3,178 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

    The Application of Data Analytics Technologies for the Predictive Maintenance of Industrial Facilities in Internet of Things (IoT) Environments

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    In industrial production environments, the maintenance of equipment has a decisive influence on costs and on the plannability of production capacities. In particular, unplanned failures during production times cause high costs, unplanned downtimes and possibly additional collateral damage. Predictive Maintenance starts here and tries to predict a possible failure and its cause so early that its prevention can be prepared and carried out in time. In order to be able to predict malfunctions and failures, the industrial plant with its characteristics, as well as wear and ageing processes, must be modelled. Such modelling can be done by replicating its physical properties. However, this is very complex and requires enormous expert knowledge about the plant and about wear and ageing processes of each individual component. Neural networks and machine learning make it possible to train such models using data and offer an alternative, especially when very complex and non-linear behaviour is evident. In order for models to make predictions, as much data as possible about the condition of a plant and its environment and production planning data is needed. In Industrial Internet of Things (IIoT) environments, the amount of available data is constantly increasing. Intelligent sensors and highly interconnected production facilities produce a steady stream of data. The sheer volume of data, but also the steady stream in which data is transmitted, place high demands on the data processing systems. If a participating system wants to perform live analyses on the incoming data streams, it must be able to process the incoming data at least as fast as the continuous data stream delivers it. If this is not the case, the system falls further and further behind in processing and thus in its analyses. This also applies to Predictive Maintenance systems, especially if they use complex and computationally intensive machine learning models. If sufficiently scalable hardware resources are available, this may not be a problem at first. However, if this is not the case or if the processing takes place on decentralised units with limited hardware resources (e.g. edge devices), the runtime behaviour and resource requirements of the type of neural network used can become an important criterion. This thesis addresses Predictive Maintenance systems in IIoT environments using neural networks and Deep Learning, where the runtime behaviour and the resource requirements are relevant. The question is whether it is possible to achieve better runtimes with similarly result quality using a new type of neural network. The focus is on reducing the complexity of the network and improving its parallelisability. Inspired by projects in which complexity was distributed to less complex neural subnetworks by upstream measures, two hypotheses presented in this thesis emerged: a) the distribution of complexity into simpler subnetworks leads to faster processing overall, despite the overhead this creates, and b) if a neural cell has a deeper internal structure, this leads to a less complex network. Within the framework of a qualitative study, an overall impression of Predictive Maintenance applications in IIoT environments using neural networks was developed. Based on the findings, a novel model layout was developed named Sliced Long Short-Term Memory Neural Network (SlicedLSTM). The SlicedLSTM implements the assumptions made in the aforementioned hypotheses in its inner model architecture. Within the framework of a quantitative study, the runtime behaviour of the SlicedLSTM was compared with that of a reference model in the form of laboratory tests. The study uses synthetically generated data from a NASA project to predict failures of modules of aircraft gas turbines. The dataset contains 1,414 multivariate time series with 104,897 samples of test data and 160,360 samples of training data. As a result, it could be proven for the specific application and the data used that the SlicedLSTM delivers faster processing times with similar result accuracy and thus clearly outperforms the reference model in this respect. The hypotheses about the influence of complexity in the internal structure of the neuronal cells were confirmed by the study carried out in the context of this thesis

    AI: Limits and Prospects of Artificial Intelligence

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    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Plant-derived bioactive compounds for inflammatory diseases

