14,806 research outputs found

    Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

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    This paper provides an overview of the current state-of-the-art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labour costs, and minimise food waste by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning and control. The paper also discusses the potential benefits of integrating AI and soft robots and data-driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

    Passive Radio Frequency-based 3D Indoor Positioning System via Ensemble Learning

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    Passive radio frequency (PRF)-based indoor positioning systems (IPS) have attracted researchers' attention due to their low price, easy and customizable configuration, and non-invasive design. This paper proposes a PRF-based three-dimensional (3D) indoor positioning system (PIPS), which is able to use signals of opportunity (SoOP) for positioning and also capture a scenario signature. PIPS passively monitors SoOPs containing scenario signatures through a single receiver. Moreover, PIPS leverages the Dynamic Data Driven Applications System (DDDAS) framework to devise and customize the sampling frequency, enabling the system to use the most impacted frequency band as the rated frequency band. Various regression methods within three ensemble learning strategies are used to train and predict the receiver position. The PRF spectrum of 60 positions is collected in the experimental scenario, and three criteria are applied to evaluate the performance of PIPS. Experimental results show that the proposed PIPS possesses the advantages of high accuracy, configurability, and robustness.Comment: DDDAS 202

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    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

    Identifying and responding to people with mild learning disabilities in the probation service

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    It has long been recognised that, like many other individuals, people with learningdisabilities find their way into the criminal justice system. This fact is not disputed. Whathas been disputed, however, is the extent to which those with learning disabilities arerepresented within the various agencies of the criminal justice system and the ways inwhich the criminal justice system (and society) should address this. Recently, social andlegislative confusion over the best way to deal with offenders with learning disabilities andmental health problems has meant that the waters have become even more muddied.Despite current government uncertainty concerning the best way to support offenders withlearning disabilities, the probation service is likely to continue to play a key role in thesupervision of such offenders. The three studies contained herein aim to clarify the extentto which those with learning disabilities are represented in the probation service, toexamine the effectiveness of probation for them and to explore some of the ways in whichprobation could be adapted to fit their needs.Study 1 and study 2 showed that around 10% of offenders on probation in Kent appearedto have an IQ below 75, putting them in the bottom 5% of the general population. Study 3was designed to assess some of the support needs of those with learning disabilities in theprobation service, finding that many of the materials used by the probation service arelikely to be too complex for those with learning disabilities to use effectively. To addressthis, a model for service provision is tentatively suggested. This is based on the findings ofthe three studies and a pragmatic assessment of what the probation service is likely to becapable of achieving in the near future

    Visualisation of Fundamental Movement Skills (FMS): An Iterative Process Using an Overarm Throw

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    Fundamental Movement Skills (FMS) are precursor gross motor skills to more complex or specialised skills and are recognised as important indicators of physical competence, a key component of physical literacy. FMS are predominantly assessed using pre-defined manual methodologies, most commonly the various iterations of the Test of Gross Motor Development. However, such assessments are time-consuming and often require a minimum basic level of training to conduct. Therefore, the overall aim of this thesis was to utilise accelerometry to develop a visualisation concept as part of a feasibility study to support the learning and assessment of FMS, by reducing subjectivity and the overall time taken to conduct a gross motor skill assessment. The overarm throw, an important fundamental movement skill, was specifically selected for the visualisation development as it is an acyclic movement with a distinct initiation and conclusion. Thirteen children (14.8 ± 0.3 years; 9 boys) wore an ActiGraph GT9X Link Inertial Measurement Unit device on the dominant wrist whilst performing a series of overarm throws. This thesis illustrates how the visualisation concept was developed using raw accelerometer data, which was processed and manipulated using MATLAB 2019b software to obtain and depict key throw performance data, including the trajectory and velocity of the wrist during the throw. Overall, this thesis found that the developed visualisation concept can provide strong indicators of throw competency based on the shape of the throw trajectory. Future research should seek to utilise a larger, more diverse, population, and incorporate machine learning. Finally, further work is required to translate this concept to other gross motor skills

    Learning disentangled speech representations

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    A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

    Estudo da remodelagem reversa miocárdica através da análise proteómica do miocárdio e do líquido pericárdico

