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  • Paladi, Nicolae
    et al.
    Lund University and RISE.
    Svenningsson, Jakob
    RISE.
    Medina, Jorge
    New Jersey Institute of Technology.
    Arlos, Patrik
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Protecting OpenFlow Flow Tables with Intel SGX2019In: Proceedings of the ACM SIGCOMM 2019 Conference Posters and Demos, Beijing: ACM Publications, 2019, p. 146-147Conference paper (Refereed)
  • Nordahl, Christian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Netz Persson, Marie
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Monitoring Household Electricity Consumption Behaviour for Mining Changes2019Conference paper (Refereed)
    Abstract [en]

    In this paper, we present an ongoing work on using a household electricity consumption behavior model for recognizing changes in sleep patterns. The work is inspired by recent studies in neuroscience revealing an association between dementia and sleep disorders and more particularly, supporting the hypothesis that insomnia may be a predictor for dementia in older adults. Our approach initially creates a clustering model of normal electricity consumption behavior of the household by using historical data. Then we build a new clustering model on a new set of electricity consumption data collected over a predefined time period and compare the existing model with the built new electricity consumption behavior model. If a discrepancy between the two clustering models is discovered a further analysis of the current electricity consumption behavior is conducted in order to investigate whether this discrepancy is associated with alterations in the resident’s sleep patterns. The approach is studied and initially evaluated on electricity consumption data collected from a single randomly selected anonymous household. The obtained results show that our approach is robust to mining changes in the resident daily routines by monitoring and analyzing their electricity consumption behavior model.

  • García Martín, Eva
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Rodrigues, Crefeda Faviola
    University of Manchester, GBR.
    Riley, Graham
    University of Manchester, GBR.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Estimation of energy consumption in machine learning2019In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, p. 75-88Article in journal (Refereed)
    Abstract [en]

    Energy consumption has been widely studied in the computer architecture field for decades. While the adoption of energy as a metric in machine learning is emerging, the majority of research is still primarily focused on obtaining high levels of accuracy without any computational constraint. We believe that one of the reasons for this lack of interest is due to their lack of familiarity with approaches to evaluate energy consumption. To address this challenge, we present a review of the different approaches to estimate energy consumption in general and machine learning applications in particular. Our goal is to provide useful guidelines to the machine learning community giving them the fundamental knowledge to use and build specific energy estimation methods for machine learning algorithms. We also present the latest software tools that give energy estimation values, together with two use cases that enhance the study of energy consumption in machine learning.

  • Minniti, Maria
    et al.
    Martin J. Whitman School of Management, USA.
    Andersson, Martin
    Blekinge Institute of Technology, Faculty of Engineering, Department of Industrial Economics.
    Braunerhjelm, Pontus
    KTH, SWE.
    Delmar, Frederic
    EMLYON Business School, FRA.
    Rickne, Annika
    Linköping University, SWE.
    Thorburn, Karin
    Wharton School at University of Pennsylvania, USA.
    Wennberg, Karl
    Linköping University, SWE.
    Stenkula, Mikael
    Research Institute of Industrial Economics (IFN), SWE.
    Boyan Jovanovic: recipient of the 2019 Global Award for Entrepreneurship Research2019In: Small Business Economics, ISSN 0921-898X, E-ISSN 1573-0913, Vol. 53, no 3, p. 547-553Article in journal (Refereed)
    Abstract [en]

    The 2019 Global Award for Entrepreneurship Research has been awarded to Professor Boyan Jovanovic at New York University in the USA. Boyan Jovanovic has developed pioneering research that advances our understanding of the competitive dynamics between incumbent firms and new entrants, entrepreneurial learning and selection processes, and the importance of entrepreneurship for the economy. Key perspectives in his research are that the entrepreneur makes employment choices based on the comparative advantage of his or her skills and that entrepreneurial firms are vehicles of technological change and knowledge diffusion that influence industry dynamics and, in turn, economic growth. © 2019, The Author(s).

