Hydrochemistry of Lake Rara: A high mountain lake in western Nepal

High altitude ecosystems have important natural ecological functions but are under increasing impacts from human activities and climate change. A detailed analysis of the water chemistry of Lake Rara, a high mountain lake in western Nepal, was carried out in October 2015 and April 2016. A total of 31 water samples were collected. Major ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, SO₄^2?, NO₃^? and Cl^?) were analysed by ion chromatography. Si and PO₄^3? were analysed following the standard protocols. Conductivity, pH, total dissolved solids (TDS), turbidity and dissolved oxygen (DO) were measured on‐site. The lake is oligotrophic characterized by low PO₄^3? concentration (0.06 ± 0.01 mg/L), high DO values (6.73 ± 0.06 and 10.89 ± 0.86 mg/L), alkaline pH (8.42 ± 0.3 and 8.32 ± 0.23) and low conductivity (189.93 ± 5.3 and 189.22 ± 5.8 μS/cm). The concentrations of the major cations were in the order of Ca²⁺ > Mg²⁺ > K⁺ > Na⁺ (during both seasons), and for anions, it was HCO₃^? > SO₄^2? > Cl^? > NO₃^? and HCO₃^? > Cl^? > NO₃^? > SO₄^2? during postmonsoon and premonsoon, respectively. One‐way ANOVA revealed significant seasonal variations (p  < 0.05) in most of the physicochemical parameters. The increased concentrations of most of the ions in the premonsoon time probably reflect long‐range transport of materials through dry deposition, whereas higher concentrations of NO₃^? and Cl^? in some sites possibly reflect the localized impacts of settlement and grazing. The lake water was classified as Ca(Mg)HCO₃. High (Ca²⁺ + Mg²⁺)/Tz⁺ ratio (0.97 in postmonsoon and 0.95 in premonsoon) and low (Na⁺ + K⁺)/Tz⁺ ratio (0.03 in postmonsoon and 0.04 in premonsoon) confirm carbonate weathering as the principal source of major ions with bedrock geology governing the water chemistry. The findings of this study build on the baseline dataset for assessing future anthropogenic influence on the lake and subsequent development for future lake management strategies.     

SEIAQRDT model for the spread of novel coronavirus (COVID-19): A case study in India

Applied Intelligence - COVID-19 is a global pandemic declared by WHO. This pandemic requires the execution of planned control strategies, incorporating quarantine, self-isolation, and tracing of...     

Image denoising by supervised adaptive fusion of decomposed images restored using wave atom,curvelet and wavelet transform

This paper presents an efficient image denoising method that adaptively combines the features of wavelets, wave atoms and curvelets. Wavelet shrinkage is used to denoise the smooth regions in the image while wave atoms are employed to denoise the textures, and the edges will take advantage of curvelet denoising. The received noisy image is firstly decomposed into a homogenous (smooth/cartoon) part and a textural part. The cartoon part of the noisy image is denoised using wavelet transform, and the texture part of the noisy image is denoised using wave atoms. The two denoised images are then fused adaptively. For adaptive fusion, different weights are chosen from the variance map of the denoised texture image. Further improvement in denoising results is achieved by denoising the edges through curvelet transform. The information about edge location is gathered from the variance map of denoised cartoon image. The denoised image results in perfect presentation of the smooth regions and efficient preservation of textures and edges in the image.     

Nanoarchitectonics of Nanoporous Carbon Materials from Natural Resource for Supercapacitor Application

Nanoarchitectonics of nanoporous carbon materials (NCMs) derived from natural resource; Areca Catechu Nut (ACN) with enhanced electrochemical supercapacitance properties is reported. ACN powder is chemically activated in a tubular furnace at 400?°C and the effect of activating agent sodium hydroxide (NaOH), zinc chloride (ZnCl₂) and phosphoric acid (H₃PO₄) on the textural properties, surface functional groups and electrochemical supercapacitance properties was thoroughly examined. We found that ACN derived NCMs are amorphous in nature comprising of macropores, micropores and hierarchical micro- and mesopore architecture depending on the activating agent. Surface area and pore volume are found in the range 25–1985 m² g^?1 and 0.12–3.42 cm³ g^?1, respectively giving the best textural properties for H₃PO₄ activated NCM. Nevertheless, despite the different chemical activating agent used, all the prepared NCMs showed similar oxygen-containing surface functional groups (carboxyl, carboxylate, carbonyl and phenolic groups). The H₃PO₄ activated NCM showed excellent supercapacitance properties giving a high specific capacitance of ca. 342 F g^?1 at a scan rate of 5 mV s^?1 together with the high cyclic stability sustaining capacitance retention of about 97% after 5000 charging/discharging cycles. Electrochemical supercapacitance properties have demonstrated that the ACN derived novel nanoporous carbon material would be a potential material in energy storage application.     

Intelligent fault diagnosis of rolling bearing and gear system under fluctuating load conditions using image processing technique

Health monitoring of a rotating machine is mainly done by investigation of the vibration patterns generated by the machine. Leveraging the fact that faults occurring in different parts of a machine generate unique fault signatures, a fault diagnosis methodology is proposed that can identify nine different healthy and faulty categories under varying load and noisy conditions. Neural network is employed for classification of faults in various categories. The robustness of features such as semivariance, kurtosis and Shannon entropy make them strong candidates to train the artificial neural network. The matching of vibration textural patterns with wave atom basis functions ensures removal of noise. As a result, the enhanced features used to train the neural network have led to high accuracy in classification. The algorithm is tested at various load conditions for both bearing and gear fault experimental data sets acquired by machinery fault simulator in laboratory. Simulation results show high degree of accuracy for both bearing and gear fault diagnosis under no load to heavy load noisy conditions.

     

Nanoporous carbon materials with enhanced supercapacitance performance and non-aromatic chemical sensing with C1/C2 alcohol discrimination

We have investigated the textural properties, electrochemical supercapacitances and vapor sensing performances of bamboo-derived nanoporous carbon materials (NCM). Bamboo, an abundant natural biomaterial, was chemically activated with phosphoric acid at 400 °C and the effect of impregnation ratio of phosphoric acid on the textural properties and electrochemical performances was systematically investigated. Fourier transform-infrared (FTIR) spectroscopy confirmed the presence of various oxygen-containing surface functional groups (i.e. carboxyl, carboxylate, carbonyl and phenolic groups) in NCM. The prepared NCM are amorphous in nature and contain hierarchical micropores and mesopores. Surface areas and pore volumes were found in the range 218–1431 m² g^?1 and 0.26–1.26 cm³ g^?1, respectively, and could be controlled by adjusting the impregnation ratio of phosphoric acid and bamboo cane powder. NCM exhibited electrical double-layer supercapacitor behavior giving a high specific capacitance of c.256 F g^?1 at a scan rate of 5 mV s^?1 together with high cyclic stability with capacitance retention of about 92.6% after 1000 cycles. Furthermore, NCM exhibited excellent vapor sensing performance with high sensitivity for non-aromatic chemicals such as acetic acid. The system would be useful to discriminate C₁ and C₂ alcohol (methanol and ethanol).