Exploring Regional Profile of Drought History- a New Procedure to Characterize and Evaluate Multi-Scaler Drought Indices Under Spatial Poisson Log-Normal Model

Water Resources Management - Drought is recurrently occurring in many parts of the globe. In contrast to other natural hazards, drought has complex climatic characteristics. Several environmental...     

Correlation between optical basicity, electronegativity and electronic polarizability for some oxides and oxysalts

A new method for calculating the optical basicity, electronic polarizability and oxygen electronegativity of oxides and oxysalts has been evaluated based on the concept of average electronegativity given by Asokamani and Manjula. The estimated parameters are in good agreement with the Duffy values. The results are also discussed in relation to the nature of bonding. It is suggested that the impurity of average electronegativity in predicting different parameters is well emphasised.     

Purification and identification of antioxidant peptide from black pomfret, <Emphasis Type="Italic">Parastromateus niger</Emphasis> (Bloch, 1975) viscera protein hydrolysate

To utilize fish waste, black pomfret, Parastromateus niger viscera was analysed for its proximate and amino acid composition followed by hydrolysis using various proteases to extract antioxidant peptide. Antioxidant activities of the crude hydrolysate was evaluated using DPPH (54%), metal chelating (78.6%) at a concentration of 1 mg/mL, whereas the reducing power assay was done with different concentration (0.5–2.5 mg/mL) and the activity also increased with increasing concentration (0.021–0.068). Furthermore, the hydrolysate was purified by diethylaminoethyl (DEAE) ion-exchange and Sephadex G-25 gel filtration chromatography. Finally, the purified peptide had a mass of 701.9 Da, and the amino acid sequence was identified as Ala-Met-Thr-Gly-Leu-Glu-Ala using electrospray ionization-tandem mass spectrometry (ESI-MS/MS). Moreover, the protection ability of the peptide toward hydroxyl radical-induced oxidative DNA damage and inhibiting lipid peroxidation was evaluated and compared with natural antioxidant α-tocopherol.     

Diurnal and seasonal variabilities in surface ozone and its precursor gases at a semi‐arid site Anantapur (14.62°N, 77.65°E, 331 m asl) in India

Measurements of surface ozone and its precursor gases (NO_x and CO) have been made at a semi‐arid site Anantapur (14.62°N, 77.65°E, 331 m asl) in tropical Indian region for the period, 2001–2003. NO_x and CO levels were the highest during morning and late night hours at this site. Diurnal variations of ozone concentrations varied from 25 ppbv to 50 ppbv and were observed to increase gradually after sunrise, attaining a maximum value by the evening and decreasing gradually thereafter. During monsoon months, the diurnal amplitude of ozone was found to be small (20–25 ppbv). Seasonal variation in ozone showed a pronounced maximum (40–50 ppbv) in the winter and summer. Local pollutants were major contributors to the ozone levels during this period. Ozone shows a yearly mean mixing ratio of about 35.9 ± 8.8 ppbv. The daytime concentration of CO and NO_x varied between 200 to 1200 ppbv and 3 to 20 ppbv respectively for the period studied. Annual average mixing ratios of oxides of nitrogen (NO_x) and CO were observed to be 3.9±0.6 ppbv and 436±64 ppbv, respectively.     

Urea-N uptake by dasheen (Colocasia esculenta L. Schott) in relation to the fertilizer placement method

Urea has become the most important N carrier in many parts of the world and its reaction when added to soil is unique in many ways. Two field experiments were therefore undertaken using¹⁵N to investigate the uptake efficiency of the added urea-¹⁵N which was banded in Experiment I and broadcast in Experiment II. In both experiments the uptake efficiencies were not affected by N-rate and cropping system (Exp. I) or crop residue management (Exp. II) and averaged 17.4 and 16.9% respectively. These low values were supported by evidences of high losses; high pH increases following urea application (volatilization), downward movement of N (leaching), and cycles of waterlogged and well drained conditions in the soil (de-nitrification). Evidence of leaching at least down to 30 cm in the profile was observed in the first experiment where urea was banded but not in experiment II where it was broadcast. The proportion of N in the crop that was derived from added urea (%Ndff) was 57.7% and 36.4% in experiments I and II respectively, suggesting that band application resulted in a higher proportion of the added N in the root zone compared to that for broadcast application. The results indicate the need to investigate other management strategies, such as higher application frequencies and placement closer to the root zone, in order to improve the uptake efficiency of added urea-N in upland rainfed dasheen.     

Electrochemical assessment of inhibitive behavior of some antibacterial drugs on 316 stainless steel in acidic medium

In this work, the inhibitive effect of some antibacterial drugs against the corrosion of 316 stainless steel in 1M HCl has been studied by weight loss, potentiodynamic polarization and electrochemical impedance spectroscopy (EIS). The inhibiting effect explained by adsorption of the additives on steel surface. The inhibition efficiency increases with increasing the inhibitors concentrations and decreases with increasing the temperature. The data obtained fit well to Langmuir adsorption isotherm and the kinetic-thermodynamic model. The results of polarization studies indicate that the investigated antibacterial drugs are mixed type inhibitors. Increasing the inhibition efficiency of the investigated inhibitors with the addition of iodide ions indicates that iodide ions play important role in the adsorption process. The efficiencies obtained from the different electrochemical techniques were in good agreement which prove the validity of these tools in the measurements of the tested inhibitors.     

A machine learning approach for feature selection traffic classification using security analysis

Class imbalance has become a big problem that leads to inaccurate traffic classification. Accurate traffic classification of traffic flows helps us in security monitoring, IP management, intrusion detection, etc. To address the traffic classification problem, in literature, machine learning (ML) approaches are widely used. Therefore, in this paper, we also proposed an ML-based hybrid feature selection algorithm named WMI_AUC that make use of two metrics: weighted mutual information (WMI) metric and area under ROC curve (AUC). These metrics select effective features from a traffic flow. However, in order to select robust features from the selected features, we proposed robust features selection algorithm. The proposed approach increases the accuracy of ML classifiers and helps in detecting malicious traffic. We evaluate our work using 11 well-known ML classifiers on the different network environment traces datasets. Experimental results showed that our algorithms achieve more than 95% flow accuracy results.     

Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

Face recognition is a big challenge in the research field with a lot of problems like misalignment, illumination changes, pose variations, occlusion, and expressions. Providing a single solution to solve all these problems at a time is a challenging task. We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching. The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching and max-pooling. Finally, the input image is recognized using a robust kernel representation method using extracted features. The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets. Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR, ORL, LFW, and FERET face recognition datasets.     

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different accident datasets i.e., IRTAD, NCDB, and FARS. The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC, RMSE, Kappa rate, classification accuracy, and performs better than state-of-the-art approaches for the prediction of casualty severity level. Moreover, the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash. Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.