232 research outputs found
Modern techniques for agricultural disease management and crop yield prediction Advances in environmental engineering and green technologies (AEEGT) book series./ N. Pradeep, Sandeep Kautish, C.R. Nirmala, Vishal Goyal, Sonia Abdellatif, [editors].
No full text"eISSN: 2326-9170."Includes compilation of bibliographical references (pages 259-284) and index."This book explores the current practices and widespread open challenges in the field of applications of computational techniques in smart agriculture. It also examines computational methods that will aid in agriculture disease management, weed detection, and crop yield prediction"--Provided by publisher.Issues and challenges in smart farming for sustainable agriculture / Immanuel Zion Ramdinthara, Shanthi Bala P -- Image processing techniques aiding smart agriculture / Aspira S. Tripathy, Deepak Kumar Sharma -- Expert system design for diagnosis of diseases for paddy crop / Sreekantha Desai Karanam, Deepthi M.B. -- Deep learning and computer vision in smart agriculture / Shiv Kumar, Agrima Yadav, Deepak Kumar Sharma -- Computer vision for green plant segmentation and leaf count / Praveen Kumar J., Domnic S. -- Automatic data acquisition and spot disease identification system in plants pathology domain : agricultural intelligence system in plant pathology domain / Rajesh T.M., Kavyashree Dalawai, Pradeep N. -- Applications of data mining techniques in smart farming for sustainable agriculture / Anandhavalli Muniasamy -- Agro guardian : a smart agriculture framework for precision farming / Sanjeevakumar M. Hatture, Susen P. Naik -- A study on technology-LED solution for fruit grading to address post-harvest handling issues of horticultural crops / Arun Kumar R., Vijay S. Rajpurohit, Sandeep Kautish -- Optimized data mining techniques for outlier detection, removal, and management zone delineation for yield prediction / Roopa G.M., Arun Kumar G.H., Naveen Kumar J.R., Nirmala C.R.1 online resource (xix, 291 pages)
The State of Teacher Education in France: A Critique
No full textPradeep Kumar Misra, The State of Teacher Education in France: A Critique, FMSH-WP-2014-58, january 2014. Pour télécharger ce document sur HALSHS The author Dr. Pradeep Kumar Misra is an Associate Professor in the Faculty of Education and Allied Sciences of M.J.P. Rohilkhand University, Bareilly, India. His research specializations are Teacher Education, Educational Technology and Lifelong Learning. Acknowledgements I am extremely grateful to the Fondation maison des Sciences de l'Homme (FMSH..
The State of Teacher Education in France: A Critique
No full textPradeep Kumar Misra, The State of Teacher Education in France: A Critique, FMSH-WP-2014-58, january 2014. Pour télécharger ce document sur HALSHS The author Dr. Pradeep Kumar Misra is an Associate Professor in the Faculty of Education and Allied Sciences of M.J.P. Rohilkhand University, Bareilly, India. His research specializations are Teacher Education, Educational Technology and Lifelong Learning. Acknowledgements I am extremely grateful to the Fondation maison des Sciences de l'Homme (FMSH..
An analysis of the marketing strategy of American motors corporation from a local frame of reference, 1969
No full textClinico-histopathological study of varicose vein and role of matrix metalloproteinases-1, matrix metalloproteinases-9 and tissue inhibitor of matrix metalloproteinase-1 in varicose vein formation
No full textBackground and Aims: Varicose vein (VV) is an important cause of morbidity in the young and elderly population. Many studies of the Western country suggest that matrix metalloproteinases (MMPs) and tissue inhibitors of MMPs have a crucial role in the pathogenesis of VV, but limited work has been done in Indian population. The aim of this study is to study detailed histology of VV and to see the expression of MMP-1, MMP-9 and tissue inhibitor of matrix metalloproteinase-1 (TIMP-1). Materials and Methods: A total of 63 cases of VV and 10 control leg veins were included in this prospective study. Paraffin sections of VV were prepared. Hematoxylin and eosin (H and E), Masson trichrome and Verhoeff′s staining were performed. Immunohistochemistry of VV was done with MMP-1, MMP-9, and TIMP-1 antibodies. Cytoplasmic expression of MMP-1, MMP-9 and TIMP-1 were graded as intense positive (++), weak/slight positive (+), and absent (−). Results: Focal intimal thickening (47.6%), increased medial thickening (73%) and fragmentation of elastin fibers (84.1%) were the major histological changes noted in H and E and special stained sections. MMP-1 expression increased in all layers of VV in 58 cases (92.1%) as compared to control veins. As compared to the control veins, intimal and adventitial expression of MMP-9 were increased in 31 (49.2%) and 40 (63.5%) cases, respectively. Expression of TIMP-1 was absent in both the varicose and the control veins. Conclusion: Increased expression of MMP-1 and MMP-9 suggests they have an important role in the pathogenesis of VV
Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics
No full textLogistics planning is a complex optimization problem involving multiple decision makers. Automated scheduling systems offer support to human planners; however state-of-the-art approaches often employ a centralized control paradigm. While these approaches have shown great value, their application is hindered in dynamic settings with no central authority. Motivated by real-world scenarios, we present a decentralized approach to collaborative multi-agent scheduling by casting the problem as a Distributed Constraint Optimization Problem (DCOP). Our model-based heuristic approach uses message passing with a novel pruning technique to allow agents to cooperate on mutual agreement, leading to a near-optimal solution while offering low computational costs and flexibility in case of disruptions. Performance is evaluated in three real-world field trials with a logistics carrier and compared against a centralized model-free Deep Q-Network (DQN)-based Reinforcement Learning (RL) approach, a Mixed-Integer Linear Programming (MILP)-based solver, and both human and heuristic baselines. The results demonstrate that it is feasible to have virtual agents make autonomous decisions using our DCOP method, leading to an efficient distributed solution. To facilitate further research in Self-Organizing Logistics (SOL), we provide a novel real-life dataset.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Algorithmic
Co-creating Responsible Energy Systems
No full textEnergy system projects in countries like India are often failing. Not only because of technical or economical barriers, but mainly institutional and social issues are at the base of these failures. A co-creation, or participatory, process to align all demands and requirements of the different stakeholders is required. This paper takes evidence from literature on co-creation and energy systems and from case study research in India to help define an approach towards such a co-creation process as a use case for the application of the Responsible Innovation Systems framework. A discussion on co-creation as a solution generates a number of recommendations, after which a set of characteristics is concluded that the co-creation process of energy systems should have towards a responsible approach, so that more robust and sustainable innovations might emerge.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Energy and IndustryDelft Centre for Entrepreneurshi
Structural and electronic properties of LaPd2As2 superconductor: First-principle calculations
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