Comparative Study of Networking Protocols in WSN Implementation for Greenhouse Monitoring

Wireless sensor networks consist of number of small devices called sensor nodes formed by combining a sensing unit, processor unit, wireless communication unit and power source unit. WSN has gained a lot of importance in recent years because of its use in various fields where monitoring and controlling are important aspects. This paper discusses implementation of wireless sensor network in greenhouse for the growth of crop yields. Wireless sensor networkcan use various types of networking protocols for implementing WSN in greenhouse monitoring.Main focus of this paper is on comparative study of various networking protocols available for implementing WSN

ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements

HICF: A MATLAB PACKAGE FOR HYPERSPECTRAL IMAGE CLASSIFICATION AND FUSION FOR EDUCATIONAL LEARNING AND RESEARCH

A significant surge has been observed with the development and research in remote sensing in recent years for hyperspectral applications in Earth observation. Subsequently, the development of software and tools have also experienced an unprecedented rise, both in research as well as in academia. Although commercial software and tools such as ENVI by ITT Visual Information Solutions, Boulder, CO, USA are available for visualizing and analyzing the hyperspectral images, such software are expensive. Some open source toolboxes such as the MATLAB-based Hyperspectral Image Analysis Toolbox (HIAT) are also available. However, mostly these toolboxes have not been packaged for dissemination and operation without the MATLAB software which is commercial. In this paper, we introduce the Hyperspectral Image Classification and Fusion (HICF) package which is being developed at the Geoinformatics laboratory, Department of Civil Engineering, Indian Institute of Technology Kanpur (IITK) in MATLAB that can be used by standalone installation with an open source supplementary MATLAB compiler. This software is intended to provide a collection of algorithms both conventional and those developed at the Geoinformatics laboratory that utilizes the numerical computing capability of MATLAB for the processing of hyperspectral and multispectral imagery. The HICF software comprises a simple design of the graphical user interface which can be efficiently used particularly for academic purposes

Correlation of serum transaminases with dengue serology

Aim: Clinical correlation of dengue serology with serum transaminases Introduction: Dengue, caused by dengue virus, spread by AEDES genus mosquito, is one of the most significant arthropod borne disease. In severe and less severe forms dengue may affect liver enzymes. The goal of this research was to examine and compare dengue serology with serum transaminases. Methodology: The study comprised of 109 cases for which informed consent was taken and the patients were monitored throughout their hospital stay. Dengue antibody detection was used to confirm dengue infection and serum transaminases were monitored. Conclusion: All types of dengue infection frequently result in elevated transaminases values with SGOT rising much more thn SGPT. Patients with NS1 antibody + dengue, have significantly higher serum transaminases levels. Also, high SGOT and  SGPT values may be a poor prognostic indication and an early sign of dengue infection

Chirality Dependent Charge Transfer Rate in Oligopeptides

It is shown that “spontaneous magnetization” occurs when chiral oligopeptides are attached to ferrocene and are self-assembled on a gold substrate. As a result, the electron transfer, measured by electrochemistry, shows asymmetry in the reduction and oxidation rate constants; this asymmetry is reversed between the two enantiomers. The results can be explained by the chiral induced spin selectivity of the electron transfer. The measured magnetization shows high anisotropy and the “easy axis” of magnetization is along the molecular axis

Glacial lake outburst flood risk assessment of a rapidly expanding glacial lake in the Ladakh region of Western Himalaya, using hydrodynamic modeling

The ongoing trend of warming climate has made Glacial Lake Outburst Floods (GLOFs) a major cryospheric hazard worldwide, especially in the Himalayas. GLOFs in the Himalayan region are mostly caused by moraine-dammed proglacial lakes and ice-dammed lakes. These sporadic disasters have resulted in significant loss of life and property. This study offers a comprehensive analysis of the GLOF hazard potential of a potentially dangerous proglacial lake (PDGL) in the Ladakh region. This research explores the GLOF threat from the lake using multi-criteria analysis and advanced 2D hydrodynamic modeling approaches. The mass balance response of the mother glacier, its flow dynamics, and glacier-lake interactions were examined for the past 22 years. The findings show that over this period, the PDGL has had a notable expansion of 78.7%, accompanied by a significant recession of 13.2% in its feeding glacier. The glacier has witnessed an average thickness loss of ⁓7 m at the rate of 0.32 m a−1 during this period. The average, lowest, and maximum depth of the glacier were found to be 30.95, 14.30, and 50.57 m, respectively and the average velocity of the glacier was estimated as 3.38 m a−1. Because of the lake’s rapid expansion and steep surrounding slopes, it was classified as a high-hazard lake. The risk to the downstream community was assessed through 2D hydrodynamic modeling using the HEC-RAS tool. The maximum discharge under the worst-case scenario for the piping and overtopping failures was estimated as 3890.99 m3s−1 and 5111.39 m3s−1, respectively. The area potentially under the threat of inundation was calculated to be 4.74 and 5.38 km2 for the moderate and worst-case scenarios respectively. The expected maximum flood velocities range from 18.26 to 23.78 meters, respectively for the moderate and worst-case scenarios. At several locations in the downstream area, routed hydrographs representing the GLOF propagation were generated. The findings show that the flood wave in the worst-case scenario would arrive at the first settlement in 50 min, with a peak velocity of 12.36 m s−1. The potentially inundated area includes critical infrastructure such as bridges, residential houses, and roads. To mitigate the potential risk associated with this lake, a more detailed and on-site study is highly recommended

Nanocomposite hydrogel as a template for synthesis of mono and bimetallic nanoparticles

Nanocomposite hydrogels (NC gels) with polymer–clay network assemblies are useful in a number of applications because of their unique properties and characteristics. Herein, we describe a distinctive strategy for the preparation of discrete monometallic (Ag, Au, and Pd) and bimetallic (Pt-Pd, Au-Pd) nanoparticles that uses a nanocomposite hydrogel composed of a polymer–clay network. Thermoresponsive NC gels were synthesized by the in-situ free-radical polymerization of N-isopropylacrylamide in the presence of clay (synthetic hectorite) nanosheets (CNSs). Since CNSs have strong affinities for metals ions that facilitate the concentration of metal precursors around them, the reduction of metal ions by ascorbic acid in NC gels provides well-dispersed, non-aggregated spherical monometallic and bimetallic nanoparticles (NPs) that are strongly immobilized within the polymer-clay network. The resulting hybrid NP-NC gels, which contain monometallic or bimetallic NPs, exhibit high catalytic activities for the hydrogenation of nitrophenol to aminophenol. The combination of well-defined metal NPs and mechanically tough NC gels opens up new possibilities for the design of environmentally friendly and sustainable functional NP-NC-gel materials