Services trade contribution on global income generation (2000 - 2014)

This paper investigates the contribution of services trade to the variation of global income generation for the period of 2000 to 2014, applying a structural decomposition analysis in a global multi-regional input-output framework. We disentangle impacts of determinants of this variation for 56 sectors (of which, 29 are services) on a global level and on seven world regions, considering primary inputs, technology, components of final demand (private consumption, government expenditures and investment), trade and trade structure of both intermediate inputs and final products as drivers. Empirical findings suggest that overall, intermediate trade of services contributed to 5,38% of global income generation while final trade of services to 4,56% for the 15 years-period analyzed. This significant contribution seems to be explained mainly through the increase of demand of services as the negative effect of the structure of trade suggests that per unit of services traded, the value–added generated decreased over this period. At the sectoral level, wholesale trade, the financial sector, administrative and support services, legal and accounting services along with land transport appear to be the most important contributors of the services sectors through trade. Despite having northern European countries along with the BRIIC countries and the northern American ones as the most important contributors through services trade, when looking at the share of contribution of services trade of different groups relative to only their own total contribution, the eastern European countries is the group that makes it to the top

Mobile Transaction Supports for DBMS

National audienceIn recent years data management in mobile environments has generated a great interest. Several proposals concerning mobile transactions have been done. However, it is very difficult to have an overview of all these approaches. In this paper we analyze and compare several contributions on mobile transactions and introduce our ongoing research: the design and implementation of a Mobile Transaction Service. The focus of our study is on execution models, the manner ACID properties are provided and the way geographical movements of hosts (during transaction executions) is supported

Adaptable Mobile Transactions and Environment Awareness

National audienceMobile environments are characterized by high variability (e.g. variable bandwidth, disconnections, different communication prices) as well as by limited mobile host resources. Such characteristics lead to high rates of transaction failures and unpredictable execution costs. This paper introduces an Adaptable Mobile Transaction model (AMT) that allows defining transactions with several execution alternatives associated to a particular context. The principal goal is to adapt transaction execution to context variations. An analytical study shows that using AMTs increases commit probabilities and that it is possible to choose the way transactions will be executed according to their costs. In addition, the middleware TransMobi is proposed. It manages environment awareness and implements the AMT model with suitable protocols.Les environnements mobiles sont caractérisés par une grande variabilité (bande passante variable, déconnexions, prix de communication différents, etc.) ainsi que par des uni-tés mobiles à ressources limitées. Ces caractéristiques entraînent un nombre important de défaillances transactionnels et des coûts d'exécution imprévus. Cet article introduit un modèle de transactions mobiles adaptables (AMT) permettant de définir des transactions avec plusieurs alternatives d'exécution. Le principal objectif est d'adapter l'exécution des transactions aux variations du contexte. Une étude analytique montre que les AMT augmentent la probabilité de validation et qu'il est possible de choisir le type d'exécution en fonction de son coût. Nous proposons également l'intergiciel TransMobi gérant la perception de l'environnement et implantant le modèle AMT à l'aide de protocoles appropriés

CrowdED: Guideline for Optimal Crowdsourcing Experimental Design

Crowdsourcing involves the creating of HITs (Human Intelligent Tasks), submitting them to a crowdsourcing platform and providing a monetary reward for each HIT. One of the advantages of using crowdsourcing is that the tasks can be highly parallelized, that is, the work is performed by a high number of workers in a decentralized setting. The design also offers a means to cross-check the accuracy of the answers by assigning each task to more than one person and thus relying on majority consensus as well as reward the workers according to their performance and productivity. Since each worker is paid per task, the costs can significantly increase, irrespective of the overall accuracy of the results. Thus, one important question when designing such crowdsourcing tasks that arise is how many workers to employ and how many tasks to assign to each worker when dealing with large amounts of tasks. That is, the main research questions we aim to answer is: 'Can we a-priori estimate optimal workers and tasks' assignment to obtain maximum accuracy on all tasks?'. Thus, we introduce a two-staged statistical guideline, CrowdED, for optimal crowdsourcing experimental design in order to a-priori estimate optimal workers and tasks' assignment to obtain maximum accuracy on all tasks. We describe the algorithm and present preliminary results and discussions. We implement the algorithm in Python and make it openly available on Github, provide a Jupyter Notebook and a R Shiny app for users to re-use, interact and apply in their own crowdsourcing experiments

