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  • 1.
    Afzal, Wasif
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Caporuscio, M.
    Linnaeus University, Sweden.
    Conboy, H.
    University of Massachusetts Amherst, MA, United States.
    Di Marco, A.
    University of l'Aquila, Italy.
    Duchien, D. L.
    University of Lille, France.
    Pérez, D.
    University of British Columbia, Canada.
    Seceleanu, C.
    Kyushu University, Japan.
    Shahbazian, A.
    University of California, Berkeley, CA, United States.
    Spalazzese, R.
    Microsoft, WA, United States.
    Tivoli, M.
    Florida State University, FL, United States.
    Vasilescu, B.
    University College Dublin and Lero, Ireland.
    Washizaki, H
    Mälardalens högskola.
    Weyns, D.
    University of Southern California, CA, United States.
    Pasquale, L.
    Malmö University, Sweden.
    Nistor, A.
    Malmö University, Sweden.
    Muşlu, K.
    Waseda University, Japan.
    Kamei, Y.
    Waseda University, Japan.
    Hanam, Q.
    Carnegie Mellon University, PA, United States.
    Ying, A. T. T.
    Katholieke Universiteit Leuven, Belgium.
    Program committee for icse 2018 posters track2018Ingår i: Proceedings / International Conference of Software Engineering, ISSN 0270-5257, E-ISSN 1558-1225, Vol. Part F137351Artikel i tidskrift (Refereegranskat)
  • 2.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Andersson, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Andersson, Tim
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Tomas Aparicio, Elena
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi. Malardalen Univ, Future Energy Ctr, Sch Business Soc & Engn, SE-72123 Vasteras, Sweden.;Malarenergi AB, Sjohagsvagen 3, S-72103 Vasteras, Sweden..
    Baaz, Hampus
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Bergström, Albert
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Bengtsson, Daniel
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Orisio, Daniele
    State Inst Higher Educ Guglielmo Marconi, Dalmine, Italy..
    Skvaril, Jan
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
    Zambrano, Jesus
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
    A Machine Learning Approach for Biomass Characterization2019Ingår i: INNOVATIVE SOLUTIONS FOR ENERGY TRANSITIONS / [ed] Yan, J Yang, HX Li, H Chen, X, ELSEVIER SCIENCE BV , 2019, s. 1279-1287Konferensbidrag (Refereegranskat)
    Abstract [en]

    The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SGi) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications.

  • 3.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Andersson, Peter
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Andersson, Tim
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Tomas Aparicio, Elena
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi. Mälarenergi AB, Sweden.
    Baaz, Hampus
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Barua, Shaibal
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. RISE SICS, Sweden.
    Bergström, Albert
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Bengtsson, Daniel
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Skvaril, Jan
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
    Zambrano, Jesus
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
    Real-time Biomass Characterization in Energy Conversion Processes using Near Infrared Spectroscopy: A Machine Learning Approach2019Ingår i: “Innovative Solutions for Energy Transitions” / [ed] Elsevier, 2019, Vol. 158, s. 1279-1287Konferensbidrag (Refereegranskat)
    Abstract [en]

    The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts: a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SG1) and second (SG2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications.

  • 4. Boulougari, Andromachi
    et al.
    Lundengård, Karl
    Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, Utbildningsvetenskap och Matematik.
    Rancic, Milica
    Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, Utbildningsvetenskap och Matematik.
    Silvestrov, Sergei
    Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, Utbildningsvetenskap och Matematik.
    Strass, Belinda
    University of Dar es salaam, Dar es salaam, Tanzania.
    Application of a power-exponential function-based model to mortality rates forecasting2019Ingår i: Communications in Statistics: Case Studies, Data Analysis and Applications, E-ISSN 2373-7484, Vol. 5, nr 1, s. 3-10Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    There are many models for mortality rates. A well-known problem that complicates modeling of human mortality rates is the “accident hump” occurring in early adulthood. Here, two models of mortality rate based on power-exponential functions are presented and compared to a few other models. The models will be fitted to known data of measured death rates from several different countries using numerical techniques for curve-fitting with the nonlinear least-squares method. The properties of the model with respect to forecasting with the Lee–Carter method will be discussed.

  • 5.
    Campana, Pietro Elia
    et al.
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi. Royal Institute of Technology, Stockholm, Sweden.
    Wästhage, Louise
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
    Nookuea, Worrada
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
    Tan, Y.
    Royal Institute of Technology, Stockholm, Sweden.
    Yan, Jinyue
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi. Royal Institute of Technology, Stockholm, Sweden.
    Optimization and assessment of floating and floating-tracking PV systems integrated in on- and off-grid hybrid energy systems2019Ingår i: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 177, s. 782-795Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Considering the targets of Thailand in terms of renewable energy exploitation and decarbonization of the shrimp farming sector, this work evaluates several scenarios for optimal integration of hybrid renewable energy systems into a representative shrimp farm. In particular, floating and floating-tracking PV systems are considered as alternatives for the exploitation of solar energy to meet the shrimp farm electricity demand. By developing a dynamic techno-economic simulation and optimization model, the following renewable energy systems have been evaluated: PV and wind based hybrid energy systems, off-grid and on-grid PV based hybrid energy systems, ground mounted and floating PV based hybrid energy systems, and floating and floating-tracking PV based hybrid energy systems. From a water-energy nexus viewpoint, floating PV systems have shown significant impacts on the reduction of evaporation losses, even if the energy savings for water pumping are moderate due to the low hydraulic head. Nevertheless, the study on the synergies between water for food and power production has highlighted that the integration of floating PV represents a key solution for reducing the environmental impacts of shrimp farming. For the selected location, the results have shown that PV systems represent the best renewable solution to be integrated into a hybrid energy system due to the abundance of solar energy resources as compared to the moderate wind resources. The integration of PV systems in off-grid configurations allows to reach high renewable reliabilities up to 40% by reducing the levelized cost of electricity. Higher renewable reliabilities can only be achieved by integrating energy storage solutions but leading to higher levelized cost of electricity. Although the floating-tracking PV systems show higher investment costs as compared to the reference floating PV systems, both solutions show similar competiveness for reliabilities up to 45% due to the higher electricity production of the floating-tracking PV systems. The higher electricity production from the floating-tracking PV systems leads to a better competitiveness for reliabilities higher than 90% due to lower capacity requirements for the storage systems.

  • 6.
    Causevic, Aida
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Lisova, Elena
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ashjaei, Seyed Mohammad Hossein
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ashgar, Syed Usman
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    On incorporating security parameters in service level agreements2019Ingår i: CLOSER 2019 - Proceedings of the 9th International Conference on Cloud Computing and Services Science, SciTePress , 2019, s. 48-57Konferensbidrag (Refereegranskat)
    Abstract [en]

    With development of cloud computing new ways for easy, on-demand, Internet-based access to computing resources have emerged. In such context a Service Level Agreement (SLA) enables contractual agreements between service providers and users. Given an SLA, service users are able to establish trust in that the service outcome corresponds to what they have demanded during the service negotiation process. However, an SLA provides a limited support outside of basic Quality of Service (QoS) parameters, especially when it comes to security. We find security as an important factor to be included in adjusting an SLA according to user defined objectives. Incorporating it in an SLA is challenging due to difficulty to provide complete and quantifiable metrics, thus we propose to focus on a systematic way of addressing security using the security process. In this paper we investigate ways in which security might be incorporated already in the service negotiation process and captured in an SLA. We propose a corresponding process to develop and maintain an SLA that considers both design-, and run-time. To demonstrate the approach we built upon the existing SLAC language and extend its syntax to support security. An example of a service being provided with security guarantees illustrates the concept.

  • 7.
    Dahlquist, Erik
    et al.
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
    Hellstrand, StefanMälardalens högskola, Akademin för ekonomi, samhälle och teknik, Industriell ekonomi och organisation. Nolby Ekostrategi, Kil, Sweden.
    Natural resources available today and in the future: How to perform change management for achieving a sustainable world2017Samlingsverk (redaktörskap) (Övrigt vetenskapligt)
    Abstract [en]

    This book focuses on providing an overview of all our available natural resources, considering the sustainability and potential for power generation of each. Energy efficiency prospects of each natural resource are examined in the context of society's key energy needs- Heating/cooling, Electric Power, Transportation and Industrial Production. Geography, climate and demographics are all discussed as key vectors impacting the comparative opportunities for self-sustenance around the globe. The authors provide in-depth coverage of renewable energy upscale and energy efficiency improvements in industry and society within a historical context, including a keen look at the variable effectiveness of different policy tools that have been used to support the transition away from unsustainable resource use. Finally, suggestions for more sustainable futures are provided, from improved policy measures, to new technological horizons in areas from offshore wind and marine energy to biogas and energy storage. 

