المؤلفون: Amado, Conceição, Bianco, Ana M., Boente, Graciela, Rodrigues, Isabel M.

مصطلحات موضوعية: Statistics - Methodology

الوصف: We collect robust proposals given in the field of regression models with heteroscedastic errors. Our motivation stems from the fact that the practitioner frequently faces the confluence of two phenomena in the context of data analysis: non--linearity and heteroscedasticity. The impact of heteroscedasticity on the precision of the estimators is well--known, however the conjunction of these two phenomena makes handling outliers more difficult. An iterative procedure to estimate the parameters of a heteroscedastic non--linear model is considered. The studied estimators combine weighted $MM-$regression estimators, to control the impact of high leverage points, and a robust method to estimate the parameters of the variance function.

الوصول الحر: http://arxiv.org/abs/2311.02822Test

المؤلفون: Leite, Rita, Amado, Conceição, Azeitona, Margarida

المصدر: Expert Systems with Applications ; volume 248, page 123379 ; ISSN 0957-4174

مصطلحات موضوعية: Artificial Intelligence, Computer Science Applications, General Engineering

الإتاحة: https://doi.org/10.1016/j.eswa.2024.123379Test
https://api.elsevier.com/content/article/PII:S0957417424002446?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S0957417424002446?httpAccept=text/plainTest

المؤلفون: Santos, Kenny, Firme, Bernardo, Dias, João P., Amado, Conceição

المصدر: Transportation Research Record: Journal of the Transportation Research Board ; volume 2678, issue 1, page 736-748 ; ISSN 0361-1981 2169-4052

مصطلحات موضوعية: Mechanical Engineering, Civil and Structural Engineering

الوصف: Motorcycle road traffic accidents represent a big concern, as vulnerable road users account for more than half of all road deaths worldwide. The estimation of the influential factors associated with the increase of injury severity of motorcyclists involved in a road accident is of extreme importance as it provides a necessary basis for the development of an appropriate and targeted sustainable prevention plan for improving road safety. This study adopted the decision tree, ordered logistic regression (LR), random forest (RF), gradient boosting, extreme gradient boosting, k-nearest neighbor, and support vector machine methods to predict the injury severity outcome of motorcycle accidents. All the developed models were compared with six different performance metrics. A 10-year (2010–2019) dataset with motorcycle accidents that occurred in Portugal was used for analysis. As usual in traffic accidents datasets, this dataset is class unbalanced which was dealt with by under-sampling. The developed models made it possible to determine the factors associated with the increase of injury severity of motorcyclists involved in road accidents. The interpretation of each factor is based on the Shapley additive explanations values. The RF and LR models (developed with the balanced dataset) outperformed the other models. Risk factors associated with alcohol consumption, road type, road conditions, location, motorcycle age, rider’s gender, and when the accident occurs were estimated. This study provides a suitable framework analysis to build a proper predictive model, allowing researchers and practitioners to evaluate more accurately the risk factors of motorcycle injury severity.

الإتاحة: https://doi.org/10.1177/03611981231172507Test

المؤلفون: Santos, Kenny, Silva, Nuno M., Dias, João P., Amado, Conceição

المساهمون: Fundação para a Ciência e a Tecnologia

المصدر: Engineering Failure Analysis ; volume 152, page 107505 ; ISSN 1350-6307

مصطلحات موضوعية: General Engineering, General Materials Science

الإتاحة: https://doi.org/10.1016/j.engfailanal.2023.107505Test
https://api.elsevier.com/content/article/PII:S1350630723004594?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S1350630723004594?httpAccept=text/plainTest

المؤلفون: Almeida Silva, Maria, Amado, Conceiçāo, Loureiro, Dália

المصدر: Journal of Water Resources Planning and Management ; volume 150, issue 8 ; ISSN 0733-9496 1943-5452

الإتاحة: https://doi.org/10.1061/jwrmd5.wreng-6404Test

المؤلفون: Farinha, Miguel, Amado, Conceição, Morgado, Pedro, Cabral, Joana

المصدر: Farinha , M , Amado , C , Morgado , P & Cabral , J 2022 , ' Increased Excursions to Functional Networks in Schizophrenia in the Absence of Task ' , Frontiers in Neuroscience , vol. 16 , 821179 . https://doi.org/10.3389/fnins.2022.821179Test

مصطلحات موضوعية: dynamic functional connectivity, dynamical systems theory, functional networks, LEiDA, resting-state functional magnetic resonance imaging, schizophrenia

