8,747 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

    Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

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    In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

    Comparing the autism phenotype in children born extremely preterm and children born at term

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    BACKGROUND AND OBJECTIVE: It has been well established that children born preterm are at an increased risk of developing Autism Spectrum Disorder (ASD), and that risk increases as gestational age decreases. However, there is limited knowledge on how the ASD phenotype in preterm-born children compares to ASD presentation in children born at term. The objective of this study is to compare ASD core symptoms and characteristics commonly associated with ASD in children born extremely preterm (EP) and children born at term. METHODS: Extremely preterm (EP) participants (n=59) from the Extremely Low Gestational Age Newborn (ELGAN) Study who met diagnostic criteria for ASD at approximately 10 years of age were matched with term participants (n=59) from the Simons Simplex Collection (SSC) on age, sex, and nonverbal IQ. Differences in core ASD symptomatology were evaluated using the Autism Diagnostic Interview-Revised (ADI-R), an in-depth parent interview, and the Autism Diagnostic Observation Schedule, 2nd edition (ADOS-2), a semi-structured clinical observation assessment. Developmental milestones, anthropometrics, seizure disorder, and psychiatric symptoms were also investigated as associated characteristics of ASD. Analyses excluding multiplex EP individuals and their term matches, as well female-only analyses, were also conducted. RESULTS: On the ADI-R, the EP group had lower scores (decreased symptom severity) on verbal communication, specifically stereotypic language, and restricted and repetitive behaviors (RRBs). However, no between-group differences were observed with direct observation based on the ADOS-2 assessment. The EP group was more likely to have delayed speech milestones, lower height, weight, and head circumference, and lower rates of depression and anxiety symptoms. When 7 multiplex EP participants and their term control matches were eliminated from the sample, there were no differences from the primary analyses. Female-only analyses were similar to primary analyses on core ASD symptomatology findings. Regarding associated characteristics, females only differed on height, head circumference, and anxiety symptoms. CONCLUSIONS: Accounting for age, sex, nonverbal IQ, and prior ASD diagnosis status, EP children had less severe stereotypic language and RRB symptoms compared to term children based on ADI-R parent report, but exhibited no differences on parent-reported nonverbal communication or reciprocal social interaction symptoms, or with direct observation of social affective and repetitive and restricted ASD symptoms on the ADOS-2. EP children with ASD also showed decreased physical growth and delayed language relative to those born at term, possibly reflecting the developmental effects of being born EP. In sum, the ASD phenotype was generally similar between EP and term born children, with the exception of less severity of retrospectively parent-reported stereotypic behaviors, lower physical growth parameters, and increased delays in language milestones among EP born children with ASD

    Desarrollo de una batería de memoria semántica para pacientes con epilepsia del lóbulo temporal

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    La epilepsia focal más frecuente es aquella epilepsia cuyo foco epileptógeno está localizado en el lóbulo temporal medial y es secundaria a una esclerosis con atrofia de la región amígdalo-hipocámpica, con una red epileptógena que abarca la porción anterior del lóbulo temporal. En ocasiones los pacientes requieren de un tratamiento quirúrgico que incluye la resección unilateral de ambas regiones, tanto del polo anterior, como del complejo amígdala-hipocampo. Estas estructuras han demostrado tener gran importancia para el procesamiento de la memoria semántica (región anterotemporal) y episódica (región amígdalo-hipocámpica), por lo que los pacientes que son sometidos a esta intervención suelen presentar quejas cognitivas relacionadas con ambos tipos de memoria. Sin embargo, parece que las evaluaciones neuropsicológicas que realizamos de forma rutinaria en las diferentes Unidades de Epilepsia no son capaces de detectar todos los problemas cognitivos que ocurren en estos pacientes ya que, a pesar de las dificultades expresadas por estos, las evaluaciones no muestran alteraciones. La hipótesis principal del presente trabajo es que estas quejas se deben a tipos de memoria que no están incluidos en las pruebas neuropsicológicas actuales y, por tanto, no somos capaces de identificar bien sus problemas. En primer lugar, se propone que la memoria semántica está afectada, pero solamente para palabras de baja frecuencia de uso en la vida diaria, no analizadas en las evaluaciones convencionales actuales. En segundo lugar, otros problemas no objetivados se deben a un problema de la memoria de consolidación, medida como olvido a largo plazo acelerado que se detecta cuando se amplia el periodo de evaluación del recuerdo. Además, estas alteraciones van a manifestarse con mayor intensidad en pacientes cuyo foco epileptógeno está localizado en el lóbulo temporal izquierdo. Los objetivos fundamentales de este trabajo son evaluar en pacientes con epilepsia del lóbulo temporal medial intervenidos quirúrgicamente mediante lobectomía temporal anterior con amigdalohipocampectomía la presencia de alteraciones de la memoria verbal tanto semántica como episódica, así como conocer su valor lateralizador según el hemisferio afectado. El estudio se basó en la comparación de pacientes con epilepsia del lóbulo temporal (ELT) tratados con lobectomía temporal anterior con amigdalohipocampectomía con un grupo control de personas sanas, comparables respecto a edad, nivel educativo y coeficiente intelectual (CI). Las pruebas de memoria semántica mostraron que únicamente los pacientes con ELT izquierda tenían alteraciones, especialmente para ítems de baja frecuencia y tanto en tares de expresión como de comprensión verbal. Asimismo, el tiempo de reacción fue mayor en el grupo de pacientes con ELT izquierda para todos los ítems y únicamente para las palabras o conceptos de baja frecuencia en aquellos con ELT derecha. Además, se incluyó una prueba de memoria episódica estándar (RAVLT) que en lugar de restringir la evaluación a 30 minutos, se evaluó a 7 días para medir el olvido a largo plazo. Los resultados mostraron que los dos grupos de pacientes, tanto los de ELT izquierda como aquellos con ELT derecha, desarrollaron olvido a largo plazo. Por último los resultados mostraron que la presencia de crisis epilépticas no afectó a la presencia de olvido a largo plazo acelerado

    Mathematical models to evaluate the impact of increasing serotype coverage in pneumococcal conjugate vaccines

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    Of over 100 serotypes of Streptococcus pneumoniae, only 7 were included in the first pneumo- coccal conjugate vaccine (PCV). While PCV reduced the disease incidence, in part because of a herd immunity effect, a replacement effect was observed whereby disease was increasingly caused by serotypes not included in the vaccine. Dynamic transmission models can account for these effects to describe post-vaccination scenarios, whereas economic evaluations can enable decision-makers to compare vaccines of increasing valency for implementation. This thesis has four aims. First, to explore the limitations and assumptions of published pneu- mococcal models and the implications for future vaccine formulation and policy. Second, to conduct a trend analysis assembling all the available evidence for serotype replacement in Europe, North America and Australia to characterise invasive pneumococcal disease (IPD) caused by vaccine-type (VT) and non-vaccine-types (NVT) serotypes. The motivation behind this is to assess the patterns of relative abundance in IPD cases pre- and post-vaccination, to examine country-level differences in relation to the vaccines employed over time since introduction, and to assess the growth of the replacement serotypes in comparison with the serotypes targeted by the vaccine. The third aim is to use a Bayesian framework to estimate serotype-specific invasiveness, i.e. the rate of invasive disease given carriage. This is useful for dynamic transmission modelling, as transmission is through carriage but a majority of serotype-specific pneumococcal data lies in active disease surveillance. This is also helpful to address whether serotype replacement reflects serotypes that are more invasive or whether serotypes in a specific location are equally more invasive than in other locations. Finally, the last aim of this thesis is to estimate the epidemiological and economic impact of increas- ing serotype coverage in PCVs using a dynamic transmission model. Together, the results highlight that though there are key parameter uncertainties that merit further exploration, divergence in serotype replacement and inconsistencies in invasiveness on a country-level may make a universal PCV suboptimal.Open Acces