A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles

[EN] Electricity consumption patterns reveal energy demand behaviors and enable strategY implementation to increase efficiency using monitoring systems. However, incorrect patterns can be obtained when the time-series components of electricity demand are not considered. Hence, this research proposes a new method for handling time-series components that significantly improves the ability to obtain patterns and detect anomalies in electrical consumption profiles. Patterns are found using the proposed method and two widespread methods for handling the time-series components, in order to compare the results. Through this study, the conditions that electricity demand data must meet for making the time-series analysis useful are established. Finally, one year of real electricity consumption is analyzed for two different cases to evaluate the effect of time-series treatment in the detection of anomalies. The proposed method differentiates between periods of high or low energy demand, identifying contextual anomalies. The results indicate that it is possible to reduce time and effort involved in data analysis, and improve the reliability of monitoring systems, without adding complex procedures.Serrano-Guerrero, X.; Escrivá-Escrivá, G.; Luna-Romero, S.; Clairand, J. (2020). A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles. Energies. 13(5):1-23. https://doi.org/10.3390/en13051046S123135Hong, T., Yang, L., Hill, D., & Feng, W. (2014). Data and analytics to inform energy retrofit of high performance buildings. Applied Energy, 126, 90-106. doi:10.1016/j.apenergy.2014.03.052Ogunjuyigbe, A. S. O., Ayodele, T. R., & Akinola, O. A. (2017). User satisfaction-induced demand side load management in residential buildings with user budget constraint. Applied Energy, 187, 352-366. doi:10.1016/j.apenergy.2016.11.071Huang, Y., Sun, Y., & Yi, S. (2018). Static and Dynamic Networking of Smart Meters Based on the Characteristics of the Electricity Usage Information. Energies, 11(6), 1532. doi:10.3390/en11061532Lin, R., Ye, Z., & Zhao, Y. (2019). OPEC: Daily Load Data Analysis Based on Optimized Evolutionary Clustering. Energies, 12(14), 2668. doi:10.3390/en12142668Hunt, L. C., Judge, G., & Ninomiya, Y. (2003). Underlying trends and seasonality in UK energy demand: a sectoral analysis. Energy Economics, 25(1), 93-118. doi:10.1016/s0140-9883(02)00072-5Serrano-Guerrero, X., Escrivá-Escrivá, G., & Roldán-Blay, C. (2018). Statistical methodology to assess changes in the electrical consumption profile of buildings. Energy and Buildings, 164, 99-108. doi:10.1016/j.enbuild.2017.12.059Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection. ACM Computing Surveys, 41(3), 1-58. doi:10.1145/1541880.1541882Escrivá-Escrivá, G., Álvarez-Bel, C., Roldán-Blay, C., & Alcázar-Ortega, M. (2011). New artificial neural network prediction method for electrical consumption forecasting based on building end-uses. Energy and Buildings, 43(11), 3112-3119. doi:10.1016/j.enbuild.2011.08.008Serrano-Guerrero, X., Prieto-Galarza, R., Huilcatanda, E., Cabrera-Zeas, J., & Escriva-Escriva, G. (2017). Election of variables and short-term forecasting of electricity demand based on backpropagation artificial neural networks. 2017 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). doi:10.1109/ropec.2017.8261630Jain, R. K., Smith, K. M., Culligan, P. J., & Taylor, J. E. (2014). Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy, 123, 168-178. doi:10.1016/j.apenergy.2014.02.057Singh, S., & Yassine, A. (2018). Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting. Energies, 11(2), 452. doi:10.3390/en11020452Jota, P. R. S., Silva, V. R. B., & Jota, F. G. (2011). Building load management using cluster and statistical analyses. International Journal of Electrical Power & Energy Systems, 33(8), 1498-1505. doi:10.1016/j.ijepes.2011.06.034Shareef, H., Ahmed, M. S., Mohamed, A., & Al Hassan, E. (2018). Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers. IEEE Access, 6, 24498-24509. doi:10.1109/access.2018.2831917Crespo Cuaresma, J., Hlouskova, J., Kossmeier, S., & Obersteiner, M. (2004). Forecasting electricity spot-prices using linear univariate time-series models. Applied Energy, 77(1), 87-106. doi:10.1016/s0306-2619(03)00096-5Janczura, J., Trück, S., Weron, R., & Wolff, R. C. (2013). Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling. Energy Economics, 38, 96-110. doi:10.1016/j.eneco.2013.03.013Angelos, E. W. S., Saavedra, O. R., Cortés, O. A. C., & de Souza, A. N. (2011). Detection and Identification of Abnormalities in Customer Consumptions in Power Distribution Systems. IEEE Transactions on Power Delivery, 26(4), 2436-2442. doi:10.1109/tpwrd.2011.2161621Milton, M.-A., Pedro, C.-O., Xavier, S.-G., & Guillermo, E.-E. (2018). Characterization and Classification of Daily Electricity Consumption Profiles: Shape Factors and k-Means Clustering Technique. E3S Web of Conferences, 64, 08004. doi:10.1051/e3sconf/20186408004Chicco, G. (2012). Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy, 42(1), 68-80. doi:10.1016/j.energy.2011.12.031Seem, J. E. (2005). Pattern recognition algorithm for determining days of the week with similar energy consumption profiles. Energy and Buildings, 37(2), 127-139. doi:10.1016/j.enbuild.2004.04.004Seem, J. E. (2007). Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy and Buildings, 39(1), 52-58. doi:10.1016/j.enbuild.2006.03.033Li, X., Bowers, C. P., & Schnier, T. (2010). Classification of Energy Consumption in Buildings With Outlier Detection. IEEE Transactions on Industrial Electronics, 57(11), 3639-3644. doi:10.1109/tie.2009.2027926Capozzoli, A., Piscitelli, M. S., Brandi, S., Grassi, D., & Chicco, G. (2018). Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings. Energy, 157, 336-352. doi:10.1016/j.energy.2018.05.127Jokar, P., Arianpoo, N., & Leung, V. C. M. (2016). Electricity Theft Detection in AMI Using Customers’ Consumption Patterns. IEEE Transactions on Smart Grid, 7(1), 216-226. doi:10.1109/tsg.2015.2425222Fenza, G., Gallo, M., & Loia, V. (2019). Drift-Aware Methodology for Anomaly Detection in Smart Grid. IEEE Access, 7, 9645-9657. doi:10.1109/access.2019.2891315Araya, D. B., Grolinger, K., ElYamany, H. F., Capretz, M. A. M., & Bitsuamlak, G. (2017). An ensemble learning framework for anomaly detection in building energy consumption. Energy and Buildings, 144, 191-206. doi:10.1016/j.enbuild.2017.02.058Hayes, M. A., & Capretz, M. A. (2015). Contextual anomaly detection framework for big sensor data. Journal of Big Data, 2(1). doi:10.1186/s40537-014-0011-yCui, W., & Wang, H. (2017). A New Anomaly Detection System for School Electricity Consumption Data. Information, 8(4), 151. doi:10.3390/info8040151Fan, C., Xiao, F., Zhao, Y., & Wang, J. (2018). Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data. Applied Energy, 211, 1123-1135. doi:10.1016/j.apenergy.2017.12.005Cai, H., Shen, S., Lin, Q., Li, X., & Xiao, H. (2019). Predicting the Energy Consumption of Residential Buildings for Regional Electricity Supply-Side and Demand-Side Management. IEEE Access, 7, 30386-30397. doi:10.1109/access.2019.2901257Khan, I., Huang, J. Z., Masud, M. A., & Jiang, Q. (2016). Segmentation of Factories on Electricity Consumption Behaviors Using Load Profile Data. IEEE Access, 4, 8394-8406. doi:10.1109/access.2016.2619898Al-Jarrah, O. Y., Al-Hammadi, Y., Yoo, P. D., & Muhaidat, S. (2017). Multi-Layered Clustering for Power Consumption Profiling in Smart Grids. IEEE Access, 5, 18459-18468. doi:10.1109/access.2017.2712258Park, K.-J., & Son, S.-Y. (2019). A Novel Load Image Profile-Based Electricity Load Clustering Methodology. IEEE Access, 7, 59048-59058. doi:10.1109/access.2019.2914216Serrano-Guerrero, X., Siavichay, L.-F., Clairand, J.-M., & Escrivá-Escrivá, G. (2019). Forecasting Building Electric Consumption Patterns Through Statistical Methods. Advances in Emerging Trends and Technologies, 164-175. doi:10.1007/978-3-030-32033-1_16Li, Y., Zhang, H., Liang, X., & Huang, B. (2019). Event-Triggered-Based Distributed Cooperative Energy Management for Multienergy Systems. IEEE Transactions on Industrial Informatics, 15(4), 2008-2022. doi:10.1109/tii.2018.2862436Khalid, A., Javaid, N., Guizani, M., Alhussein, M., Aurangzeb, K., & Ilahi, M. (2018). Towards Dynamic Coordination Among Home Appliances Using Multi-Objective Energy Optimization for Demand Side Management in Smart Buildings. IEEE Access, 6, 19509-19529. doi:10.1109/access.2018.2791546Borovkova, S., & Geman, H. (2006). Analysis and Modelling of Electricity Futures Prices. Studies in Nonlinear Dynamics & Econometrics, 10(3). doi:10.2202/1558-3708.137

Techno-Economic Assessment of Renewable Energy-based Microgrids in the Amazon Remote Communities in Ecuador

This is the peer reviewed version of the following article: Clairand, J., Serrano-Guerrero, X., González-Zumba, A. and Escrivá-Escrivá, G. (2022), Techno-Economic Assessment of Renewable Energy-based Microgrids in the Amazon Remote Communities in Ecuador. Energy Technol., 10: 2100746, which has been published in final form at https://doi.org/10.1002/ente.202100746. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.[EN] Several remote communities have limited electricity access and are mainly dependent on environmentally damaging fossil fuels. The installation of microgrid networks and green energy initiatives are currently addressing this issue. Thus, the techno-economic assessment of a microgrid that comprises photovoltaic arrays, a micro-hydro turbine, and diesel generation is proposed herein. Two scenarios are evaluated considering the inclusion or not of diesel generation. This model is performed in HOMER. The results demonstrate that the best option in economics is to invest in a PV/hydro/diesel microgrid, resulting in an net present cost of 2.33 M$, and a cost of energy of 0.194$ kWh(-1). Furthermore, to address diesel price uncertainties, a sensitivity analysis is carried out based on three different projected diesel prices.Clairand, J.; Serrano-Guerrero, X.; González-Zumba, A.; Escrivá-Escrivá, G. (2022). Techno-Economic Assessment of Renewable Energy-based Microgrids in the Amazon Remote Communities in Ecuador. Energy Technology. 10(2):1-13. https://doi.org/10.1002/ente.202100746S11310

Speech and Speaker Recognition for Home Automation: Preliminary Results

International audienceIn voice controlled multi-room smart homes ASR and speaker identification systems face distance speech conditionswhich have a significant impact on performance. Regarding voice command recognition, this paper presents an approach whichselects dynamically the best channel and adapts models to the environmental conditions. The method has been tested on datarecorded with 11 elderly and visually impaired participants in a real smart home. The voice command recognition error ratewas 3.2% in off-line condition and of 13.2% in online condition. For speaker identification, the performances were below veryspeaker dependant. However, we show a high correlation between performance and training size. The main difficulty was the tooshort utterance duration in comparison to state of the art studies. Moreover, speaker identification performance depends on the sizeof the adapting corpus and then users must record enough data before using the system

Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

[EN] The market for electric vehicles (EVs) has grown with each year, and EVs are considered to be a proper solution for the mitigation of urban pollution. So far, not much attention has been devoted to the use of EVs for public transportation, such as taxis and buses. However, a massive introduction of electric taxis (ETs) and electric buses (EBs) could generate issues in the grid. The challenges are different from those of private EVs, as their required load is much higher and the related time constraints must be considered with much more attention. These issues have begun to be studied within the last few years. This paper presents a review of the different approaches that have been proposed by various authors, to mitigate the impact of EBs and ETs on the future smart grid. Furthermore, some projects with regard to the integration of ETs and EBs around the world are presented. Some guidelines for future works are also proposed.This research was funded by the project SIS.JCG.19.03 of Universidad de las Americas, Ecuador.Clairand-Gómez, J.; Guerra-Terán, P.; Serrano-Guerrero, JX.; González-Rodríguez, M.; Escrivá-Escrivá, G. (2019). Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies. Energies. 12(16):1-22. https://doi.org/10.3390/en12163114S1221216Emadi, A. (2011). Transportation 2.0. IEEE Power and Energy Magazine, 9(4), 18-29. doi:10.1109/mpe.2011.941320Fahimi, B., Kwasinski, A., Davoudi, A., Balog, R., & Kiani, M. (2011). Charge It! IEEE Power and Energy Magazine, 9(4), 54-64. doi:10.1109/mpe.2011.941321Yilmaz, M., & Krein, P. T. (2013). Review of Battery Charger Topologies, Charging Power Levels, and Infrastructure for Plug-In Electric and Hybrid Vehicles. IEEE Transactions on Power Electronics, 28(5), 2151-2169. doi:10.1109/tpel.2012.2212917Tagliaferri, C., Evangelisti, S., Acconcia, F., Domenech, T., Ekins, P., Barletta, D., & Lettieri, P. (2016). Life cycle assessment of future electric and hybrid vehicles: A cradle-to-grave systems engineering approach. Chemical Engineering Research and Design, 112, 298-309. doi:10.1016/j.cherd.2016.07.003Zackrisson, M., Fransson, K., Hildenbrand, J., Lampic, G., & O’Dwyer, C. (2016). Life cycle assessment of lithium-air battery cells. Journal of Cleaner Production, 135, 299-311. doi:10.1016/j.jclepro.2016.06.104Wu, Y., Yang, Z., Lin, B., Liu, H., Wang, R., Zhou, B., & Hao, J. (2012). Energy consumption and CO2 emission impacts of vehicle electrification in three developed regions of China. Energy Policy, 48, 537-550. doi:10.1016/j.enpol.2012.05.060Shen, W., Han, W., Chock, D., Chai, Q., & Zhang, A. (2012). Well-to-wheels life-cycle analysis of alternative fuels and vehicle technologies in China. Energy Policy, 49, 296-307. doi:10.1016/j.enpol.2012.06.038Wang, R., Wu, Y., Ke, W., Zhang, S., Zhou, B., & Hao, J. (2015). Can propulsion and fuel diversity for the bus fleet achieve the win–win strategy of energy conservation and environmental protection? Applied Energy, 147, 92-103. doi:10.1016/j.apenergy.2015.01.107Clement-Nyns, K., Haesen, E., & Driesen, J. (2010). The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid. IEEE Transactions on Power Systems, 25(1), 371-380. doi:10.1109/tpwrs.2009.2036481Shafiee, S., Fotuhi-Firuzabad, M., & Rastegar, M. (2013). Investigating the Impacts of Plug-in Hybrid Electric Vehicles on Power Distribution Systems. IEEE Transactions on Smart Grid, 4(3), 1351-1360. doi:10.1109/tsg.2013.2251483Pieltain Fernandez, L., Gomez San Roman, T., Cossent, R., Mateo Domingo, C., & Frias, P. (2011). Assessment of the Impact of Plug-in Electric Vehicles on Distribution Networks. IEEE Transactions on Power Systems, 26(1), 206-213. doi:10.1109/tpwrs.2010.2049133Lucas, A., Bonavitacola, F., Kotsakis, E., & Fulli, G. (2015). Grid harmonic impact of multiple electric vehicle fast charging. Electric Power Systems Research, 127, 13-21. doi:10.1016/j.epsr.2015.05.012Turker, H., Bacha, S., Chatroux, D., & Hably, A. (2012). Low-Voltage Transformer Loss-of-Life Assessments for a High Penetration of Plug-In Hybrid Electric Vehicles (PHEVs). IEEE Transactions on Power Delivery, 27(3), 1323-1331. doi:10.1109/tpwrd.2012.2193423Kempton, W., & Tomić, J. (2005). Vehicle-to-grid power fundamentals: Calculating capacity and net revenue. Journal of Power Sources, 144(1), 268-279. doi:10.1016/j.jpowsour.2004.12.025Guille, C., & Gross, G. (2009). A conceptual framework for the vehicle-to-grid (V2G) implementation. Energy Policy, 37(11), 4379-4390. doi:10.1016/j.enpol.2009.05.053Geng, Z., Conejo, A. J., Chen, Q., Xia, Q., & Kang, C. (2017). Electricity production scheduling under uncertainty: Max social welfare vs. min emission vs. max renewable production. Applied Energy, 193, 540-549. doi:10.1016/j.apenergy.2017.02.051Verbruggen, A., Fischedick, M., Moomaw, W., Weir, T., Nadaï, A., Nilsson, L. J., … Sathaye, J. (2010). Renewable energy costs, potentials, barriers: Conceptual issues. Energy Policy, 38(2), 850-861. doi:10.1016/j.enpol.2009.10.036Oda, T., Aziz, M., Mitani, T., Watanabe, Y., & Kashiwagi, T. (2018). Mitigation of congestion related to quick charging of electric vehicles based on waiting time and cost–benefit analyses: A japanese case study. Sustainable Cities and Society, 36, 99-106. doi:10.1016/j.scs.2017.10.024Arkin, E. M., Carmi, P., Katz, M. J., Mitchell, J. S. B., & Segal, M. (2019). Locating battery charging stations to facilitate almost shortest paths. Discrete Applied Mathematics, 254, 10-16. doi:10.1016/j.dam.2018.07.019Gallardo-Lozano, J., Milanés-Montero, M. I., Guerrero-Martínez, M. 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M., Ramachandaramurthy, V. K., & Yong, J. Y. (2016). Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques. Renewable and Sustainable Energy Reviews, 53, 720-732. doi:10.1016/j.rser.2015.09.012Raslavičius, L., Azzopardi, B., Keršys, A., Starevičius, M., Bazaras, Ž., & Makaras, R. (2015). Electric vehicles challenges and opportunities: Lithuanian review. Renewable and Sustainable Energy Reviews, 42, 786-800. doi:10.1016/j.rser.2014.10.076Rahman, I., Vasant, P. M., Singh, B. S. M., Abdullah-Al-Wadud, M., & Adnan, N. (2016). Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures. Renewable and Sustainable Energy Reviews, 58, 1039-1047. doi:10.1016/j.rser.2015.12.353Faddel, S., Al-Awami, A., & Mohammed, O. (2018). Charge Control and Operation of Electric Vehicles in Power Grids: A Review. Energies, 11(4), 701. doi:10.3390/en11040701Ercan, T., Onat, N. C., & Tatari, O. (2016). Investigating carbon footprint reduction potential of public transportation in United States: A system dynamics approach. Journal of Cleaner Production, 133, 1260-1276. doi:10.1016/j.jclepro.2016.06.051Kwan, S. C., & Hashim, J. H. (2016). A review on co-benefits of mass public transportation in climate change mitigation. Sustainable Cities and Society, 22, 11-18. doi:10.1016/j.scs.2016.01.004Kolbe, K. (2019). Mitigating urban heat island effect and carbon dioxide emissions through different mobility concepts: Comparison of conventional vehicles with electric vehicles, hydrogen vehicles and public transportation. Transport Policy, 80, 1-11. doi:10.1016/j.tranpol.2019.05.007Zalakeviciute, R., Rybarczyk, Y., López-Villada, J., & Diaz Suarez, M. V. (2018). Quantifying decade-long effects of fuel and traffic regulations on urban ambient PM 2.5 pollution in a mid-size South American city. 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Real-time detection of the aluminium contribution during laser welding of Usibor1500 tailor-welded blanks

The identification and intensity estimation of some aluminium emission lines have been proposed to perform an on-line quantification of the Al contribution to the laser-welding process of Usibor blanks. This boron steel is protected by an Al-Si coating that is removed by laser ablation before welding. If this process fails to remove Al from the joint surface, its contribution may affect the final properties of the resulting seams, therefore compromising their quality. Experimental tests have been performed, some of them in a real production scenario. They have been analysed and compared to the results of welding test specimens, analysis of the associated tensile properties and fracture locations and seam macrographs. These studies have indicated that on-line quantification of the Al contribution to the process is feasible and that a correlation can be established between the Al content estimated in real-time and the results derived from the off-line tests considered.The authors would like to thank the staff of Autotech Engineering and Solblank (both Gestamp companies) for their valuable help during the design, implementation and test of the monitoring system. This work has been supported by the project TEC2013- 47264-C2-1-