WOMID: A mentoring initiative for women working in international development aims to connect research and practice.

Balancing the early stages of a research career, while simultaneously keeping up to date with developments in the field generates some unique requirements for researchers in international development. WOMID is a new global mentoring initiative for women, facilitating mentorship between early career academics and practitioners. Alex Dorgan and Beth Harrison, who co-founded WOMID based on their own experiences of doing PhDs, explain what WOMID is all about and how you can get involved

Private Sector Investment and the Green Economy: The Local Consequences of Tree Plantations in Southern Tanzania

Increasing interest and investment on privately funded carbon finance and neoliberal conservation sees capital transforming landscapes across the global south. With this transformation and movement of capital are concerns of land grabs, dispossession, exploitation and loss of access to resources. Understanding these concerns requires understanding investments, responding to markets, changing ecologies, changing livelihoods and land-use practices, power dynamics and social structures. This research explores the direct and broader consequences of large-scale private sector investment in carbon forestry on the society, ecologies and economies of two rural communities in Kilombero, Morogoro, Tanzania. These communities have over the last two decades, seen the establishment of large tree plantations by a private-sector company: Green Resources Ltd (GRL). Fieldwork in the two rural villages, Uchindile and Kitete, was undertaken in 2013, and utilised mixed qualitative methods, including focus group discussions, semi-structured interviews, household surveys. The presence and activities of GRL have brought about immediate and more gradual fundamental shifts in the economies and society of Uchindile and Kitete. These impacts have affected all parts of local society, to varying degrees. Some have opened up new opportunities for welcome change, while others have deepened inequalities. The impacts are significant and far reaching but are not straightforward. Nor are they viewed as such by local residents. Findings suggest we should pay more attention to the economic and employment opportunities created by such investments relative to the attention placed in the literature on land alienation. I argue the indirect, broader consequences of private sector investment are as important as the direct consequences because of the changes that it sets in motion. I argue that the degradation and multi-win narratives we see in the literature are sticky locally, and that local people (re) appropriate these narratives and leverage fire as a tool to further their own interests

Wearable EEG-based Activity Recognition in PHM-related Service Environment via Deep Learning

It is of paramount importance to track the cognitive activity or cognitve attenion of the service personnel in a Prognostics and Health Monitoring (PHM) service related training or operation environment. The electroencephalography (EEG) data is one of the good candidates for cognitive activity recognition of the user. Analyzing electroencephalography (EEG) data in an unconstrained (natural) environment for understanding cognitive state and classifying human activity is a challenging task due to multiple reasons such as low signal-to-noise ratio, transient nature, lack of baseline availability and uncontrolled mixing of various tasks. This paper proposes a framework based on an emerging tool named deep learning that monitors human activity by fusing multiple EEG sensors and also selects a smaller sensor suite for a lean data collection system. Real-time classification of human activity from spatially non collocated multi-probe EEG is executed by applying deep learning techniques without performing any significant amount of data preprocessing and manual feature engineering. Two types of deep neural networks, deep belief network (DBN) and deep convolutional neural network (DCNN) are used at the core of the proposed framework, which automatically learns necessary features from EEG for a given classification task. Validation on extensive amount of data, which was collected from several subjects while they were performing multiple tasks (listening and watching) in PHM service training session, is presented and significant parallels are drawn from existing domain knowledge on EEG data understanding. Comparison with machine learning benchmark techniques shows that deep learning based tools are better at understanding EEG data for task classification. It is observed via sensor selection that a significantly smaller EEG sensor suite can perform at a comparable accuracy as the original sensor suite

100 key research questions for the post-2015 development agenda

The Sustainable Development Goals (SDGs) herald a new phase for international development. This article presents the results of a consultative exercise to collaboratively identify 100 research questions of critical importance for the post-2015 international development agenda. The final shortlist is grouped into nine thematic areas and was selected by 21 representatives of international and non-governmental organisations and consultancies, and 14 academics with diverse disciplinary expertise from an initial pool of 704 questions submitted by 110 organisations based in 34 countries. The shortlist includes questions addressing long-standing problems, new challenges and broader issues related to development policies, practices and institutions. Collectively, these questions are relevant for future development-related research priorities of governmental and non-governmental organisations worldwide and could act as focal points for transdisciplinary research collaborations

A Hundred Key Questions for the Post-2015 Development Agenda

Marcia Vera Espinoza - ORCID: 0000-0001-6238-7683 https://orcid.org/0000-0001-6238-7683Item not available in this repository.This project was financially supported by SIID, the University of Sheffield’s Research and Innovation Services (R&IS), and the Innovation, Impact and Knowledge Exchange (IIKE) programme.https://www.unrisd.org/en/library/publications/a-hundred-key-questions-for-the-post-2015-development-agendapubpu

Bis(8-quinolinolato)aluminum ethyl complexes: Iso-Selective Initiators for rac-Lactide Polymerization

The synthesis and characterization of a series of bis(8-quinolinato)aluminum ethyl complexes, substituted at the 2-, 5-, and 7-positions on the 8-quinolinol ligand, are presented. These complexes are viable initiators for the ring-opening polymerization of rac-lactide in the presence of 1 equiv of isopropyl alcohol. The polymerization control is good, it shows a linear evolution of molecular weight as the polymerization progresses, the polylactide molecular weights are in close agreement with those determined on the basis of the reaction stoichiometry, and the polydispersity indices are narrow. The polymerization kinetics have been monitored, and the influence of the site of ligand substitution has been related to the rates. Some of the initiators show stereocontrol, producing PLA with a good probability of isotactic enchainment (P-i = 0.76)