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    Tese de doutoramento em Engenharia de Tecidos, Medicina Regenerativa e Células EstaminaisA Organização Mundial da Saúde qualifica as doenças inflamatórias crónicas como a principal causa de morbilidade e mortalidade no mundo. A inflamação crónica é caracterizada por uma resposta inflamatória anormal e persistente que conduz à disfunção de tecidos e órgãos (p. ex. artrite). Nas últimas décadas, foram observadas melhorias significativas no tratamento destas doenças. No entanto, a contínua administração de fármacos anti-inflamatórios é limitada devido à sua associação com efeitos secundários graves. Assim, terapias mais seguras e eficazes devem ser exploradas. As plantas, sendo a base da medicina tradicional em muitas culturas por milhares de anos, são uma excelente fonte de moléculas bioativas, tornando-se algumas delas marcos na indústria farmacêutica (p. ex. morfina). Duas plantas tradicionalmente utilizadas no tratamento de doenças imunológicas são a Salvia officinalis e a Echinacea purpurea. Todavia, a sua atividade imunomoduladora ainda não foi amplamente estudada de forma a fornecer evidências científicas sólidas acerca da sua eficácia. Neste trabalho foram preparados extratos de diferentes órgãos dessas plantas (flores, folhas e raízes) para explorar o seu potencial como formulações pró- ou anti-inflamatórias. Diferentes solventes e métodos de extração foram usados para preparar extratos com diferentes características. Em particular, os extratos da E. purpurea foram separados em duas frações (fenóis/ácidos carboxílicos e alquilamidas) para permitir identificar a classe de compostos responsável pela maior bioatividade. A composição química dos extratos e das frações foi caracterizada por diferentes técnicas cromatográficas. A atividade antioxidante das diferentes formulações foi avaliada na presença de espécies reativas relevantes. Os efeitos pró- e anti-inflamatórios dos diferentes extratos e frações foram investigados, respetivamente, em macrófagos não estimulados e estimulados com lipopolissacarídeos. Relativamente às propriedades pró-inflamatórias, somente os extratos aquosos de E. purpurea demonstraram bioatividade ao induzir as principais vias de sinalização inflamatória e os mediadores pró-inflamatórios. Considerando as atividades antioxidantes e anti inflamatórias, todos os extratos e frações preparados apresentaram grande eficácia, a qual foi influenciada pelo método de extração, solvente utilizado e órgão da planta selecionado. Posteriormente, o extrato mais promissor foi encapsulado em vesículas unilamelares grandes, funcionalizadas com ácido fólico, com o objetivo de melhorar a sua biodistribuição. Por fim, demonstrou-se a segurança e a eficácia terapêutica desta formulação num modelo experimental de inflamação em ratos. Assim, concluiu-se que os extratos de plantas são formulações com grande potencial para serem posteriormente utilizadas como base no tratamento eficaz de doenças que afetam o sistema imunológico, seja quando este está comprometido ou hiper-reativo.Chronic inflammation-related diseases are ranked by the World Health Organization as the major cause of morbidity and mortality in the world. Chronic inflammation is characterized by a persistent and abnormal inflammatory response that leads to tissue damage and/or dysfunction (e.g., arthritis). There were remarkable improvements in the last decades in the management of chronic inflammatory diseases. However, the constant administration of the clinically available anti-inflammatory drugs is limited due to their association with serious side effects. Therefore, alternative, safer and more effective therapies must be investigated. Plants, being the basis of traditional medicine in many cultures for thousands of years, are a rich source of bioactive molecules. Some of them became landmarks in the pharmaceutical field (e.g., morphine). Two plants traditionally used in the treatment of immune-related diseases are Salvia officinalis and Echinacea purpurea. However, their immunomodulatory activity has not been extensively studied in a scientifically soundness. Therefore, in this work, we obtained extracts from different organs of those plants (flowers, leaves, and roots) to explore their potential as pro- or anti-inflammatory formulations. Different solvents and extraction methods were used to prepare a variety of extracts. Particularly for E. purpurea extracts were fractionated into phenolic/carboxylic acids and alkylamide fractions to identify the class of compounds responsible for the strongest bioactivity. Then, the chemical fingerprint in the extracts and fractions was evaluated by different chromatographic techniques. The antioxidant activity of the different formulations was evaluated against relevant reactive species. The proand anti-inflammatory effects of the different extracts and fractions were evaluated using non-stimulated and lipopolysaccharide-stimulated macrophages, respectively. Regarding pro-inflammatory properties, aqueous E. purpurea extracts were the most promising by the induction of main inflammatory signaling pathways and pro-inflammatory mediators. Considering antioxidant and anti-inflammatory activities, all the developed extracts displayed strong efficacy that was influenced by the extraction method, solvent used, and source organ of the plant. Afterward, the most promising extract was loaded in folic acidfunctionalized large unilamellar vesicles (FLUVs) to improve its therapeutic biodistribution. Finally, it was demonstrated in an experimental rat model of inflammation the safety and enhanced therapeutic efficacy of the most powerful extracts loaded in FLUVs. Therefore, we showed that the plant extracts are promising natural formulations that can be further used as a basis for the effective treatment for disorders in which the immune system is either overactive or impaired.Fundação para a Ciência e Tecnologia (FCT) for my Ph.D. scholarships (PD/BD/135246/2017 and COVID/BD/152012/2021) and the Ph.D. programme in Advanced Therapies for Health (PATH, PD/00169/2013)
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