Monitoring of water stress in wheat using multispectral indices derived from Landsat-TM

Detection of crop water stress is crucial for efficient irrigation water management. Potential of Satellite data to provide spatial and temporal dynamics of crop growth conditions makes it possible to monitor crop water stress at regional level. This study was conducted in parts of western Uttar Pradesh and Haryana. Multi-temporal Landsat data were used for detecting wheat crop water stress using vegetation indices (VIs), viz. vegetation water stress index (VWSI) and land surface wetness index water stress factor (Ws_LSWI). The estimated water stress from satellite data-based VIs was validated by water stress factor (Ws) derived from flux-tower data. The study observed Ws_LSWI to be better index for water stress detection. The results indicated that Ws_LSWI was superior over other index showing RMSE = 0.12, R² = 0.65, whereas VWSI showed overestimated values with mean RD 4%.     

Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya,India

Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world. The number of landslides and the level of damage across the globe has been increasing over time. Therefore, landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region. Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study. The prime goal of the study is to prepare landslide susceptibility maps(LSMs) using computer-based advanced machine learning techniques and compare the performance of the models.To properly understand the existing spatial relation with the landslide, twenty factors, including triggering and causative factors, were selected. A deep learning algorithm viz. convolutional neural network model(CNN) and three popular machine learning techniques, i.e., random forest model(RF), artificial neural network model(ANN), and bagging model, were employed to prepare the LSMs. Two separate datasets including training and validation were designed by randomly taken landslide and nonlandslide points. A ratio of 70:30 was considered for the selection of both training and validation points.Multicollinearity was assessed by tolerance and variance inflation factor, and the role of individual conditioning factors was estimated using information gain ratio. The result reveals that there is no severe multicollinearity among the landslide conditioning factors, and the triggering factor rainfall appeared as the leading cause of the landslide. Based on the final prediction values of each model, LSM was constructed and successfully portioned into five distinct classes, like very low, low, moderate, high, and very high susceptibility. The susceptibility class-wise distribution of landslides shows that more than 90% of the landslide area falls under higher landslide susceptibility grades. The precision of models was examined using the area under the curve(AUC) of the receiver operating characteristics(ROC) curve and statistical methods like root mean square error(RMSE) and mean absolute error(MAE). In both datasets(training and validation), the CNN model achieved the maximum AUC value of 0.903 and 0.939, respectively. The lowest value of RMSE and MAE also reveals the better performance of the CNN model. So, it can be concluded that all the models have performed well, but the CNN model has outperformed the other models in terms of precision.     

Improving integrity of RC beam-column joints with deficient beam rebar anchorage

In recent earthquakes in developing countries, severe damage was observed on reinforced concrete buildings. This study focuses on exterior beam-column joints with substandard beam rebar anchorage and seismic strengthening by installing wing walls. First, a series of experiments was conducted to investigate the seismic behavior of exterior joints with substandard beam rebar anchorage representing typical Bangladeshi buildings. Two 0.7-scaled exterior joint specimens were tested, and these specimens showed beam rebar anchorage failure and/or joint shear failure. Prior to strengthening of the joint, a series of pullout tests was conducted on postinstalled bonded anchors in low-strength concrete for strengthening design. Then, an experiment was performed to apply the strengthening method by wing walls to one of the exterior joint specimens to improve the integrity, and this method was intended to prevent the failure of beam rebar anchorage. The strengthening method is proposed to extend the development length of beam longitudinal bars by considering the embedment length along the wing walls. The test results verified the effectiveness and applicability of the proposed strengthening method to upgrade exterior RC beam-column joints with deficient beam rebar anchorage.     

Neoarchean high-pressure margarite–phengitic muscovite–chlorite corona mantled corundum in quartz-free high-Mg,Al phlogopite–chlorite schists from the Bundelkhand craton,north central India

In quartz-free Fe, Na-poor and high-Mg, Al schists from the Bundelkhand craton in north central India, corundum porphyroblasts in finely interleaved phlogopite–chlorite aggregates with rare clinozoisite are mantled by fine-grained (<3 μm) intergrowths of outer collars (>200 μm wide) of phengitic-muscovite and chlorite (phlogopite + corundum + H₂O → phengitic-muscovite + chlorite), and <100 μm wide inner collars of margarite–muscovite (corundum + clinozoisite + phengitic muscovite → margarite + muscovite + H₂O). Wide-beam electron probe microanalyses indicate Mg in the bi-layered corona increases from corundum outwards, with a complementary decrease in Al and K. Si and Ca increase and then decrease to matrix values. The sharp chemical gradients across the highly structured bi-layered corona are inferred to suggest that the corona-forming reactions were promoted by local grain-boundary-controlled thermodynamic instability as opposed to element transport by advective diffusion. The P–T convergence of KMASH reactions and NCKMASH pseudosection phase relations computed using micro-domain compositions indicate the chlorite–phengitic muscovite outer collar formed at 18–20 kbar and ca. 630°C. The NCKMASH margarite–muscovite inner collar yielded lower metamorphic P–T conditions of 11 ± 3 kbar, ca. 630°C. U–Th–Pb chemical dating of metamorphic monazite and LA-ICPMS U–Pb isotope dating of re-equilibrated zircon yield ca. 2.78 Ga ages, which are interpreted to date corona formation and Neoarchean high-P metamorphism in the Bundelkhand craton, hitherto unknown in the Indian Precambrian. (220)     

Hydrochemistry and isotopic studies to identify Ganges River and riverbank groundwater interaction, southern Bangladesh