  • 8.
    Gharehbaghi, Arash
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Babic, A.
    Department of Biomedical Engineering, Linköping University, Sweden.
    Structural Risk Evaluation of a Deep Neural Network and a Markov Model in Extracting Medical Information from Phonocardiography2018Ingår i: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 251, s. 157-160Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents a method for exploring structural risk of any artificial intelligence-based method in bioinformatics, the A-Test method. This method provides a way to not only quantitate the structural risk associated with a classification method, but provides a graphical representation to compare the learning capacity of different classification methods. Two different methods, Deep Time Growing Neural Network (DTGNN) and Hidden Markov Model (HMM), are selected as two classification methods for comparison. Time series of heart sound signals are employed as the case study where the classifiers are trained to learn the disease-related changes. Results showed that the DTGNN offers a superior performance both in terms of the capacity and the structural risk. The A-Test method can be especially employed in comparing the learning methods with small data size.

  • 9.
    Hansen, Eric M.
    et al.
    Mälardalens högskola, Akademin för hälsa, vård och välfärd, Hälsa och välfärd.
    Håkansson Eklund, Jakob
    Mälardalens högskola, Akademin för hälsa, vård och välfärd, Hälsa och välfärd.
    Hallen, Anna
    Mälardalens högskola, Akademin för hälsa, vård och välfärd, Hälsa och välfärd.
    Bjurhager, Carmen
    Mälardalens högskola, Akademin för hälsa, vård och välfärd, Hälsa och välfärd. Malardalen Univ, Psychol Studies, Vasteras, Sweden..
    Norrström, Emil
    Mälardalens högskola, Akademin för hälsa, vård och välfärd, Hälsa och välfärd.
    Viman, Adam
    Mälardalens högskola, Akademin för hälsa, vård och välfärd, Hälsa och välfärd.
    Stocks, Eric L.
    Univ Texas Tyler, Psychol, Tyler, USA..
    Does Feeling Empathy Lead to Compassion Fatigue or Compassion Satisfaction?: The Role of Time Perspective2018Ingår i: Journal of Psychology, ISSN 0022-3980, E-ISSN 1940-1019, Vol. 152, nr 8, s. 630-645Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Research has shown that feeling empathy sometimes leads to compassion fatigue and sometimes to compassion satisfaction. In three studies, participants recalled an instance when they felt empathy in order to assess the role time perspective plays in how empathizers perceive the consequences of empathy. Study 1 revealed that college students perceive empathy as having more negative consequences in the short term, but more positive consequences in the long term. Study 2 showed that service industry professionals perceive the consequences of feeling empathy for customers who felt bad as less negative, and the consequences of feeling empathy for people who felt good as less positive, in the long as opposed to the short term. Because Studies 1 and 2 confounded time perspective with event specificity a third study was conducted in which event specificity was held constant across time perspectives. The same pattern of results emerged. The results of these studies indicate that perceptions of the effects of feeling empathy, whether positive or negative, become less extreme over time. These findings shed light on the relation between empathy and compassion fatigue and satisfaction by suggesting that situations that initially are experienced as stressful can over time make the empathizer stronger.

  • 10.
    He, Fan
    et al.
    Mälardalens högskola.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Big Data Stream Learning Based on Hybridized Kalman Filter and Backpropagation Through Time Method2017Ingår i: 2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD) / [ed] Liu, Y Zhao, L Cai, G Xiao, G Li, KL Wang, L, IEEE , 2017, s. 2886-2891Konferensbidrag (Refereegranskat)
    Abstract [en]

    Most real-time control systems are often accompanied with various changes such as variations of working load and changes of the environment. Hence it is necessary to perform real-time process modeling so that the model can adjust itself in runtime to maintain high accuracy of states under control. This paper considers process model represented as a deep recurrent neural network. We propose a new hybridized learning method for online updating the weights of such recurrent neural networks by exploiting both fast convergence of Kalman filter and stable search of the Backpropagation through time algorithm. Several experiments were made to show that the proposed learning method has fast convergence, high accuracy and good adaptivity. It can not only achieve high modeling accuracy for a static process but also quickly adapt to changes of characteristics in a time -varying process.

