Ecological balance of Iron ore mines land in Chhattisgarh by using vesicular arbuscular mycorrhiza fungi.

The State of Chhattisgarh is situated in the Mid Eastern India, bounded between North latitude 170 46' to 240 06' and East Longitude 800 15' to 84025' and the states is geologically one of the most important terrains in the Indian shield comprising of litho logical sequence ranging in age from Archaean to Recent. Bountiful nature has bestowed Chhattisgarh with vast reserves of all important minerals. Chhattisgarh state has rich sources of mineral resource especially iron and coal. Open cast mining is the dominant form of mining. The immediate effect of open cast mining is the removal of soil and vegetation cover.  The extent of damage depends on location of mining site, scale of operation, mining methods, degree of mechanization etc. The effects of mining activity as well as mining waste causes such as soil erosion, air and water pollution, toxicity, geo-environmental disasters, loss of biodiversity, and ultimately loss of economic wealth. This research paper presenting the how Vesicular Arbuscular Mycorrhiza fungi used for ecological balances. VAM fungi are types of endomycorrhizae and used as biofertilizer for revegetation of mining destroyed sites especially iron ore mines in Chhattisgarh. Mycorrhizae associate plants shows more root and shoot height, fresh and dry biomass weight, high content of soluble protein and low rate of mortality when planting in actual mining sites

On weighted cumulative residual extropy and weighted negative cumulative extropy

In this paper, we define general weighted cumulative residual extropy (GWCRJ) and general weighted negative cumulative extropy (GWNCJ). We obtain its simple estimators for complete and right censored data. We obtain some results on GWCREJ and GWNCJ. We establish its connection to reliability theory and coherent systems. We also propose empirical estimators of weighted negative cumulative extropy (WNCJ)

Sliding Mode Control of Photovoltaic Energy Conversion Systems

Increasing interest and investment in renewable energy give rise to rapid development of high penetration solar energy. The focus has been on the power electronic converters which are typically used as interface between the dc output of the photo voltaic (PV) panels and the terminals of the ac utility network. In the dual-stage grid-connected PV (GPV) system, the dc dc stage plays a significant role in converting dc power from PV panel at low voltage to high dc bus voltage. However, the output of solar arrays varies due to change in solar irradiation and weather conditions. More importantly, high initial cost and limited lifespan of PV panels make it more critical to extract as much power from them as possible. It is, therefore, necessary one to employ the maximum power point tracking (MPPT) techniques in order to operate PV array at its maximum power point (MPP). A fast-and-robust analog-MPP tracker is thus proposed by using the concepts of Utkin’s equivalent control theory and fast-scale stability analysis. Analytical demonstration has also been presented to show the effectiveness of the proposed MPPT control technique. After the dc stage, the dc-ac inverter stage is employed to convert dc power into ac power and feed the power into the utility grid. The dc-ac stage is realized through the conventional full-bridge voltage source inverter (VSI) topologies. A fixed frequency hysteresis current (FFHC) controller, as well as an ellipsoidal switching surface based sliding mode control (SMC) technique are developed to improve the steady state and dynamic response under sudden load fluctuation. Such a control strategy is used not only maintains good voltage regulation, but also exhibits fast dynamic response under sudden load variation .Moreover, VSI can be synchronized with the ac utility grid. The current injected into the ac grid obeys the regulations standards (IEEE Std 519 and IEEE Std 1547)and ful fills the maximum allowable amount of injected current harmonics. Apart from that, controlling issues of stand-alone and grid-connected operation PV have also been discussed. A typical stand-alone PV system comprises a solar array and battery which is used as a backup source for power management between the source and the load .A control approach is developed for a 1-_ dual-stage transformer less inverter system to achieve voltage regulation with low steady state error and low total harmonic distortion (THD) and fast transient response under various load disturbances. The SMC technique is employed to address the power quality issues. A control technique for battery charging and discharging is also presented to keep the dc-link voltage constant during change in load demand or source power. This battery controller is employed for bidirectional power flow between battery and dc-link through a buck-boost converter in order to keep the input dc voltage constant. The robust stability of the closed-loop system is also analyzed. Finally, modeling and control of a 1-_ dual-stage GPV system has been analyzed. A small-signal average model has been developed for a 1-_ bridge inverter. The proposed controller has three cascaded control loops. The simulation results and theoretical analysis indicate that the proposed controller improves the efficiency of the system by reducing the THD of the injected current to the grid and increases the robustness of the system against uncertainties

Compact schemes for variable coefficient convection-diffusion equations

Fourth order accurate compact schemes for variable coefficient convection-diffusion equations are considered. A sufficient condition for stability of the schemes have been derived using a difference equation based approach. The constant coefficient problems are considered as a special case, and the unconditional stability of compact schemes for such case is proved theoretically. The condition number of the amplification matrix is also analysed, and an estimate for the same is derived. In order to verify the derived conditions numerically, MATLAB codes are provided in Appendix of the manuscript. An example is provided to support the assumption taken to assure stability

Te mperature Dependent Decline in Soil Methane Oxidizing Bacterial Population in Tropical Dry Deciduous Forest Ecosystems

