Sharing perspectives on public-private sector interaction Proceedings of a workshop, 10 April 2001 ICRISAT, Patancheru, India

This book presents several papers presented at a workshop to gather perspectives on the patterns of interaction between private and public agricultural research institutions. The importance of research partnerships, the types of interaction that are possible, and some new ways of exploring this from a policy perspective, are explained

Post-harvest innovations in innovation: reflections on partnership and learning

In the post-harvest area and in agriculture research in general, both in India and internationally, policy attention is returning to the question of how innovation can be encouraged and promoted and thus how impact on the poor can be achieved. This publication assembles several cases from the post-harvest sector. These provide examples of successful innovation that emerged in quite different ways. Its purpose is to illustrate and analyze the diversity and often highly context-specific nature of the processes that lead to and promote innovation. The presented cases suggest a number of generic principles needed to develop the capacity of innovation systems: the need to pay more attention to revealing and managing the historical and institutional context of partnerships and relationship; the need to build on local contexts and circumstance rather than introducing external blueprints; and the need to strengthen the learning process and to link this to the broader agenda of institutional change, particularly concerning the governance of public science endeavors

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding technique

New Agendas for Agricultural Research in Developing Countries: Policy Analysis and Institutional Implications

This article argues that the goals of agricultural research in poor countries have changed substantially over the last four decades. In particular they have broadened from the early (and narrow) emphasis on food production to a much wider agenda that includes poverty alleviation, environmental degradation, and social inclusion. Conversely, agricultural research systems have proved remarkably resistant to the concomitant need for changes in research focus. As a result many, at both the national and international level, are under great strain. In terms of public policy the article goes on to suggest that shortcomings of existing conceptual approaches to technology development could be supplemented by adopting analytical principles that view innovation in systemic terms. An approach where flows of knowledge between institutional nodes is a key to innovative performance (the “National Systems of Innovation” approach) is suggested as one such conceptual framework that might help supplement conventional policy analysis. An earlier version of this paper was presented at a workshop “New Policy Agendas for Agricultural Research: Implications for Institutional Arrangements” held on 28 March 2000 at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India. The workshop was supported by the UK Department of International Development (DFID) Crop Post-Harvest Programme as an output of the project “Optimising Institutional Arrangements.

CaO–MgO–SiO2 glass ceramics: Transferred arc plasma (TAP) synthesis and microstructural characterization

In this paper, synthesis of CaO–MgO–SiO2 glass ceramic using transferred arc plasma (TAP) processingmethod is illustrated. Homogeneous mixture of 51.6% SiO2, 35.6% CaO and 12.8% MgO prepared by drymixing in a ball mill was kept in the anode well (which is the melting bed) of the 10 kW transferredarc plasma torch. It was melted in plasma at an operating power of 5 kW (by varying the processing timefor 3, 5 and 8 min). The melt was cooled to solidify by applying forced air on it. The resulting sampleswere characterized for microstructure and phase composition. The phases were identified by scanningelectron microscopy (SEM), using the back-scattered electron (BSE) image mode and X-ray diffraction(XRD) and energy dispersive X-ray analysis (EDX). The microstructure was examined using opticalmicroscopy (OM) and scanning electron microscopy. The micro-hardness, density and porosity measurementsfor the synthesized samples were carried out. Differential thermal analysis (DTA) was performedto study the thermal evolution. The results show the formation of diopside phase in the transferred arcplasma melted CaO–MgO–SiO2 glass ceramic system achieved with in a quite considerable short time ofplasma processing. The method indicated that TAP technique could be a promising, time saving and onestepmanufacturing process for the production of functional bulk glass ceramics