7,924 research outputs found

    A Bayesian hierarchical assessment of night shift working for offshore wind farms

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    This article presents a Bayesian data‐modelling approach to assessing operational efficiency at offshore wind farms. Input data are provided by an operational database provided by a large offshore wind farm which employs an advanced data management system. We explore the combination of datasets making up the database, using them to train a Bayesian hierarchical model which predicts weekly lost production from corrective maintenance and time‐based availability. The approach is used to investigate the effect of technician work shift patterns, specifically addressing a strategy involving night shifts for corrective maintenance which was employed at the site throughout the winter. It was found that, for this particular site, there is an approximate annual increase in time‐based technical availability of 0.64%. We explore the effect of modelling assumptions on cost savings; specifically, we explore variations in failure rate, price of electricity, number of technicians working night shift, extra staff wages, months of the year employing 24/7 working and extra vessel provision. Results vary quite significantly among the scenarios investigated, exemplifying the need to consider the question on a farm‐by‐farm basis

    Molecular Mechanisms and Therapies of Colorectal Cancer

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    Colorectal cancer (CRC) is currently the third leading cause of cancer-related mortality, with 1.9 million incidence cases and 0.9 million deaths worldwide. The global number of new CRC cases is predicted to reach 3.2 million in 2040, based on the projection of aging, population growth, and human development.In clinics, despite advances of diagnosis and surgical procedures, 20% of the patients with CRC present with metastasis at the time of diagnosis, caused by residual tumor cells that have spread to distant organs prior to surgery, affecting the patient survival rate. Standard systemic chemotherapy, alternative therapies that target mechanisms involved in cancer progression and metastasis, immunotherapy, and combination therapies are the major CRC-treatment strategies. In the advanced stage of CRC the transforming growth factor-beta (TGF-β) plays an oncogenic role by promoting cancer cell proliferation, cancer cell self-renewal, epithelial-to-mesenchymal transition, invasion, tumor progression, metastatic spread, and immune escape. Furthermore, high levels of TGF-β1 confers poor prognosis and is associated with early recurrence after surgery, resistance to chemo- or immunotherapy, and shorter survival. Based on the body of experimental evidence indicating that TGF-β signaling has the potential to be a good therapeutic target in CRC, several anti-TGF-β drugs have been investigated in cancer clinical trials. Here, we presented a comprehensive collection of manuscripts regarding studies on targeting the TGF-β signaling in CRC to improve patient’s prognosis and personalized treatments

    Land Use and Land Cover Mapping in a Changing World

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    It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classification systems

    Affinity-Based Reinforcement Learning : A New Paradigm for Agent Interpretability

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    The steady increase in complexity of reinforcement learning (RL) algorithms is accompanied by a corresponding increase in opacity that obfuscates insights into their devised strategies. Methods in explainable artificial intelligence seek to mitigate this opacity by either creating transparent algorithms or extracting explanations post hoc. A third category exists that allows the developer to affect what agents learn: constrained RL has been used in safety-critical applications and prohibits agents from visiting certain states; preference-based RL agents have been used in robotics applications and learn state-action preferences instead of traditional reward functions. We propose a new affinity-based RL paradigm in which agents learn strategies that are partially decoupled from reward functions. Unlike entropy regularisation, we regularise the objective function with a distinct action distribution that represents a desired behaviour; we encourage the agent to act according to a prior while learning to maximise rewards. The result is an inherently interpretable agent that solves problems with an intrinsic affinity for certain actions. We demonstrate the utility of our method in a financial application: we learn continuous time-variant compositions of prototypical policies, each interpretable by its action affinities, that are globally interpretable according to customers’ financial personalities. Our method combines advantages from both constrained RL and preferencebased RL: it retains the reward function but generalises the policy to match a defined behaviour, thus avoiding problems such as reward shaping and hacking. Unlike Boolean task composition, our method is a fuzzy superposition of different prototypical strategies to arrive at a more complex, yet interpretable, strategy.publishedVersio

    A comparative study of cortical distribution and density of calbindin inhibitory interneurons and neurons with calcium-binding proteins colocalization between mice and monkeys frontal and visual areas

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    The mouse is a widely used animal model in neuroscience, but the translational relationship from mice to primates is still under question. Previous studies have shown that between-area and between-species differences of pyramidal neurons exist between monkey and mouse frontal areas and V1 (Gilman et al., 2017). To provide more insights into this issue, we compared the laminar distribution and density of calcium-binding protein (CaBP) expressing inhibitory interneurons, which have different functions and properties. We specifically focused on calbindin (CB) interneurons and the colocalization of calbindin and with calretinin (CR) and parvalbumin (PV) in the same neuron, within monkey lateral prefrontal cortex (LPFC) and primary visual cortex (V1), and in functionally equivalent frontal and visual cortices in mouse (FC and V1). We stained coronal brain sections from each brain area and species by using the immunohistochemistry technique. The somata of CB+ interneurons were imaged across the entire cortical depth of each area using high-resolution confocal laser scanning microscopy and stereologically counted to quantify laminar distribution. Our data revealed a dense CB+ neuronal distribution in layers II/III in all monkey and mouse frontal and visual cortices; however, only monkey V1 showed a second peak in layer IV. When cortical depth was normalized, between-area differences in CB+ neuronal distribution were more pronounced in mouse compared to monkey frontal and visual cortices. The data also showed a higher density of CB+ neurons in superficial layers in the mouse compared to deep layers, but the density of CB+ neurons showed a similar pattern across superficial and deep layers in the monkey. In addition, there was a significantly higher density of CB+CR+ and CB+PV+ neurons in mouse compared to the monkey. Triple CB+CR+PV+ expressing neurons were rarely observed in monkey or mouse frontal and visual cortices. These findings contribute to understanding differences in inhibitory neuronal populations between rodents and primate brains

    Deep learning for speech to text transcription for the portuguese language

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    Automatic speech recognition (ASR) is the process of transcribing audio recordings into text, i.e. to transform speech into the respective sequence of words. This process is also commonly known as speechto- text. Machine learning (ML), the ability of machines to learn from examples, is one of the most relevant areas of artificial intelligence in today’s world. Deep learning is a subset of ML which makes use of Deep Neural Networks, a particular type of Artificial Neural Networks (ANNs), which are intended to mimic human neurons, that possess a large number of layers. This dissertation reviews the state-of-the-art on automatic speech recognition throughout time, from early systems which used Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs) to the most up-to-date end-to-end (E2E) deep neural models. Considering the context of the present work, some deep learning algorithms used in state-of-the-art approaches are explained in additional detail. The current work aims to develop an ASR system for the European Portuguese language using deep learning. This is achieved by implementing a pipeline composed of stages responsible for data acquisition, data analysis, data pre-processing, model creation and evaluation of results. With the NVIDIA NeMo framework was possible to implement the QuartzNet15x5 architecture based on 1D time-channel separable convolutions. Following a data-centric methodology, the model developed yielded state-of-the-art Word Error Rate (WER) results of WER = 0.0503; Sumário: Aprendizagem profunda para transcrição de fala para texto para a Língua Portuguesa - O reconhecimento automático de fala (ASR) é o processo de transcrever gravações de áudio em texto, i.e., transformar a fala na respectiva sequência de palavras. Esse processo também é comumente conhecido como speech-to-text. A aprendizagem de máquina (ML), a capacidade das máquinas de aprenderem através de exemplos, é um dos campos mais relevantes da inteligência artificial no mundo atual. Deep learning é um subconjunto de ML que faz uso de Redes Neurais Profundas, um tipo particular de Redes Neurais Artificiais (ANNs), que se destinam a imitar neurónios humanos, que possuem um grande número de camadas Esta dissertação faz uma revisão ao estado da arte do reconhecimento automático de fala ao longo do tempo, desde os primeiros sistemas que usavam Hidden Markov Models (HMMs) e Gaussian Mixture Models (GMMs até sistemas end-to-end (E2E) mais recentes que usam modelos neuronais profundos. Considerando o contexto do presente trabalho, alguns algoritmos de aprendizagem profunda usados em abordagens de ponta são explicados mais detalhadamente. O presente trabalho tem como objetivo desenvolver um sistema ASR para a língua portuguesa europeia utilizando deep learning. Isso é conseguido por meio da implementação de um pipeline composto por etapas responsáveis pela aquisição de dados, análise dos dados, pré-processamento dos dados, criação do modelo e avaliação dos resultados. Com o framework NVIDIA NeMo foi possível implementar a arquitetura QuartzNet15x5 baseada em convoluções 1D separáveis por canal de tempo. Seguindo uma metodologia centrada em dados, o modelo desenvolvido produziu resultados de taxa de erro de palavra (WER) semelhantes aos de estado da arte de WER = 0.0503

    Deep learning-powered vision-based energy management system for next-gen built environment

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    Heating, ventilation and air-conditioning (HVAC) systems provide thermally comfortable spaces for occupants, and their consumption is strongly related to how occupants utilise the building. The over- or under-utilisation of spaces and the increased adoption of flexible working hours lead to unnecessary energy usage in buildings with HVAC systems operated using static or fixed schedules during unoccupied periods. Demand-driven methods can enable HVAC systems to adapt and make timely responses to dynamic changes in occupancy. Approaches central to the implementation of a demand-driven approach are accurate in providing real-time information on occupancy, including the count, localisation and activity levels. While conventional occupancy sensors exist and can provide information on the number and location of occupants, their ability to detect and recognise occupancy activities is limited. This includes the operation of windows and appliances, which can impact the building’s performance. Artificial intelligence (AI) has recently become a critical tool in enhancing the energy performance of buildings and occupant satisfaction and health. Recent studies have shown the capabilities of AI methods, such as computer vision and deep learning in detecting and recognising human activities. The recent emergence of deep learning algorithms has propelled computer vision applications and performance. While several studies used deep learning and computer vision to recognise human motion or activity, there is limited work on integrating these methods with building energy systems. Such methods can be used to obtain accurate and real-time information about the occupants for assisting in the operation of HVAC systems. In this research, a demand-driven deep learning framework was proposed to detect and recognise occupancy behaviour for optimising the operation of building HVAC systems. The computer vision-based deep learning algorithm, convolutional neural network (CNN), was selected to develop the vision-based detector to recognise common occupancy activities such as sitting, standing, walking and opening and closing windows. A dataset consisting of images of occupants in buildings performing different activities was formed to perform the training the model. The trained model was deployed to an AI-powered camera to perform real-time detection within selected case study building spaces, which include university tutorial rooms and offices. Two main types of detectors were developed to show the capabilities of the proposed approach; this includes the occupancy activity detector and the window opening detector. Both detectors were based on the Faster R-CNN with Inception V2 model, which was trained and tested using the same approach. In addition, the influence of different parameters on the performance, such as the training data size, labelling method, and how real-time detection was conducted in different indoor spaces was evaluated. The results have shown that a single response 'people detector’ can accurately understand the number of people within a detected space. The ‘occupancy activity detector’ could provide data towards the prediction of the internal heat emissions of buildings. Furthermore, window detectors were formed to recognise the times when windows are opened, providing insights into the potential ventilation heat losses through this type of ventilation strategy employed in buildings. The information generated by the detector is then outputted as profiles, which are called Deep Learning Influence Profiles (DLIP). Building energy simulation (BES) was used to assess the potential impact of the use of detection and recognition methods on building performance, such as ventilation heat loss and energy demands. The generated DLIPs were inputted into the BES tool. Comparisons with static or scheduled occupancy profiles, currently used in conventional HVAC systems and building energy modelling were made. The results showed that the over- or under-estimation of the occupancy heat gains could lead to inaccurate heating and cooling energy predictions. The deep learning detection method showed that the occupancy heat gains could be represented more accurately compared to static office occupancy profiles. A difference of up to 55% was observed between occupancy DLIP and static heat gain profile. Similarly, the window detection method enabled accurate recognition of the opening and closing of windows and the prediction of ventilation heat losses. BES was conducted for various scenario-based cases that represented typical and/or extreme situations that would occur within selected case study buildings. Results showed that the detection methods could be useful for modulating heating and cooling systems to minimise building energy losses while providing adequate indoor air quality and thermal conditions. Based on the developed individual detectors, combined detectors were formed and also assessed during experimental tests and analysis using BES. The vision-based technique’s integration with the building control system was discussed. A heat gain prediction and optimisation strategy were proposed along with a hybrid controller that optimises energy use and thermal comfort. This should be further developed in future works and assessed in real building installations. This work also discussed the limitations and practical challenges of implementing the proposed technology. Initial results of survey-based questionnaires highlighted the importance of informing occupants about the framework approach and how DLIPs were formed. In all, preference is towards a less intrusive and effective approach that could meet the needs of optimising building energy loads for the next-gen built environment

    Computational prediction of Short Linear Motif candidates in the proteome of the Apicomplexan parasite Toxoplasma gondii

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    Toxoplasma gondii is a unicellular parasite of the Apicomplexan family with the unique ability to infect a wide spectrum of warm-blooded animals, including mammals and birds. Host infection is established by distinct secreted proteins that interact with the cellular machinery and signaling networks of the host cells, hijacking their immune response and subverting cellular processes to their advantage. Short linear motifs (SLiMs) are small functional modules within protein sequences known to mediate protein-protein interaction between parasite and host proteins. By integrating SLiM information with sequences, structural, and experimental data I developed a computational pipeline to identify motif candidates relevant for T. gondii infection. Among these candidates, I identified motifs in microneme, rhoptry, and dense granule proteins that potentially link them to processes like cell attachment, nuclear targeting and cytoskeleton rearrangements. As a proof of concept, the protein-protein interaction of a group of motif candidates related to the innate immune response were tested experimentally in collaboration with the EMBL Protein expression and purification facility. This provided proof of binding and affinity measurements for some of them, and showed that the pipeline is able to identify true binding motifs. Taken together, I developed a computational pipeline that can potentially predict motif candidates relevant for T. gondii infection and provide a resource for further experimental validation and understanding of parasite infection
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