Anatomization of Soil Fertility Status of Chaka Block, Yamuna River Bank, Prayagraj, Uttar Pradesh, India

The present investigation was carried at Sam Higginbottom university of Agriculture Technology and Sciences in the department of Soil Science and Agricultural Chemistry lab. In this study, a total of 24 soil samples were collected from eight different villages on 01 November 2020 in Chaka block of Prayagraj district and from each village 3 soil samples were collected and analyzed for their Physico-chemical parameters by using standard laboratory techniques. According to the critical limits of soil nutrients the results observed, 70 to 80% soil samples were in low to medium range for Nitrogen (N) (51-648 kg ha-1), Phosphorus (P) (0 – 48 kg ha-1) and Potassium (K) (78.4 – 392 kg ha-1). The micro-nutrients (Zn, Fe, Mn, Cu) of soil samples are observed deficiency range due to inverse relationship with pH i.e., increase in pH causes reduction in availability of Zn, Fe, Mn, Cu. According to the Nutrient Index Values, Chaka block was found to be medium category for Organic carbon (2.25), Nitrogen (1.70), Phosphorous (2.29), and Manganese (1.70). Low category was found in Potassium (1.37), Sulphur (1.29), Zinc (1.08) and Iron (1.41). High category was found in Copper (2.66). The results showed that improvement has to be done for improving soil fertility and quality by practicing the improving cropping pattern, decomposition of organic waste, mulching and tillage practices.

Finding the best-fit background function for whole-powder-pattern fitting using LASSO combined with tree search

A new linear function for modelling the background in whole-powder-pattern fitting has been derived by applying LASSO (least absolute shrinkage and selection operator) and the technique of tree search. The background function (BGF) consists of terms b n L(2θ/180)−n/2 and b n H(1 − 2θ/180)−n/2 for the low- and high-angle sides, respectively. Some variable parameters of the BGF should be fixed at zero while others should be varied in order to find the best fit for a given data set without inducing overfitting. The LASSO algorithm can automatically select the variables in linear regression analysis. However, it finds the best-fit BGF with a set of adjustable parameters for a given data set while it derives a different set of parameters for a different data set. Thus, LASSO derives multiple solutions depending on the data set used. By regarding the individual solutions from LASSO as nodes of trees, tree structures were constructed from these solutions. The root node has the maximum number of adjustable parameters, P. P decreases with descending levels of the tree one by one, and leaf nodes have just one parameter. By evaluating individual solutions (nodes) by their χ2 index, the best-fit single path from a root node to a leaf node was found. The present BGF can be used simply by varying P in the range 1–10. The BGF thus derived as a final single solution was incorporated into computer programs for Pawley-based whole-powder-pattern decomposition and Rietveld refinement, and the performance of the BGF was tested in comparison with the polynomials currently widely used as the BGF. The present BGF has been demonstrated to be stable and to give an excellent fit, comparable to polynomials but with a smaller number of adjustable parameters and without introducing undulation into the calculated background curve. Basic algorithms used in statistics and machine learning have been demonstrated to be useful in developing an analytical model in X-ray crystallography.

ANALYSIS OF STRUCTURAL TRANSFORMATION OF LABOR FROM AGRICULTURE TO NON-AGRICULTURE IN ASIA

Through the dataset from APO (Asian Productivity Organization) comprising 22 countries in Asia from 1980 to 2015, this study is to investigate the growth pattern, decomposition, and determinants of structural transformation in Asia. A decomposition method measuring within-effect, between-static-effect, and between-dynamic-effect was adopted to explain the structural change within sectors of agriculture, industry, and services. The results show that the agriculture sector of all countries in Asia declines slowly. The agriculture sector is no longer the largest contributor to GDP in all Asian countries even though it still has the largest labor in Asian developing countries. Workers moving from the agriculture sector to the services sector as the productivity of the service sector is higher than agriculture. The structural changes positively contribute to productivity growth in Asia as a result of the positive static reallocation effects and negative dynamic reallocation effects. Overall, the structural changes contribute to a large part of labor productivity growth. The important determinants of structural transformation are the employment share in agriculture and trade. Final, the policy implication was proposed for structural changes.

Generative lesion pattern decomposition of cognitive impairment after stroke

SummaryCognitive impairment is a frequent and disabling sequela of stroke. There is however incomplete understanding of how lesion topographies in the left and right cerebral hemisphere brain interact to cause distinct cognitive deficits. We integrated machine learning and Bayesian hierarchical modeling to enable hemisphere-aware analysis of 1080 subacute ischemic stroke patients with deep profiling ∼3 months after stroke. We show relevance of the left hemisphere in the prediction of language and memory assessments, while global cognitive impairments were equally well predicted by lesion topographies from both sides. Damage to the hippocampal and occipital regions on the left were particularly informative about lost naming and memory function. Global cognitive impairment was predominantly linked to lesioned tissue in supramarginal and angular gyrus, the postcentral gyrus as well as the lateral occipital and opercular cortices of the left hemisphere. Hence, our analysis strategy uncovered that lesion patterns with unique hemispheric distributions are characteristic of how cognitive capacity is lost due to ischemic brain tissue damage.

Interdecadal Variations in the Walker Circulation and Its Connection to Inhomogeneous Air Temperature Changes from 1961–2012

The tropical Pacific Walker circulation (PWC) is fundamentally important to global atmospheric circulation, and changes in it have a vital influence on the weather and climate systems. A novel three-pattern decomposition of a global atmospheric circulation (3P-DGAC) method, which can be used to investigate atmospheric circulations including the PWC, was proposed in our previous study. Therefore, the present study aims to examine the capability of this 3P-DGAC method to acquire interdecadal variations in the PWC and its connection to inhomogeneous air temperature changes in the period from 1961–2012. Our findings reveal that interdecadal variations in the PWC, i.e., weakening (strengthening) between the periods 1961–1974 and 1979–1997 (1979–1997 and 1999–2012), can be observed using the zonal stream function (ZSF) derived from the 3P-DGAC method. Enhancement of the PWC is also associated with the strengthening and weakening of zonal circulations in the tropical Indian Ocean (IOC) and Atlantic (AOC), respectively, and vice versa, implying a connection between these zonal overturning circulations in the tropics. The interdecadal variations in the zonal circulations correspond well to inhomogeneous air temperature changes, i.e., an enhancement of the PWC is associated with a warming (cooling) of the air temperature from 1000 to 300 hPa in the western (mid–eastern) Pacific Ocean and a cooling (warming) of the air temperature in the tropopause in the western (mid–eastern) Pacific Ocean. Furthermore, a novel index for the PWC intensity based on air temperature is defined, and the capability of the novel index in representing the PWC intensity is evaluated. This novel index is potentially important for the prediction of the PWC by using dynamic equations derived from the 3P-DGAC method.