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    Valve replacement remains as the standard therapeutic option for aortic stenosis patients, aiming at abolishing pressure overload and triggering myocardial reverse remodeling. However, despite the instant hemodynamic benefit, not all patients show complete regression of myocardial hypertrophy, being at higher risk for adverse outcomes, such as heart failure. The current comprehension of the biological mechanisms underlying an incomplete reverse remodeling is far from complete. Furthermore, definitive prognostic tools and ancillary therapies to improve the outcome of the patients undergoing valve replacement are missing. To help abridge these gaps, a combined myocardial (phospho)proteomics and pericardial fluid proteomics approach was followed, taking advantage of human biopsies and pericardial fluid collected during surgery and whose origin anticipated a wealth of molecular information contained therein. From over 1800 and 750 proteins identified, respectively, in the myocardium and in the pericardial fluid of aortic stenosis patients, a total of 90 dysregulated proteins were detected. Gene annotation and pathway enrichment analyses, together with discriminant analysis, are compatible with a scenario of increased pro-hypertrophic gene expression and protein synthesis, defective ubiquitinproteasome system activity, proclivity to cell death (potentially fed by complement activity and other extrinsic factors, such as death receptor activators), acute-phase response, immune system activation and fibrosis. Specific validation of some targets through immunoblot techniques and correlation with clinical data pointed to complement C3 β chain, Muscle Ring Finger protein 1 (MuRF1) and the dual-specificity Tyr-phosphorylation regulated kinase 1A (DYRK1A) as potential markers of an incomplete response. In addition, kinase prediction from phosphoproteome data suggests that the modulation of casein kinase 2, the family of IκB kinases, glycogen synthase kinase 3 and DYRK1A may help improve the outcome of patients undergoing valve replacement. Particularly, functional studies with DYRK1A+/- cardiomyocytes show that this kinase may be an important target to treat cardiac dysfunction, provided that mutant cells presented a different response to stretch and reduced ability to develop force (active tension). This study opens many avenues in post-aortic valve replacement reverse remodeling research. In the future, gain-of-function and/or loss-of-function studies with isolated cardiomyocytes or with animal models of aortic bandingdebanding will help disclose the efficacy of targeting the surrogate therapeutic targets. Besides, clinical studies in larger cohorts will bring definitive proof of complement C3, MuRF1 and DYRK1A prognostic value.A substituição da válvula aórtica continua a ser a opção terapêutica de referência para doentes com estenose aórtica e visa a eliminação da sobrecarga de pressão, desencadeando a remodelagem reversa miocárdica. Contudo, apesar do benefício hemodinâmico imediato, nem todos os pacientes apresentam regressão completa da hipertrofia do miocárdio, ficando com maior risco de eventos adversos, como a insuficiência cardíaca. Atualmente, os mecanismos biológicos subjacentes a uma remodelagem reversa incompleta ainda não são claros. Além disso, não dispomos de ferramentas de prognóstico definitivos nem de terapias auxiliares para melhorar a condição dos pacientes indicados para substituição da válvula. Para ajudar a resolver estas lacunas, uma abordagem combinada de (fosfo)proteómica e proteómica para a caracterização, respetivamente, do miocárdio e do líquido pericárdico foi seguida, tomando partido de biópsias e líquidos pericárdicos recolhidos em ambiente cirúrgico. Das mais de 1800 e 750 proteínas identificadas, respetivamente, no miocárdio e no líquido pericárdico dos pacientes com estenose aórtica, um total de 90 proteínas desreguladas foram detetadas. As análises de anotação de genes, de enriquecimento de vias celulares e discriminativa corroboram um cenário de aumento da expressão de genes pro-hipertróficos e de síntese proteica, um sistema ubiquitina-proteassoma ineficiente, uma tendência para morte celular (potencialmente acelerada pela atividade do complemento e por outros fatores extrínsecos que ativam death receptors), com ativação da resposta de fase aguda e do sistema imune, assim como da fibrose. A validação de alguns alvos específicos através de immunoblot e correlação com dados clínicos apontou para a cadeia β do complemento C3, a Muscle Ring Finger protein 1 (MuRF1) e a dual-specificity Tyr-phosphoylation regulated kinase 1A (DYRK1A) como potenciais marcadores de uma resposta incompleta. Por outro lado, a predição de cinases a partir do fosfoproteoma, sugere que a modulação da caseína cinase 2, a família de cinases do IκB, a glicogénio sintase cinase 3 e da DYRK1A pode ajudar a melhorar a condição dos pacientes indicados para intervenção. Em particular, a avaliação funcional de cardiomiócitos DYRK1A+/- mostraram que esta cinase pode ser um alvo importante para tratar a disfunção cardíaca, uma vez que os miócitos mutantes responderam de forma diferente ao estiramento e mostraram uma menor capacidade para desenvolver força (tensão ativa). Este estudo levanta várias hipóteses na investigação da remodelagem reversa. No futuro, estudos de ganho e/ou perda de função realizados em cardiomiócitos isolados ou em modelos animais de banding-debanding da aorta ajudarão a testar a eficácia de modular os potenciais alvos terapêuticos encontrados. Além disso, estudos clínicos em coortes de maior dimensão trarão conclusões definitivas quanto ao valor de prognóstico do complemento C3, MuRF1 e DYRK1A.Programa Doutoral em Biomedicin
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