  • Östling, Dan
    et al.
    Sandvik Coromant Trondheim AS, NOR.
    Magnevall, Martin
    Modelling the dynamics of a large damped boring bar in a lathe.2019In: Procedia CIRP, Elsevier B.V. , 2019, Vol. 82, p. 285-289Conference paper (Refereed)
    Abstract [en]

    Boring bars with tuned mass dampers have a passive damper tuned with respect to the frequency of the first bending mode of the tool. When the tool is clamped into the machine tool there is a stiffness loss that lowers the natural frequency of the bar compared to ideal clamping conditions. For large tools the difference can be more than 35%, depending on clamping structure, tool size and overhang. In this paper we investigate a simple two-degree-of-freedom model for the tool-machine interaction consisting of a bending mode coupled with a rotational stiff mode. The model gives good insight into the system behavior and fits well with measurements. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of The 17th CIRP Conference on Modelling of Machining Operations

  • Moraes, Ana Luiza Dallora
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Kvist, Ola
    KI, SWE.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Ruiz, Sandra
    KI, SWE.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis2019In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 7, article id e0220242Article, review/survey (Refereed)
    Abstract [en]

    Background The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment for the diagnosis of pediatric endocrinology, orthodontics and orthopedic disorders, and legal environment in what concerns if an individual is a minor or not when there is a lack of documents. Being a time-consuming activity that can be prone to inter- and intra-rater variability, the use of methods which can automate it, like Machine Learning techniques, is of value. Objective The goal of this paper is to present the state of the art evidence, trends and gaps in the research related to bone age assessment studies that make use of Machine Learning techniques. Method A systematic literature review was carried out, starting with the writing of the protocol, followed by searches on three databases: Pubmed, Scopus and Web of Science to identify the relevant evidence related to bone age assessment using Machine Learning techniques. One round of backward snowballing was performed to find additional studies. A quality assessment was performed on the selected studies to check for bias and low quality studies, which were removed. Data was extracted from the included studies to build summary tables. Lastly, a meta-analysis was performed on the performances of the selected studies. Results 26 studies constituted the final set of included studies. Most of them proposed automatic systems for bone age assessment and investigated methods for bone age assessment based on hand and wrist radiographs. The samples used in the studies were mostly comprehensive or bordered the age of 18, and the data origin was in most of cases from United States and West Europe. Few studies explored ethnic differences. Conclusions There is a clear focus of the research on bone age assessment methods based on radiographs whilst other types of medical imaging without radiation exposure (e.g. magnetic resonance imaging) are not much explored in the literature. Also, socioeconomic and other aspects that could influence in bone age were not addressed in the literature. Finally, studies that make use of more than one region of interest for bone age assessment are scarce. Copyright: © 2019 Dallora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

  • Christiansen, Line
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Lindberg, Catharina
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Skär, Lisa
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Health-related quality of life and related factors among a sample of older people with cognitive impairment2019In: Nursing Open, E-ISSN 2054-1058, Vol. 6, no 3, p. 849-859Article in journal (Refereed)
    Abstract [en]

    Aim: This study aimed to identify factors affecting health-related quality of life (HRQoL) of older adults with cognitive impairment and to describe the association of these factors with different components of HRQoL. Design: A cross-sectional, descriptive research design was used. Methods: Data were collected from 247 individuals aged 60 years and older from a Swedish longitudinal cohort study. The Short-Form Health Survey-12 (SF-12) and EuroQol (EQ-5D) were used to assess HRQoL. The data were analysed using descriptive and comparative statistics. Results: The present study identified several factors that influenced HRQoL of older adults with cognitive impairment. The results of a multiple logistic regression analysis revealed that the following factors were associated with physical and mental HRQoL: dependency in activities of daily living (ADL), receiving informal care and feelings of loneliness and pain. © 2019 The Authors. Nursing Open published by John Wiley & Sons Ltd.

