Social networks of young Serbian migrants living in Malmö

The general aim of the research is to explore the formation, maintenance, and use of social networks or social capital, which young migrants of Serbian origin living in Malmö have at their disposal. The research is circumscribed to second-generation Serbian immigrants (i.e. ‘young migrants of Serbian origin’) in Malmö, who were born or have lived here since childhood, with both parents of Serbian origin (first generation (im)migrants), and who have obtained a university education or are on working their way towards achieving one. Malmö is considered to be a city with a significant number of Serbian migrants and with its very strong co-native community which is what makes this study interesting. The study is qualitative and it represents migrants’ perspectives gained through eight semi-structured, in-depth interviews where the sampling was based on the snowball technique, which seemed most useful. Research questions are focused on how second-generation migrants of Serbian origin in Malmö create and portray their social networks at local and transnational levels and how they use them, how they maintain them, and what significance and meaning they have to them. Theories mostly related with these subjects are Bourdieu’s Social capital theory, Migration network theory, and Transnational network theory, which also represent the theoretical grounds of this study. Results of the study show that all the participants maintain better or closer connections in Malmö with people of their or Balkan origin since they share common experiences. The contacts that they have are mostly formed through co-native associations, school, work, and neighborhoods. Further, the findings showed that most of the respondents did use their own or even their parents’ social capital for various matters in life. Additionally, findings and the analysis demonstrated that maintaining transnational relationships is meaningful for the young migrants of Serbian origin and that they mostly maintain those relationships through travel or via the Internet. Keywords: social networks, social capital, transnationalism, young migrants, Serbian origin, qualitative study

Improving adaptation and interpretability of a short-term traffic forecasting system

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyse the situation in a commercial real-time prediction system with its current problems and limitations. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. We test this predictive system in different real-work scenarios, and evaluate its performance integrating a multi-task learning paradigm for the sake of the traffic prediction task.Peer ReviewedPostprint (published version

Advanced traffic data for dynamic OD demand estimation: the state of the art and benchmark study

In this paper, the use of advanced traffic data is discussed to contribute to the ongoing debate about their applications in dynamic OD estimation. This is done by discussing the advantages and disadvantages of traffic data with support of the findings of a benchmark study. The benchmark framework is designed to assess the performance of the dynamic OD estimation methods using different traffic data. Results show that despite the use of traffic condition data to identify traffic regime, the use of unreliable prior OD demand has a strong influence on estimation ability. The greatest estimation occurs when the prior OD demand information is aligned with the real traffic state or omitted and using information from AVI measurements to establish accurate and meaningful values of OD demand. A common feature observed by methods in this paper indicates that advanced traffic data require more research attention and new techniques to turn them into usable information.Peer ReviewedPostprint (author's final draft

Improving adaptation and interpretability of a short-term traffic forecasting system

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyse the situation in a commercial real-time prediction system with its current problems and limitations. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. We test this predictive system in different real-work scenarios, and evaluate its performance integrating a multi-task learning paradigm for the sake of the traffic prediction task.Peer Reviewe

Improving adaptation and interpretability of a short-term traffic forecasting system

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyse the situation in a commercial real-time prediction system with its current problems and limitations. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. We test this predictive system in different real-work scenarios, and evaluate its performance integrating a multi-task learning paradigm for the sake of the traffic prediction task.Peer Reviewe

Advanced traffic data for dynamic OD demand estimation: the state of the art and benchmark study

In this paper, the use of advanced traffic data is discussed to contribute to the ongoing debate about their applications in dynamic OD estimation. This is done by discussing the advantages and disadvantages of traffic data with support of the findings of a benchmark study. The benchmark framework is designed to assess the performance of the dynamic OD estimation methods using different traffic data. Results show that despite the use of traffic condition data to identify traffic regime, the use of unreliable prior OD demand has a strong influence on estimation ability. The greatest estimation occurs when the prior OD demand information is aligned with the real traffic state or omitted and using information from AVI measurements to establish accurate and meaningful values of OD demand. A common feature observed by methods in this paper indicates that advanced traffic data require more research attention and new techniques to turn them into usable information.Peer Reviewe

Advanced traffic data for dynamic OD demand estimation: the state of the art and benchmark study

In this paper, the use of advanced traffic data is discussed to contribute to the ongoing debate about their applications in dynamic OD estimation. This is done by discussing the advantages and disadvantages of traffic data with support of the findings of a benchmark study. The benchmark framework is designed to assess the performance of the dynamic OD estimation methods using different traffic data. Results show that despite the use of traffic condition data to identify traffic regime, the use of unreliable prior OD demand has a strong influence on estimation ability. The greatest estimation occurs when the prior OD demand information is aligned with the real traffic state or omitted and using information from AVI measurements to establish accurate and meaningful values of OD demand. A common feature observed by methods in this paper indicates that advanced traffic data require more research attention and new techniques to turn them into usable information.Peer Reviewe

POSITIVE IMPACT OF PRESCRIBED PHYSICAL ACTIVITY ON SYMPTOMS OF SCHIZOPHRENIA: RANDOMIZED CLINICAL TRIAL

Background: The purpose of this study was to examine functional capacity of cardio-respiratory system in patients with schizophrenia, and to evaluate the effects of 12 weeks prescribed physical activity on aerobic capacity and symptoms of schizophrenia. Subjects and methods: Study involved 80 hospitalized patients with any of the subtypes of schizophrenia (42 men, 38 women). They were divided into two groups: exercise and control group, both with 40 patients. Maximal aerobic capacity (VO2 max) as an indicator of cardiovascular fitness has been obtained by cardiopulmonary stress test on a treadmill. Twelve weeks program of prescribed physical activity (45 minutes, four times per week) was made for every patient individually. Patients in exercise group practiced in training zone between 65 and 75% of their maximum heart rate (HR). Target HR was controlled by Polar F4 monitors. Symptoms of schizophrenia were measured by using Positive and Negative Symptoms Scale (PANSS). Results: Before the exercise program was introduced, measured VO2 max was significantly lower in patients with schizophrenia, than the expected average value in matched healthy subjects (p<0.001). After twelve weeks, patients in exercise group showed a significant increase of VO2max (p=0.002), and significantly higher level of VO2max compared to the control group (p=0.000). Significant differences were also observed on PANSS general psychopathology subscale (p=0.007) and on PANSS total score (p=0.001). The pharmacotherapy and exercise had influence on PANSS general psychopathology (p=0.002) and PANSS total score (p=0.001). Conclusions: Individuals with schizophrenia have lower levels of aerobic capacity compared to general population. Prescribed physical activity significantly improves aerobic capacity in people with schizophrenia and it is effective in amelioration of some psychiatric symptoms. Prescribed physical activity could be an effective adjunctive treatment for patients with schizophrenia, not only for prevention and treatment of comorbidities, but also having an impact on symptoms of schizophrenia