Abstract: Culturable methanotrophic bacteria (CMB) were studied in the soils of forest and savanna of tropical dry deciduous forest ecosystems employing most probable number (MPN) technique. The spatiotemporal study was conducted at the six sites differing in the soil physicochemical properties and vegetational cover. CMB population was high in the moist sites compared to the dry sites and in sub soil below 10 cm depth. The top soil population ranged between 7.0 × 10 4 to

Raman evidence for Orbiton-Mediated Multiphonon Scattering in Multiferroic TbMnO3_3

Temperature-dependent Raman spectra of TbMnO$_3$ from 5 K to 300 K in the spectral range of 200 to 1525 cm$^{-1}$ show five first-order Raman allowed modes and two high frequency modes. The intensity ratio of the high frequency Raman band to the corresponding first order Raman mode is nearly constant and high ($\sim$ 0.6) at all temperatures, suggesting a orbiton-phonon mixed nature of the high frequency mode. One of the first order phonon modes shows anomalous softening below T$_N$ ($\sim$ 46 K), suggesting a strong spin-phonon coupling.Comment: 14 pages, 3 figures, 1 tabl

Genetic diversity and population structure of Indian golden silkmoth (Antheraea assama)

Background The Indian golden saturniid silkmoth (Antheraea assama), popularly known as muga silkmoth, is a semi-domesticated silk producing insect confined to a narrow habitat range of the northeastern region of India. Owing to the prevailing socio-political problems, the muga silkworm habitats in the northeastern region have not been accessible hampering the phylogeography studies of this rare silkmoth. Recently, we have been successful in our attempt to collect muga cocoon samples, although to a limited extent, from their natural habitats. Out of 87 microsatellite markers developed previously for A. assama, 13 informative markers were employed to genotype 97 individuals from six populations and analyzed their population structure and genetic variation. Methodology/Principal Findings We observed highly significant genetic diversity in one of the populations (WWS-1, a population derived from West Garo Hills region of Meghalaya state). Further analysis with and without WWS-1 population revealed that dramatic genetic differentiation (global FST = 0.301) was due to high genetic diversity contributed by WWS-1 population. Analysis of the remaining five populations (excluding WWS-1) showed a marked reduction in the number of alleles at all the employed loci. Structure analysis showed the presence of only two clusters: one formed by WWS-1 population and the other included the remaining five populations, inferring that there is no significant genetic diversity within and between these five populations, and suggesting that these five populations are probably derived from a single population. Patterns of recent population bottlenecks were not evident in any of the six populations studied. Conclusions/Significance A. assama inhabiting the WWS-1 region revealed very high genetic diversity, and was genetically divergent from the five populations studied. The efforts should be continued to identify and study such populations from this region as well as other muga silkworm habitats. The information generated will be very useful in conservation of dwindling muga culture in Northeast India

Evaluating air quality and criteria pollutants prediction disparities by data mining along a stretch of urban-rural agglomeration includes coal-mine belts and thermal power plants

Air pollution has become a threat to human life around the world since researchers have demonstrated several effects of air pollution to the environment, climate, and society. The proposed research was organized in terms of National Air Quality Index (NAQI) and air pollutants prediction using data mining algorithms for particular timeframe dataset (01 January 2019, to 01 June 2021) in the industrial eastern coastal state of India. Over half of the study period, concentrations of PM2.5, PM10 and CO were several times higher than the NAQI standard limit. NAQI, in terms of consistency and frequency analysis, revealed that moderate level (ranges 101–200) has the maximum frequency of occurrence (26–158 days), and consistency was 36%–73% throughout the study period. The satisfactory level NAQI (ranges 51–100) frequency occurrence was 4–43 days with a consistency of 13%–67%. Poor to very poor level of air quality was found 13–50 days of the year, with a consistency of 9%–25%. Random Forest (RF), Support Vector Machine (SVM), Bagged Multivariate Adaptive Regression Splines (MARS) and Bayesian Regularized Neural Networks (BRNN) are the data mining algorithms, that showed higher efficiency for the prediction of PM2.5, PM10, NO2 and SO2 except for CO and O3 at Talcher and CO at Brajrajnagar. The Root Mean Square Error (RMSE) between observed and predicted values of PM2.5 (ranges 12.40–17.90) and correlation coefficient (r) (ranges 0.83–0.92) for training and testing data indicate about slightly better prediction of PM2.5 by RF, SVM, bagged MARS, and BRNN models at Talcher in comparison to PM2.5 RMSE (ranges 13.06–21.66) and r (ranges 0.64–0.91) at Brajrajnagar. However, PM10 (RMSE: 25.80–43.41; r: 0.57–0.90), NO2 (RMSE: 3.00–4.95; r: 0.42–0.88) and SO2 (RMSE: 2.78–5.46; r: 0.31–0.88) at Brajrajnagar are better than PM10 (RMSE: 35.40–55.33; r: 0.68–0.91), NO2 (RMSE: 4.99–9.11; r: 0.48–0.92), and SO2 (RMSE: 4.91–9.47; r: 0.20–0.93) between observed and predicted values of training and testing data at Talcher using RF, SVM, bagged MARS and BRNN models, respectively. Taylor plots demonstrated that these algorithms showed promising accuracy for predicting air quality. The findings will help scientific community and policymakers to understand the distribution of air pollutants to strategize reduction in air pollution and enhance air quality in the study region