Annual Report 2017-18

Not AvailableI am extremely happy and privileged to present the annual report of ICAR-CRIDA for the year 2017- 18. During the reporting year, ICAR-CRIDA has made eloquent progress in technology development and dissemination associated with climate change in rainfed agriculture and dealing contingencies in agriculture and allied sector. The institute has received copyright for “Unreaped yield potentials in major rainfed crops and scope for bridging yield gaps - A decision support system”. ICAR-CRIDA along with SAUs and KVKs prepared contingency plans at district level for all the 126 agro-climatic zones of the country (623 districts) to deal with weather related aberrations. An IFS module with cotton, vegetables, fodder and small ruminants with farm pond using portable raingun at Chenchu tribal farmer field implemented in Petrallachenu village of Nagarkurnool district showed positive impact on socio economic condition of the farmer with total net income of Rs. 96,605/- over the traditional system of growing only rainfed cotton, which gave negative returns of Rs. (-) 3600. A small scale solar powered micro-irrigation system was designed and installed for small farmers having one acre or less land under farm pond system for growing vegetables. The assessment based on daily rainfall dataset, annual average effective rainfall and runoff percentages helped in developing the expected runoff in various rainfall zones, which could be used to estimate the runoff in meso-scale watersheds. Seven inbreds of maize (DTL2, SNJ2011- 03, SNJ2011-37, SNJ2011-26, Z101-15, Z32-12 and HKI7660) were found to be promising for use in crop improvement programme under rainfed conditions. 4:4 strip intercropping system of sorghum and pigeonpea with relay horse gram performed better compared to traditional 2:1 intercropping system. In a study on resource conserving technologies, conventional tillage recorded 15% lower maize yields as compared to conservation agriculture practices. Intensive system of rearing livestock not only improved the profitability but also significantly reduced methane emissions as compared to semi-intensive and extensive systems. Heat Load Index (HLI) and Temperature Humidity Index (THI) was found to be better choice for comparing heat stress in extensively and intensively reared sheep, respectively. A rotary implement for weeding operation was developed to effectively utilize low horse power tractor for field applications. A raised bed planter cum herbicide applicator was developed and the design was transferred to Avanthi Bufa Industries Ltd., Jahirabad. Farmers’ first project, envisaged to transfer rainfed technologies with objective of doubling farmers income is being implemented in 4 villages of Pudur mandal of Vikarabad district. Among 12 pigeonpea genotypes AKT-8811, PUSA-33, GRG-276-1 and RVK-274 were the high yielders in both unstressed and rainfed conditions. An econometric analysis of impact of climate change on crop yields showed that the impacts would be more severe and widespread towards the end of the century. Under changing climatic scenarios, runoff is not expected to vary much in Vijayapura district under low or medium emission scenarios, but the high runoff potential available under the present scenario itself shows substantial scope for rainwater harvesting and its utilization for supplemental irrigation. Decreased grub duration with increased predation capacity of M. sexmaculatus on A. craccivora with elevated CO2 indicated increased predation in future climate change scenarios. For assessing the real time climate change impacts on crop water requirements, SCADA Preface based rainfall simulator and precision lysimeter was designed and developed by using state of art process automation instrumentation in climate change research complex at Hayathnagar. Rotavator, cultivator and disc plough + harrow recorded higher GHG emissions and global warming potential, whereas animal drawn implements recorded lower emissions. Evaluation of the performance of different crops under organic, inorganic and integrated production systems showed that yield of sunflower was 14 and 7% higher under integrated management (1374 kg/ha) than that of under inorganic and organic management, respectively. Supplementation of chromium propionate @ 200 ppb can help in mitigation of heat stress in grazing lambs. An experiment to evaluate 36 elite clones of short rotation and high biomass yielding multipurpose tree species (M. dhubia, Casuarina, B. balcoa, D. sisoo and Eucalyptus) was established at Hayathnagar Research Farm. The KVK under technology assessment and refinement has assessed 17 technologies through 115 trials on crop varieties, integrated crop management, horticulture and livestock management. 269 Frontline demonstrations on 19 technologies were conducted in different disciplines. It also organized 115 need based and skill oriented training programmes on various aspects of improved technologies to 3005 clientele farmers and filed level extension workers. Two special skill development programmes allotted by Department of Horticulture, Government of Telangana in the disciplines of “ farm pond construction and lining” were organized for 520 rural youths. Exemplary performance of its scientists were visible as two scientists attended trainings/exposure visit outside the country and 52 graduate and post graduate students carried out research work at ICAR-CRIDA. Sustained performance of its scientists were exhibited in terms of 20 scientists of Institute receiving several awards, fellowships, copyright and recognition from national academies, professional societies and other institutions. The scientists of the institute published a total of 116 research articles in international and national journals, 29 books/bulletins including 2 in Hindi and 112 book chapters. The contributions of scientists also appeared in the form of a number of policy papers, bulletins, popular articles, presentations in conferences, e-publications and radio and television programmes. The collaborations with several Ministries and Departments, SAUs, NGOs and Private Industries reflect its commitment to work hand-to-hand with grow together and finding the technological solutions to the problems of farmers in rainfed regions of India. I would like to place on record my sincere gratitude to Indian Council of Agricultural Research for its continued guidance and support. I appreciate all the committee members of annual report for their timely compilation and shaping this report in time.Not Availabl

