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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms
Vertical farming (VF) is the practice of growing crops or animals using the vertical dimension via multi-tier racks or vertically inclined surfaces. In this thesis, I focus on the emerging industry of plant-specific VF. Vertical plant farming (VPF) is a promising and relatively novel practice that can be conducted in buildings with environmental control and artificial lighting. However, the nascent sector has experienced challenges in economic viability, standardisation, and environmental sustainability. Practitioners and academics call for a comprehensive financial analysis of VPF, but efforts are stifled by a lack of valid and available data.
A review of economic estimation and horticultural software identifies a need for a decision support system (DSS) that facilitates risk-empowered business planning for vertical farmers. This thesis proposes an open-source DSS framework to evaluate business sustainability through financial risk and environmental impact assessments. Data from the literature, alongside lessons learned from industry practitioners, would be centralised in the proposed DSS using imprecise data techniques. These techniques have been applied in engineering but are seldom used in financial forecasting. This could benefit complex sectors which only have scarce data to predict business viability.
To begin the execution of the DSS framework, VPF practitioners were interviewed using a mixed-methods approach. Learnings from over 19 shuttered and operational VPF projects provide insights into the barriers inhibiting scalability and identifying risks to form a risk taxonomy. Labour was the most commonly reported top challenge. Therefore, research was conducted to explore lean principles to improve productivity.
A probabilistic model representing a spectrum of variables and their associated uncertainty was built according to the DSS framework to evaluate the financial risk for VF projects. This enabled flexible computation without precise production or financial data to improve economic estimation accuracy. The model assessed two VPF cases (one in the UK and another in Japan), demonstrating the first risk and uncertainty quantification of VPF business models in the literature. The results highlighted measures to improve economic viability and the viability of the UK and Japan case.
The environmental impact assessment model was developed, allowing VPF operators to evaluate their carbon footprint compared to traditional agriculture using life-cycle assessment. I explore strategies for net-zero carbon production through sensitivity analysis. Renewable energies, especially solar, geothermal, and tidal power, show promise for reducing the carbon emissions of indoor VPF. Results show that renewably-powered VPF can reduce carbon emissions compared to field-based agriculture when considering the land-use change.
The drivers for DSS adoption have been researched, showing a pathway of compliance and design thinking to overcome the âproblem of implementationâ and enable commercialisation. Further work is suggested to standardise VF equipment, collect benchmarking data, and characterise risks. This work will reduce risk and uncertainty and accelerate the sectorâs emergence
Educating Sub-Saharan Africa:Assessing Mobile Application Use in a Higher Learning Engineering Programme
In the institution where I teach, insufficient laboratory equipment for engineering education pushed students to learn via mobile phones or devices. Using mobile technologies to learn and practice is not the issue, but the more important question lies in finding out where and how they use mobile tools for learning. Through the lens of Kearney et al.âs (2012) pedagogical model, using authenticity, personalisation, and collaboration as constructs, this case study adopts a mixed-method approach to investigate the mobile learning activities of students and find out their experiences of what works and what does not work. Four questions are borne out of the over-arching research question, âHow do students studying at a University in Nigeria perceive mobile learning in electrical and electronic engineering education?â The first three questions are answered from qualitative, interview data analysed using thematic analysis. The fourth question investigates their collaborations on two mobile social networks using social network and message analysis. The study found how studentsâ mobile learning relates to the real-world practice of engineering and explained ways of adapting and overcoming the mobile toolsâ limitations, and the nature of the collaborations that the students adopted, naturally, when they learn in mobile social networks. It found that mobile engineering learning can be possibly located in an offline mobile zone. It also demonstrates that investigating the effectiveness of mobile learning in the mobile social environment is possible by examining usersâ interactions. The study shows how mobile learning personalisation that leads to impactful engineering learning can be achieved. The study shows how to manage most interface and technical challenges associated with mobile engineering learning and provides a new guide for educators on where and how mobile learning can be harnessed. And it revealed how engineering education can be successfully implemented through mobile tools
Industry 4.0: product digital twins for remanufacturing decision-making
Currently there is a desire to reduce natural resource consumption and expand circular business principles whilst Industry 4.0 (I4.0) is regarded as the evolutionary and potentially disruptive movement of technology, automation, digitalisation, and data manipulation into the industrial sector. The remanufacturing industry is recognised as being vital to the circular economy (CE) as it extends the in-use life of products, but its synergy with I4.0 has had little attention thus far. This thesis documents the first investigating into I4.0 in remanufacturing for a CE contributing a design and demonstration of a model that optimises remanufacturing planning using data from different instances in a productâs life cycle.