The Ganges River water and riverbank shallow groundwater were studied during a single wet season using the hydrochemical and isotopic composition of its dissolved load. The dissolved concentrations of major ions (Cl^?, SO₄ ^2?, NO₃ ^?, HCO₃ ^?, Ca²⁺, Na⁺, Mg²⁺, and K⁺), trace elements (barium (Ba) and strontium (Sr)) and stable isotopes (O and D) were determined on samples collected from the Ganges River and its riverbank shallow aquifers. In the present study, the shallow groundwater differs significantly from the Ganges River water; it shows distinct high concentrations of Ca²⁺, Mg²⁺, HCO₃ ^?, Ba, and Sr due to water–rock interaction and this in particular suggests that the Ganges River may not contribute significantly to the riverbank shallow aquifers during wet season. Besides, the sum of the total cationic charge (∑⁺, in milliequivalents per liter) in the groundwater shows high values (2.48 to 13.91 meq/L, average 9.12 meq/L), which is much higher than the sum of the cations observed in the Ganges water (1.36 to 3.10 meq/L, average 1.94 meq/L). Finally, the more depleted stable isotopic (δ ¹⁸O and δ ²H) compositions of the Ganges River water are in contrast to those of the riverbank aquifer having enriched stable isotopic values during the wet season and the riverbank groundwater thus has a purely local origin from precipitation.     

Mobility centre-oriented urban regeneration: examining place value of railway stations

Urban regeneration involves the notion of reconstruction of places, where regeneration initiatives are undertaken to ameliorate the quality of physical and social spaces, accrue economic profitability of space, and finally encourage environmental sustainability. Railway stations all around the world have a very important role to play in contemporary urban regeneration strategies as places of both social and material interest. With the progress of the twentieth century several issues like rapidly growing population, seemingly relentless urbanisation, an alarming rate of vehicle emission, and consequent pollution emphasised railway stations as critical places with great potentials of infrastructure development for the optimised functioning of urban centres. Thus the huge value of railway stations and their adjacent land has been acknowledged since the last quarter of the previous century. This paper aims to analyse the place value of railway stations and their immediate neighbourhoods within the urban fabric of a city using a relevant theoretical concept i.e. the ‘Node-Place Model’. Simultaneously it also draws attention to the notion of (re)development of functional spaces in and around the railway stations as a dimension of contemporary urban regeneration strategies, with the help of some global examples. This paper thereby reveals that in the urban centres throughout the entire world the railway stations are progressively attaining the focal position within integrated transport and land use planning strategies, either under the larger concept of 'Transit-Oriented Development' (TOD) or more simply, with the label of (re)development programme. This paper will significantly contribute to the ability of spatial policymakers to direct the future of urban growth towards public transit stations so as to achieve sustainable urban development.

     

Seasonal prediction of Indian summer monsoon in NCEP coupled and uncoupled model

This study has identified probable factors that govern ISMR predictability. Furthermore, extensive analysis has been performed to evaluate factors leading to the predictability aspect of Indian Summer Monsoon Rainfall (ISMR) using uncoupled and coupled version of National Centers for Environmental Prediction Coupled Forecast System (CFS). It has been found that the coupled version (CFS) has outperformed the uncoupled version [Global Forecast System (GFS)] of the model in terms of prediction of rainfall over Indian land points. Even the spatial distribution of rainfall is much better represented in the CFS as compared to that of GFS. Even though these model skills are inadequate for the reliable forecasting of monsoon, it imparts the capacious knowledge about the model fidelity. The mean monsoon features and its evolution in terms of rainfall and large-scale circulation along with the zonal and meridional shear of winds, which govern the strength of the monsoon, are relatively closer to the observation in the CFS as compared to the GFS. Furthermore, sea surface temperature–rainfall relation is fairly realistic and intense in the coupled version of the model (CFS). It is found that the CFS is able to capture El Niño Southern Oscillation ISMR (ENSO-ISMR) teleconnections much strongly as compared to GFS; however, in the case of Indian Ocean Dipole ISMR teleconnections, GFS has the larger say. Coupled models have to be fine-tuned for the prediction of the transition of El Niño as well as the strength of the mature phase has to be improved. Thus, to sum up, CFS tends to have better predictive skill on account of following three factors: (a) better ability to replicate mean features, (b) comparatively better representation of air–sea interactions, and (c) much better portrayal of ENSO-ISMR teleconnections. This study clearly brings out that coupled model is the only way forward for improving the ISMR prediction skill. However, coupled model’s spurious representation of SST variability and mean model bias are detrimental in seasonal prediction.     

Testing isotropy of cosmic microwave background radiation

We introduce new symmetry-based methods to test for isotropy in cosmic microwave background (CMB) radiation. Each angular multipole is factored into unique products of power eigenvectors, related multipoles and singular values that provide two new rotationally invariant measures mode by mode. The power entropy and directional entropy are new tests of randomness that are independent of the usual CMB power. Simulated Galactic plane contamination is readily identified. The ILC– WMAP data maps show seven axes well aligned with one another and the direction Virgo. Parameter free statistics find 12 independent cases of extraordinary axial alignment, low power entropy, or both having 5 per cent probability or lower in an isotropic distribution. Isotropy of the ILC maps is ruled out to confidence levels of better than 99.9 per cent, whether or not coincidences with other puzzles coming from the Virgo axis are included. Our work shows that anisotropy is not confined to the low l region, but extends over a much larger l range.     

Periodicities of the neutrino burst from Supernova 1987A

The neutrino burst from Supernova 1987A detected by Mont Blanc, Kamiokande II, IMB, and BAKSAN have been studied by Jurkevich's mathematical technique of search for periodicities. It is found that all the data exhibit 11±0.2 ms period. There are also other periods, but they are almost exact multiples of 11 ms. We suggests that the 11 ms period is the pulsation period of the neutron core of the supernova remnant. From the observed period of neutrino data it is also possible to predict the masses of the neutrinos.