1,521 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    Runway Safety Improvements Through a Data Driven Approach for Risk Flight Prediction and Simulation

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    Runway overrun is one of the most frequently occurring flight accident types threatening the safety of aviation. Sensors have been improved with recent technological advancements and allow data collection during flights. The recorded data helps to better identify the characteristics of runway overruns. The improved technological capabilities and the growing air traffic led to increased momentum for reducing flight risk using artificial intelligence. Discussions on incorporating artificial intelligence to enhance flight safety are timely and critical. Using artificial intelligence, we may be able to develop the tools we need to better identify runway overrun risk and increase awareness of runway overruns. This work seeks to increase attitude, skill, and knowledge (ASK) of runway overrun risks by predicting the flight states near touchdown and simulating the flight exposed to runway overrun precursors. To achieve this, the methodology develops a prediction model and a simulation model. During the flight training process, the prediction model is used in flight to identify potential risks and the simulation model is used post-flight to review the flight behavior. The prediction model identifies potential risks by predicting flight parameters that best characterize the landing performance during the final approach phase. The predicted flight parameters are used to alert the pilots for any runway overrun precursors that may pose a threat. The predictions and alerts are made when thresholds of various flight parameters are exceeded. The flight simulation model simulates the final approach trajectory with an emphasis on capturing the effect wind has on the aircraft. The focus is on the wind since the wind is a relatively significant factor during the final approach; typically, the aircraft is stabilized during the final approach. The flight simulation is used to quickly assess the differences between fight patterns that have triggered overrun precursors and normal flights with no abnormalities. The differences are crucial in learning how to mitigate adverse flight conditions. Both of the models are created with neural network models. The main challenges of developing a neural network model are the unique assignment of each model design space and the size of a model design space. A model design space is unique to each problem and cannot accommodate multiple problems. A model design space can also be significantly large depending on the depth of the model. Therefore, a hyperparameter optimization algorithm is investigated and used to design the data and model structures to best characterize the aircraft behavior during the final approach. A series of experiments are performed to observe how the model accuracy change with different data pre-processing methods for the prediction model and different neural network models for the simulation model. The data pre-processing methods include indexing the data by different frequencies, by different window sizes, and data clustering. The neural network models include simple Recurrent Neural Networks, Gated Recurrent Units, Long Short Term Memory, and Neural Network Autoregressive with Exogenous Input. Another series of experiments are performed to evaluate the robustness of these models to adverse wind and flare. This is because different wind conditions and flares represent controls that the models need to map to the predicted flight states. The most robust models are then used to identify significant features for the prediction model and the feasible control space for the simulation model. The outcomes of the most robust models are also mapped to the required landing distance metric so that the results of the prediction and simulation are easily read. Then, the methodology is demonstrated with a sample flight exposed to an overrun precursor, and high approach speed, to show how the models can potentially increase attitude, skill, and knowledge of runway overrun risk. The main contribution of this work is on evaluating the accuracy and robustness of prediction and simulation models trained using Flight Operational Quality Assurance (FOQA) data. Unlike many studies that focused on optimizing the model structures to create the two models, this work optimized both data and model structures to ensure that the data well capture the dynamics of the aircraft it represents. To achieve this, this work introduced a hybrid genetic algorithm that combines the benefits of conventional and quantum-inspired genetic algorithms to quickly converge to an optimal configuration while exploring the design space. With the optimized model, this work identified the data features, from the final approach, with a higher contribution to predicting airspeed, vertical speed, and pitch angle near touchdown. The top contributing features are altitude, angle of attack, core rpm, and air speeds. For both the prediction and the simulation models, this study goes through the impact of various data preprocessing methods on the accuracy of the two models. The results may help future studies identify the right data preprocessing methods for their work. Another contribution from this work is on evaluating how flight control and wind affect both the prediction and the simulation models. This is achieved by mapping the model accuracy at various levels of control surface deflection, wind speeds, and wind direction change. The results saw fairly consistent prediction and simulation accuracy at different levels of control surface deflection and wind conditions. This showed that the neural network-based models are effective in creating robust prediction and simulation models of aircraft during the final approach. The results also showed that data frequency has a significant impact on the prediction and simulation accuracy so it is important to have sufficient data to train the models in the condition that the models will be used. The final contribution of this work is on demonstrating how the prediction and the simulation models can be used to increase awareness of runway overrun.Ph.D

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    The Omics basis of human health: investigating plasma proteins and their genetic effects on complex traits

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    Over the past decade, the advancements in technology and the growing amount of identified genetic variants have led to a high number of important discoveries in the field of precision medicine concerning human biology and pathophysiology. However, it became evident that genomics alone could not properly explain the onset and regulation of the specific molecular mechanisms of certain phenotypes. Studying omics helped complement this gap in genetic research, providing detailed information on the quantification of molecules that are involved in structural and functional processes in the organism. Specifically, protein production, levels, and regulation are dynamic and change during the course of one’s lifetime. This information has proven fundamental to understanding how certain proteins affect complex phenotypes such as neurological and psychiatric disorders. In this thesis, I describe the three groups of analyses I conducted over the course of my doctoral programme on different sets of blood plasma proteins and over a broad range of neurological, psychiatric, cardiovascular, and electrophysiology phenotypes. The underlying mechanisms that trigger the onset of psychiatric and neurological conditions are often not limited to the nervous system, but rather stem from multi-system molecular triggers. The first part of the work I carried out aims at investigating the frequent co-occurrence and comorbidity of neurological and cardiovascular phenotypes by conducting a genome-wide association (GWA) meta-analysis of 183 neurology-related blood proteins on data from over 12000 individuals. The second part concerns the bivariate and multivariate analyses conducted on 276 cardiology and inflammatory proteins, while the third illustrates the contribution to consortia focussed on heart rate and electrophysiology. Results from the second and third parts of the work provided information that played an important role in understanding a part of the genetic mechanisms of the complex traits of interest. Overall, the results presented in this thesis strongly support the notion that proteomics is an important tool to be used to study complex traits and drug discovery and development should focus on targeting protein synthesis and regulation. Furthermore, the results also support the notion that complex diseases involve more than one biological system, and in order to gain a better understanding of human pathology, it is fundamental to study the causes and effects across the entire organism

