2,152 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Natural and Technological Hazards in Urban Areas

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    Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

    Sociotechnical Imaginaries, the Future and the Third Offset Strategy

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    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

    Undergraduate Catalog of Studies, 2022-2023

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    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ļ¬fth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ļ¬elds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiļ¬ed Proportional Conļ¬‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiļ¬ers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiļ¬cation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiļ¬cation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiļ¬cation, and hybrid techniques mixing deep learning with belief functions as well

    2023-2024 Graduate School Catalog

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    You and your peers represent more than 67 countries and your shared scholarship spans 140 programs - from business administration and biomedical engineering to history, horticulture, musical performance, marine science, and more. Your ideas and interests will inform public health, create opportunities for art and innovation, contribute to the greater good, and positively impact economic development in Maine and beyond

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Integrating ecosystemā€“based management and marine spatial planning for sustainable ocean governance in the Bay of Bengal

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    In the contemporary world, oceans are increasingly realized as ā€˜threatened placesā€™ in need of environmental protection, at risk from pollution, habitat loss, and overfishing. On the other hand, nations around the world are turning their attention to oceans as a new source of economic development and growth, seeing them as ā€˜industrialized spacesā€™. The concept of integrating Ecosystem-Based Management (EBM) and Marine Spatial Planning (MSP) is a new approach for sustainable Ocean Governance (SOG). As an effective strategic tool for planning and managing conflicting ocean uses and their interactions with marine ecosystems, the EBM-MSP approach creates an opportunity for long-term development in relation to ocean and its resources. This thesis scrutinizes the contemporary concepts, definitions, and approaches pertinent to the establishment of a comprehensive Ecosystem-based Management and Marine Spatial Planning (EBM-MSP) framework for Sustainable Ocean Governance that reflects global and regional standards. The study also analyses various scientific data ā€“ especially the pollutantsā€™ concentration at spatial and temporal scales ā€“ with special reference to EBM-MSP. The research analyses international laws, declarations, conventions, and agreements that are relevant to the proposition of a new dynamic approach to SOG based on EBM-MSP. This new approach could be useful to support necessary reforms, filling gaps in legal regimes and achieving integrated and effective ocean governance mechanisms to prevent, reduce, and control pollution in the marine environment, as well as promoting sustainable exploration of marine resources. Specifically, the research critically analyses the existing legal frameworks in relation to SOG in the Bay of Bengal (BOB). Based on an analysis of sectoral legislation and institutional arrangements in the BOB, the thesis recommends the modification and adoption of legislation, as well as integration among the relevant departments of Bangladesh Government, to match transboundary SOG, particularly along with EBM-MSP development processes. The study focuses on national policies and strategies along with sectoral legislation and institutional arrangements to contribute towards EBM-MSP at national level for SOG, by considering socioeconomic balance and jurisdictional overlays. Based on experiences in the Baltic Sea, Mediterranean Sea, and Great Barrier Reef Marine Park (GBRMP), the research determines numerous key features to assist with the generation and application of EBM-MSP in the BOB region, specifically in Bangladesh, by integrating EBM-MSP with particular reference to a Comprehensive Ocean Zoning (COZ). The substantial outcome of the study is to suggest a COZ Framework for Bangladesh to protect priority seascapes and sites, species of special concern, and their critical habitats, by designing Marine Protected Area networks throughout the transboundary coast
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