Women’s Empowerment through Self-help Groups and its Impact on Health Issues: Empirical Evidence

Based on an empirical study in West Bengal, this paper attempts toexamine whether women’s involvement in the microcredit programmethrough SHGs makes any positive change on women’s empowerment.From the assessment of various criteria of empowerment(power,autonomy and self-reliance, entitlement, participation and awarenessand capacity-building), the study suggests that if women participatingin the microcredit programme through SHGs sustain for some longerperiod (eight years or more), such programme might contribute tohigher level of women’s empowerment than women’s empowermentunder all types of control group. This paper also finds that women’searnings from saving and credit have positive and significant effect onnutritional status of the children of women members of SHGs and onthe protein-intake for their household compared with that of amongcontrol groups

Duality between imperfect resources and measurements for propagating entanglement in networks

We propose a measurement-based entanglement propagation strategy for networks in which all nodes except two are initially occupied by a suitably chosen single-qubit system and the two nodes share a bipartite noisy entangled state. The connections between the sites are established using unsharp two-qubit measurements. When only a single node performs measurements, we refer to it as a unidirectional protocol while when both parts of the initial entangled states perform measurements, we call it a bidirectional scheme. When the measurement outcome is post-selected, we demonstrate that in the presence of a local amplitude damping channel acting on a single site, entanglement shareability, as measured by the monogamy score, of the resulting state after measurement can be higher for all values of the strength of the noise than that of the scenario without noise. We observe that irrespective of the channel, there exists a range of unsharpness parameter where a higher monogamy score may be obtained starting from the initial nonmaximally entangled states than from the initial maximally entangled state. We report that the effect of noise on the average monogamy score entered from the resource state may be reduced faster with the unidirectional protocol than with the bidirectional one.Comment: 13 pages, 11 figure

Sequential Reattempt of Telecloning

The task of a telecloning protocol is to send an arbitrary qubit possessed by a sender to multiple receivers. Instead of performing Bell measurement at the sender's node, if one applies unsharp measurement, we show that the shared state can be recycled for further telecloning protocol. Specifically, in case of a single sender and two receivers, the maximal attempting number, which is defined as the maximum number of rounds used by the channel to obtain quantum advantage in the fidelity, turns out to be three both for optimal and nonoptimal shared states for telecloning while the maximal number reduces to two in case of three receivers. Although the original telecloning with quantum advantage being possible for arbitrary numbers of receivers, we report that the recycling of resources is not possible in telecloning involving a single sender and more than three receivers, thereby demonstrating a no-go theorem. We also connect the maximal achievable fidelities in each round with the bipartite entanglement content of the reduced state between the sender and one of the receivers as well as with the monogamy score of entanglement.Comment: v1: 12 pages, 5 figures; v2: 13 pages, 5 figures; close to the publish versio

Sustainable Biomaterials: Current Trends, Challenges and Applications

Biomaterials and sustainable resources are two complementary terms supporting the development of new sustainable emerging processes. In this context, many interdisciplinary approaches including biomass waste valorization and proper usage of green technologies, etc., were brought forward to tackle future challenges pertaining to declining fossil resources, energy conservation, and related environmental issues. The implementation of these approaches impels its potential effect on the economy of particular countries and also reduces unnecessary overburden on the environment. This contribution aims to provide an overview of some of the most recent trends, challenges, and applications in the field of biomaterials derived from sustainable resource

Microwave-Assisted Conversion of Levulinic Acid to γ-Valerolactone Using Low-Loaded Supported Iron Oxide Nanoparticles on Porous Silicates

The microwave-assisted conversion of levulinic acid (LA) has been studied using low-loaded supported Fe-based catalysts on porous silicates. A very simple, productive, and highly reproducible continuous flow method has been used for the homogeneous deposition of metal oxide nanoparticles on the silicate supports. Formic acid was used as a hydrogen donating agent for the hydrogenation of LA to effectively replace high pressure H2 mostly reported for LA conversion. Moderate LA conversion was achieved in the case of non-noble metal-based iron oxide catalysts, with a significant potential for further improvements to compete with noble metal-based catalyst

Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring.publishedVersio

Sufficient conditions for curvature invariants to avoid divergencies in Hyperextended Scalar Tensor theory for Bianchi models

We look for sufficient conditions such that the scalar curvature, Ricci and Kretchmann scalars be bounded in Hyperextended Scalar Tensor theory for Bianchi models. We find classes of gravitation functions and Brans-Dicke coupling functions such that the theories thus defined avoid the singularity. We compare our results with these found by Rama in the framework of the Generalised Scalar Tensor theory for the FLRW models.Comment: 13 page

Acceleration of the universe with a simple trigonometric potential

In this paper we investigate the quintessence model with a minimally coupled scalar field in the context of recent supernovae observations. By choosing a particular form of the deceleration parameter q, which gives an early deceleration and late time acceleration for dust dominated model, we show that this sign flip in q can be obtained by a simple trigonometric patential. The early matter dominated model expands with q=1/2 as desired and enters a negative q phase quite late during the evolution.Comment: 9 pages; 5 figures; to be published in GRG Journa