Latrine adoption and use in rural Odisha, India: Constraints and challenges

An estimated 2.4 billion people worldwide lack access to improved sanitation. This includes nearly 1 billion people practicing open defecation, of which 60 percent reside in India. Open defecation is especially common among rural populations, and has been linked to health problems like the occurrence of diarrheal disease and malnutrition. Despite decades of efforts by the Indian government to improve sanitation, open defecation continues to be a common practice even in households possessing a functional latrine. The main aims of this research were 1) to understand the reasons for poor adoption (uptake) and use of government subsidized latrines, and 2) to identify the constraints causing latrine non adoption and use. From the constraints identified in the literature review, three constraints were selected for in-depth investigation in this dissertation :1) socio-cultural beliefs and customs around handling adult human faeces, 2) programmatic challenges in mobilising communities for latrine promotion, and 3) household level challenges with sanitation decision making, especially exploring inability of women to take decisions on sanitation installation. The study was conducted in rural areas of Odisha through a mixed methods approach. The research revealed that in this study population, latrine adoption and use by all family members is influenced by socio-cultural and behavioural rituals and restrictions on handling and containing adult human faeces close to the home. In some cases, study subjects expressed a preference for open defecation over latrine use and were able to articulate benefits and advantages. Diverse communities and lack of capacity and skill among implementers negatively impacted the implementation of sanitation campaigns. Power hierarchies, inter-generational and household dynamics prevented female family members from participating in household decisions, including latrine installation decision-making

Assessment of WRF-3DVAR Data Assimilation on Simulation of Heavy Rainfall Events Associated with Monsoon Depressions over Bay of Bengal

The present study examines the performance of the Advance Research Weather Research and Forecasting model with three-dimensional variational data assimilation (WRF-3DVAR) associated with four heavy rainfall events (HREs) in the presence of monsoon depressions (MDs) over the Bay of Bengal (BoB). We have carried out two numerical experiments, control experiment (CNTL; without data assimilation) and 3DV (assimilation of observations from Global Telecommunication system). The resultant high-resolution analysis obtained from the successful insertion of additional observations through 3DVAR assimilation technique recaptures the better convection and synoptic features associated with the MDs. The 3DV-simulated values of hydrometeors (rainwater, cloud water, and ice + snow + graupel) are found to be reasonably well captured, compared to CNTL simulation. The MDs evolution at various phases of its life span is reasonably well simulated in the 3DV compared to the CNTL experiment. The qualitative and quantitative precipitations are examined with respect to satellite-estimated rainfall data. The quantitative validation of model simulated 24-h accumulated precipitation is evaluated through the feature-based diagnostic evaluation method. Numerous statistical skill scores are evaluated by virtue of the object-oriented tool and results revealed that the simulated rainfall is remarkably improved in 3DV experiment. The study envisages that the assimilation of observations through 3DVAR have positive impact for simulation of HREs due to the presence of MDs

The finite range simple effective interaction including tensor terms

The prediction of single particle level crossing phenomenon between $2p_{3/2}$ and $1f_{5/2}$ orbitals in $Ni$- and $Cu$-isotopic chains by the finite range simple effective interaction without requiring the tensor part is discussed. In this case the experimentally observed crossing could be studied as a function of nuclear matter incompressibility, $K(\rho_0)$. The estimated crossing for the neutron number $N$=46 could be reproduced by the equation of state corresponding to $K(\rho_0)$=240 MeV. However, the observed proton gaps between the $1h_{11/2}$ and $1g_{7/2}$ shells in $Sn$ and $Sb$ isotopic chain, and the neutron gaps between the $1i_{13/2}$ and $1h_{9/2}$ shells in $N$=82 isotones, as well as the shell closure properties at $N$=28 require explicit consideration of a tensor part as the central contribution is not enough to initiate the required level splittings

Long-Short Term Memory for an Effective Short-Term Weather Forecasting Model Using Surface Weather Data

Part 7: Deep Learning - Convolutional ANNInternational audienceNumerical Weather Prediction (NWP) requires considerable computer power to solve complex mathematical equations to obtain a forecast based on current weather conditions. In this article, we propose a lightweight data-driven weather forecasting model by exploring state-of-the-art deep learning techniques based on Artificial Neural Network (ANN). Weather information is captured by time-series data and thus, we explore the latest Long Short-Term Memory (LSTM) layered model, which is a specialised form of Recurrent Neural Network (RNN) for weather prediction. The aim of this research is to develop and evaluate a short-term weather forecasting model using the LSTM and evaluate the accuracy compared to the well-established Weather Research and Forecasting (WRF) NWP model. The proposed deep model consists of stacked LSTM layers that uses surface weather parameters over a given period of time for weather forecasting. The model is experimented with different number of LSTM layers, optimisers, and learning rates and optimised for effective short-term weather predictions. Our experiment shows that the proposed lightweight model produces better results compared to the well-known and complex WRF model, demonstrating its potential for efficient and accurate short-term weather forecasting

Reexamination of the N = 50 and Z = 28 shell closure

Recent experiments performed in neutron-rich copper isotopes have revealed a crossing in the nucleus Cu75 between the 3/2− and 5/2− levels, which correspond to the ground state and the first excited state in isotopes with mass number below A = 75. Due to the strong single-particle character of these states, this scenario can be investigated through the analysis of the proton spectrum provided by mean-field models in nickel isotopes with neutron numbers between N = 40 and N = 50. In this work, we show that the aforementioned crossing is mainly driven by the mean field provided by the effective nucleon-nucleon and spin-orbit interactions. We also analyze the impact of the tensor interaction and find that in some mean-field models it is essential to reproduce the crossing of the 2p3/2 and 1 f5/2 proton single-particle levels, as in the case of the SAMi-T Skyrme force and the D1M Gogny interaction, whereas in other cases, as for example the SLy5 Skyrme force, a reasonable tensor force appears to be unable to modify the mean-field enough to reproduce this level crossing. Finally, in the calculations performed with the so-called simple effective interaction (SEI), it is shown that the experimental data in nickel and copper isotopes considered in this work can be explained satisfactorily without any explicit consideration of the tensor interactio