Paulo Ferreira

• Helmy, Maged; Truong, Trung Tuyen; Dykky, Anastasiya; Ferreira, Paulo & Jul, Eric Bartley (2022). CapillaryNet: An automated system to quantify skin capillary density and red blood cell velocity from handheld vital microscopy. Artificial Intelligence in Medicine. ISSN 0933-3657. 127. doi: 10.1016/j.artmed.2022.102287. Fulltekst i vitenarkiv
• Kamalian, Mahdieh & Ferreira, Paulo (2022). A survey on local transport mode detection on the edge of the network. Applied intelligence (Boston). ISSN 0924-669X. doi: 10.1007/s10489-022-03214-y.
• Tavakolisomeh, Sanaz; Bruno, Rodrigo & Ferreira, Paulo (2021). Selecting a GC for Java Applications. Norsk Informatikkonferanse (NIK). Fulltekst i vitenarkiv
• Matre, Dagfinn; Christensen, Jan Olav; Mork, Paul Jarle; Ferreira, Paulo; Sand, Trond & Nilsen, Kristian Bernhard (2021). Shift work, inflammation and musculoskeletal pain. The HUNT Study. Occupational Medicine. ISSN 0962-7480. 71(9), s. 422–427. doi: 10.1093/occmed/kqab133. Vis sammendrag

  Background: Studies have indicated that shift work, in particular night work, is associated with chronic musculoskeletal pain but the mechanisms are unclear. It has been suggested that sleep disturbance, a common complaint among shift and night workers, may induce low-grade inflammation as well as heightened pain sensitivity. Aims: Firstly, this study was aimed to examine the cross-sectional associations between shift work, C-reactive protein (CRP) level and chronic musculoskeletal pain, and secondly, to analyse CRP as a mediator between shift work and chronic musculoskeletal pain. Methods: The study included 23 223 vocationally active women and men who participated in the HUNT4 Survey of the Trøndelag Health Study (HUNT). Information was collected by questionnaires, interviews, biological samples and clinical examination. Results: Regression analyses adjusted for sex, age and education revealed significant associations between shift work and odds of any chronic musculoskeletal pain (odd ratio [OR] 1.11, 95% confidence interval [CI] 1.04-1.19), between shift work and CRP level (OR 1.09, 95% CI 1.03-1.16) and between CRP level 3.00-10 mg/L and any chronic musculoskeletal pain (OR 1.38, 95% CI 1.27-1.51). Shift work and CRP were also associated with number of chronic pain sites. Mediation analysis indicated that shift work was indirectly associated with any chronic musculoskeletal pain through CRP (OR 1.03, 95% CI 1.01-1.06). Conclusions: The results support the hypothesis that shift work is associated with chronic musculoskeletal pain, and that systemic inflammation may be a biological mechanism linking shift work to chronic pain.

• Ferreira, Paulo; Zavgorodnii, Constantin & Veiga, Luìs (2020). edgeTrans - Edge transport mode detection. Pervasive and Mobile Computing. ISSN 1574-1192. 69. doi: 10.1016/j.pmcj.2020.101268. Fulltekst i vitenarkiv
• Bruno, Rodrigo; Patricio, Duarte; Simão, José; Veiga, Luìs & Ferreira, Paulo (2019). Runtime Object Lifetime Profiler for Latency Sensitive Big Data Applications, EuroSys '19: Fourteenth EuroSys Conference 2019. Association for Computing Machinery (ACM). ISSN 978-1-4503-6281-8. doi: 10.1145/3302424.3303988.
• Ferreira, Paulo; Zabolotny, Andriy & Barreto, João (2019). Bicycle Mode Activity Detection with Bluetooth Low Energy Beacons. I Gkoulalas-Divanis, Aris; Marchetti, Mirco & Avresky, Dimiter R. (Red.), 2019 IEEE 18th International Symposium on Network Computing and Applications (NCA). IEEE. ISSN 978-1-7281-2522-0. doi: 10.1109/NCA.2019.8935030.

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• Ferreira, Paulo (2018). Proceedings of the 19th International Middleware Conference. Association for Computing Machinery (ACM). ISBN 978-1-4503-5702-9. 299 s.

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• Kamalian, Mahdieh & Ferreira, Paulo (2022). FogTMDetector - Fog Based Transport Mode Detection using Smartphones. Vis sammendrag

  A user’s transport mode (e.g., walk, car, etc.) can be detected by using a smartphone. Such devices exist in a great number with enough computation power and sensors to run a classifier (i.e., for transport mode detection). Using a smartphone in a fog environment ensures low latency, high generalization, high accuracy, and low battery consumption. We propose a fog- based real-time (at human time scale) transport mode detection, called FogTMDetector; it consists of a Random Forest classifier trained with magnetometer, accelerometer, and GPS data. The overall accuracy achieved by our system is 93% when detecting 8 different modes (i.e., stationary, walk, bicycle, car, bus, train, tram, and subway). We compared FogTMDetector with another recent system (called EdgeTrans). The comparison results suggest that our solution achieves 10% higher motorized accuracy (i.e., 94.4%) with more fine-grained motorized transport modes (i.e., subway, tram, etc.) thanks to the magnetometer sensor readings. FogTMDetector uses a low sampling rate (1Hz) for logging accelerometer and magnetometer and (every 10 seconds) for GPS to ensure low battery consumption. FogTMDetector is also generalizable as it is robust against variation of users and smartphone positions.

• Ferreira, Paulo (2018). Foreword for the 19th ACM/IFIP/USENIX Middleware Conference. I Ferreira, Paulo (Red.), Proceedings of the 19th International Middleware Conference. Association for Computing Machinery (ACM). ISSN 978-1-4503-5702-9. s. 1–3.
• Ferreira, Paulo & Bruno, Rodrigo (2018). Live Migration and Garbage Collection for Big Data Java Applications.

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