Search

Baseline toxicity of neonicotinoid insecticides viz., thiamethoxam 25WG and sulfoxaflor 21.8SC was assessed in the field collected populations of the rice white backed planthopper Sogatella furcifera (Horvath). The major rice growing regions of Tamil Nadu when studied revealed that the field populations vary in their susceptibility. Coimbatore and Bhavani populations were more susceptible compared to the Nagapattinam one. Baseline toxicity of thiamethoxam 25WG was relatively high with narrow variation (LC50-0.3130 to 1.7882 ppm; LC95-1.0993 to 6.3840 ppm); LC50 of sulfoxaflor 21.8SC ranged from 1.3121 to 3.8392 ppm, while the LC95 values were 3.1507 to 5.2754 ppm. The resistance ratios varied among the populations viz., thiamethoxam 25WG (2.5 to 5.6 folds) and sulfoxaflor 21.8SC (1.8 to 2.9 folds). Thus, the population from Nagapattinam exhibited maximum resistance. [ABSTRACT FROM AUTHOR]

A field experiment was conducted during March-April, 2016 with jasmine crop. The crop was sprayed with Alanto 240 SC (thiacloprid) at the recommended dose of 72 g ai ha-1 and residues of thiacloprid were monitored in soil . The results revealed that the residues in soil persisted up to 3 days and become non-detectable (<0.01 mg kg-1) on the 5th day. The calculated half-life of thiacloprid was 0.69 days (≈16 hrs) showing the less persistent nature of the compound. Hence, thiacloprid at the recommended dose is compatible with the environmental quality, posing no threat to the soil. [ABSTRACT FROM AUTHOR]

In the current era, as there is an abundant expansion of functionally similar web services, it becomes a prodigious issue for the web service discovery process. The service classification plays a significant role to greatly reduce the search space and retrieves the desirable service quickly and accurately. The classification is performed using the functional values. Recent research activities recommend RNN (Recurrent Neural Network) deep learning algorithms for efficient classification process. The state-of-the-art of GRU (Gated Recurrent Unit) one of the RNN model, provides a proficient classification process. However, the ratio of training and testing dataset, and hyperparameters namely neural network size, and batch size etc, affects the classification accuracy. The objective of the paper is to incorporate GRU model for efficient classification process. The novelty of the proposed model lies in implementing the GRU model for semantic web service classification. Furthermore, the genetic algorithm is used to predict the optimal ratio of training and testing dataset and optimal hidden neural Network units of GRU model in order to attain optimal classification. The experimental results exemplifies that the semantic web service classification is efficiently deliberated using the proposed GA-GRU model that outperforms the classification process as compared with the conventional semantic extraction using accuracy, precision, F-measure, recall and FDR (False Date Rate) rate. [ABSTRACT FROM AUTHOR]

The parameters like pH, electrical conductivity, moisture content and Fiehe's test have been analysed in all the honey samples. The pH of stingless bee honey ranged from 3.7±0.00 to 5.92±0.02 whereas the pH of honey bee honey samples ranged from 3.00±0.02 to 4.67±0.02. The electrical conductivity (mS/cm) of stingless bee honey was 0.94±0.01 to 2.51±0.06 and honey from Apis sp. ranged between 0.16±0.00 to 1.08±0.00. The moisture content (%) of stingless bee honey was 15.0±0.06 to 30.0±0.06 whereas the Apis honey had moisture content from 18.10±0.15 to 40.30±0.20. The Fiehe's test conducted revealed that four samples of stingless bee honey including a sample from Nagaland showed positiveness to hydroxymethyl furfural content (HMF) and the remaining honey showed negative result to HMF content. Generally, the honey quality parameters depend on the floral resources. [ABSTRACT FROM AUTHOR]

This article presents the detailed analysis of the local pixel grouping-principle component analysis (LPG-PCA) algorithm in denoising and deblurring of medical images. Inefficient diagnosis of the medical images containing lot of information is often affected by the noise and artifacts. In order to remove these noises and artifacts, a statistical decorrelation technique, LPG-PCA is used which is found to be one of the efficient methods, which could be used in improving the performance of medical images. For better preservation of local structures of the image, a pixel and its nearest neighbors are modeled as a vector variable, which leads to the selection of similar intensity characteristics. Denoising method used in this article is done in two stages for improving the denoising performance. The smoothening caused by the denoising process is removed by using LPG-PCA along with adaptive sparse domain representations in the deblurring process. This involves clustering of data and finding the subdictionary of each cluster using LPG-PCA. Experimental results show that an average improvement of 2.9 and 5.1 dB is found in the computed tomography and magnetic resonance imaging images using denoising and deblurring process. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 157-170, 2013 [ABSTRACT FROM AUTHOR]

Investigations were carried out on the seasonal incidence of Spodoptera litura on groundnut at the Regional Research Station, Vriddhachalam, Tamil Nadu. Seven field experiments were conducted during Kharif 2001, Kharif 2002, Kharif 2003, Kharif 2004, Rabi 2001-02, Rabi 2002-03, and Rabi 2003-04 using groundnut variety VRI 2. The presence of Spodoptera was noticed two weeks after sowing and continued to exist up till harvesting of the crop. The peak incidence of pest occurs around 50-60 days after sowing. The correlation and regression studies showed that during Kharif season, the individual weather parameters have no significant correlation with the abundance of Spodoptera, but all weather parameters together can have influence up to 90% on the incidence of Spodoptera. During Rabi season also the effect of weather parameters individually on the abundance of Spodoptera was not significant. But, the R² value indicated that the influence of weather parameters on the incidence of Spodoptera was 98%. The forecasting model developed for Kharif season was Y = 12.309 + 2.37 X23, where, Y-Percent pest Incidence, X23 = Adjusted Minimum Temperature (2-week lag). The model developed for Rabi season was Y = 0.189 + 2.039 X12, where, Y-Percent pest Incidence, X12 = Adjusted Maximum Temperature (1-week lag). These models were validated with individual season data. [ABSTRACT FROM AUTHOR]