Strengthening Primary Health Care Through Community Health Workers: Investment Case And Financing Recommendations

A report released this week at the Third International Conference on Financing for Development found that there is a strong case for investing in Community Health Worker (CHW) programs as part of integrated health systems. The report was released by leaders from the Federal Democratic Republic of Ethiopia, the Republic of Liberia, the U.N. Secretary General's Special Envoy for Financing the Health MDGs and for Malaria, Partners in Health, the Clinton Foundation, the African Leaders Malaria Alliance, and the MDG Health Alliance. The authors encourage domestic governments, international financers, bilateral and multilateral donors, and the broader global health community to finance and support the scale up of CHW programs as part of community-based primary health care through a set of specific recommendations. The authors participated in the crafting of the report and its recommendations as part of a distinguished panel chaired by Ray Chambers, the UN Secretary General's Special Envoy for Financing the Health MDGs and for Malaria, and Prime Minister Hailemariam Dessalegn, President of the Federal Democratic Republic of Ethiopia

Agile Data Offloading over Novel Fog Computing Infrastructure for CAVs

Future Connected and Automated Vehicles (CAVs) will be supervised by cloud-based systems overseeing the overall security and orchestrating traffic flows. Such systems rely on data collected from CAVs across the whole city operational area. This paper develops a Fog Computing-based infrastructure for future Intelligent Transportation Systems (ITSs) enabling an agile and reliable off-load of CAV data. Since CAVs are expected to generate large quantities of data, it is not feasible to assume data off-loading to be completed while a CAV is in the proximity of a single Road-Side Unit (RSU). CAVs are expected to be in the range of an RSU only for a limited amount of time, necessitating data reconciliation across different RSUs, if traditional approaches to data off-load were to be used. To this end, this paper proposes an agile Fog Computing infrastructure, which interconnects all the RSUs so that the data reconciliation is solved efficiently as a by-product of deploying the Random Linear Network Coding (RLNC) technique. Our numerical results confirm the feasibility of our solution and show its effectiveness when operated in a large-scale urban testbed.Comment: To appear in IEEE VTC-Spring 201

Robust, Recognizable and Legitimate: Strengthening India's Appliance Efficiency Standards and Labels Through Greater Civil Society Involvement

Residential use accounts for 14 percent of global energy consumption. Appliance standards alone could achieve 17 percent energy reductions in the residential sector. Although appliance efficiency standards and labeling programs (AES&L) aim to influence consumer behavior, consumers and civil society often play a limited role in the design, implementation, and monitoring of these programs. This report considers the contribution that civil society organizations can make at each stage of an appliance efficiency standards and labeling program (AES&L), based on experiences in 10 developed and developing countries

Evaluation of the Sustainable Employment in a Green US Economy (SEGUE)

The Rockefeller Foundation's Sustainable Employment in a Green US Economy(SEGUE) initiative has been a central player in green job discussions since 2009, andeven earlier through the Foundation's Campaign for American Workers. In its earliestdevelopmental stages, the initiative sought "to maximize the 'green' growth areas ofthe economy while benefiting low- and moderate-income workers" (RockefellerFoundation, 2009b). SEGUE focused on creating jobs by supporting green economicactivities. Initially, the focus was the building energy-retrofit market in the constructionindustry and, later, on water infrastructure and waste management. The demandfor workers became recognized as the bottleneck that needed to be released, in orderto realize the benefits of the green economy.To document and expand upon the learning and exploration that SEGUE has started,the Rockefeller Foundation provided a grant to the research firm, Abt Associates,Inc., in April 2012, to conduct a short-term, developmental evaluation of SEGUE. Theevaluation focused on three areas: learning for the purposes of determining SEGUE'sfuture direction, documenting SEGUE's grant and non-grant outputs for accountabilityneeds, and providing public knowledge on green jobs and evaluations in general.This report provides the results from the evaluation

Bayesian peak-bagging of solar-like oscillators using MCMC: A comprehensive guide

Context: Asteroseismology has entered a new era with the advent of the NASA Kepler mission. Long and continuous photometric observations of unprecedented quality are now available which have stimulated the development of a number of suites of innovative analysis tools. Aims: The power spectra of solar-like oscillations are an inexhaustible source of information on stellar structure and evolution. Robust methods are hence needed in order to infer both individual oscillation mode parameters and parameters describing non-resonant features, thus making a seismic interpretation possible. Methods: We present a comprehensive guide to the implementation of a Bayesian peak-bagging tool that employs a Markov chain Monte Carlo (MCMC). Besides making it possible to incorporate relevant prior information through Bayes' theorem, this tool also allows one to obtain the marginal probability density function for each of the fitted parameters. We apply this tool to a couple of recent asteroseismic data sets, namely, to CoRoT observations of HD 49933 and to ground-based observations made during a campaign devoted to Procyon. Results: The developed method performs remarkably well at constraining not only in the traditional case of extracting oscillation frequencies, but also when pushing the limit where traditional methods have difficulties. Moreover it provides an rigorous way of comparing competing models, such as the ridge identifications, against the asteroseismic data.Comment: Accepted for publication in A&

Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial feature from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. Besides, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with several state-of-the-art methods, including the CNN framework, on three widely used HSIs. The obtained results show that Bi-CLSTM can improve the classification performance as compared to other methods