The initial aim of this work was to identify the I4.0 technology that would enhance the stability in remanufacturing with a view to reducing resource consumption. As the project progressed it narrowed to focus on the development of a product digital twin (DT) model to support data-driven decision making for operations planning. The modelâs architecture was derived using a bottom-up approach where requirements were extracted from the identified complications in production planning and control that differentiate remanufacturing from manufacturing. Simultaneously, the benefits of enabling visibility of an assetâs through-life health were obtained using a DT as the modus operandi. A product simulator and DT prototype was designed to use Internet of Things (IoT) components, a neural network for remaining life estimations and a search algorithm for operational planning optimisation. The DT was iteratively developed using case studies to validate and examine the real opportunities that exist in deploying a business model that harnesses, and commodifies, early life product data for end-of-life processing optimisation. Findings suggest that using intelligent programming networks and algorithms, a DT can enhance decision-making if it has visibility of the product and access to reliable remanufacturing process information, whilst existing IoT components provide rudimentary âsmartâ capabilities, but their integration is complex, and the durability of the systems over extended product life cycles needs to be further explored
Assessing the Benefits, Challenges and Scientific Value of Community Science Programs: A Case Study Using Bumble Bee Watch
We are experiencing a biodiversity crisis but resources to help species are limited. Scientists are turning to community science to complement traditional scientific methods. Bumble bees (Bombus spp.) are important pollinators in temperate regions, but many are in decline, and more information is needed to conserve them. The Bumble Bee Watch (BBW) program collects this through photos submitted by volunteers and identified by experts. Yet many community science programs struggle. Chapter 2 reviews common successes and challenges, offering best practices for developing and running programs.
To determine whether BBW is filling knowledge gaps, Chapter 3 compares its data to the Bumble Bees of North America database (BBNA) over all years and 2010-2020. BBW recorded 41 species (BBNA had 48) from all parts of the continental US and Canada, confirmed persistence, and provided novel locations for species outside of and within the known extent of occurrence. BBW showed its greatest impact from 2010-2020 by contributing 25% of all records, 28% of all unique locations, and 32% new plant forage genera.
BBW does not replace traditional surveys, but does complement them. Chapter 4 shows that B. pensylvanicus is critically endangered in Canada according to IUCN Red List criteria. BBW provided 20% of all B. pensylvanicus records and 36% of its sites over the 2007-2016 period assessed, and thus provided important information on its current abundance and distribution.
No experience is required to participate in BBW, but having participants able to accurately identify species is beneficial. Chapter 5 explores the percent agreement and veracity of participant species identifications compared to experts, with the average being 53% and 56%, respectively. With better educational resources, participants may be better trained to identify species more accurately.
Understanding the motivations and insights of community science participants is important. Chapter 6 discusses the results of a BBW user and expert survey: participants want to contribute to science and save the bees, and report an increase in knowledge and skills after participating. Although areas for improvement are noted, BBW is an important tool for Bombus researchers, and demonstrates the value that community science has for species conservation
Adaptive task selection using threshold-based techniques in dynamic sensor networks
Sensor nodes, like many social insect species, exist in harsh environments in large groups, yet possess very limited amount of resources. Lasting for as long as possible, and fulfilling the network purposes are the ultimate goals of sensor networks. However, these goals are inherently contradictory. Nature can be a great source of inspiration for mankind to find methods to achieve both extended survival, and effective operation. This work aims at applying the threshold-based action selection mechanisms inspired from insect societies to perform action selection within sensor nodes. The effect of this micro-model on the macro-behaviour of the network is studied in terms of durability and task performance quality. Generally, this is an example of using bio-inspiration to achieve adaptivity in sensor networks
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