    Wildlife trade in Latin America: people, economy and conservation

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    Wildlife trade is among the main threats to biodiversity conservation and may pose a risk to human health because of the spread of zoonotic diseases. To avoid social, economic and environmental consequences of illegal trade, it is crucial to understand the factors influencing the wildlife market and the effectiveness of policies already in place. I aim to unveil the biological and socioeconomic factors driving wildlife trade, the health risks imposed by the activity, and the effectiveness of certified captive-breeding as a strategy to curb the illegal market in Latin America through a multidisciplinary approach. I assess socioeconomic correlates of the emerging international trade in wild cat species from Latin America using a dataset of >1,000 seized cats, showing that high levels of corruption and Chinese private investment and low income per capita were related to higher numbers of jaguar seizures. I assess the effectiveness of primate captive-breeding programmes as an intervention to curb wildlife trafficking. Illegal sources held >70% of the primate market share. Legal primates are more expensive, and the production is not sufficiently high to fulfil the demand. I assess the scale of the illegal trade and ownership of venomous snakes in Brazil. Venomous snake taxa responsible for higher numbers of snakebites were those most often kept as pets. I uncover how online wildlife pet traders and consumers responded to campaigns associating the origin of the COVID-19 pandemic. Of 20,000 posts on Facebook groups, only 0.44% mentioned COVID-19 and several stimulated the trade in wild species during lockdown. Despite the existence of international and national wildlife trade regulations, I conclude that illegal wildlife trade is still an issue that needs further addressing in Latin America. I identify knowledge gaps and candidate interventions to amend the current loopholes to reduce wildlife trafficking. My aspiration with this thesis is to provide useful information that can inform better strategies to tackle illegal wildlife trade in Latin America

    RNA pull-down-confocal nanoscanning (RP-CONA), a novel method for studying RNA/protein interactions in cell extracts that detected potential drugs for Parkinson’s disease targeting RNA/HuR complexes

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    MicroRNAs (miRNAs, miRs) are a class of small non-coding RNAs that regulate gene expression through specific base-pair targeting. The functional mature miRNAs usually undergo a two-step cleavage from primary miRNAs (pri-miRs), then precursor miRNAs (pre-miRs). The biogenesis of miRNAs is tightly controlled by different RNA-binding proteins (RBPs). The dysregulation of miRNAs is closely related to a plethora of diseases. Targeting miRNA biogenesis is becoming a promising therapeutic strategy. HuR and MSI2 are both RBPs. MiR-7 is post-transcriptionally inhibited by the HuR/MSI2 complex, through a direct interaction between HuR and the conserved terminal loop (CTL) of pri-miR-7-1. Small molecules dissociating pri-miR-7/HuR interaction may induce miR-7 production. Importantly, the miR-7 levels are negatively correlated with Parkinson’s disease (PD). PD is a common, incurable neurodegenerative disease causing serious motor deficits. A hallmark of PD is the presence of Lewy bodies in the human brain, which are inclusion bodies mainly composed of an aberrantly aggregated protein named α-synuclein (α-syn). Decreasing α-syn levels or preventing α-syn aggregation are under investigation as PD treatments. Notably, α-syn is negatively regulated by several miRNAs, including miR-7, miR-153, miR-133b and others. One hypothesis is that elevating these miRNA levels can inhibit α-syn expression and ameliorate PD pathologies. In this project, we identified miR-7 as the most effective α-syn inhibitor, among the miRNAs that are downregulated in PD, and with α-syn targeting potentials. We also observed potential post-transcriptional inhibition on miR-153 biogenesis in neuroblastoma, which may help to uncover novel therapeutic targets towards PD. To identify miR-7 inducers that benefit PD treatment by repressing α-syn expression, we developed a novel technique RNA Pull-down Confocal Nanoscaning (RP-CONA) to monitor the binding events between pri-miR-7 and HuR. By attaching FITC-pri-miR-7-1-CTL-biotin to streptavidin-coated agarose beads and incubating them in human cultured cell lysates containing overexpressed mCherry-HuR, the bound RNA and protein can be visualised as quantifiable fluorescent rings in corresponding channels in a confocal high-content image system. A pri-miR-7/HuR inhibitor can decrease the relative mCherry/FITC intensity ratio in RP-CONA. With this technique, we performed several small-scale screenings and identified that a bioflavonoid, quercetin can largely dissociate the pri-miR-7/HuR interaction. Further studies proved that quercetin was an effective miR-7 inducer as well as α-syn inhibitor in HeLa cells. To understand the mechanism of quercetin mediated α-syn inhibition, we tested the effects of quercetin treatment with miR-7-1 and HuR knockout HeLa cells. We found that HuR was essential in this pathway, while miR-7 hardly contributed to the α-syn inhibition. HuR can directly bind an AU-rich element (ARE) at the 3’ untranslated region (3’-UTR) of α-syn mRNA and promote translation. We believe quercetin mainly disrupts the ARE/HuR interaction and disables the HuR-induced α-syn expression. In conclusion, we developed and optimised RP-CONA, an on-bead, lysate-based technique detecting RNA/protein interactions, as well as identifying RNA/protein modulators. With RP-CONA, we found quercetin inducing miR-7 biogenesis, and inhibiting α-syn expression. With these beneficial effects, quercetin has great potential to be applied in the clinic of PD treatment. Finally, RP-CONA can be used in many other RNA/protein interactions studies

    Epilepsy Mortality: Leading Causes of Death, Co-morbidities, Cardiovascular Risk and Prevention

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    a reuptake inhibitor selectively prevents seizure-induced sudden death in the DBA/1 mouse model of sudden unexpected ... Bilateral lesions of the fastigial nucleus prevent the recovery of blood pressure following hypotension induced by ..

    Predictive Maintenance of Critical Equipment for Floating Liquefied Natural Gas Liquefaction Process

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    Predictive Maintenance of Critical Equipment for Liquefied Natural Gas Liquefaction Process Meeting global energy demand is a massive challenge, especially with the quest of more affinity towards sustainable and cleaner energy. Natural gas is viewed as a bridge fuel to a renewable energy. LNG as a processed form of natural gas is the fastest growing and cleanest form of fossil fuel. Recently, the unprecedented increased in LNG demand, pushes its exploration and processing into offshore as Floating LNG (FLNG). The offshore topsides gas processes and liquefaction has been identified as one of the great challenges of FLNG. Maintaining topside liquefaction process asset such as gas turbine is critical to profitability and reliability, availability of the process facilities. With the setbacks of widely used reactive and preventive time-based maintenances approaches, to meet the optimal reliability and availability requirements of oil and gas operators, this thesis presents a framework driven by AI-based learning approaches for predictive maintenance. The framework is aimed at leveraging the value of condition-based maintenance to minimises the failures and downtimes of critical FLNG equipment (Aeroderivative gas turbine). In this study, gas turbine thermodynamics were introduced, as well as some factors affecting gas turbine modelling. Some important considerations whilst modelling gas turbine system such as modelling objectives, modelling methods, as well as approaches in modelling gas turbines were investigated. These give basis and mathematical background to develop a gas turbine simulated model. The behaviour of simple cycle HDGT was simulated using thermodynamic laws and operational data based on Rowen model. Simulink model is created using experimental data based on Rowen’s model, which is aimed at exploring transient behaviour of an industrial gas turbine. The results show the capability of Simulink model in capture nonlinear dynamics of the gas turbine system, although constraint to be applied for further condition monitoring studies, due to lack of some suitable relevant correlated features required by the model. AI-based models were found to perform well in predicting gas turbines failures. These capabilities were investigated by this thesis and validated using an experimental data obtained from gas turbine engine facility. The dynamic behaviours gas turbines changes when exposed to different varieties of fuel. A diagnostics-based AI models were developed to diagnose different gas turbine engine’s failures associated with exposure to various types of fuels. The capabilities of Principal Component Analysis (PCA) technique have been harnessed to reduce the dimensionality of the dataset and extract good features for the diagnostics model development. Signal processing-based (time-domain, frequency domain, time-frequency domain) techniques have also been used as feature extraction tools, and significantly added more correlations to the dataset and influences the prediction results obtained. Signal processing played a vital role in extracting good features for the diagnostic models when compared PCA. The overall results obtained from both PCA, and signal processing-based models demonstrated the capabilities of neural network-based models in predicting gas turbine’s failures. Further, deep learning-based LSTM model have been developed, which extract features from the time series dataset directly, and hence does not require any feature extraction tool. The LSTM model achieved the highest performance and prediction accuracy, compared to both PCA-based and signal processing-based the models. In summary, it is concluded from this thesis that despite some challenges related to gas turbines Simulink Model for not being integrated fully for gas turbine condition monitoring studies, yet data-driven models have proven strong potentials and excellent performances on gas turbine’s CBM diagnostics. The models developed in this thesis can be used for design and manufacturing purposes on gas turbines applied to FLNG, especially on condition monitoring and fault detection of gas turbines. The result obtained would provide valuable understanding and helpful guidance for researchers and practitioners to implement robust predictive maintenance models that will enhance the reliability and availability of FLNG critical equipment.Petroleum Technology Development Funds (PTDF) Nigeri
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