الوصف: Schizophrenia is a chronic psychotic disorder characterized by the disruption of thought processes, perception, cognition, and behaviors, for which there is still a lack of objective and quantitative biomarkers in brain activity. Using functional magnetic resonance imaging (fMRI) data from an open-source database, this study investigated differences between the dynamic exploration of resting-state networks in 71 schizophrenia patients and 74 healthy controls. Focusing on recurrent states of phase coherence in fMRI signals, brain activity was examined for intergroup differences through the lens of dynamical systems theory. Results showed reduced fractional occupancy and dwell time of a globally synchronized state in schizophrenia. Conversely, patients exhibited increased fractional occupancy, dwell time and limiting probability of being in states during which canonical functional networks—i.e., Limbic, Dorsal Attention and Somatomotor—synchronized in anti-phase with respect to the rest of the brain. In terms of state-to-state transitions, patients exhibited increased probability of switching to Limbic, Somatomotor and Visual networks, and reduced probability of remaining in states related to the Default Mode network, the Orbitofrontal network and the globally synchronized state. All results revealed medium to large effect sizes. Combined, these findings expose pronounced differences in the temporal expression of resting-state networks in schizophrenia patients, which may relate to the pathophysiology of this disorder. Overall, these results reinforce the utility of dynamical systems theory to extend current knowledge regarding disrupted brain dynamics in psychiatric disorders.

العلاقة: https://pure.au.dk/portal/en/publications/9d9e5e70-a67a-49d4-bf10-01c9aa0514aeTest

الإتاحة: https://doi.org/10.3389/fnins.2022.821179Test
https://pure.au.dk/portal/en/publications/9d9e5e70-a67a-49d4-bf10-01c9aa0514aeTest
http://www.scopus.com/inward/record.url?scp=85127612517&partnerID=8YFLogxKTest

المؤلفون: Silva, Maria Almeida, Amado, Conceição, Ribeiro, Álvaro, Loureiro, Dália

المصدر: Measurement ; volume 196, page 111196 ; ISSN 0263-2241

مصطلحات موضوعية: Applied Mathematics, Electrical and Electronic Engineering, Condensed Matter Physics, Instrumentation

الإتاحة: https://doi.org/10.1016/j.measurement.2022.111196Test
https://api.elsevier.com/content/article/PII:S0263224122004493?httpAccept=text/xmlTest
https://api.elsevier.com/content/article/PII:S0263224122004493?httpAccept=text/plainTest

المؤلفون: Santos, Kenny, Firme, Bernardo, Dias, João P., Amado, Conceição

المصدر: Transportation Research Record; January 2024, Vol. 2678 Issue: 1 p736-748, 13p

مستخلص: Motorcycle road traffic accidents represent a big concern, as vulnerable road users account for more than half of all road deaths worldwide. The estimation of the influential factors associated with the increase of injury severity of motorcyclists involved in a road accident is of extreme importance as it provides a necessary basis for the development of an appropriate and targeted sustainable prevention plan for improving road safety. This study adopted the decision tree, ordered logistic regression (LR), random forest (RF), gradient boosting, extreme gradient boosting, k-nearest neighbor, and support vector machine methods to predict the injury severity outcome of motorcycle accidents. All the developed models were compared with six different performance metrics. A 10-year (2010–2019) dataset with motorcycle accidents that occurred in Portugal was used for analysis. As usual in traffic accidents datasets, this dataset is class unbalanced which was dealt with by under-sampling. The developed models made it possible to determine the factors associated with the increase of injury severity of motorcyclists involved in road accidents. The interpretation of each factor is based on the Shapley additive explanations values. The RF and LR models (developed with the balanced dataset) outperformed the other models. Risk factors associated with alcohol consumption, road type, road conditions, location, motorcycle age, rider’s gender, and when the accident occurs were estimated. This study provides a suitable framework analysis to build a proper predictive model, allowing researchers and practitioners to evaluate more accurately the risk factors of motorcycle injury severity.

المؤلفون: Santos, Kenny, Firme, Bernardo, Dias, João P., Amado, Conceição

مصطلحات موضوعية: FOS: Social and economic geography, 120599 Urban and Regional Planning not elsewhere classified

الوصف: Supplemental material, sj-docx-1-trr-10.1177_03611981231172507 for Analysis of Motorcycle Accident Injury Severity and Performance Comparison of Machine Learning Algorithms by Kenny Santos, Bernardo Firme, João P. Dias and Conceição Amado in Transportation Research Record

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::a5e437037aecda59663c0ec712dbec5bTest

المؤلفون: Pacheco, Mafalda, Falcão, Ana Paula, Amado, Conceição, Almeida, Joana, Garcia, João, Portela, Manuel, de Sá, Ana Morais, Afonso, Nuno

المصدر: Cultural Sustainable Tourism ; Advances in Science, Technology & Innovation ; page 45-55 ; ISSN 2522-8714 2522-8722 ; ISBN 9783031078187 9783031078194

الإتاحة: https://doi.org/10.1007/978-3-031-07819-4_5Test