  • 11.
    Johnstone, Leanne
    et al.
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Industriell ekonomi och organisation. Orebro Univ, Sch Business, Orebro, Sweden.
    Monteiro, Mariana Pio
    Escola Super Educ Coimbra, Coimbra, Portugal.
    Ferreira, Ines
    Escola Super Educ Coimbra, Coimbra, Portugal.
    Westerlund, Johanna
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Industriell ekonomi och organisation. Uppsala Univ, Sweden.
    Aalto, Roosa
    Mikkeli Univ Appl Sci, Dept Business Management, Mikkeli, Finland.
    Marttinen, Jenni
    Language ability and entrepreneurship education: Necessary skills for Europe's start-ups?2018Ingår i: Journal of International Entrepreneurship, ISSN 1570-7385, E-ISSN 1573-7349, Vol. 16, nr 3, s. 369-397Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Language ability and entrepreneurial education are seen as essential resources for start-ups operating in intensified landscapes of internationalisation and globalisation. Deemed as the necessary skills for corporate effectiveness vis-a-vis rivals, this paper responds to calls for increased understandings of cultural components as vital to entrepreneurship and the product of institutional forces. Thus, it explores (a) the impact language ability has on start-up expansion; (b) the perceptions of international relations as based on language ability as a tool for cross-cultural communication; and (c) the role of educational context from the entrepreneurs' perspective. Based on interviews from European online start-ups across three discrete contexts-Finland, Portugal and Sweden-it concludes that contextual trends regarding language and education are founded upon the cultural-cognitive and normative pillars of institutionalisation. Further, by combining actor-context perspectives, it poses that language ability and education are resources borne from the domestic environment which positively moderate the start-up's international success. Nevertheless, the notion of learnt entrepreneurship remains contested. Taken together, this study contributes by offering deeper insight into the role of context on entrepreneurial tendencies by combining resource and institutional perspectives.

  • 12.
    Lindh, Cecilia
    et al.
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Industriell ekonomi och organisation.
    Anastasiadou, Elena
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Industriell ekonomi och organisation.
    Vasse, Thibault
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Industriell ekonomi och organisation.
    Are consumers international?: A study of CSR, Cross-Border Shopping Commitment and Purchase Intent among Online Consumers2018Ingår i: Journal of global marketingArtikel i tidskrift (Refereegranskat)
    Abstract [en]

    The purpose of this study is to investigate purchase intent in online marketplaces as an international phenomenon. A profile of the international online consumer is established, taking into account factors such as indicators of socio-economic development of their home countries. A structural model is analyzed using LISREL, testing the importance of CSR, the propensity to buy from international online vendors and commitment with purchase intent as the dependent variable. A cross-national dataset of 804 respondents from 57 countries is analyzed, showing that CSR activities alone do not increase purchasing intent, but they do when mediated by commitment.

  • 13.
    Martinsson, Gunilla
    et al.
    Mälardalens högskola, Akademin för hälsa, vård och välfärd.
    Wiklund Gustin, Lena
    Mälardalens högskola, Akademin för hälsa, vård och välfärd.
    Fagerberg, Ingegerd
    Mälardalens högskola, Akademin för hälsa, vård och välfärd.
    Lindholm, Christina
    Karolinska Institutet.
    Mental disorders affect older persons in Sweden: a register-based study2011Ingår i: International Journal of Geriatric Psychiatry, ISSN 0885-6230, E-ISSN 1099-1166, Vol. 26, nr 3, s. 277-283Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    OBJECTIVE: The study aimed to estimate the prevalence of mental disorders based on pharmaceutical use among the old (age >/= 65) in Sweden for the years 2006-2008. METHODS: Data on the mental health of older persons were approximated on the basis of recommended prescriptions for pharmaceuticals, gathered from the Swedish Register on Prescribed Pharmaceuticals (SRPP). Each disorder (ICD-10, F20-F42, and F60-F61) was analyzed to identify associated recommended pharmaceuticals. Anatomical Therapeutic Chemical Classification codes were applied. The data covered 188 024 individuals who received 2 013 079 prescriptions for pharmaceuticals for mental disorders during a 3-year period. Persons with pharmaceuticals for dementia disorders were excluded from the calculations of the prevalence of mental disorders. RESULTS: The prevalence of mental disorders among the old in Sweden, measured on the basis of pharmaceutical use, was 6.6% in 2006, 2007, and 2008, respectively. Men constituted one-third of cases and women two-thirds. Prevalence was lowest in the age group 65-69 and increased subsequently with age. CONCLUSIONS: This fundamental register-based study included a great number of older persons and shows that mental disorders affect every fifteenth older person in Sweden. The prevalence of mental disorders increases with increasing age. The results highlight the extent of mental disorders among older persons, which is important to know when planning care for these patients. This study, by investigating a large population, provides a solid basis for general planning as well as for future mental disorder research.

