Traffic event detection framework using social media

This is an accepted manuscript of an article published by IEEE in 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC) on 18/09/2017, available online: https://ieeexplore.ieee.org/document/8038595 The accepted version of the publication may differ from the final published version.© 2017 IEEE. Traffic incidents are one of the leading causes of non-recurrent traffic congestions. By detecting these incidents on time, traffic management agencies can activate strategies to ease congestion and travelers can plan their trip by taking into consideration these factors. In recent years, there has been an increasing interest in Twitter because of the real-time nature of its data. Twitter has been used as a way of predicting revenues, accidents, natural disasters, and traffic. This paper proposes a framework for the real-time detection of traffic events using Twitter data. The methodology consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated and further classified into positive, negative, or neutral class using sentiment analysis. In addition, stress and relaxation strength detection is performed, with the purpose of further analyzing user emotions within the tweet. Future work will be carried out to implement the proposed framework in the West Midlands area, United Kingdom.Published versio

‘Challenging’ doesn’t sum it up: Exploring probation practitioners’ experiences managing high-risk individuals during the COVID-19 pandemic

The COVID-19 pandemic has been, and still is, a worldwide health crisis. Despite the surge of literature on this phenomenon, little research has been conducted with the Probation Service during this time. The aim of this research was to explore Probation Practitioners’ (PPs’) experiences of the COVID-19 restrictions with a specific focus on those who access the Psychologically Informed Consultation Service (PICS). Further, to explore the experiences of key aspects of the COVID-19 pandemic through the lens of the Community Offender Personality Disorder Service. Semi-structured interviews were conducted with 9 PPs who represented a broad cross-section in terms of age and years of experience in the role. Interpretative Phenomenological Analysis was used to explore the experiences of PPs and revealed 5 main themes: unmet support needs, problematic working environments, an emotionally distressing time, the use of PICS, and a silver lining. These findings are discussed with implications for further research

Sibling relationships and family functioning in siblings of early adolescents, adolescents and young adults with autism spectrum disorder

The purpose of the study was to investigate how family functioning (defined as the ability that family members hold to manage stressful events, and intimate and social relationships), the degree to which family members feel happy and fulfilled with each other (called family satisfaction), and the demographical characteristics of siblings (age and gender) impacted on sibling relationships. The Circumplex Model of Marital and Family Systems and Behavioral Systems constituted the theoretical frameworks that guided our study. Eighty-six typically developing adolescents and young adults having a sister or a brother with autism spectrum disorder were enrolled. Results indicated that the youngest age group (early adolescents) reported to engage more frequently in negative behaviors with their siblings with ASD than the two older age groups (middle adolescents and young adults). No significant differences were found among the three age groups regarding behaviors derived from attachment, caregiving and affiliative systems. Family satisfaction and age significantly predicted behaviors during sibling interactions. Suggestions on prevention and intervention programs were discussed in order to prevent parentification among typically developing siblings and decrease episodes of quarrels and overt conflicts between brothers and sisters with and without AS

Comparison of two-phase pipe flow in openFOAM with a mechanistic model

Two-phase pipe flow is a common occurrence in many industrial applications such as power generation and oil and gas transportation. Accurate prediction of liquid holdup and pressure drop is of vast importance to ensure effective design and operation of fluid transport systems. In this paper, a Computational Fluid Dynamics (CFD) study of a two-phase flow of air and water is performed using OpenFOAM. The two-phase solver, interFoam is used to identify flow patterns and generate values of liquid holdup and pressure drop, which are compared to results obtained from a two-phase mechanistic model developed by Petalas and Aziz (2002). A total of 60 simulations have been performed at three separate pipe inclinations of 0°, +10° and -10° respectively. A three dimensional, 0.052m diameter pipe of 4m length is used with the Shear Stress Transport (SST) k - turbulence model to solve the turbulent mixtures of air and water. Results show that the flow pattern behaviour and numerical values of liquid holdup and pressure drop compare reasonably well to the mechanistic model

An artificial fish swarm filter-based Method for constrained global optimization

Ana Maria A.C. Rocha, M. Fernanda P. Costa and Edite M.G.P. Fernandes, An Artificial Fish Swarm Filter-Based Method for Constrained Global Optimization, B. Murgante, O. Gervasi, S. Mirsa, N. Nedjah, A.M. Rocha, D. Taniar, B. Apduhan (Eds.), Lecture Notes in Computer Science, Part III, LNCS 7335, pp. 57–71, Springer, Heidelberg, 2012.An artificial fish swarm algorithm based on a filter methodology for trial solutions acceptance is analyzed for general constrained global optimization problems. The new method uses the filter set concept to accept, at each iteration, a population of trial solutions whenever they improve constraint violation or objective function, relative to the current solutions. The preliminary numerical experiments with a wellknown benchmark set of engineering design problems show the effectiveness of the proposed method.Fundação para a Ciência e a Tecnologia (FCT

