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Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, data sets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task.To realize the potential of integration of recommenders in science gateways in order to spur research productivity, we present a novel "OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons). K E Y W O R D Schatbot-guided user interface, knowledge discovery, microservices, recommender system, science gateway INTRODUCTIONRecent science and engineering research tasks are increasingly becoming data-intensive and thus relying on workflows to automate integration and analysis of voluminous data to test hypotheses. For example, research and training in neural science and engineering increasingly deal with diverse and voluminous multiparameter data, 1 posing unique challenges outlined in an NSF iNeuro report 2 as limited access to: multisomics data archives, 3 heterogeneous software 4 and computing resources (Neuroscience Gateway, 5 Amazon Web Services [AWS]), and multisite interdisciplinary expertise (e.g., engineering, biology and psychology). Existing distributed high-performance computing resources (HPC) and other cyberinfrastructure (CI) tools for data management support the related data analysis and visualization capabilities. However, to fully utilize such capabilities, neuroscientists (often with limited CI skills) are required to take valuable time away from the focus of knowledge discovery in neuroscience, in order to learn about how to use the various technologies.

Abstract.A flat mobile ad hoc network has an inherent scalability limitation. When the network size increases, per node throughput of an ad hoc network rapidly decreases. This is due to the act that in large scale networks, flat structure of networks results in long hop paths which are prone to breaks. These long hop paths can be avoided by building a physically hierarchical backbone network. These networks have some specific backbone capable nodes that have powerful radios and are functionally more capable than ordinary nodes.In this paper, a hybrid routing protocol for large scale networks with mobile backbones has been proposed. This routing protocol uses different types of routing schemes in different layers of hierarchical network which makes it easily extendable to support QoS as well. Along with hierarchical structure, a lowoverhead clustering scheme to elect backbone nodes has been proposed and works with our routing protocol without causing any extra overhead.

Drought is amongst the most precarious natural hazards associated with severe repercussions. The characterization of droughts is usually carried out by the sector-specific (meteorological/agricultural/hydrological) indices that are mostly based on hydroclimatic variables. Groundwater is the major source of water supply during drought periods, and the socio-economic factors control the aftermaths of droughts; however, they are often ignored by the sector-specific indices, thereby failing to capture the overall impacts of droughts. This study aims to circumvent this issue by incorporating hydroclimatic, socio-economic and physiographic information to assess the overall drought vulnerability over Narmada River Basin, India, which is an agriculture-dominated basin highly dependent on groundwater resources. A Comprehensive Drought Vulnerability Indicator (CDVI) is proposed that assimilates the information on meteorological fluctuations, depth to groundwater level, slope, distance from river reach, population density, land use/land cover, soil type, and elevation through a geospatial approach. The CDVI showed a remarkable geospatial variation over the basin, with a majority (66.4%) of the area under highly to extremely vulnerable conditions. Out of 35 constituent districts of the basin, 9, 22, and 4 districts exhibited moderate, high, and extreme vulnerability to droughts, respectively. These results urge an immediate attention towards reducing drought vulnerability and enhancing resilience towards drought occurrences. The proposed multi-dimensional approach for drought vulnerability mapping would certainly help policy-makers to proactively plan and manage water resources over the basin, especially to ameliorate the pernicious impacts of droughts.

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