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The communications satellite industry as an element in Nigeriaâs attempt to modernise its economy and society
There is general consensus that Nigeriaâs inordinate reliance on oil has not had a positive impact on its social and economic development â indeed, that Nigeria has suffered from the âresource curseâ. In 2009, the National Planning Commission of Nigeria, the custodian of the Vision 20:2020 document as well as the 30-year National Integrated Infrastructure Master Plan (NIIMP), which stressed the need for Nigeria to reduce its reliance on hydrocarbons, a crucial element in this goal is Information and Communications Technology. This paper
examines the establishment of the communications satellite industry and its strategic role as critical ICT
backbone infrastructure in driving Nigeriaâs national ICT revolution beyond cities and urban areas to unserved
and underserved areas and its growing value chain in key economic sectors of the Nigerian economy and society
The Communications Satellite Industry as an Element in Nigeriaâs Attempt to Modernise Its Economy and Society
There is general consensus that Nigeriaâs inordinate reliance on oil has not had a positive impact on its social and economic development â indeed, that Nigeria has suffered from the âresource curseâ. In 2009, the National Planning Commission of Nigeria, the custodian of the Vision 20:2020 document as well as the 30-year National Integrated Infrastructure Master Plan (NIIMP), which stressed the need for Nigeria to reduce its reliance on hydrocarbons, a crucial element in this goal is Information and Communications Technology. This paper examines the establishment of the communications satellite industry and its strategic role as critical ICT backbone infrastructure in driving Nigeriaâs national ICT revolution beyond cities and urban areas to unserved and underserved areas and its growing value chain in key economic sectors of the Nigerian economy and society. Keywords: Nigeria, Resource Curse, ICT, Communication Satellites
A Framework for Leveraging Artificial Intelligence in Project Management
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThis dissertation aims to support the project manager in their daily tasks. As we use artificial
intelligence (AI) and machine learning (ML) in everyday life, it is necessary to include them in business
and change traditional ways of working. For the purpose of this study, it is essential to understand
challenges and areas of project management and how artificial intelligence can contribute to them. A
theoretical overview, applying the knowledge of project management, will show a holistic view of the
current situation in the enterprises. The research is about artificial intelligence applications in project
management, the common activities in project management, the biggest challenges, and how AI and
ML can support it. Understanding project managers help create a framework that will contribute to
optimizing their tasks. After designing and developing the framework for applying artificial intelligence
to project management, the project managers were asked to evaluate. This study is essential to
increase awareness among the stakeholders and enterprises on how automation of the processes can
be improved and how AI and ML can decrease the possibility of risk and cost along with improving the
happiness and efficiency of the employees
Leakage Detection Framework using Domain-Informed Neural Networks and Support Vector Machines to Augment Self-Healing in Water Distribution Networks
The reduction of water leakage is essential for ensuring sustainable and resilient water supply systems. Despite recent investments in sensing technologies, pipe leakage remains a significant challenge for the water sector, particularly in developed nations like the UK, which suffer from aging water infrastructure. Conventional models and analytical methods for detecting pipe leakage often face reliability issues and are generally limited to detecting leaks during nighttime hours. Moreover, leakages are frequently detected by the customers rather than the water companies. To achieve substantial reductions in leakage and enhance public confidence in water supply and management, adopting an intelligent detection method is crucial. Such a method should effectively leverage existing sensor data for reliable leakage identification across the network. This not only helps in minimizing water loss and the associated energy costs of water treatment but also aids in steering the water sector towards a more sustainable and resilient future. As a step towards âself-healingâ water infrastructure systems, this study presents a novel framework for rapidly identifying potential leakages at the district meter area (DMA) level. The framework involves training a domain-informed variational autoencoder (VAE) for real-time dimensionality reduction of water flow time series data and developing a two-dimensional surrogate latent variable (LV) mapping which sufficiently and efficiently captures the distinct characteristics of leakage and regular (non-leakage) flow. The domain-informed training employs a novel loss function that ensures a distinct but regulated LV space for the two classes of flow groupings (i.e., leakage and non-leakage). Subsquently, a binary SVM classifier is used to provide a hyperplane for separating the two classes of LVs corresponding to the flow groupings. Hence, the proposed framework can be efficiently utilised to classify the incoming flow as leakage or non-leakage based on the encoded surrogates LVs of the flow time series using the trained VAE encoder. The framework is trained and tested on a dataset of over 2000 DMAs in North Yorkshire, UK, containing water flow time series recorded at 15-minute intervals over one year. The framework performs exceptionally well for both regular and leakage water flow groupings with a classification accuracy of over 98 % on the unobserved test datase
Wright State University\u27s Celebration of Research, Scholarship and Creative Activities Book of Abstracts from Friday, April 21, 2017
The student abstract booklet is a compilation of abstracts from students\u27 oral and poster presentations at Wright State University\u27s Annual Celebration of Research, Scholarship and Creative Activities on April 21, 2017.https://corescholar.libraries.wright.edu/urop_celebration/1024/thumbnail.jp
Is Metaverse in education a blessing or a curse: a combined content and bibliometric analysis
The Metaverse has been the centre of attraction for educationists for quite some time. This field got renewed interest with the announcement of social media giant Facebook as it rebranding and positioning it as Meta. While several studies conducted literature reviews to summarize the findings related to the Metaverse in general, no study to the best of our knowledge focused on systematically summarizing the finding related to the Metaverse in education. To cover this gap, this study conducts a systematic literature review of the Metaverse in education. It then applies both content and bibliometric analysis to reveal the research trends, focus, and limitations of this research topic. The obtained findings reveal the research gap in lifelogging applications in educational Metaverse. The findings also show that the design of Metaverse in education has evolved over generations, where generation Z is more targeted with artificial intelligence technologies compared to generation X or Y. In terms of learning scenarios, there have been very few studies focusing on mobile learning, hybrid learning, and micro learning. Additionally, no study focused on using the Metaverse in education for students with disabilities. The findings of this study provide a roadmap of future research directions to be taken into consideration and investigated to enhance the adoption of the Metaverse in education worldwide, as well as to enhance the learning and teaching experiences in the Metaverse
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and âenablersâ, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Open Access Publishing: A Literature Review
Within the context of the Centre for Copyright and New Business Models in the Creative Economy (CREATe) research scope, this literature review investigates the current trends, advantages, disadvantages, problems and solutions, opportunities and barriers in Open Access Publishing (OAP), and in particular Open Access (OA) academic publishing. This study is intended to scope and evaluate current theory and practice concerning models for OAP and engage with intellectual, legal and economic perspectives on OAP. It is also aimed at mapping the field of academic publishing in the UK and abroad, drawing specifically upon the experiences of CREATe industry partners as well as other initiatives such as SSRN, open source software, and Creative Commons. As a final critical goal, this scoping study will identify any meaningful gaps in the relevant literature with a view to developing further research questions. The results of this scoping exercise will then be presented to relevant industry and academic partners at a workshop intended to assist in further developing the critical research questions pertinent to OAP
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
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