On the Heterogeneity of Dowry Motives

Dowries have been modeled as pre-mortem bequests to daughters or as groom-prices paid to in-laws. These two classes of models yield mutually exclusive predictions, but empirical tests of these predictions have been mixed. We argue that the heterogeneity of findings can be explained by a heterogeneous world--some households use dowries as a bequest and others use dowries as a price. We estimate a model with heterogeneous dowry motives and use the predictions from the competing theories in an exogenous switching regression to place households in the price or bequest regime. Our empirical strategy generates multiple, independent checks on the validity of regime assignment. Using retrospective marriage data from rural Bangladesh, we find robust evidence of heterogeneity in dowry motives in the population; that bequest dowries have declined in prevalence and amount over time; and that bequest households are better off compared to price households on a variety of welfare measures.

Memecylon edule leaf extract mediated green synthesis of silver and gold nanoparticles

We used an aqueous leaf extract of Memecylon edule (Melastomataceae) to synthesize silver and gold nanoparticles. To our knowledge, this is the first report where M. edule leaf broth was found to be a suitable plant source for the green synthesis of silver and gold nanoparticles. On treatment of aqueous solutions of silver nitrate and chloroauric acid with M. edule leaf extract, stable silver and gold nanoparticles were rapidly formed. The gold nanoparticles were characterized by UV-visible spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive X-ray analysis (EDAX) and Fourier transform infra-red spectroscopy (FTIR). The kinetics of reduction of aqueous silver and gold ions during reaction with the M. edule leaf broth were easily analyzed by UV-visible spectroscopy. SEM analysis showed that aqueous gold ions, when exposed to M. edule leaf broth, were reduced and resulted in the biosynthesis of gold nanoparticles in the size range 20–50 nm. TEM analysis of gold nanoparticles showed formation of triangular, circular, and hexagonal shapes in the size range 10–45 nm. The resulting silver nanoparticles were predominantly square with uniform size range 50–90 nm. EDAX results confirmed the presence of triangular nanoparticles in the adsorption peak of 2.30 keV. Further FTIR analysis was also done to identify the functional groups in silver and gold nanoparticles. The characterized nanoparticles of M. edule have potential for various medical and industrial applications. Saponin presence in aqueous extract of M. edule is responsible for the mass production of silver and gold nanoparticles

Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making

This study evaluates the performance of different data clustering approaches for searching the profitable consumer segments in the UK hospitality industry. The paper focuses on three aspects of datasets including the ordinal nature of data, high dimensionality and outliers. Data collected from 513 sample points are analysed in this paper using four clustering approaches: Hierarchical clustering, K-Medoids, fuzzy clustering, and Self-Organising Maps (SOM). The findings suggest that Fuzzy and SOM based clustering techniques are comparatively more efficient than traditional approaches in revealing the hidden structure in the data set. The segments derived from SOM has more capability to provide interesting insights for data-driven decision making in practice. This study makes a significant contribution to literature by comparing different clustering approaches and addressing misconceptions of using these for market segmentation to support data-driven decision making in business practices

Developing procedures for assessment of ecological status of Indian River basins in the context of environmental water requirements

River basins / Ecology / Indicators / Environmental flows / Environmental management / Habitats / Biota / Fish / Ecosystems / India / Krishna River Basin / Chauvery River Basin / Narmada River Basin / Periyar River Basin / Ganga River Basin

Genetic analysis of some exotic x Indian crosses in sorghum. VI. Dynamics of character association under selection

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Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice

In the era of Big Data, many organisations have successfully leveraged Big Data Analytics (BDA) capabilities to improve their performance. However, past literature on BDA have put limited focus on understanding the capabilities required to extract value from big data. In this context, this paper aims to provide a systematic literature review of BDA capabilities in supply chain and develop the capabilities maturity model. The paper presents the bibliometric and thematic analysis of research papers from 2008 to 2016. This paper contributes in theorizing BDA capabilities in context of supply chain, and provides future direction of research in this field

Retinal boundary segmentation in stargardt disease optical coherence tomography images using automated deep learning

Purpose: To use a deep learning model to develop a fully automated method (fully semantic network and graph search [FS-GS]) of retinal segmentation for optical coherence tomography (OCT) images from patients with Stargardt disease. Methods: Eighty-seven manually segmented (ground truth) OCT volume scan sets (5171 B-scans) from 22 patients with Stargardt disease were used for training, validation and testing of a novel retinal boundary detection approach (FS-GS) that combines a fully semantic deep learning segmentation method, which generates a per-pixel class prediction map with a graph-search method to extract retinal boundary positions. The performance was evaluated using the mean absolute boundary error and the differences in two clinical metrics (retinal thickness and volume) compared with the ground truth. The performance of a separate deep learning method and two publicly available software algorithms were also evaluated against the ground truth. Results: FS-GS showed an excellent agreement with the ground truth, with a boundary mean absolute error of 0.23 and 1.12 pixels for the internal limiting membrane and the base of retinal pigment epithelium or Bruch's membrane, respectively. The mean difference in thickness and volume across the central 6 mm zone were 2.10 µm and 0.059 mm3. The performance of the proposed method was more accurate and consistent than the publicly available OCTExplorer and AURA tools. Conclusions: The FS-GS method delivers good performance in segmentation of OCT images of pathologic retina in Stargardt disease. Translational Relevance: Deep learning models can provide a robust method for retinal segmentation and support a high-throughput analysis pipeline for measuring retinal thickness and volume in Stargardt disease