Estimating groundwater inputs from Sankarabarani River Basin, South India to the Bay of Bengal evaluated by Radium (226Ra) and nutrient fluxes

Sankarabarani river basin gains significance due to presence of major industrial, agricultural, urban development and tourist related activities has influenced the water quality in the estuarine environment.  Investigations about river water quality has been attempted but not more studies focus about the evaluation of groundwater discharge a significant process that connects groundwater and the coastal seawater have been attempted.  For the present study, radium (226Ra) a naturally occurring isotope was measured at three locations and used as effective tracers for estimating the groundwater discharge along with nutrient inputs to the Bay. Groundwater samples representing north east monsoon (December, 2017) has been collected during tidal variation in three locations (Location A- away from the coast towards inland, Location B-intermediate between Location A and the coast and Location C-at the estuary). 226Ra mass balance calculated groundwater fluxes irrespective of tidal variations were 2.27×108 m3/d, 2.19×108 m3/d and 5.22×107m3/d for A, B and C locations respectively. The nutrients like Dissolved inorganic nitrogen (DIN), Dissolved inorganic Phosphate (DIP) and Dissolved Silica (DSi) were found to be influencing the coastal groundwater by contributing fluxes to the sea of about 679.33 T mol/day. The study suggests increasing radium and nutrient fluxes to the Bay altering the coastal ecosystems would result in surplus algal blooms creating hypoxia

Quantifying the shifts and intensification in the annual cycles of diurnal temperature extremes for human comfort and crop production

Any significant change in climate is known to have a significant impact on crop production and human resources, which are generally difficult to quantify. In the present study, two indices are defined: (i) refined growing season (GS) characteristics and (ii) transition period, based on the annual cycles of diurnal temperature extremes, to unravel any possible impact on these productive elements. Multi-dimensional ensemble empirical mode decomposition, a nonlinear, non-stationary approach is used to extract the annual cycles of diurnal temperature extremes. Since the adverse impact is reportedly more critical over tropical regions, the Indian region is chosen as the study area, and 1° × 1° gridded daily minimum and daily maximum temperature data are used. Results reveal earlier onset and lengthening of GS, with notable spatial variations. Further, a drastic reduction in the transition (i.e. comfortable) period is observed over the warm humid regions, majorly due to the encroachment of summer days. On the contrary, over semi-arid regions, the transition period is found to be increasing, majorly due to the shortening of winter. The quantification of these changes may aid in implementing regional adaptation strategies related to the two productive elements

Unravelling Diurnal Asymmetry of Surface Temperature in Different Climate Zones.

Understanding the evolution of Diurnal Temperature Range (DTR), which has contradicting global and regional trends, is crucial because it influences environmental and human health. Here, we analyse the regional evolution of DTR trend over different climatic zones in India using a non-stationary approach known as the Multidimensional Ensemble Empirical Mode Decomposition (MEEMD) method, to explore the generalized influence of regional climate on DTR, if any. We report a 0.36 °C increase in overall mean of DTR till 1980, however, the rate has declined since then. Further, arid deserts and warm-temperate grasslands exhibit negative DTR trends, while the west coast and sub-tropical forest in the north-east show positive trends. This transition predominantly begins with a 0.5 °C increase from the west coast and spreads with an increase of 0.25 °C per decade. These changes are more pronounced during winter and post-monsoon, especially in the arid desert and warm-temperate grasslands, the DTR decreased up to 2 °C, where the rate of increase in minimum temperature is higher than the maximum temperature. We conclude that both maximum and minimum temperature increase in response to the global climate change, however, their rates of increase are highly local and depend on the underlying climatic zone

Web Phishing Detection In Machine Learning Using Heuristic Image Based Method

Phishing attacks are significant threat to users of the Internet causing tremendous economic loss every year. In combating phish Industry relies heavily on manual verification to achieve a low false positive rate, which however tends to be slows in responding to the huge volume created by toolkits. The goal here is to combine the best aspects of human verified blacklists and heuristic-based methods which are the low false positive rate of the former and the broad coverage of the latter. The key insight behind our detection algorithm is to leverage existing human-verified blacklists and apply the shingling technique, a popular near duplicate detection algorithm used by search engines, to detect phish in a probabilistic fashion with very high accuracy. The features introduced in Carnegie Mellon Anti-Phishing and Network Analysis Tool (CANTINA), in similarity feature to a machine learning based phishing detection system. By preliminarily experimented with a small set of 200 web data, consisting of 100 phishing webs and another 100 non-phishing webs. The evaluation result in terms of f-measure was upto 0.9250, with 7.50 % of error rate is implemented