4,472 research outputs found

    Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions

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    In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request

    Corporate Social Responsibility: the institutionalization of ESG

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    Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective

    A Decision Support System for Economic Viability and Environmental Impact Assessment of Vertical Farms

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    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 ‘problem of implementation’ 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 sector’s emergence

    The Adirondack Chronology

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    The Adirondack Chronology is intended to be a useful resource for researchers and others interested in the Adirondacks and Adirondack history.https://digitalworks.union.edu/arlpublications/1000/thumbnail.jp

    Industry 4.0: product digital twins for remanufacturing decision-making

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    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 product’s 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 model’s 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 asset’s 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 “smart” capabilities, but their integration is complex, and the durability of the systems over extended product life cycles needs to be further explored

    Fast forward: technography of the social integration of connected and automated vehicles into UK society

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    The emerging connected and automated vehicles (CAV) have caught much research attention in the past few years. However, a techno-centric bias in the CAV research domain implies the lack of in-depth qualitative studies. To fill the gap, this Ph.D. project bridges the fields of Social Anthropology with STS by adopting technography, an ethnography of technology, to enable a thick description of the CAV technology’s social integration into UK society. By critically drawing a holistic view of the ongoing process of the CAV social deployment, it aims to (1) unfold CAV’s potential problems and dynamic contributions to everyday life through the lens of sociotechnical imaginaries, and (2) reveal and analyse the institutional practice on its social rollout. Based on pilot research and one-year-long fieldwork in London and Edinburgh, the thesis investigated a wide range of important socio-political aspects where fundamental topics such as trust, human-and-machine relationship, social safety, political transparency, and equity in transport systems were explicated. Different from the planners’ top-down CAV imaginaries that focused on its contribution to functional safety, environment, and the economy, the public’s bottom-up imaginaries highlighted issues that were related to their travelling experiences, such as inequity of transport service distribution and sexual harassment during commutes. These findings inspired thinking and rethinking on what constitutes the success of technology’s social deployment from multiple perspectives. In particular, it critically pointed out that safety means not only technological feasibility but also social safety that refers to a safe commuting environments. Such finding in my thesis thus suggests that CAV technology is not a one-size-fits-all solution to problems in our transport system and calls for research effort to the broader socio-political and ethical areas of this technology. through an investigation of the institutional practice, it identified four major institutional forces, including technicians, industry stakeholders, researchers, and policymakers who have been working on these aspects with different approaches and priorities. Apart from acknowledging their efforts in building safety cases, pushing forward the CAV legislation, and engaging the public in trials, it critically explained challenges such as technical uncertainty and political tension in developing and implementing a legal framework. Hence, the project contributes to an understanding of a close encounter between the CAV technology and its imaginaries, in which, technical and socio-political problems and potentials fabricate the richness in its social deployment. It also explicates the importance of embracing multiple perspectives and calls for continuous research in this field

    Solving the Rubik’s cube of Indian sport: exploring impactful factors and alternative ways to facilitate success

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    The heavy investment of nations in high-performance sport seems justified by the belief that high-performance sporting success can lead to national pride and mass participation. This would then provide a larger pool of talent for selection of future successful athletes, whilst also promoting participation and greater physical activity for others. Although India too seems to follow a similar philosophy, and has consistently been investing in sport, its performance at international sport, especially the Olympic Games has not been impressive. This is particularly distressing when considered against the country’s large population. Given that India sees worth in investing in high-performance sport, potential ways to facilitate sporting success need to be explored. Consequently, this thesis adopted a pragmatic approach to explore sport development in India. Specifically, potential factors contributing to the limited success were explored and potential alternatives to facilitate India’s ongoing efforts of achieving sporting success on the international stage were proposed. The first step involved exploring Indian sport from a policy viewpoint to gain deeper knowledge about potential reasons that might be limiting the impact of numerous policies implemented so far. A long-standing issue with policy implementation and a potential lack of policy learning were concluded as two of the main reasons impacting sporting success. A potential for India to adopt bottom-up and top-down approaches to policy implementation and policy transfer were proposed as alternative ways for India to overcome the policy issues. There was, however, a need to gather a rich picture of the current scenario of Indian sport. Therefore, perceptions of high-level key stakeholders were explored through a semi structured interview to gain in-depth knowledge about Indian sport. Reflecting the challenges of size and scope, and the consequent need to triangulate and generalise the conclusions, further exploration was completed through a quantitative survey. Significant findings from these empirically driven studies included: i) a lack of sporting culture; ii) a need to develop quality Indian coaches and a coaching system; and iii) a need to increase use and knowledge of sport science support. Of these conclusions, coach development was prioritised for three main reasons, its significance in the wider literature, the fact that India lacked a coaching system and Indian coaches being criticised for their relatively poor knowledge (including misconceptions and limited use of sport sciences). Therefore, an India-specific model aimed at developing quality Indian coaches and a coaching system was proposed. Given the policy implementation issues, however, the feasibility of the model was tested through another empirically driven study. Finally, a revised model for coach development was offered that might contribute to India’s efforts of succeeding at international sports

    ‘We the People: Supporting Food SMEs towards a Circular Food Economy’

