AMA, Agricultural Mechanization in Asia, Africa and Latin America (AMA) (issn: 00845841) is a peer reviewed journal first published online after indexing scopus in 1982. AMA is published by Farm Machinery Industrial Research Corp and Shin-Norinsha Co. AMA publishes every subjects of general engineering and agricultural engineering.
AMA, Agricultural Mechanization in Asia, Africa and Latin America (ISSN: 00845841) is a peer-reviewed journal. The journal covers Agricultural and Biological Sciences and all sort of engineering topic. the journal's scopes are in the following fields but not limited to:Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Interventional Pulmonology
In the analysis of high-dimensional data the challenging problem is selecting a useful set of variables among the set of large number of variables. Feature selection reduces the dimensionality of feature space, removes redundant, irrelevant, or noisy data. In this study, comparisons between different variable selection methods were performed. These methods include four methods such as Raoˊs F test, Wilkˊs lambda (Backward and Forward) and Random Forests. A Monte Carlo Simulation study was conducted to compare the performance of various methods of variable selection for classification and discrimination. Random samples with varying sizes (50, 100, 200, 500) were generated using Monte Carlo simulation using means and variance covariance matrices of groups formed on the basis of seed yield and oil content of the 310 genotypes of Indian mustard data set. For samples generated on the basis of seed yield of equal size three methods viz Rao's F test, Wilkˊs lambda (Backward) and Wilkˊs lambda (Forward) were found to have equal performance for (N1=200, N2=200) with least error rate of 18.50 per cent. On comparing the equal sized samples ((N1=50, N2=50), (N1=100, N2=100), (N1=200, N2=200) and (N1=500, N2=500) the most suitable methods for selection of variables affecting oil content with least leave one out cross validation 31.50 percent error rate are Wilkˊs lambda (Backward) and Wilkˊs lambda (Forward) for sample size (N1=100, N2=100).
The present study has been accomplished in two parts i.e. the development of ARIMA and ARIMAX models for mustard yield forecasting in Bhiwani, Fatehabad, Hisar and Sirsa districts of Haryana. The ARIMA models have been fitted using mustard yield data for the period 1980-81 to 2011-12 of Bhiwani, Hisar and Sirsa districts (Fatehabad from 1997-98 to 2011-12). However, the fortnightly weather data from 1980-81 to 2016-17have been utilized as input for ARIMAX model building. The validity of fitted models have been checked for subsequent years i.e.2012-13 to 2016-17, not included in the development of the models. The ARIMAX models performed well with lower error metrics as compared to the ARIMA models in all time regimes.
Guava is a small, tropical fruit tree grown in various tropical and subtropical regions. Salicylic acid (SA) is a phenolic compound that enhances disease resistance and delays the fruit ripening process. Calcium is an essential cell component that delays ripening, particularly softening of the fruit. The effect of foliar spray of CaCl2, and SA, on guava's physical and biochemical traits were investigated in the present investigation. The application of CaCl2 2% + SA 2mM was more effective as compared with both when applied alone. The data were recorded on fruit set (%), fruit weight (g), fruit length (cm), fruit diameter (cm), fruit yield (kg), ripening period (days), TSS, acidity, total sugar, ascorbic acid, nitrogen, phosphorus, potassium. CaCl2 2% + SA 2mM was showed better performance in all cases, followed by SA 2mM and CaCl2 2%. Overall, this work determines the influence on guava's essential traits by pre-harvest calcium chloride and salicylic acid.
This paper introduces an optimal model named Self-Organizing Type-2 Recurrent Wavelet Fuzzy Brain Emotional Learning Network controller (SET2RWFBELNC) with self-evolving algorithm to gain optimal structure from zero initial rule, which merges Interval Type-2 Recurrent Wavelet Fuzzy System and Brain Emotional Learning Network(BELN). As an ideal controller, SET2RWFBELNC not only solves the problem of less information between master and slave systems, but also reduces the influence of external disturbance on synchronization of chaotic systems. Consequently, one model-free adaptive sliding mode controller based on SET2RWFBELNC, sliding model theory, and the asymptotic stability of the synchronization error is realized by robust compensation, in which the strong compensation used for the compensation of the network error. Besides, the Lyapunov function improves the stability of the model. Finally, simulation results of the chaotic system presented in this paper show the superiority of this method.
This paper presents the results of an optimization study on powder-mixed electrical discharge machining (PMEDM) when machining cylindrical parts made of SKD11 tool steel. The objective of this study was to investigate the influence of main process parameters on the ratio of the surface roughness (SR) to the material removal speed (MRS). To do that, an experiment was conducted. Also, the Taguchi method in Minitab 19 software was used to design and analyze the experimental results. The influence of the input process parameters, including the powder concentration, the powder size, the pulse on time, the pulse off time, the servo current, and the servo voltage on the ratio between SR and MRS was evaluated. Furthermore, the optimal input parameters to achieve the minimum ratio of SR to MRS were found.