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Verma, Ajay
- Development of Pedal Operated Thresher for Finger Millets
Abstract Views :348 |
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Authors
Affiliations
1 Krishi Vigyan Kendra, Bhatapara (C.G.), IN
2 Department of Farm Machinery and Power Engineering, Indira Gandhi Krishi Vishwavidyalaya, Raipur (C.G.), IN
3 Division of Crop Production, ICAR-Central Rice Research Institute, Cuttack (Odisha), IN
1 Krishi Vigyan Kendra, Bhatapara (C.G.), IN
2 Department of Farm Machinery and Power Engineering, Indira Gandhi Krishi Vishwavidyalaya, Raipur (C.G.), IN
3 Division of Crop Production, ICAR-Central Rice Research Institute, Cuttack (Odisha), IN
Source
International Journal of Agricultural Engineering, Vol 8, No 2 (2015), Pagination: 175-180Abstract
This paper deals with the development of a pedal operated millet thresher. Millet is one of the oldest human foods and believed to be the first domesticated cereal grain. Millets are important food for sustaining tribal population in Bastar region of Chhattisgarh. Available evidence suggests that the mode of threshing finger millet is by traditional method like beating with sticks, rubbing and trampling finger-heads under bullocks feet or men feet. Traditional method is time wasting, energy sapping and often the grains are broken. Pedal operation is the most efficient way of utilizing power from human muscles. Keeping this thing in mind, pedal operated thresher for minor millets with spike-tooth type threshing cylinder was designed, fabricated and tested. This machine basically consists of four major components: feeding, threshing (consisting of threshing cylinder, concave and cylinder casing), cleaning and power transmission mechanism. The developed millet thresher has the ability to winnow the premature grains and leaves, which are often lighter, thus, leaving aside the massy grains that, will be collected. It is beneficial for farmers with reduced time of operation, reduction in breakage of the grains and separation of the stalk from the grains. The machine is economically viable can be used by farmers easily.Keywords
Finger Millet Thresher, Spike-Tooth Type Threshing Cylinder, Pedal Operated Thresher.References
- Gbabo, A., Gana, I.M. and Amoto, M.S. (2013). Design, fabrication and testing of a millet thresher. Net. J. Agril. Sci., 1(4) : 100-106.
- Hatwalne, A.P., Ambadkar, S.T., Paropate, R.V., Gandhwar, V.R. and Wankhade, A.M. (2011). Design and development of a pedal-operated flour mill. New York Sci. J., 4 (5) : 74-77.
- Khurmi, R.S. and Gupta, J.K. (2005). A text of machine design, Eurasia Publishing House (Pvt.) Ltd., Ram Nagar,NEW DELHI, INDIA.
- Kumar, Naveen, D.B., Kumar, Prasanna, Arun Kumar, H.S., Sandeep, T.N. and Sudhadevi, G. (2013). Efficiency of mechanical thresher over traditional method of threshing finger millet. Internat. J. Agril. Engg., 6(1) : 184-188.
- Modak, J. and Bapat, A. (1987). Design of experimentation for establishing generalized experimental model for a manually driven flywheel motor. Proc. International Conference on Modeling and Simulation. New Delhi, India, 8(2) : 127-140.
- Tiwari, P.S., Gite, L.P., Pandey, M.M. and Shrivastav, A.K. (2011). Pedal power for occupational activities :Effect of power output and pedaling rate on physiological responses. Internat. J. Industrial Ergonomics, 41 : 261-267.
- Verma, P.K. and Mishra, N. (2010). Traditional techniques of processing on minor millets in Bastar district of Chhattisgarh, India. Res. J. Agril. Sci., 1(4) : 465-467.
- Understanding Different ADC Parameters used in Software Radio for 3G/4G Mobile Receiver
Abstract Views :206 |
PDF Views:3
Authors
Preeti Trivedi
1,
Ajay Verma
2
Affiliations
1 G.S. Institute of Technology and Science, Indore, M.P., IN
2 Department of Electronics and Instrumentation, IET, DAVV, Indore, M.P., IN
1 G.S. Institute of Technology and Science, Indore, M.P., IN
2 Department of Electronics and Instrumentation, IET, DAVV, Indore, M.P., IN
Source
Wireless Communication, Vol 2, No 7 (2010), Pagination: 151-158Abstract
A software defined radio adopts a fully reconfigurable front end and is believed to be the right answer to realize 3G/4G mobile systems. This paper will give a review of different ADC architecture used in SDR mobile receiver with different parameter of ADC. In an ideal software radio, the data conversion process occurs immediately after the antenna in the receiver chain. This paper also presents a multi-standard reconfigurable modulator, which are able to support the predictable standards of fourth generation of mobile communication systems (4G). The down conversion process is entirely in the digital domain. The proper selection of data converters, both analog to digital converters and digital to analog converters (DACs) is one of the most challenging steps in designing software radio. Different types of ADCs are discussed here.Keywords
Analog to Digital Converter (ADC), Intermediate Frequency (IF),Software Defined Radio (SDR), SNR, Radio Frequency (RF).- Interpreting Genotype X Environment by Non-Parametric Methods for Malt Barley Evaluated under North Western Plains Zone
Abstract Views :223 |
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Authors
Affiliations
1 Statistics and Computer Center, ICAR-Indian Institute of Wheat and Barley Research, Karnal (Haryana), IN
1 Statistics and Computer Center, ICAR-Indian Institute of Wheat and Barley Research, Karnal (Haryana), IN
Source
International Research Journal of Agricultural Economics and Statistics, Vol 8, No 2 (2017), Pagination: 236-242Abstract
The present study was carried out to identify malt barley genotypes with high yield and stability across eight different environments, using non-parametric statistical measures. Descriptive statistics MR, SD and CV identified DWRB147, DWRB150 and RD2943 stable genotypes. BH902 and PL890 were identified as unstable genotypes by CMR CSD and CCV. Non-parametric measures selected DWRB147 and DWRB150 as the stable genotypes and BH902 and PL890 unstable genotypes. Significant tests for Si 1 and Si 2 were based on sum of Zi 1 and Zi 2 measures and sum of Zi 1 was greater than critical value confirmed significant differences among the twenty genotypes. Results of the NPi 2, NPi 3 and NPi 4were similar for unstable performance of BH902, DWRB150 and DWRB147. Biplot analysis of PCA1 and PCA2 accounting for 70.08 per cent showed three distinguish groups among non-parametric measures. Clustering by Ward’s hierarchical method expressed four clusters by using the squared Euclidean distance as dissimilarity measure.Keywords
Non-Parametric Measurements, Rank Correlation, Biplot Analysis, Hierarchical Clustering.References
- Akcura, M., Kaya,Y. and Tanner, S. (2009). Evaluation of durum wheat genotypes using parametric and non-parametric stability statistics.Turkish J. Field Crops, 14(2):111–122.
- Delic N., Stankovic, G. and Konstatinov, K. (2009). Use of non parametric statistics in estimation of genotypes stability. Maydica, 54 : 155-160.
- Farshadfar, E., Mahmudi, N. and Sheibanirad, A. (2014). Non-parametric methods for interpreting genotype × environment interaction in bread wheat genotypes. J. Bio. & Env. Sci. 4 : 55-62.
- Huehn, M. (1996). Non-parametric analysis of genotype x environment interactions by ranks. In: Kang, M.S. and Gauch, H.G. (Ed.) Genotype by Environment Interaction. CRC Press, Boca Raton, pp. 213-228.
- Hussein, M.A., Bjornstad, A. and Aastveit, A.H.(2000). SASG × ESTAB: A SAS program for computing genotype 3 environment stability statistics. Agron. J., 92 : 454-459.
- Kadi, Z., Adje, F. and Bouzerzour, H. (2010). Analysis of the genotype by environment interaction of barley grain yield (Hordeum vulgare L.) under semi arid conditions. Adv. Environ., Biol., 4 (1) : 34-40.
- Karimizadeh, R., Mohammadi, M., Sabaghnia, N. and Shefazadeh, M.K. (2012). Using Huehn’s non-parametric stability statistics to investigate genotype × environment interaction. Not. Bot. Hort. Agrobo., 40 : 195-200.
- Kaya, Y. and Taner, S. (2003). Estimating genotypic ranks by nonparametric stability analysis in bread wheat (Triticuma estivum L.). J. Central Eur. Agric., 4 : 47-54.
- Kilic, H., Akcura, M. and Aktas, H. (2010). Assessment of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in multienvironments. Not. Bot. Hort. Agrobo., 38 : 271-279.
- Kumar, V., Khippal, A., Singh, J., Selvakumar, R., Malik, R., Kumar, D., Kharub, A.S., Verma, R.P.S. and Sharma, I. (2014). Barley research in India: Retrospect and prospects. J. Wheat Res., 6 (1) : 1-20.