Filter-based stochastic algorithm for global optimization

We propose the general Filter-based Stochastic Algorithm (FbSA) for the global optimization of nonconvex and nonsmooth constrained problems. Under certain conditions on the probability distributions that generate the sample points, almost sure convergence is proved. In order to optimize problems with computationally expensive black-box objective functions, we develop the FbSA-RBF algorithm based on the general FbSA and assisted by Radial Basis Function (RBF) surrogate models to approximate the objective function. At each iteration, the resulting algorithm constructs/updates a surrogate model of the objective function and generates trial points using a dynamic coordinate search strategy similar to the one used in the Dynamically Dimensioned Search method. To identify a promising best trial point, a non-dominance concept based on the values of the surrogate model and the constraint violation at the trial points is used. Theoretical results concerning the sufficient conditions for the almost surely convergence of the algorithm are presented. Preliminary numerical experiments show that the FbSA-RBF is competitive when compared with other known methods in the literature.The authors are grateful to the anonymous referees for their fruitful comments and suggestions.The first and second authors were partially supported by Brazilian Funds through CAPES andCNPq by Grants PDSE 99999.009400/2014-01 and 309303/2017-6. The research of the thirdand fourth authors were partially financed by Portuguese Funds through FCT (Fundação para Ciência e Tecnologia) within the Projects UIDB/00013/2020 and UIDP/00013/2020 of CMAT-UM and UIDB/00319/2020

On a smoothed penalty-based algorithm for global optimization

This paper presents a coercive smoothed penalty framework for nonsmooth and nonconvex constrained global optimization problems. The properties of the smoothed penalty function are derived. Convergence to an ε -global minimizer is proved. At each iteration k, the framework requires the ε(k) -global minimizer of a subproblem, where ε(k)→ε . We show that the subproblem may be solved by well-known stochastic metaheuristics, as well as by the artificial fish swarm (AFS) algorithm. In the limit, the AFS algorithm convergence to an ε(k) -global minimum of the real-valued smoothed penalty function is guaranteed with probability one, using the limiting behavior of Markov chains. In this context, we show that the transition probability of the Markov chain produced by the AFS algorithm, when generating a population where the best fitness is in the ε(k)-neighborhood of the global minimum, is one when this property holds in the current population, and is strictly bounded from zero when the property does not hold. Preliminary numerical experiments show that the presented penalty algorithm based on the coercive smoothed penalty gives very competitive results when compared with other penalty-based methods.The authors would like to thank two anonymous referees for their valuable comments and suggestions to improve the paper. This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundac¸ao para a Ci ˜ encia e Tecnologia within the projects UID/CEC/00319/2013 and ˆ UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio

Siblings of children with autism:The Siblings Embedded Systems Framework

Purpose of review: a range of interacting factors/mechanisms at the individual, family, and wider systems levels influences siblings living in families where one sibling has autism. We introduce the Sibling Embedded Systems Framework which aims to contextualise siblings’ experience and characterise the multiple and interacting factors influencing family and, in particular, sibling outcomes.Recent findings: findings from studies that have reported outcomes for siblings of children with autism are equivocal, ranging from negative impact, no difference, to positive experience. This is likely due to the complex nature of understanding the sibling experience. We focus on particular elements of the framework and review recent novel literature to help guide future directions for research and practice including the influence of culture, methodological considerations, and wider participatory methods.Summary: the Siblings Embedded System Framework can be used to understand interactive factors that affect sibling adjustment and to develop clinically, educationally and empirically based work that aims to enhance and support sibling adjustment, relationships, and well-being in families of children with autism.<br/

Theoretical and practical convergence of a self-adaptive penalty algorithm for constrained global optimization

This paper proposes a self-adaptive penalty function and presents a penalty-based algorithm for solving nonsmooth and nonconvex constrained optimization problems. We prove that the general constrained optimization problem is equivalent to a bound constrained problem in the sense that they have the same global solutions. The global minimizer of the penalty function subject to a set of bound constraints may be obtained by a population-based meta-heuristic. Further, a hybrid self-adaptive penalty firefly algorithm, with a local intensification search, is designed, and its convergence analysis is established. The numerical experiments and a comparison with other penalty-based approaches show the effectiveness of the new self-adaptive penalty algorithm in solving constrained global optimization problems.The authors would like to thank the referees, the Associate Editor and the Editor-in-Chief for their valuable comments and suggestions to improve the paper. This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Funda¸c˜ao para a Ciˆencia e Tecnologia within the projects UID/CEC/00319/2013 and UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio