Emerg Infect Dis

The identification of new materials capable of sustaining a high electron emission current is a key requirement in the development of the next generation of cold cathode devices and technology. Compatibility with large volume material production methods is a further important practical consideration with solution chemistry-based methods providing for route to industrial scale-up. Here we demonstrate a new class of organic-inorganic hybrid material based on polypyrrole and zinc oxide (PPy/ZnO) nanofibers for use as a low-cost large-area cathode material. Solution chemistry based surfactant chemical oxidation polymerisation is used to synthesise the nanofibers and the macroscopic turn-on electric field for emission has been measured to be as low as 1.8 V/μm, with an emission current density of 1 mA/cm2 possible for an applied electric field of less than 4 V/μm. Specfic surface area measurements reveal a linear increase in the nanofiber surface area with ZnO incorporation, which when coupled with electron microscopy and x-ray diffraction analysis reveals that the wurtzite ZnO nanoparticles (around 45 nm in size) act as nucleation sites for the growth of PPy nanofibers. Our study demonstrates for the first time how an inorganic nanocrystal acting as a nucleation site allows for the tailored growth of the organic component without diminishing the overall electrical properties and opens the potential of a new type of organic-inorganic hybrid large-area cathode material. The broader impacts and advantages of using hybrid materials, when compared to other composite nanomaterial systems, as large area cathode materials are also discusse

Developing a 3D geometry for Urban energy modelling of Indian cities

The advancement in the field of Urban Building Energy Modelling (UBEM) is assisting urban planners and managers to design and operate cities to meet environmental emission targets. The usefulness of the UBEM depends upon the quality and level of details (LoD) of the inputs to the model. The inadequacy and quality of relevant input data pose challenges. This paper analyses the usefulness of different methodologies for developing a 3D building stock model of Ahmedabad, India, recognizing data gaps and heterogenous development of the city over time. It evaluates the potentials, limitations, and challenges of remote sensing techniques namely (a) Satellite imagery (b) LiDAR and (c) Photogrammetry for this application. Further, the details and benefits of data capturing through UAV assisted Photogrammetry technique for the development of the 3D city model are discussed. The research develops potential techniques for feature detection and model reconstruction using Computer vision on the Photogrammetry reality mesh. Preliminary results indicate that the use of supervised learning for Image based segmentation on the reality mesh detects building footprints with higher accuracy as compared to geometrybased segmentation of the point cloud. This methodology has the potential to detect complex building features and remove redundant objects to develop the semantic model at different LoDs for urban simulations. The framework deployed and demonstrated for the part of Ahmedabad has a potential for scaling up to other parts of the city and other Indian cities having similar urban morphology and no previous data for developing a UBEM

Capacity building for GIS-based SDG indicators analysis with global high-resolution land cover datasets

The support of geospatial data and technologies for the United Nations Sustainable Development Goals (SDG) framework is critical for assessing and monitoring key indicators, revealing the planet’s trajectory towards sustainability. The availability of global open geospatial datasets, especially high-resolution land cover datasets, provides significant opportunities for computing and comparing indicators across different regions and scales. However, barriers to their proficient use remain due to a lack of data awareness, management and processing capacities using geographic information systems software. To address this, the ”Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets” project created open training material on discovering, accessing, and manipulating global geospatial datasets for computing SDG indicators. The material focuses on water and terrestrial ecosystems, urban environments, and climate, by leveraging world-class global geospatial datasets and using the Free and Open Source Software QGIS. The training material is released under a Creative Commons Attribution 4.0 License, ensuring broad accessibility and facilitating continuous improvement.The Educational and Capacity Building Initiative 2022 of the International Society for Photogrammetry and Remote Sensing (ISPRS).https://www.isprs.org/publications/archives.aspxam2024Geography, Geoinformatics and MeteorologySDG-02:Zero HungerSDG-06:Clean water and sanitationSDG-09: Industry, innovation and infrastructureSDG-11:Sustainable cities and communitiesSDG-12:Responsible consumption and productionSDG-13:Climate actionSDG-14:Life below waterSDG-15:Life on lan

Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets

The support of geospatial data and technologies for the United Nations Sustainable Development Goals (SDG) framework is critical for assessing and monitoring key indicators, revealing the planet’s trajectory towards sustainability. The availability of global open geospatial datasets, especially high-resolution land cover datasets, provides significant opportunities for computing and comparing indicators across different regions and scales. However, barriers to their proficient use remain due to a lack of data awareness, management and processing capacities using geographic information systems software. To address this, the”Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets” project created open training material on discovering, accessing, and manipulating global geospatial datasets for computing SDG indicators. The material focuses on water and terrestrial ecosystems, urban environments, and climate, by leveraging world-class global geospatial datasets and using the Free and Open Source Software QGIS. The training material is released under a Creative Commons Attribution 4.0 License, ensuring broad accessibility and facilitating continuous improvement

CAPACITY BUILDING FOR GIS-BASED SDG INDICATORS ANALYSIS WITH GLOBAL HIGH-RESOLUTION LAND COVER DATASETS

The support of geospatial data and technologies for the United Nations Sustainable Development Goals (SDG) framework is critical for assessing and monitoring key indicators, revealing the planet’s trajectory towards sustainability. The availability of global open geospatial datasets, especially high-resolution land cover datasets, provides significant opportunities for computing and comparing indicators across different regions and scales. However, barriers to their proficient use remain due to a lack of data awareness, management and processing capacities using geographic information systems software. To address this, the ”Capacity Building for GIS-based SDG Indicator Analysis with Global High-resolution Land Cover Datasets” project created open training material on discovering, accessing, and manipulating global geospatial datasets for computing SDG indicators. The material focuses on water and terrestrial ecosystems, urban environments, and climate, by leveraging world-class global geospatial datasets and using the Free and Open Source Software QGIS. The training material is released under a Creative Commons Attribution 4.0 License, ensuring broad accessibility and facilitating continuous improvement

Aid conditionalities, international Good Manufacturing Practice standards and local production rights: a case study of local production in Nepal

© 2015 Brhlikova et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http:// creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.This work was supported by the Economic and Social Research Council and the Department for International Development [RES-167-25-0110] through the collaborative research project Tracing Pharmaceuticals in South Asia (2006 – 2009). In addition to the authors of this paper, the project team included: Soumita Basu, Gitanjali Priti Bhatia, Erin Court, Abhijit Das, Stefan Ecks, Patricia Jeffery, Roger Jeffery, Rachel Manners, and Liz Richardson. Martin Chautari (Kathmandu) and the Centre for Health and Social Justice (New Delhi) provided resources drawn upon in writing this paper but are not responsible for the views expressed, nor are ESRC or DFID. Ethical review was provided by the School of Social and Political Science at the University of Edinburgh, and ethical approval in Nepal for the study granted by the Nepal Health Research Council (NHRC)

Increased risk of second malignancies after in situ breast carcinoma in a population-based registry

Among 1276 primary breast carcinoma in situ (BCIS) patients diagnosed in 1972–2002 in the Southern Netherlands, 11% developed a second cancer. Breast carcinoma in situ patients exhibited a two-fold increased risk of second cancer (standardised incidence ratios (SIR): 2.1, 95% confidence interval (CI): 1.7–2.5). The risk was highest for a second breast cancer (SIR: 3.4, 95% CI: 2.6–4.3; AER: 66 patients per 10 000 per year) followed by skin cancer (SIR: 1.7, 95% CI: 1.1–2.6; AER: 17 patients per 10 000 per year). The increased risk of second breast cancer was similar for the ipsilateral (SIR: 1.9, 95% CI: 1.3–2.7) and contralateral (SIR: 2.0, 95% CI: 1.4–2.8) breast. Risk of second cancer was independent of age at diagnosis, type of initial therapy, histologic type of BCIS and period of diagnosis. Standardised incidence ratios of second cancer after BCIS (SIR: 2.3, 95% CI: 1.8–2.8) resembled that after invasive breast cancer (SIR: 2.2, 95% CI: 2.1–2.4). Surveillance should be directed towards second (ipsi- and contra-lateral) breast cancer