How do intrahousehold dynamics change when assets are transferred to women? Evidence from BRAC’s “Targeting the Ultra Poor” program in Bangladesh

BRAC’s Challenging the Frontiers of Poverty Reduction Targeting the Ultra Poor (CFPR ¬‐ TUP) program aims to assist the ultra poor in rural Bangladesh to rise out of extreme poverty and access mainstream development programming. CFPR—TUP Phase 2 —the focus of the Gender, Agriculture, and Assets Project’s study — operated from 2007 to 2011 in the poorest regions of Bangladesh. The program provided female members of ultra poor households with assets that could be maintained at home (primarily livestock such as cattle, goats, and poultry birds), as well as intensive training on how to use the assets for income -generating activities. Training subject matter included management practices and how to use improved technology. The GAAP study’s aim was to explore how CFPR¬‐TUP affected intrahousehold dynamics in beneficiary households, including men’s and women’s ownership of and control over various assets (the transferred asset, as well as other assets) and roles in intrahousehold decision making. It also aimed to understand men’s and women’s perceptions of these changes

How do intrahousehold dynamics change when assets are transferred to women? Evidence from BRAC’s challenging the frontiers of poverty reduction—targeting the ultra poor program in Bangladesh

Growing evidence shows that the distribution of individuals' ownership and control of assets within a household can have important implications for women’s empowerment and children’s well-being. Interventions that target assets to specific individuals can shift these intrahousehold dynamics, yet little evidence exists from rigorous evaluations. We study BRAC’s Challenging the Frontiers of Poverty Reduction—Targeting the Ultra Poor (CFPR-TUP) program in Bangladesh, which targets asset transfer (primarily livestock) and training to rural women in poor households. Previous research has shown large, significant positive program impacts at the household level. In this paper, we examine intrahousehold impacts using mixed methods. We focus on the Specially Targeted Ultra-Poor(STUP) component of the program, which targets households selected following a randomized controlled trial design. Adding a new round of data collection with quantitative sex-disaggregated information and qualitative exploration, we exploit the randomized design to assess intrahousehold impacts of STUP. Our analysis confirms that the program significantly increases household ownership of various assets but has complex effects on the targeted women. Quantitative estimates show increases in women’s sole and joint ownership of or control over transferred assets such as livestock, but a much greater increase in men’s sole ownership over nearly all other assets (including agricultural and nonagricultural productive assets, land, and consumer durables). These findings suggest that while the transferred assets tend to remain with women, new investments from mobilized resources are controlled by men. Moreover, the program reduces women’s mobility outside the home and their control over income, consistent with the transferred asset’s requiring maintenance at home. Qualitative findings are consistent with these quantitative results , but women’s contribution to their households is perceived as increasing their confidence and social capital, which they themselves value. Therefore, while provision of assets and training to women has ambiguous effects on women’s empowerment in terms of tangible assets and decisionmaking, women take intangibles into account and largely perceive positive (though still mixed) effects. The analysis shows that asset transfer targeted to women can increase women’s ownership of and control over the transferred asset itself but may not necessarily increase women’s intrahousehold bargaining position. Moreover, it reveals that outcomes valued by individuals may not always be tangible, highlighting the complexity of assessing whether interventions improve women’s empowerment

An Intelligent Decision Support System for the Detection of Meat Spoilage using Multispectral Images

In food industry, quality and safety are considered important issues worldwide that are directly related to health and social progress. The use of vision technology for quality testing of food production has the obvious advantage of being able to continuously monitor a production using non-destructive methods, thus increasing the quality and minimizing cost. The performance of an intelligent decision support system has been evaluated in monitoring the spoilage of minced beef stored either aerobically or under modified atmosphere packaging, at different storage temperatures (0, 5, 10, and 15 °C) utilising multispectral imaging information. This paper utilises a neuro-fuzzy model which incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. Initially, meat samples are classified according to their storage conditions, while identification models are then utilised for the prediction of the Total Viable Counts of bacteria. The innovation of the proposed approach is further extended to the identification of the temperature used for storage, utilizing only imaging spectral information. Results indicated that spectral information in combination with the proposed modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage

The places parents go: understanding the breadth, scope, and experiences of activity spaces for parents

The final publication is available at Springer via https://doi.org/10.1007/s10708-015-9690-yNeighborhood environments are related to parenting behaviors, which in turn have a life-long effect on children’s health and well-being. Activity spaces, which measure individual routine patterns of movement, may be helpful in assessing how physical and social environments shape parenting. In this study we use qualitative data and GIS mapping from four California cities to examine parental activity spaces. Parents described a number of factors that shape their activity spaces including caregiving status, the age of their children, and income. Parental activity spaces also varied between times (weekends vs. weekdays) and places (adult-only vs. child-specific places). Knowing how to best capture and study parental activity spaces could identify mechanisms by which environmental factors influence parenting behaviors and child health

Identifying Recent Cholera Infections Using a Multiplex Bead Serological Assay

Estimates of incidence based on medically attended cholera can be severely biased. Vibrio cholerae O1 leaves a lasting antibody signal and recent advances showed that these can be used to estimate infection incidence rates from cross-sectional serologic data. Current laboratory methods are resource intensive and challenging to standardize across laboratories. A multiplex bead assay (MBA) could efficiently expand the breadth of measured antibody responses and improve seroincidence accuracy. We tested 305 serum samples from confirmed cholera cases (4 to 1083 d postinfection) and uninfected contacts in Bangladesh using an MBA (IgG/IgA/IgM for 7 Vibrio cholerae O1-specific antigens) as well as traditional vibriocidal and enzyme-linked immunosorbent assays (2 antigens, IgG, and IgA). While postinfection vibriocidal responses were larger than other markers, several MBA-measured antibodies demonstrated robust responses with similar half-lives. Random forest models combining all MBA antibody measures allowed for accurate identification of recent cholera infections (e.g., past 200 days) including a cross-validated area under the curve (cvAUC200) of 92%, with simpler 3 IgG antibody models having similar accuracy. Across infection windows between 45 and 300 days, the accuracy of models trained on MBA measurements was non-inferior to models based on traditional assays. Our results illustrated a scalable cholera serosurveillance tool that can be incorporated into multipathogen serosurveillance platforms. IMPORTANCE Reliable estimates of cholera incidence are challenged by poor clinical surveillance and health-seeking behavior biases. We showed that cross-sectional serologic profiles measured with a high-throughput multiplex bead assay can lead to accurate identification of those infected with pandemic Vibrio cholerae O1, thus allowing for estimates of seroincidence. This provides a new avenue for understanding the epidemiology of cholera, identifying priority areas for cholera prevention/control investments, and tracking progress in the global fight against this ancient disease

Understanding interactions in face-to-face and remote undergraduate science laboratories

This paper reviews the ways in which interactions have been studied, and the findings of such studies, in science education in both face-to-face and remote laboratories. Guided by a systematic selection process, 27 directly relevant articles were analysed based on three categories: the instruments used for measuring interactions, the research findings on student interactions, and the theoretical frameworks used in the studies of student interactions. In face-to-face laboratories, instruments for measuring interactions and the characterisation of the nature of interactions were prominent. For remote laboratories, the analysis of direct interactions was found to be lacking. Instead, studies of remote laboratories were mainly concerned with their practical scope. In addition, it is found that only a limited number of theoretical frameworks have been developed and applied in the research design. Existent theories are summarised and possible theoretical frameworks that may be implemented in studies of interactions in undergraduate laboratories are proposed. Finally, future directions for research on the interrelationship between student interactions and laboratory learning are suggested

An efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation

Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts