Logging-on to Sai Baba: the poetics of sacred globalization

Bhagavan Sri Sathya Sai Baba is the leader of a progressive religious movement steeped in the Hindu tradition. The Sathya Sai Baba Organization claims to have over thirty million members in approximately 170 countries. The dedicated followers of the movement believe Sai Baba to be an avatar or incarnation of God in human form. Sai Baba utilizes the Internet to transmit his universalistic philosophies around the world. With this digital universe, devotees can log-on to a multitude of official Sai websites that act as training ground for achieving liberation of the mind and soul. This path of devotion that Sai Baba teaches his followers has its foundation in Hinduism\u27s sacred scriptures. The Sathya Sai Baba Organization is attempting to restructure the Hindu tradition, marketing a new hegemony fit for global consumption. The organization is one of many spiritual movements that have embraced globalization and become part of the religious landscapes of the World Wide Web. Using websites as field sites, I conducted an in-depth case study on the cyberspace activities of this organization. This annointment of cyberspace as sacred space illustrates how the Internet can be a powerful source in cultural production. The religious subculture that links a global network of Sai Centers and dedicated participants utilizes information technology to spread the ideology of the movement. The Internet has the potential to change how social scientists engage in data collection and cultural documentation. The fieldwork that I conducted in cyberspace and during interviews with my consultant reveals the Internet as both a vessel for sacred space and a venue for a cyberperformance that is shaped by the poetics of sacred globalization

A Service-Oriented Approach for Network-Centric Data Integration and Its Application to Maritime Surveillance

Maritime-surveillance operators still demand for an integrated maritime picture better supporting international coordination for their operations, as looked for in the European area. In this area, many data-integration efforts have been interpreted in the past as the problem of designing, building and maintaining huge centralized repositories. Current research activities are instead leveraging service-oriented principles to achieve more flexible and network-centric solutions to systems and data integration. In this direction, this article reports on the design of a SOA platform, the Service and Application Integration (SAI) system, targeting novel approaches for legacy data and systems integration in the maritime surveillance domain. We have developed a proof-of-concept of the main system capabilities to assess feasibility of our approach and to evaluate how the SAI middleware architecture can fit application requirements for dynamic data search, aggregation and delivery in the distributed maritime domain

Influence of Donor Aid Policy on Disability Inclusion in Myanmar

Article 32 of the UNCRPD requires that international aid programs are inclusive and accessible to people with disabilities. Myanmar is both a signatory to the UNCRPD and is also a major recipient of aid from signatory countries. This study aimed to identify if the requirements of Article 32. 1 (a) are reflected in donor-funded aid programmes in Myanmar. The primary purpose was to analyse compliance along ‘the aid delivery chain’ understood to encompass policy commitment by donor agencies and in-country partners, identifying influencing factors on disability-inclusive development practices. The research used a multi-method design in a two-step approach involving purposive sampling of three bilateral donors active in Myanmar to analyse their policy commitment of disability inclusion. The second step involved interviews with aid and development stakeholders focusing on respondents’ experiences and understanding about disability inclusion in aid programs in Myanmar. The findings demonstrate the inclusion of persons with disabilities in the aid delivery chain is not yet regarded as a key priority as prescribed by the UNCRPD. Yet, disability inclusion still occurred where a leader within an organization has commitment. The presence of an activist further strengthened disability inclusion. Employing persons with disabilities in organisations helped to raise awareness and understanding about disability in their own organisation and their network of stakeholders. The outcome point to the need to identify policy compliance on the Article 32 by donors who are signatories to the UNCRPD and to recognise the key influencing factors on disability inclusion at all levels of the aid delivery chain. The findings of this study would lead to better understanding of the need to monitor compliance with UNCRPD by the government, donor agencies and disability advocates and activists for disability inclusion in international cooperation and aid and development programmes

A novel framework for scalable resilience analyses in complex networks

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringBalasubramaniam NatarajanResilience has emerged as a crucial and desirable characteristic of complex systems due to the increasing frequency of cyber intrusions and natural disasters. In systems such as power grids and transportation networks, resilience analysis typically deals with the assessment of system robustness in terms of identifying and safeguarding key system attributes. Robustness evaluation methods can be broadly classified into two types, namely network-based and performance-based. Network-based methodologies involve topological properties of the system, whereas performance-based methods deal with specific performance attributes such as voltage fluctuations in a power distribution network. Existing approaches to evaluate robustness have limitations in terms of (1) inaccurate modeling of the underlying system; (2) high computational complexity; and (3) lack of scalability. This dissertation addresses these challenges by developing computationally efficient frameworks to identify key entities of the system. First, it develops a probabilistic framework for a performance-based robustness attribute. Specifically, using power grid as a case study, this work focuses on the performance measure of interest, i.e., voltage fluctuations. This work first derives an analytical approximation for voltage change at any node of the network due to a change in power at other nodes of a three-phase unbalanced radial distribution network. Next, the probability distribution of voltage changes at a certain node due to random power changes at multiple locations in the network is derived. Then, these distributions with information theoretic metrics are used to derive a novel voltage influencing score (VIS) that quantifies the voltage influencing capacity of nodes with distributed energy resources (DERs) and active loads. VIS is then employed to identify the dominant voltage influencer nodes. Results demonstrate the high efficacy and low computational complexity of the proposed approach, enabling various future applications (e.g., voltage control). In the second part, this dissertation emphasizes on network-based robustness measures. Particularly, it focuses on the task of identifying critical nodes in complex systems so that preemptive actions can be taken to improve the system's resilience. Critical nodes represent a set of sub-systems and/or their interconnections whose removal from the graph maximally disconnects the network, and thus severely disrupts the operation of the system. The majority of the critical node identification methods in literature are based on an iterative approach, and thus suffer from high computational complexity and are not scalable to larger networks. Therefore, this work proposes a scalable and generic graph neural network (GNN) based framework for identifying critical nodes in large complex networks. The proposed framework defines a GNN-based model that learns the node criticality score on a small representative subset of nodes and can identify critical nodes in larger networks. Furthermore, the problem of quantifying the uncertainty in GNN predictions is also considered. Essentially, Assumed Density Filtering is used to quantify aleatoric uncertainty and Monte-Carlo dropout captures uncertainty in model parameters. Finally, the two sources of uncertainty are aggregated to estimate the total uncertainty in predictions of a GNN. Results in real-world datasets demonstrate that the Bayesian model performs at par with a frequentist model. Furthermore, the combinatorial case of critical node identification is also addressed in this dissertation, where the node criticality scores would be associated with a set of nodes. This simulates a concurrent scenario where multiple nodes are being disrupted simultaneously. Essentially, this problem falls under the generic category of graph combinatorial problems. This problem is approached through a novel deep reinforcement learning (DRL) based framework. Specifically, GNNs are used for encoding the underlying graph structure and DRL for learning to identify the optimal node sequence. Moreover, the framework is first developed for Influence Maximization (IM), where one is interested in identifying a set of seed nodes, which when activated, will result in the activation of a maximal number of nodes in the graph. This generic framework can be used for various use-cases, including the identification of critical nodes set related to concurrent disruption. The results on real world networks demonstrate the scalability and generalizability of the proposed methodology. Thirdly, this dissertation presents a comparative study of different performance and network-based robustness metrics in terms of ranking critical nodes of a power distribution network. The efficacy of failure-based metrics in characterizing voltage fluctuations is also investigated. Results show that hybrid failure-based metrics can quantify voltage fluctuations to a reasonable extent. Additionally, several other challenges in existing robustness frameworks are highlighted, including the lack of mechanism to effectively incorporate various performance and network-based resilience factors. Then, a novel modeling framework, namely hetero-functional graph theory (HFGT) is leveraged to model both power distribution networks as well as other dependent infrastructure networks. Results demonstrate that HFGT can address key modeling limitations, and can be used to accurately assess system robustness to failures

adaptations in electronic structure calculations in heterogeneous environments

Modern quantum chemistry deals with electronic structure calculations of unprecedented complexity and accuracy. They demand full power of high-performance computing and must be in tune with the given architecture for superior efficiency. To make such applications resource-aware, it is desirable to enable their static and dynamic adaptations using some external software (middleware), which may monitor both system availability and application needs, rather than mix science with system-related calls inside the application. The present work investigates scientific application interlinking with middleware based on the example of the computational chemistry package GAMESS and middleware NICAN. The existing synchronous model is limited by the possible delays due to the middleware processing time under the sustainable runtime system conditions. Proposed asynchronous and hybrid models aim at overcoming this limitation. When linked with NICAN, the fragment molecular orbital (FMO) method is capable of adapting statically and dynamically its fragment scheduling policy based on the computing platform conditions. Significant execution time and throughput gains have been obtained due to such static adaptations when the compute nodes have very different core counts. Dynamic adaptations are based on the main memory availability at run time. NICAN prompts FMO to postpone scheduling certain fragments, if there is not enough memory for their immediate execution. Hence, FMO may be able to complete the calculations whereas without such adaptations it aborts

Performance Evaluation of v-eNodeB using Virtualized Radio Resource Management

With the demand upsurge for high bandwidth services, continuous increase in the number of cellular subscriptions, adoption of Internet of Things (IoT), and marked growth in Machine-to-Machine (M2M) traffic, there is great stress exerted on cellular network infrastructure. The present wireline and wireless networking technologies are rigid in nature and heavily hardware-dependent, as a result of which the process of infrastructure upgrade to keep up with future demand is cumbersome and expensive. Software-defined networks (SDN) hold the promise to decrease network rigidity by providing central control and flow abstraction, which in current network setups are hardware-based. The embrace of SDN in traditional cellular networks has led to the implementation of vital network functions in the form of software that are deployed in virtualized environments. This approach to move crucial and hardware intensive network functions to virtual environments is collectively referred to as network function virtualization (NFV). Our work evaluates the cost reduction and energy savings that can be achieved by the application of SDN and NFV technologies in cellular networks. In this thesis, we implement a virtualized eNodeB component (Radio Resource Management) to add agility to the network setup and improve performance, which we compare with a traditional resource manager. When combined with dynamic network resource allocation techniques proposed in Elastic Handoff, our hardware agnostic approach can achieve a greater reduction in capital and operational expenses through optimal use of network resources and efficient energy utilization. Advisor: Jitender S. Deogu

Innovative Financing for Urban Rail in Indian Cities: Land-based Strategic Value Capture Mechanisms

Emerging cities are seeking urban rail but have difficulty with funding. This research uses the Bangalore Metro rail to develop an innovative land-based ‘strategic value capture' (VC) financing system suitable for Indian cities and other emerging cities. It shows significant land value uplift that could be used for VC funding. The four frameworks and strategic interventions developed in this research are novel contributions in India and apply to other emerging cities as well