A GaAs-based self-aligned stripe distributed feedback laser

We demonstrate operation of a GaAs-based self-aligned stripe (SAS) distributed feedback (DFB) laser. In this structure, a first order GaInP/GaAs index-coupled DFB grating is built within the p-doped AlGaAs layer between the active region and the n-doped GaInP opto-electronic confinement layer of a SAS laser structure. In this process no Al-containing layers are exposed to atmosphere prior to overgrowth. The use of AlGaAs cladding affords the luxury of full flexibility in upper cladding design, which proved necessary due to limitations imposed by the grating infill and overgrowth with the GaInP current block layer. Resultant devices exhibit single-mode lasing with high side-mode-suppression of >40 dB over the temperature range 20 °C–70 °C. The experimentally determined optical profile and grating confinement correlate well with those simulated using Fimmwave

[Accepted Manuscript] Smartphone tool to collect repeated 24 h dietary recall data in Nepal.

To outline the development of a smartphone-based tool to collect thrice-repeated 24 h dietary recall data in rural Nepal, and to describe energy intakes, common errors and researchers' experiences using the tool. We designed a novel tool to collect multi-pass 24 h dietary recalls in rural Nepal by combining the use of a CommCare questionnaire on smartphones, a paper form, a QR (quick response)-coded list of foods and a photographic atlas of portion sizes. Twenty interviewers collected dietary data on three non-consecutive days per respondent, with three respondents per household. Intakes were converted into nutrients using databases on nutritional composition of foods, recipes and portion sizes. Dhanusha and Mahottari districts, Nepal. Pregnant women, their mothers-in-law and male household heads. Energy intakes assessed in 150 households; data corrections and our experiences reported from 805 households and 6765 individual recalls. Dietary intake estimates gave plausible values, with male household heads appearing to have higher energy intakes (median (25th-75th centile): 12 079 (9293-14 108) kJ/d) than female members (8979 (7234-11 042) kJ/d for pregnant women). Manual editing of data was required when interviewers mistook portions for food codes and for coding items not on the food list. Smartphones enabled quick monitoring of data and interviewer performance, but we initially faced technical challenges with CommCare forms crashing. With sufficient time dedicated to development and pre-testing, this novel smartphone-based tool provides a useful method to collect data. Future work is needed to further validate this tool and adapt it for other contexts

Role of electromagnetically induced transparency in resonant four-wave-mixing schemes.

Published versio

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives