4,916 research outputs found

    The State of the Art in Deep Learning Applications, Challenges, and Future Prospects::A Comprehensive Review of Flood Forecasting and Management

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    Floods are a devastating natural calamity that may seriously harm both infrastructure and people. Accurate flood forecasts and control are essential to lessen these effects and safeguard populations. By utilizing its capacity to handle massive amounts of data and provide accurate forecasts, deep learning has emerged as a potent tool for improving flood prediction and control. The current state of deep learning applications in flood forecasting and management is thoroughly reviewed in this work. The review discusses a variety of subjects, such as the data sources utilized, the deep learning models used, and the assessment measures adopted to judge their efficacy. It assesses current approaches critically and points out their advantages and disadvantages. The article also examines challenges with data accessibility, the interpretability of deep learning models, and ethical considerations in flood prediction. The report also describes potential directions for deep-learning research to enhance flood predictions and control. Incorporating uncertainty estimates into forecasts, integrating many data sources, developing hybrid models that mix deep learning with other methodologies, and enhancing the interpretability of deep learning models are a few of these. These research goals can help deep learning models become more precise and effective, which will result in better flood control plans and forecasts. Overall, this review is a useful resource for academics and professionals working on the topic of flood forecasting and management. By reviewing the current state of the art, emphasizing difficulties, and outlining potential areas for future study, it lays a solid basis. Communities may better prepare for and lessen the destructive effects of floods by implementing cutting-edge deep learning algorithms, thereby protecting people and infrastructure

    Financial and Economic Review 22.

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    A narrative review of the effect of wildfire exposure on pregnancy & birth outcomes

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    Wildfires pose a significant and growing threat to human health. Current trends in climate change predict that wildfire occurrence and severity will increase in the near future, and therefore the adverse health effects associated with wildfire and its air quality effects are becoming increasingly relevant. Even with current efforts to stem future rises in temperature, wildfire activity will continue to increase due to lags in the climate system itself. Thus, in addition to the known increase in mortality, respiratory, and cardiovascular risks, there is a growing need to investigate other health outcomes associated with wildfire smoke exposure, especially their effect on pregnancy and birth outcomes. In order to provide a broad overview of the state of wildfire research on the topic of pregnancy and birth outcomes, this narrative review will summarize the existing literature on pregnancy and birth outcomes associated with wildfire smoke exposure, with consideration for the ambient air pollution literature that informs wildfire research. As research in this specific topic is still developing, a pattern of limitations to study designs is beginning to emerge, which will guide future research needs. Finally, practical considerations for implementing research findings into land management and public health policies that reduce wildfire exposure in order to mitigate the health risks associated with it will be explained

    Anuário científico da Escola Superior de Tecnologia da Saúde de Lisboa - 2021

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    É com grande prazer que apresentamos a mais recente edição (a 11.ª) do Anuário Científico da Escola Superior de Tecnologia da Saúde de Lisboa. Como instituição de ensino superior, temos o compromisso de promover e incentivar a pesquisa científica em todas as áreas do conhecimento que contemplam a nossa missão. Esta publicação tem como objetivo divulgar toda a produção científica desenvolvida pelos Professores, Investigadores, Estudantes e Pessoal não Docente da ESTeSL durante 2021. Este Anuário é, assim, o reflexo do trabalho árduo e dedicado da nossa comunidade, que se empenhou na produção de conteúdo científico de elevada qualidade e partilhada com a Sociedade na forma de livros, capítulos de livros, artigos publicados em revistas nacionais e internacionais, resumos de comunicações orais e pósteres, bem como resultado dos trabalhos de 1º e 2º ciclo. Com isto, o conteúdo desta publicação abrange uma ampla variedade de tópicos, desde temas mais fundamentais até estudos de aplicação prática em contextos específicos de Saúde, refletindo desta forma a pluralidade e diversidade de áreas que definem, e tornam única, a ESTeSL. Acreditamos que a investigação e pesquisa científica é um eixo fundamental para o desenvolvimento da sociedade e é por isso que incentivamos os nossos estudantes a envolverem-se em atividades de pesquisa e prática baseada na evidência desde o início dos seus estudos na ESTeSL. Esta publicação é um exemplo do sucesso desses esforços, sendo a maior de sempre, o que faz com que estejamos muito orgulhosos em partilhar os resultados e descobertas dos nossos investigadores com a comunidade científica e o público em geral. Esperamos que este Anuário inspire e motive outros estudantes, profissionais de saúde, professores e outros colaboradores a continuarem a explorar novas ideias e contribuir para o avanço da ciência e da tecnologia no corpo de conhecimento próprio das áreas que compõe a ESTeSL. Agradecemos a todos os envolvidos na produção deste anuário e desejamos uma leitura inspiradora e agradável.info:eu-repo/semantics/publishedVersio

    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

    CYWARN: Strategy and Technology Development for Cross-Platform Cyber Situational Awareness and Actor-Specific Cyber Threat Communication

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    Despite the merits of digitisation in private and professional spaces, critical infrastructures and societies are increasingly ex-posed to cyberattacks. Thus, Computer Emergency Response Teams (CERTs) are deployed in many countries and organisations to enhance the preventive and reactive capabilities against cyberattacks. However, their tasks are getting more complex by the increasing amount and varying quality of information dissem-inated into public channels. Adopting the perspectives of Crisis Informatics and safety-critical Human-Computer Interaction (HCI) and based on both a narrative literature review and group discussions, this paper first outlines the research agenda of the CYWARN project, which seeks to design strategies and technolo-gies for cross-platform cyber situational awareness and actor-spe-cific cyber threat communication. Second, it identifies and elabo-rates eight research challenges with regard to the monitoring, analysis and communication of cyber threats in CERTs, which serve as a starting point for in-depth research within the project

    Aerial Network Assistance Systems for Post-Disaster Scenarios : Topology Monitoring and Communication Support in Infrastructure-Independent Networks

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    Communication anytime and anywhere is necessary for our modern society to function. However, the critical network infrastructure quickly fails in the face of a disaster and leaves the affected population without means of communication. This lack can be overcome by smartphone-based emergency communication systems, based on infrastructure-independent networks like Delay-Tolerant Networks (DTNs). DTNs, however, suffer from short device-to-device link distances and, thus, require multi-hop routing or data ferries between disjunct parts of the network. In disaster scenarios, this fragmentation is particularly severe because of the highly clustered human mobility behavior. Nevertheless, aerial communication support systems can connect local network clusters by utilizing Unmanned Aerial Vehicles (UAVs) as data ferries. To facilitate situation-aware and adaptive communication support, knowledge of the network topology, the identification of missing communication links, and the constant reassessment of dynamic disasters are required. These requirements are usually neglected, despite existing approaches to aerial monitoring systems capable of detecting devices and networks. In this dissertation, we, therefore, facilitate the coexistence of aerial topology monitoring and communications support mechanisms in an autonomous Aerial Network Assistance System for infrastructure-independent networks as our first contribution. To enable system adaptations to unknown and dynamic disaster situations, our second contribution addresses the collection, processing, and utilization of topology information. For one thing, we introduce cooperative monitoring approaches to include the DTN in the monitoring process. Furthermore, we apply novel approaches for data aggregation and network cluster estimation to facilitate the continuous assessment of topology information and an appropriate system adaptation. Based on this, we introduce an adaptive topology-aware routing approach to reroute UAVs and increase the coverage of disconnected nodes outside clusters. We generalize our contributions by integrating them into a simulation framework, creating an evaluation platform for autonomous aerial systems as our third contribution. We further increase the expressiveness of our aerial system evaluation, by adding movement models for multicopter aircraft combined with power consumption models based on real-world measurements. Additionally, we improve the disaster simulation by generalizing civilian disaster mobility based on a real-world field test. With a prototypical system implementation, we extensively evaluate our contributions and show the significant benefits of cooperative monitoring and topology-aware routing, respectively. We highlight the importance of continuous and integrated topology monitoring for aerial communications support and demonstrate its necessity for an adaptive and long-term disaster deployment. In conclusion, the contributions of this dissertation enable the usage of autonomous Aerial Network Assistance Systems and their adaptability in dynamic disaster scenarios

    A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

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    The accurate modeling of landslide risk is essential pre-requisite for the development of reliable landslide control and mitigation strategies. However, landslide risk depends on the poorly known environmental and socio-economic factors for regional patterns of landslide occurrence probability and vulnerability, which constitute still a matter of research. Here, a hybrid model is described that couples data mining and multi-criteria decision-making methods for hazard and vulnerability mapping and presents its application to landslide risk assessment in Golestan Province, Northeastern Iran. To this end, landslide probability is mapped using three state-of-the-art machine learning (ML) algorithms—Maximum Entropy, Support Vector Machine and Genetic Algorithm for Rule Set Production—and combine the results with Fuzzy Analytical Hierarchy Process computations of vulnerability to obtain the landslide risk map. Based on obtained results, a discussion is presented on landslide probability as a function of the main relevant human-environmental conditioning factors in Golestan Province. In particular, from the response curves of the machine learning algorithms, it can be found that the probability p of landslide occurrence decreases nearly exponentially with the distance x to the next road, fault, or river. Specifically, the results indicated that p≈exp(−λx) where the length scale λ is about 0.0797 km−1 for road, 0.108 km−1 for fault, and 0.734 km−1 0.734 km−1 for river. Furthermore, according to the results, p follows, approximately, a lognormal function of elevation, while the equation p=p0−K(θ−θ0)2 fits well the dependence of landslide modeling on the slope-angle θ, with p0≈0.64,θ0≈25.6∘and|K|≈6.6×10−4. However, the highest predicted landslide risk levels in Golestan Province are located in the south and southwest areas surrounding Gorgan City, owing to the combined effect of dense local human occupation and strongly landslide-prone environmental conditions. Obtained results provide insights for quantitative modeling of landslide risk, as well as for priority planning in landslide risk management

    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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