Yadgar Ali Mahmood, Halgurd Nasraden Hassan, Masood Saber Mohammed
{"title":"Yield Performance of Barley Hybrids (Hordeum vulgare L.) under Drought stress and non-stressed Conditions","authors":"Yadgar Ali Mahmood, Halgurd Nasraden Hassan, Masood Saber Mohammed","doi":"10.24271/PSR.20","DOIUrl":"https://doi.org/10.24271/PSR.20","url":null,"abstract":"This study was carried out at the experiment field, Kalar Technical Institute, Garmian Region in two growing seasons of 2016-2017 and 2017-2018 in order to evaluate the growth and yield potentials of barley under water stressed using hybrids as a source of wide range of genotypic variations. Therefore, five F2 barley hybrids (Hordeum vulgare L.) were screened for grain yield, biomass dry matter, plant height and harvest index under irrigated and drought conditions. Results showed that there was no effect of drought on grain yield (P>0.05) in 2017, while significantly reduced yield in 2018 and across-year mean (P-2 (3//14) under irrigated condition, and 267.8 (3//5) to 302.3 g m-2 (3//4) under unirrigated condition (P=0.001), biomass dry matter was ranged from 1099.1 (3//1) to 1370.5 g m-2 (3//14) under irrigated condition, and 892.6 (3//1) to 1153.9 g m-2 (3//14) under unirrigated condition (P=0.05), and harvest index were from 25.1 (3//14) to 28.0 (3//1) under irrigated conditions, and 25.9 (3//14) to 31.2 (3//1) under unirrigated conditions (P=0.04). Regression analysis, averaging over years, showed a positive relationship between grain yield and biomass under irrigated (R2=0.76; P=0.05), despite that, any positive relation was not found under unirrigated conditions (R2=0.43; P=0.23) due to post-anthesis drought stress. A strong relationship was also found between plant height and biomass dry matter under both irrigated (R2=0.89; P=0.02) and unirrigated (R2=0.97; P=0.003) conditions due to the high contribution of plant height in increasing plant biomass. It is concluded that genotypes had different response to drought due to their genetic diversity, and relatively low impact of water stress was appeared on growth and grain yield of barley in this semi-arid region compared to worldwide expected range of yield reduction.","PeriodicalId":33835,"journal":{"name":"Passer Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68881991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Comparison Study of Data Mining Algorithms for blood Cancer Prediction","authors":"Noor Bahjat, Snwr Jamak","doi":"10.24271/psr.29","DOIUrl":"https://doi.org/10.24271/psr.29","url":null,"abstract":"Cancer is a common disease that threats the life of one of every three people. This dangerous disease urgently requires early detection and diagnosis. The recent progress in data mining methods, such as classification, has proven the need for machine learning algorithms to apply to large datasets. This paper mainly aims to utilise data mining techniques to classify cancer data sets into blood cancer and non-blood cancer based on pre-defined information and post-defined information obtained after blood tests and CT scan tests. This research conducted using the WEKA data mining tool with 10-fold cross-validation to evaluate and compare different classification algorithms, extract meaningful information from the dataset and accurately identify the most suitable and predictive model. This paper depicted that the most suitable classifier with the best ability to predict the cancerous dataset is Multilayer perceptron with an accuracy of 99.3967%.","PeriodicalId":33835,"journal":{"name":"Passer Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68882223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}