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    This single Case Study locates SME (small and medium-size enterprise) hospitality and food services (HaFS) within a complex food waste system. It examines collaborative support for business change from linear resource wastage (‘take, use, dump), towards a circular food economy (CfE)- where ‘designing out’ food waste may reap savings. The objective is to support SME uptake of waste aversion practices so that they may thrive. The qualitative research centers on a London-based project promoting food waste valorization and healthy nutrition, in 15 boroughs. That project’s outreach for broad-based, collective impact included HaFS that are SMEs. Cross-sector liaison was the research focus for this Case Study which utilizes a hybrid philosophy and meta-framework, based on Critical Realism and Systemic Thinking. Some reference to Interpretivism highlights stewardship values for transforming individual behaviour. The Study also uses a multi-method design, borrowing soft systems from Management Science and Operational Research. Its blended approach includes: participant observation, mapping and rich picture techniques, semi-structured interviews and focus groups. The main research questions align concepts such as: circular economy, cross-sector collaboration and food waste management- with HaFS that are SMEs. A framework method and Leximancer software supported coding and qualitative thematic analysis. Primary findings include interesting categories of analytical, NGO and policy literature. Although conversations flagged up pivotal roles for our health and education sectors, the food SME element still seems peripheral in this transition to regenerative business. A ‘people vibe’ is enabling some HaFS’ kitchen waste action and food redistribution and, academia is a potential contributor to this information resource flow among stakeholders. The Study’s unique onto-epistemological framework enhances philosophical and theoretical knowledge about promoting SME resource stewardship. It spans Systemic Thinking (overt connections and acute complexities) and Critical Realism (deep mechanisms and institutional power differentials, impacting change). As an interpretive lens, the framework’s contribution to praxis was tested by shadowing the London TRiFOCAL project. This research could inform a business policy shift from traditional supply chain thinking, towards active UK food citizenship

    Essays in cryptocurrencies’ forecasting and trading with technical analysis and advanced machine learning methods

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    This thesis mainly emphasizes two prediction fields in the cryptocurrency market: factor analysis and model examination. The first section summarises the general introduction, theoretical background, and description of performance metrics used in the empirical study (Chapter 3-5) are summarized in the first section (Chapter 1-2). Then, in Chapters 3 and 4, technical analysis and fundamental factors combined with statistical models are employed to explore the forecasting ability and profitability in the cryptocurrency market. Finally, in Chapter 5, advanced machine learning algorithms combined with leverage trading strategies and narrative sentiments are used to predict the Bitcoin (BTC) market. Chapter 3 examines technical analysis’s profitability and predictive power on cryptocurrency markets. This Chapter adopts the universe of technical rules proposed by Sullivan et al. (1999), while for data snooping purposes, I apply the Lucky Factors (LF) method proposed by Harvey and Liu (2021). Six mainstream cryptocurrencies and one cryptocurrency index from 2013 to 2018 are examined. The results demonstrate that short-term signals generated by technical rules outperform the traditional buy-and-hold strategy. However, the LF methodology shows that none of the top-performing rules in terms of profitability is consistent with actual forecasting performance. The purpose of Chapter 4 is to investigate the prediction of cryptocurrency returns by applying a large pool of factors from both technical and fundamental aspects. The results find that most trading rules perform better than the buy-and-hold strategy, especially the moving average rules. However, this profitability may not be genuine but comes from data-snooping bias. In this way, a larger pool of factors from several aspects, including blockchain information, technical indicators, online sentiment indices, and conventional financial and economic indicators, is implemented from 08/08/2015 to 08/12/2018. The overall results suggest the new proposed technical indicator, Log-price Moving Average (PMA) ratio, a moving-average likely ratio has significant forecasting ability in cryptocurrencies after taking data-snooping bias into account. Chapter 5 explores the forecasting ability of machine learning (ML) algorithms in the BTC market by combining the narrative sentiments and leverage trading strategy. First, the forecasting framework starts by selecting a pool of individual models. Secondly, ML algorithms are used further to improve the predictive performance of the individual model pool. Thirdly, both the best single predictor and ML models are fed into the process of forecasting ability examination, constructed by three different metrics. This step also takes data-snooping bias into account. At last, leverage trading strategies combined with narrative sentiments are applied to all forecasting models to examine their profitability. The results suggest that ML models consistently outperform the best individual model in forecasting ability and profitability. Gradient Boost Decision Tree (GBDT)-the family has the best performance

    The digitization of agricultural industry – a systematic literature review on agriculture 4.0

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    Agriculture is considered one of the most important sectors that play a strategic role in ensuring food security. However, with the increasing world's population, agri-food demands are growing — posing the need to switch from traditional agricultural methods to smart agriculture practices, also known as agriculture 4.0. To fully benefit from the potential of agriculture 4.0, it is significant to understand and address the problems and challenges associated with it. This study, therefore, aims to contribute to the development of agriculture 4.0 by investigating the emerging trends of digital technologies in the agricultural industry. For this purpose, a systematic literature review based on Protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses is conducted to analyse the scientific literature related to crop farming published in the last decade. After applying the protocol, 148 papers were selected and the extent of digital technologies adoption in agriculture was examined in the context of service type, technology readiness level, and farm type. The results have shown that digital technologies such as autonomous robotic systems, internet of things, and machine learning are significantly explored and open-air farms are frequently considered in research studies (69%), contrary to indoor farms (31%). Moreover, it is observed that most use cases are still in the prototypical phase. Finally, potential roadblocks to the digitization of the agriculture sector were identified and classified at technical and socio-economic levels. This comprehensive review results in providing useful information on the current status of digital technologies in agriculture along with prospective future opportunities
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