    The concepts of a legal sanction and sanction regime : a EU blueprint for international criminal and financial law and a constitutional challenge for the EU financial sectors

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    Defence date: 3 April 2023Examining Board: Professor Stefan Grundmann, Humboldt University Berlin Professor Christos V. Gortsos, National and Kapodistrian University of Athens Professor Hans-W. Micklitz, European University Institute Professor André Klip, Maastricht UniversityThis Thesis aims to define the concept of a legal ‘sanction’ and develop the sanction theory and principles that are connected with this definition. Part I of this Thesis therefore establishes the theoretical, constitutional and international architecture for sanctions, whereby the philosophical and traditional views on punishment from the old theoretical discussions of the justification for punishment is providing the broader context for the legal concept of sanctions; the case-law of European Court of Human Rights on Articles 6 and 7 and Article 4 of Protocol 7 to the European Convention of Human Rights (ECHR) is providing the main foundation for establishing the constitutional concept of sanctions by providing the architecture and principles for constructing a legal concept of sanctions; and the international standards and principles on sanctioning which the EU Member States has agreed to comply with under the Financial Sector Assessment Program, a task jointly charged on the International Monetary Fund and World Bank, is providing the international aspects on financial sanctions. The conclusions made in Part I will be applied in Part II of this Thesis, which will discuss the EU regimes of sanctions in the financial sector by first establishing the concept of ‘sanction regimes’ and determine its structures and principles. Second, the general requirements for the imposition of sanctions will then be established and discussed just as the constitutional framework will be applied in order to assess the classification of the EU financial law. Third, the specific types of EU financial sanctions will then be analysed and discussed by the application of the Engel-test and the principles establishing the constitutional concept of sanctions. Finally, the last Chapter will bring it all together and answer the research questions examined in this Thesis

    GAC-MAC-SGA 2023 Sudbury Meeting: Abstracts, Volume 46

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    Transcriptional biomarkers of toxicity - powerful tools or random noise? : An applied perspective from studies on bivalves

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    Aquatic organisms are constantly at risk of being exposed to potentially harmful chemical compounds of natural or anthropogenic origin. Biological life can for instance respond to chemical stressors by changes in gene expression, and thus, certain gene transcripts can potentially function as biomarkers, i.e. early warnings, of toxicity and chemical stress. A major challenge for biomarker application is the extrapolation of transcriptional data to potential effects at the organism level or above. Importantly, successful biomarker use also requires basal understanding of how to distinguish actual responses from background noise. The aim of this thesis is, based on response magnitude and variation, to evaluate the biomarker potential in a set of putative transcriptional biomarkers of general toxicity and chemical stress.Specifically, I addressed a selection of six transcripts involved in cytoprotection and oxidative stress: catalase (cat), glutathione-S-transferase (gst), heat shock proteins 70 and 90 (hsp70, hsp90), metallothionein (mt) and superoxide dismutase (sod). Moreover, I used metal exposures to serve as a proxy for general chemical stress, and due to their ecological relevance and nature as sedentary filter-feeders, I used bivalves as study organisms.In a series of experiments, I tested transcriptional responses in the freshwater duck mussel, Anodonta anatina, exposed to copper or an industrial waste-water effluent, to address response robustness and sensitivity, and potential controlled (e.g. exposure concentration) and random (e.g. gravidness) sources of variation. In addition, I performed a systematic review and meta-analysis on transcriptional responses in metal exposed bivalves to (1) evaluate what responses to expect from arbitrary metal exposures, (2) assess the influence from metal concentration (expressed as toxic unit), exposure time and analyzed tissue, and (3) address potential impacts from publication bias in the scientific literature.Response magnitudes were generally small in relationship to the observed variation, both for A. anatina and bivalves in general. The expected response to an arbitrary metal exposure would generally be close to zero, based on both experimental observations and on the estimated impact from publication bias. Although many of the transcripts demonstrated concentration-response relationships, large background noise might in practice obscure the small responses even at relatively high exposures. As demonstrated in A. anatina under copper exposure, this can be the case already for single species under high resolution exposures to single pollutants. As demonstrated by the meta-regression, this problem can only be expected to increase further upon extrapolation between different species and exposure scenarios, due to increasing heterogeneity and random variation. Similar patterns can also be expected for time-dependent response variation, although the meta-regression revealed a general trend of slightly increasing response magnitude with increasing exposure times.In A. anatina, gravidness was identified as a source of random variability that can potentially affect the baseline of most assessed biomarkers, particularly when quantified in gills. Response magnitudes and variability in this species were generally similar for selected transcripts as for two biochemical biomarkers included for comparison (AChE, GST), suggesting that the transcripts might not capture early warnings more efficiently than other molecular endpoints that are more toxicologically relevant. Overall, high concentrations and long exposure durations presumably increase the likelihood of a detectable transcriptional response, but not to an extent that justifies universal application as biomarkers of general toxicity and chemical stress. Consequently, without a strictly defined and validated application, this approach on its own appears unlikely to be successful for future environmental risk assessment and monitoring. Ultimately, efficient use of transcriptional biomarkers might require additional implementation of complementary approaches offered by current molecular techniques

    Optical and hyperspectral image analysis for image-guided surgery

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