- Mahtabi, E., Farshadfar, E. and Jowkar, M.M. (2013). Non-parametric estimation of phenotypic stability in chickpea (Cicer arietinumL.). Intl. J. Agric. Crop Sci., 5 : 888-895.
- Mortazavian, S. M. M. and Azizinia, S. (2014). Non-parametric stability analysis in multi-environment trial of canola. Turkish J. Field Crops, 19 (1) : 108-117.
- Mut, Z., Aydin, N., Bayramoglu, H. and Ozcan, H. (2009). Interpreting genotype × environment interaction in bread wheat (Triticum aestivum L.) genotypes using non-parametric measures. Turkish J. Agric. Sci., 33 : 127-137.
- Nassar, R. and Huehn, M. (1987). Studies on estimation of phenotypic stability: tests of significance for non-parametric measures of phenotypic stability. Biometrics, 43 : 45–53.
- Parmar, D.J., Patel, J.S., Mehta, A.M., Makwana, M.G. and Patel, S.R. (2012). Non - parametric methods for interpreting genotype x environment interaction of rice genotypes (Oryza sativa L.) J. Rice Res., 5 : 17-25.
- Sabaghnia, N., Dehghani, H. and Sabaghpour, S.H. (2006). Non-parametric methods for interpreting genotype × environment interaction in lentil genotypes. Crop Sci. 46 : 1100-1106. Doi: 10.2135/cropsci2005.06-0122.
- Sabaghnia, N., Karimizadeh, R. and Mohammadi, M. (2012). The use of corrected and uncorrected non-parametric stability measurements in durum wheat multi-environmental trials. Span. J. Agric. Res., 10 : 722-730. Doi: 10.5424/sjar/2012103-384-11.
- Sabaghnia, N., Karimizadeh, R. and Mohammadi, M. (2014). Graphic analysis of yield stability in new improved lentil (Lens culinarisMedik.) genotypes using non-parametric statistics. Acta Agric. Slov., 103 : 113-127. Doi: 10.14720/aas.2014.103.1.12.
- Thennarasu, K. (1995). On certain non-parametric procedures for studying genotype-environment interactions and yield stability. Ph.D. Thesis, P.J. School IARI, NEW DEHLI, INDIA.
- Verma, R.P.S., Kharub, A.S., Kumar, D., Sarkar, B. , Selvakumar, R., Singh, R., Malik, R., Kumar, R. and Sharma, I. (2011). Fifty years of coordinated barley research in India. Directorate of Wheat Research, Karnal-132001. Research Bulletin No. 27: 46.
- Ward, J.H. (1963). Hierarchical grouping to optimize an objective function, J. Am. Stat. Assoc., 58 : 236–224.
- Zali, H., Farshadfar, E. and Sabaghpour, S.H. (2011). Non-parametric analysis of phenotypic stability in chickpea (Cicer arietinum L.) genotypes in Iran. Crop Breed J., 1(1) : 89-100.
- Statistical Methods to Study Adaptability of Barley Genotypes Evaluated Under Multi Environment Trials
Abstract Views :237 |
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Authors
Affiliations
1 Statistics and Computer Center, ICAR- Indian Institute of Wheat and Barley Research, Karnal (Haryana), IN
1 Statistics and Computer Center, ICAR- Indian Institute of Wheat and Barley Research, Karnal (Haryana), IN
Source
International Journal of Agricultural Sciences, Vol 14, No 2 (2018), Pagination: 283-291Abstract
Genotypes G5, G8, G3, G21 and G18 had achieved higher yields besides bi > 1.0. G21 and G3 identified as appropriate one, because had higher yield value than the mean, bi values near 1.0 and low S2di. Lower values (W2i) resulted for G12, G5, G2, G21 while higher for G5, G3 and G14. Genotypes G12 followed by G2, G20, and G7 had the smallest environmental variance (S2xi). Smaller values of (CVi) considered G12, G2, G20, and G10 of stable performance. α2 i measure pointed out G12, G7 and G2 with smallest values. Desirable lower Pi values reflected by G18, G5, G21, and G4 while GAI values identified G18, G11, G4 G10 as desirable genotypes. Si (1) and Si(2) showed lower values of G12, G2 and G7 genotypes. Significant tests of Si (1) and Si(2) proved the highly significant difference in ranks among the 21 genotypes grown in 8 environments. Genotypes G12, G2, and G7 had the lower Si(3) and Si(6) values. Yield of genotypes had significant negative correlation with bi, Si(2), Si(3), Si(6), NPi (2), NPi(3), NPi(4) and significant positive correlation with GAI, Pi and Rank Sum. Hierarchical cluster analysis classified genotypes into three clusters as largest cluster included genotypes with more than average yield along with high yielders G18, G11, G3, G5, G21 and unstable performance indicated by non parametric measures. Biplot analysis while considering first two significant principal components grouped the parametric and non parametric measures into four groups. The smaller group consisted of bi and S2 di and adjacent to group of non parametric measures Si(2), Si(6), NPi(2), NPi(3) and NPi(4).Keywords
Barley, Parametric, Non-Parametric Measures, Biplot Analysis, Hierarchical Clustering.References
- Dehghani, M.R., Majidi, M.M., Mirlohi, A. and Saeidi, G. (2016). Integrating parametric and non-parametric measures to investigate genotype x environment interactions in tall fescue. Euphytica, 208 : 583–596.
- Eberhart, S.A. and Russell, W.A. (1966). Stability parameters for comparing varieties. Crop Sci., 6 : 36–40.
- Finlay, K.W. and Wilkinson, G.N. (1963). Adaptation in a plant breeding programme. Aust. J. Agric. Res., 14 :742–754.
- Francis, T.R. and Kannenberg, L.W. (1978). Yield stability studied in short-season maize. I. A descriptive method for grouping genotypes. Can. J. Plant Sci., 58 :1029–1034.
- Hussein, M.A., Bjornstad, A. and Aastveit, A.H. (2000). SASG x ESTAB: A SAS program for computing genotype x environment stability statistics. Agron. J., 92: 454-459.
- Kang, M.S. (1988). A rank-sum method for selecting highyielding, stable corn genotypes. Cereal Res. Commun., 16 : 113– 115.
- Khalili, M. and Pour-Aboughadareh, A. (2016). Parametric and non-parametric measures for evaluating yield stability and adaptability in barley doubled haploid lines. J. Agr. Sci. Tech., 18 : 789–803.
- Kilic, Hasan (2012). Assessment of parametric and nonparametric methods for selecting stable and adapted spring bread wheat genotypes in multi – environments. J. Animal & Plant Sci., 22(2) : 390-398
- Lin, C.S., Binns, M.R. and Lefkovitch, L.P. (1986). Stability analysis: where do we stand? Crop Sci., 26 : 894–900.
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- Mohammadi, R. and Amri, A. (2008). A comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica, 159 : 419–432.
- Nassar, R. and Huehn, M. (1987). Studies on estimation of phenotypic stability: tests of significance for nonparametric measures of phenotypic stability. Biometrics, 43 : 45–53.
- Piepho, H.P. and Lotito, S. (1992). Rank correlation among parametric and nonparametric measures of phenotypic stability. Euphytica, 64: 221–225.
- Rea, R., De Sousa-Vieira, O., Díaz, A., Ramon, M., Briceno, R., George, J., Nino, M. and Demey, J. (2015). Assessment of yield stability in sugarcane genotypes using non-parametric methods. Agronomía Colombiana, 33(2): 131-138.
- Scapim, C.A., Pacheco, C.A.P., Teixeira, A.A.J., Vieira, R.A., Pinto, R.J.B. and Conrado, T.V. (2010). Correlations between the stability and adaptability statistics of popcorn cultivars. Euphytica, 174 : 209–218.
- Shukla, GK. (1972). Some statistical aspects of partitioning genotype-environmental components of variability. Heredity, 29 : 237–245.
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- Thennarasu, K. (1995). On certain non-parametric procedures for studying genotype-environment interactions and yield stability. Ph.D. Thesis. P.G. School, IARI, New Delhi.
- Vaezi, B., Pour-Aboughadareh, A., Mehraban, A., Hossein-Pour, T., Mohammadi, R., Armion, M. and Dorri, M.(2017). The use of parametric and non-parametric measures for selecting stable and adapted barley lines. Arch. Agron. & Soil Sci., DOI: 10.1080/03650340.2017.1369529
- Ward, J.H. (1963). Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc., 58:236–244.
- Wricke, G. (1962). On a method of understanding the biological diversity in field research. Z. Pflanzenzucht, 47: 92–96.