  • Rennemark, Mikael
    et al.
    Linnaeus Univ, SWE.
    Jogréus, Claes
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Elmstahl, Solve
    Lund Univ, SWE.
    Weimer, Anna-Karin
    Karolinska Inst, SWE.
    Wimo, Anders
    Karolinska Inst, SWE.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Relationships Between Frequency of Moderate Physical Activity and Longevity: An 11-Year Follow-up Study2018In: Gerontology and geriatric medicine, E-ISSN 2333-7214, Vol. 4, article id 2333721418786565Article in journal (Refereed)
    Abstract [en]

    Objectives: Moderate physical activity gains survival. There are, however, several variables that may affect this relationship. In this study, the relationship between moderate physical activity and longevity was investigated, taking into account age, gender, smoking habits, cohabitation status, body mass index, leg strength and balance, education level and cognitive function. Method: A sample of 8,456 individuals aged 60 to 96 years, representative of the Swedish population, was included. Participants were followed from 2004 to 2015. Cox regression analyses were used to investigate the predictive value of physical activity on longevity. Results: Participants still alive in the follow-up measure were more physically active on a moderate level. Being active 2 to 3 times a week or more was related to a 28% lower risk of not being alive at the follow-up measure. Discussion: The low frequency of physical activity, necessary for survival benefits should be considered in public health programs.

  • Lännström, Daniel
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Chain Conditions for Epsilon-Strongly Graded Rings with Applications to Leavitt Path Algebras2019In: Algebras and Representation Theory, ISSN 1386-923X, E-ISSN 1572-9079Article in journal (Refereed)
    Abstract [en]

    Let G be a group with neutral element e and let S=⊕g∈GSg be a G-graded ring. A necessary condition for S to be noetherian is that the principal component Se is noetherian. The following partial converse is well-known: If S is strongly-graded and G is a polycyclic-by-finite group, then Se being noetherian implies that S is noetherian. We will generalize the noetherianity result to the recently introduced class of epsilon-strongly graded rings. We will also provide results on the artinianity of epsilon-strongly graded rings. As our main application we obtain characterizations of noetherian and artinian Leavitt path algebras with coefficients in a general unital ring. This extends a recent characterization by Steinberg for Leavitt path algebras with coefficients in a commutative unital ring and previous characterizations by Abrams, Aranda Pino and Siles Molina for Leavitt path algebras with coefficients in a field. Secondly, we obtain characterizations of noetherian and artinian unital partial crossed products. © 2019, The Author(s).

  • Lindberg, Terese
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Wimo, Anders
    Karolinska Inst, SWE.
    Elmstahl, Solve
    Lund Univ, SWE.
    Qiu, Chengxuan
    Karolinska Inst, SWE.
    Bohman, Doris
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Prevalence and Incidence of Atrial Fibrillation and Other Arrhythmias in the General Older Population: Findings From the Swedish National Study on Aging and Care2019In: Gerontology and geriatric medicine, E-ISSN 2333-7214, Vol. 5Article in journal (Refereed)
    Abstract [en]

    Aim: To study the prevalence and cumulative incidence of arrhythmias in the general population of adults aged 60 and older over a 6-year period. Study Design and Setting: Data were taken from the Swedish National Study on Aging and Care (SNAC), a national, longitudinal, multidisciplinary study of the general elderly population (defined as 60 years of age or older). A 12-lead resting electrocardiography (ECG) was performed at baseline and 6-year follow-up. Results: The baseline prevalence of atrial fibrillation (AF) was 4.9% (95% confidence interval [CI] = [4.5%, 5.5%]), and other arrhythmias including ventricular premature complexes (VPCs), supraventricular tachycardia (SVT), and supraventricular extrasystole (SVES) were seen in 8.4% (7.7%, 9.0%) of the population. A first- or second-degree atrioventricular (AV) block was found in 7.1% of the population (95% CI = [6.5%, 7.7%]), and there were no significant differences between men and women in baseline arrhythmia prevalence. The 6-year cumulative incidence of AF was 4.1% (95% CI = [3.5%, 4.9%]), or 6.9/1,000 person-years (py; 95% CI = [5.7, 8.0]). The incidence of AF, other arrhythmias, AV block, and pacemaker-induced rhythm was significantly higher in men in all cohorts except for the oldest. Conclusion: Our data highlight the prevalence and incidence of arrhythmias, which rapidly increase with advancing age in the general population.

  • Linde, peter
    Blekinge Institute of Technology, The Library.
    best library practice2019Report (Other academic)
    Abstract [en]

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  • Pettersson, Mats
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Dahl, Mattias
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Vu, Viet Thuy
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Javadi, Mohammad Saleh
    Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
    Future Satellite and Drone Monitoring of the Baltic‐Adriatic Corridor,Harbors, and Motorways of the Sea2019Report (Other academic)