared five methods by applying average precipitation data from 97 rain gauge stations in that province for a period of 20 years (1994–2014): Inverse Distance Weighting, Radial Basis Functions (Completely Regularized Spline, Spline with Tension, Multiquadric, Inverse Multiquadric, Thin Plate Spline), Kriging (Simple, Ordinary, Universal), Co-Kriging (Simple, Ordinary, Universal) with an auxiliary elevation parameter, and non-linear Regression. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) were used to determine the best-performing method of precipitation interpolation. Our study shows that Ordinary Co-Kriging with an auxiliary elevation parameter was the best method for determining the distribution of annual precipitation for this region, showing the highest coefficient of determination of 0.46% between estimated and observed values. Therefore, the application of this method of precipitation mapping would form a mandatory base for regional planning and policy making in the arid to semi-arid Khorasan Razavi province during the future.

scholarly journals Dynamics of potential precipitation under climate change scenarios at Cameron highlands, Malaysia

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Nuraddeen Mukhtar Nasidi ◽  
Aimrun. Wayayok ◽  
Ahmad Fikri Abdullah ◽  
Muhamad Saufi Mohd Kassim
Keyword(s):  
Environmental Sustainability ◽  
General Circulation ◽  
Greenhouse Gas Emission ◽  
Rapid Change ◽  
Absolute Error ◽  
General Circulation Models ◽  
Coefficient Of Determination ◽  
Climate Change Scenarios ◽  
Precipitation Concentration Index ◽  
Cameron Highlands

AbstractPrecipitation is sensitive to increasing greenhouse gas emission which has a significant impact on environmental sustainability. Rapid change of climate variables is often result into large variation in rainfall characteristics which trigger other forms of hazards such as floods, erosion, and landslides. This study employed multi-model ensembled general circulation models (GCMs) approach to project precipitation into 2050s and 2080s periods under four RCPs emission scenarios. Spatial analysis was performed in ArcGIS10.5 environment using Inverse Distance Weighted (IDW) interpolation and Arc-Hydro extension. The model validation indicated by coefficient of determination, Nash–Sutcliffe efficiency, percent bias, root mean square error, standard error, and mean absolute error are 0.73, 0.27, 20.95, 1.25, 0.37 and 0.15, respectively. The results revealed that the Cameron Highlands will experience higher mean daily precipitations between 5.4 mm in 2050s and 9.6 mm in 2080s under RCP8.5 scenario, respectively. Analysis of precipitation concentration index (PCI) revealed that 75% of the watershed has PCI greater than 20 units which indicates substantial variability of the precipitation. Similarly, there is varied spatial distribution patterns of projected precipitation over the study watershed with the largest annual values ranged between 2900 and 3000 mm, covering 71% of the total area in 2080s under RCP8.5 scenario. Owing to this variability in rainfall magnitudes, appropriate measures for environmental protection are essential and to be strategized to address more vulnerable areas.

scholarly journals Hierarchical Analysis of Thorax Models to Measure Tidal Volume

2018 ◽  
Vol 4 (1) ◽  
pp. 429-432
Author(s):  
Bernhard Laufer ◽  
Sabine Krueger-Ziolek ◽  
Knut Moeller ◽  
Paul David Docherty ◽  
Fabian Hoeflinger ◽  
...  
Keyword(s):  
Tidal Volume ◽  
Motion Tracking ◽  
Optimization Method ◽  
Optimal Combination ◽  
Hierarchical Optimization ◽  
Hierarchical Analysis ◽  
Hierarchical Method ◽  
Computational Costs ◽  
Sensor Configuration ◽  
Time Required

AbstractMotion tracking of thorax kinematics can be used to determine respiration. However, determining a minimal sensor configuration from 64 candidate sensor locations is associated with high computational costs. Hence, a hierarchical optimization method was proposed to determine the optimal combination of sensors. The hierarchical method was assessed by its ability to quickly determine the sensor combination that will yield optimal modelled tidal volume compared to body plethysmograph measurements. This method was able to find the optimal sensor combinations, in approximately 2% of the estimated time required by an exhaustive search.

scholarly journals Improved Backstepping Control of a DFIG based Wind Energy Conversion System using Ant Lion Optimizer Algorithm

2021 ◽  
Author(s):  
Zoubir Zeghdi ◽  
Linda Barazane ◽  
Youcef Bekakra ◽  
Abdelkader Larabi
Keyword(s):  
Fitness Function ◽  
Harmonic Distortion ◽  
Absolute Error ◽  
Optimization Method ◽  
Backstepping Control ◽  
Ant Lion Optimizer ◽  
Integral Time ◽  
Distortion Minimization ◽  
Ant Lion ◽  
Doubly Fed

In this paper, an improved Backstepping control based on a recent optimization method called Ant Lion Optimizer (ALO) algorithm for a Doubly Fed Induction Generator (DFIG) driven by a wind turbine is designed and presented. ALO algorithm is applied for obtaining optimum Backstepping control (BCS) parameters that are able to make the drive more robust with a faster dynamic response, higher accuracy and steady performance. The fitness function of the ALO algorithm to be minimized is designed using some indexes criterion like Integral Time Absolute Error (ITAE) and Integral Time Square Error (ITSE). Simulation tests are carried out in MATLAB/Simulink environment to validate the effectiveness of the proposed BCS-ALO and compared to the conventional BCS control. The results prove that the objectives of this paper were accomplished in terms of robustness, better dynamic efficiency, reduced harmonic distortion, minimization of stator powers ripples and performing well in solving the problem of uncertainty of the model parameter.

scholarly journals Alometric Model for Estimation of Leaf Area of Garcinia brasiliensis Mart. Through Non-destructive Method

2019 ◽  
Vol 11 (10) ◽  
pp. 154
Author(s):  
Vinicius de Souza Oliveira ◽  
Cássio Francisco Moreira de Carvalho ◽  
Juliany Morosini França ◽  
Flávia Barreto Pinto ◽  
Karina Tiemi Hassuda dos Santos ◽  
...  
Keyword(s):  
Leaf Area ◽  
Linear Equations ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Maximum Width ◽  
Linear Dimensions ◽  
Length Width ◽  
Independent Variable ◽  
The Mean ◽  
Non Destructive

The objective of the present study was to test and establish mathematical models to estimate the leaf area of Garcinia brasiliensis Mart. through linear dimensions of the length, width and product of both measurements. In this way, 500 leaves of trees with age between 4 and 6 years were collected from all the cardinal points of the plant in the municipality of São Mateus, North of the State of Espírito Santo, Brazil. The length (L) along the main midrib, the maximum width (W), the product of the length with the width (LW) and the observed leaf area (OLA) were obtained for all leaves. From these measurements were adjusted linear equations of first degree, quadratic and power, in which OLA was used as dependent variable as function of L, W and LW as independent variable. For the validation, the values of L, W and LW of 100 random leaves were substituted in the equations generated in the modeling, thus obtaining the estimated leaf area (ELA). The values of the means of ELA and OLA were tested by Student’s t test 5% of probability. The mean absolute error (MAE), root mean square error (RMSE) and Willmott’s index d for all proposed models were also determined. The choice of the best model was based on the non significant values in the comparison of the means of ELA and OLA, values of MAE and RMSE closer to zero and value of the index d and coefficient of determination (R2) close to unity. The equation that best estimates leaf area of Garcinia brasiliensis Mart. in a way non-destructive is the power model represented by por ELA = 0.7470(LW)0.9842 and R2 = 0.9949.

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