  • 14.
    Mustafic, Faruk
    et al.
    Mälardalens högskola.
    Herera, Francisco
    University of Granada, Spain.
    Xiong, Ning
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Gallego, Sergio
    University of Granada, Spain.
    MapReduce distributed highly random fuzzy forest for noisy big data2017Ingår i: 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery 2017 ICNC-FSKD-2017, 2017, s. 560-567Konferensbidrag (Refereegranskat)
    Abstract [en]

    Nowadays the amounts of data available to us have the ever larger growth trend. On the other hand such data often contain noise. We call them noisy Big Data. There is an increasing need for learning methods that can handle such noisy Big Data for classification tasks. In this paper we propose a highly random fuzzy forest algorithm for learning an ensemble of fuzzy decision trees from a big data set contaminated with attribute noise. We also present the distributed version of the proposed learning algorithm implemented in the MapReduce framework. Experiment results have demonstrated that the proposed algorithm is faster and more accurate than the state-of-the-art approach particularly in the presence of attribute noise. 

  • 15.
    Saravanan, Vijayalakshmi
    et al.
    Mälardalens högskola.
    Alagan, A.
    Ryerson University, Canada.
    Woungang, I.
    Ryerson University, Canada.
    Big data in massive parallel processing: A multi-core processors perspective2018Ingår i: Handbook of Research on Big Data Storage and Visualization Techniques, IGI Global , 2018, s. 276-302Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    With the advent of novel wireless technologies and Cloud Computing, large volumes of data are being produced from various heterogeneous devices such as mobile phones, credit cards, and computers. Managing this data has become the de-facto challenge in the current Information Systems. According to Moore's law, processor speeds are no longer doubling, the processing power also continuing to grow rapidly which leads to a new scientific data intensive problem in every field, especially Big Data domain. The revolution of Big Data lies in the improved statistical analysis and computational power depend on its processing speed. Hence, the need to put massively multi-core systems on the job is vital in order to overcome the physical limits of complexity and speed. It also arises with many challenges such as difficulties in capturing massive applications, data storage, and analysis. This chapter discusses some of the Big Data architectural challenges in the perspective of multi-core processors.

  • 16.
    Weishaupt, Holger
    et al.
    Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, Utbildningsvetenskap och Matematik. Uppsala University, Sweden.
    Johansson, Patrik
    Engström, Christopher
    Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, Utbildningsvetenskap och Matematik.
    Nelander, Sven
    Silvestrov, Sergei
    Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, Utbildningsvetenskap och Matematik.
    Swartling, Fredrik J.
    Prediction of high centrality nodes from reverse-engineered transcriptional regulator networks2016Ingår i: Proocedings of the 4th Stochastic Modeling Techniques and Data Analysis International Conference with Demographics Workshop / [ed] Christos H Skiadas, 2016, s. 517-531Konferensbidrag (Refereegranskat)
    Abstract [en]

    The prioritization of genes based on their centrality in biological networkshas emerged as a promising technique for the prediction of phenotype related genes.A number of methods have been developed to derive one such type of network, i.e.transcriptional regulatory networks, from expression data. In order to reliably prioritizegenes from such networks, it is crucial to investigate how well the inferencemethods reconstruct the centralities that exist in the true biological system. We haverecently reported that the correlation of centrality rankings between reference andinferred networks is only modest when using an unbiased inference approach. In thisstudy we extend on these results and demonstrate that the correlation remains modestalso when using a biased inference utilizing a priori information about transcriptionfactors. However, we show further that despite this lack of a strong correlation, theinferred networks still allow a signicant prediction of genes with high centralities inthe reference networks.

  • 17.
    Weishaupt, Holger
    et al.
    Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, Utbildningsvetenskap och Matematik. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University.
    Čančer, M.
    Uppsala University, Sweden.
    Engström, Christopher
    Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, Utbildningsvetenskap och Matematik.
    Silvestrov, Sergei
    Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, Utbildningsvetenskap och Matematik.
    Swartling, F. J.
    Uppsala University, Sweden.
    Comparing the landcapes of common retroviral insertion sites across tumor models2017Ingår i: AIP Conference Proceedings, Volume 1798 / [ed] Seenith Sivasundaram, American Institute of Physics (AIP), 2017, Vol. 1798, s. 020173-1-020173-9, artikel-id 020173Konferensbidrag (Refereegranskat)
    Abstract [en]

    Retroviral tagging represents an important technique, which allows researchers to screen for candidate cancer genes. The technique is based on the integration of retroviral sequences into the genome of a host organism, which might then lead to the artificial inhibition or expression of proximal genetic elements. The identification of potential cancer genes in this framework involves the detection of genomic regions (common insertion sites; CIS) which contain a number of such viral integration sites that is greater than expected by chance. During the last two decades, a number of different methods have been discussed for the identification of such loci and the respective techniques have been applied to a variety of different retroviruses and/or tumor models. We have previously established a retrovirus driven brain tumor model and reported the CISs which were found based on a Monte Carlo statistics derived detection paradigm. In this study, we consider a recently proposed alternative graph theory based method for identifying CISs and compare the resulting CIS landscape in our brain tumor dataset to those obtained when using the Monte Carlo approach. Finally, we also employ the graph-based method to compare the CIS landscape in our brain tumor model with those of other published retroviral tumor models. 

  • 18.
    Weishaupt, Hrafn Holger
    Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, Utbildningsvetenskap och Matematik.
    Graph theory based approaches for gene prioritization in biological networks: Application to cancer gene detection in medulloblastoma2019Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Networks provide an intuitive and highly adaptable means to model relationships between objects. When translated to mathematical graphs, they become further amenable to a plethora of mathematical operations that allow a detailed study of the underlying relational data. Thus, it is not surprising that networks have evolved to a predominant method for analyzing such data in a vast variety of research fields. However, with increasing complexity of the studied problems, application of network modeling also becomes more challenging. Specifically, given a process to be studied, (i) which interactions are important and how can they be modeled, (ii) how can relationships be inferred from complex and potentially noisy data, and (iii) which methods should be used to test hypotheses or answer the relevant questions? This thesis explores the concept and challenges of network analysis in the context of a well-defined application area, i.e. the prediction of cancer genes from biological networks, with an application to medulloblastoma research.

    Medulloblastoma represents the most common malignant brain tumor in children. Currently about 70% of treated patients survive, but they often suffer from permanent cognitive sequelae. Medulloblastoma has previously been shown to harbor at least four distinct molecular subgroups. Related studies have also greatly advanced our understanding of the genetic aberrations associated with MB subgroups. However, to translate such findings to novel and improved therapy options, further insights are required into how the dysregulated genes interact with the rest of the cellular system, how such a cross-talk can drive tumor development, and how the arising tumorigenic processes can be targeted by drugs. Establishing such understanding requires investigations that can address biological processes at a more system-wide level, a task that can be approached through the study of cellular systems as mathematical networks of molecular interactions.

    This thesis discusses the identification of cancer genes from a network perspective, where specific focus is placed on one particular type of network, i.e. so called gene regulatory networks that model relationships between genes at the expression level. The thesis outlines the bridge between biological and mathematical network concepts. Specifically, the computational challenge of inferring such networks from molecular data is presented. Mathematical approaches for analyzing these networks are outlined and it is explored how such methods might be affected by network inference. Further focus is placed on dealing with the challenges of establishing a suitable gene expression dataset for network inference in MB. Finally, the thesis is concluded with an application of various network approaches in a hypothesis-driven study in MB, in which various novel candidate genes were prioritized.  

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  • 19.
    Yan, Jinyue
    et al.
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi. KTH Royal Institute of Technology.
    Wang, C.
    Tianjin University, China.
    Yu, J.
    State Grid Tianjin Electric Power Co., China.
    Jia, H.
    Tianjin University, China.
    Wu, J.
    Cardiff University, United Kingdom.
    Xu, T.
    Tianjin University, China.
    Zhang, Yang
    Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, Framtidens energi.
    Renewable Energy Integration with Mini/Microgrid2018Ingår i: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 145, s. 1-2Artikel i tidskrift (Refereegranskat)
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