Study on Red Lentil Genotypes For Drought And Cold Tolerance And Yield Components

Abstract: This study was conducted at rainfall conditions in Southeastern Anatolia of Turkey, Diyarbakır, during 2011-2012 growing season. This work reviewed the three different lentil nurseries obtained from ICARDA; 1.Lentil International Elite Nursery-Drought Tolerance-2012, 2.Nursery-Early-2012 and 3.Nursery-Red-2012. The experiment was designed a simple lattice (7x7) and (6x6). Total one thousand twenty-two genotypes were evaluated for cold, drought tolerance and other agronomical and botanic traits. Wide variation observed for all traits among genotypes. Hopeful genotypes for earlier, more pods, average yield, drought and cold resistance were selected in first trial. The most genotypes affected by cold in all trials, also, some genotypes in plots killed by low temperature

Prediction of dust particle size effect on efficiency of photovoltaic modules with ANFIS: An experimental study in Aegean region, Turkey

In this study, the effect of coal dust in variable sizes and weight on photovoltaic (PV) modules' performance has been examined under laboratory conditions. Experimental studies have been performed under Standard Test Conditions (STC: Radiance: 1000 W/m(2); Cell temperature: 25 degrees C; Sun Spectrum: AM 1.5) for monocrystalline silicon (m-Si) and polycrystalline silicon (p-Si) PV modules. By using sieve analysis, the particle sizes of coal dust have been divided into six groups which are in mu m size and as follows: (- 38), (+ 38/- 53), (+ 53/- 75), (+ 75/-106), (+ 106/- 250), (+ 250/- 500). Artificial pollution has been created by uniformly distributing coal dust of certain size and weight onto PV modules. Three different weights of coal dust (5 g, 10 g and 15 g) have been employed for every single size of coal dust. In order to investigate the effect of any particle size and any weight of coal, the performance of PV modules has been investigated by measuring voltage, current and power. The data set consisting of electrical parameters has been used to develop a model by using Adaptive Neuro-Fuzzy Inference System (ANFIS). Comparison of experimental and ANFIS results have been given by calculating of Root Mean Square Error (RMSE) and coefficient of determination (R-2). The performance indices have been calculated as RMSE = 0.18719 and R-2 = 0.99803 for m-Si, RMSE = 0.87098 and R-2 = 0.99714 for p-Si PV modules. According to the results, for a given particle size and weight, the ANFIS model is quite successful in power estimation for PV modules

THE PREDICTION OF FLOW-RATE AND NUTRIENT LOAD IN ERGENE RIVER BASIN THROUGH ARTIFICIAL NEURAL NETWORKS

This study aims to predict the highest rate of monthly average flow and load change in Ergene River, one of the most contaminated rivers of Turkey and having a high flood frequency. For this purpose, the Flow Observation Station (FOS) of Luleburgaz district was chosen for modelling as it is located at a point in the middle of the basin, where domestic and industrial wastes of the region with the population density of basin reach and seasonal floods are observed. An artificial neural networks method, the Feed-Forward Back Propagation Neural Networks (FFBPNN), method was used to evaluate the relation among hydro-meteorological data of Luleburgaz FOS recorded for 168 months between 1997 and 2010, and the flow-rate of Ergene River Luleburgaz Station was predicted monthly for the year of 2011. The load change in the river was observed with direct calculation method on the basis of the acquired flow-rate values and long-term nutrient concentration averages

Use of Arbuscular Mycorrhizal Fungi for Boosting Antioxidant Enzyme Metabolism and Mitigating Saline Stress in Sweet Basil (Ocimum basilicum L.)

Salinity is one of the outstanding abiotic stress conditions that a significant part of the world faces. In recent years, beneficial microorganisms started to be utilized in plants to overcome several abiotic factors, including salinity. The effects of arbuscular mycorrhizal fungi (AMF) mixture on growth and enzymatic responses in basil under salt stress were investigated using saline doses of 0 mM (Control), 150 mM, and 300 mM. Results showed that AMF enhanced all growth parameters, but only the leaf number was statistically significant. However, antioxidant enzymes, such as ascorbate peroxidase (APX) by 25%, catalase (CAT) by 25%, and superoxide dismutase (SOD) by 5%, significantly enhanced. At the same time, the accumulation of oxidative enzymes, like hydrogen peroxide (H2O2) and malondialdehyde (MDA), was reduced, from 12.05 μmol g−1 fw (control) to 11.17 μmol g−1 fw (AMF) and from 14.29 μmol g−1 fw to 10.74 μmol g−1 fw, respectively. AMF also significantly alleviated the chlorophyll loss caused by increasing saline doses. Multivariate analyses revealed the co-occurrence of stress metabolism enzymes as well as the proximate effect of AMF inoculation on basil yield and enzymatic activity. As a result, AMF was considered an appropriate tool for increasing growth and reducing salt stress under both stress-free and saline conditions

MobileSkin: Classification of Skin Lesion Images Acquired Using Mobile Phone-Attached Hand-Held Dermoscopes

Dermoscopy is the visual examination of the skin under a polarized or non-polarized light source. By using dermoscopic equipment, many lesion patterns that are invisible under visible light can be clearly distinguished. Thus, more accurate decisions can be made regarding the treatment of skin lesions. The use of images collected from a dermoscope has both increased the performance of human examiners and allowed the development of deep learning models. The availability of large-scale dermoscopic datasets has allowed the development of deep learning models that can classify skin lesions with high accuracy. However, most dermoscopic datasets contain images that were collected from digital dermoscopic devices, as these devices are frequently used for clinical examination. However, dermatologists also often use non-digital hand-held (optomechanical) dermoscopes. This study presents a dataset consisting of dermoscopic images taken using a mobile phone-attached hand-held dermoscope. Four deep learning models based on the MobileNetV1, MobileNetV2, NASNetMobile, and Xception architectures have been developed to classify eight different lesion types using this dataset. The number of images in the dataset was increased with different data augmentation methods. The models were initialized with weights that were pre-trained on the ImageNet dataset, and then they were further fine-tuned using the presented dataset. The most successful models on the unseen test data, MobileNetV2 and Xception, had performances of 89.18% and 89.64%. The results were evaluated with the 5-fold cross-validation method and compared. Our method allows for automated examination of dermoscopic images taken with mobile phone-attached hand-held dermoscopes