- Wheat Genotypes Evaluated under Central Zone for Stability Analysis by Rank based Measures Considering BLUP and BLUE of Yield Values
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Authors
Ajay Verma
1,
G. P. Singh
1
Affiliations
1 ICAR-Indian Institute of Wheat and Barley Research, Karnal (Haryana), IN
1 ICAR-Indian Institute of Wheat and Barley Research, Karnal (Haryana), IN
Source
International Journal of Agricultural Sciences, Vol 16, No 2 (2020), Pagination: 105-121Abstract
Rank based measures of stability had been compared for wheat genotypes evualated in Central Zone of the country as per the BLUP and BLUE of yield values. Measures based on ranks of BLUP of original yield for 2016-17, Sis measures identified G3, G7, G4 as stable genotypes. Corrected yield measures CSis selected G4, G7, G3 for stable performance. Values of NPi(s) identified G1, G6 as of undesirable types. Association analysis observed positive correlations of Sis, with others and themselves. Positive relationships also exhibited by CSis and NPi(s) values to other measures. Biplot analysis exhibited cluster of Si6 , Si3 , CV, NPi(2), NPi(3), NPi(4) and CSi7. Larger cluster comprised of NPi(1) CCV, CSD Si1, Si2, Si4, Si 5, Si7 , CSi 1, CSi2, CSi 3, CSi 4, CSi 5, CSi6 measures. Based on BLUE’s of genotypes yield, measures Sis found G3, G7, G4 as the stable genotypes, however G1, G2 would express unstable performance. CSis identified G7, G3, G6 as opposed to G3, G5, G7 genotypes as by values NPi(s). Positive correlations exhibited by Sisexcept of negative with CMR, CMed, Z1 and Z2 values. Ranks of genotypes as per values of CSis and NPi(s) measures expressed direct relationship with most of the measures. Biplot analysis observed large cluster comprised of CCV, CSD, NPi(1), Si1, Si2, Si4,CSi1, CSi2, CSi 3, CSi4, CSi5, CSi6, CSi7 measures. Second year of study (2017-18) as per BLUP’s seen, Sis settled for G6, G5, G3 genotypes. While NPi(s) settled for G6, G3,G5 as genotypes of stable performance. Highly significant negative correlation of yield observed with most of the measures MR, CV, Med, Si3, Si6 ,CMR,NPi(2), NPi(3), NPi(4). Biplot analysis as per first two significant components (accounted for 88.7 %) marked larger cluster contains CSis with NPi(1), Si1, Si2 Si4, Si5 Si7 ,SD, CSD measures. Sis rank based measures as per BLUE’s of genotypes pointed towards G5,G4, G6, G1 whereas G6, G5, G1,G3 by CSis values. Wheat genotypes G1,G2, G3,G5 settled by least values of NPi(s) . Direct relationships portraited by Sis, CSis and NPi(s) with others. Larger cluster grouped NPi(s), CV, CCV, Z1, Z2, Yield, GAI, CSi5, CSi6 measures.Keywords
Blup, Blue, Si(s), CSi (s), NPi(s), Co-efficient Of Concordance, Biplot Analysis.References
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- Analysis of Factors Affecting Life Goals and Opportunities of Students with or without Disabilities : A Comparative Study
Abstract Views :144 |
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Authors
Affiliations
1 Division of Mathematics (SASL), VIT Bhopal University, Kothrikalan, Sehore, M. P., IN
2 Division of Languages (SASL), VIT Bhopal University, Kothrikalan, Sehore, M. P., IN
1 Division of Mathematics (SASL), VIT Bhopal University, Kothrikalan, Sehore, M. P., IN
2 Division of Languages (SASL), VIT Bhopal University, Kothrikalan, Sehore, M. P., IN
Source
Journal of Engineering Education Transformations, Vol 36, No 4 (2023), Pagination: 13-22Abstract
This paper offers a picture of disability based on census 2011, India. As per Census 2011 India, We examine the impact of demographic factors on attitudes towards students with disabilities versus non-disabled students in government and private Technical and non-technical educational institutions in India. Andrew Fisher's formula is used to determine the sample size for our investigation. Hypothesis testing has been done by using a t-test and chi-square test for comparison between the variables. Descriptive statistics are used to represent the graphical representation of the data set. This study will be helpful to know the mind-set of disabled v/s no- disabled students. At the end of the study, results were discussed with the help of graphs and tables by using Statistical Package for the Social Sciences (SPSS) and Microsoft excel.Keywords
Disability, Behaviour, Demographic Data, Educational Level.References
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