Computational Methods for Medical and Cyber Security

Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields

Application of Reinforcement Learning for Condition-based Maintenance of Multi-Unit Systems

Maintenance is a pivotal aspect of manufacturing systems, particularly those operating on a large scale. With the advent of data-driven methods and machine learning technologies, new avenues have opened for optimizing maintenance policies. In light of this, this thesis introduces advanced methodologies in Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) specifically tailored for large-scale parallel manufacturing systems. We conducted two major studies to advance the field: In the first study, an RL-based algorithm is proposed, moving beyond the traditional focus on system degradation levels to instead concentrate on the count of failed or unhealthy units. This shift allows for a more dynamic and nuanced approach to maintenance. Through Q-learning, our algorithm demonstrated significant superiority over conventional methods such as value iteration, particularly when applied to a system with four parallel units. In the second study, we delve into Deep Reinforcement Learning, developing a framework designed for multi-unit systems experiencing stochastic degradation and unforeseen failures. Unlike traditional methods, our DRL approach incorporates a more intricate reward function that considers a wide array of factors ranging from production costs to maintenance crew deployment. Notably, this study was rigorously tested on a system comprised of 30 parallel units, making it particularly relevant for real-world, large-scale applications. Our research significantly broadens the applicability of machine learning methodologies in maintenance scheduling, demonstrating both robustness and adaptability. These contributions not only validate the efficacy of data-driven approaches in real-world settings but also lay the groundwork for future research in this crucial domain

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Lost in Transitions: Analyzing Sectoral Transitions in Postcolonial Developing Island States, Investigating Theoretical and Practical Gaps in Sustainability Transitions Theory

The theory of Sustainability Transitions, a current concept that has a relevance for both policy and academic thinking, attempts to explain fundamental structural changes in individual economic sectors and in societies at large. Yet, its application outside developed industrialized societies, where the theory originated, has been limited. More research is required on the applicability of transition approaches in developing states. This study attends to this constraint by examining the explanatory power of the main theoretical tenets of sustainability transitions in the contexts of the agricultural and extractive sectors of the postcolonial developing island states of Nauru, Jamaica and Sri Lanka, which extends the geographical and historical terrain of the theory. An empirical survey of 180 individuals, participant observations, archival research, a review of 536 books and articles, in 9 fieldwork missions and some 50 research sites, provides a canvas broad enough to test Sustainability Transitions epistemology and hypotheses with local communities of knowledge: its theoretical scope – levels and units of analysis, its transition causation mechanisms, its validity outside its origin-context in developed countries, and, by extension, to establish whether or not its universal assumptions are justified. Drawing on a comprehensive multi-sectoral analysis, this study illustrates that when deployed in the context of developing countries, specifically in postcolonial island states, the multi-level perspective is deficient. While the analytical framework is useful to some extent, it falls short to provide an inclusive explanation of what drives sectoral changes in developing island states and its epistemology is not fully-representative of sectoral and societal transitions in unindustrialized island societies. The theory does not adequately consider the role of government, or agency; it fails to define the notion of sustainability in applicable, operational terms; it overemphasizes the role of niches and radical innovation networks in sectoral change processes while underemphasizing the rigidity of dominant socio-technical regimes; it offers neither a normative model nor a valid descriptive model of sectoral transformations, and therefore, subscribes no instrument for policy analysis and policy design. Further research is essential to examine whether or not these theoretical limitations are observable in other developing states, and in such event, further refinements of sustainability transitions analytical tools should be warranted

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers