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To minimize shading when interconnecting single solar cells to solar modules the shingling technique can be used. Having an electrically conductive substrate this interconnection technique can be applied without any modification of the single solar cells itself. In case of electrically isolating substrates a contacting of the back metal electrode from the backside is in general not possible without modification of the substrate. Within this paper we present a possibility to interconnect flexible CIGS thin film solar cells on polyimide substrate using the shingling technique. To expose the molybdenum layer from the backside the polyimide substrate has been removed locally with a pulsed Excimer laser (248 nm wavelength, 20 ns pulse duration). To interconnect the exposed back contact with the front contact of the next solar cell the blind vias can be filled with a conductive adhesive. The surface of the exposed back contact of the CIGS solar cells has been characterized using optical microscope and SEM to acquire information on the completeness of the polymer etching and possible damages of the thin films. To characterize the exposed thin films electrically conductive-AFM images were made. First solar cells have been contacted from backside and electrically characterized. 25th European Photovoltaic Solar Energy Conference and Exhibition / 5th World Conference on Photovoltaic Energy Conversion, 6-10 September 2010, Valencia, Spain; 3414-3417

The present research is dedicated to the study of anaerobic co-digestion process of different biomass materials. Anaerobic co-digestion of digested sludge, grass silage, haylage and cattle manure was evaluated in mesophilic tank reactors in the lab-scale experiment. Twelve laboratory scale tank reactors (1.5 L) were used during the incubation period of 45 days. First triplet of reactors was fed with pure digested sludge and the other three with different mixtures having the volumetric ratios of 30/35/25/10, 40/30/20/10 and 50/25/15/10 for digested sludge/corn silage/grass haylage/cattle manure. Methane production was analyzed for all labscale reactors individually. The resulting specific methane production of above-mentioned batches was 336.34, 238.1 and 233.23 LSTP[CH4] kg-1 [TVS], respectively. Other results such as cumulative biogas and methane yield, volumetric biogas and methane yield, volumetric biogas and methane yield per day were also assessed. These results had the highest meaning when complex substrate had no more than 30% of inoculum

We propose an on-the-fly statistical learning method to make a qualitative reduced-order model of the dynamics of a premixed flame quantitatively accurate. This physics- informed data-driven method is based on the statistically optimal combination of (i) a reduced-order model of the dynamics of a premixed flame with a level-set method, (ii) high-quality data, which can be provided by experiments and/or high-fidelity simulations, and (iii) assimilation of the data into the reduced-order model to improve the prediction of the dynamics of the premixed flame. The reduced-order model learns the state and the parameters of the premixed flame on the fly with the ensemble Kalman filter, which is a Bayesian filter used in the data assimilation of high-dimensional dynamical systems, e.g., in weather forecasting. The proposed method and algorithm are applied to two test cases with relevance to reacting flow and instability. First, the capabilities of the framework are demonstrated in a twin experiment, where the assimilated data are produced from the same model as that used in prediction. Second, the assimilated data are extracted from a high-fidelity reacting-flow direct numerical simulation (DNS). The results are analyzed by using Bayesian statistics, which provide the uncertainties of the calculations. This method opens up new possibilities for on-the-fly optimal calibration of computationally cheap reduced-order models when experimental data become available, for example, from sensors.

The innovation of this research is the development of new model called fast moving landslide risk analysis model by modifying one of the previous prominent landslide risk algorithms focusing on the fast moving type of the landslides (such as mudslides, mud flows, block slide, rock fall and topple) based on the qualitative approach using Heuristic method in GIS (Geographical Information Systems). The different event controlling parameters and criteria were used for fast moving landslide predictive risk model. The pair wise comparison method was used in which the parameters of landslide hazard and vulnerability were compared by their assigned weights. The drawback of the used approach was justified by the standard value of consistency ratio, which proved the assigned weight of the parameters as reasonable and consistent. The model was validated by using the occurred landslides inventory data and correlation coefficient test, which showed the positive relationship between the landslide risk predicted regions and the occurred landslides locations. The various landslide events occurred on 8th October, 2005 were accumulated as landslide inventory by the interpretation of satellite imagery. The validation of the model was justified by using one of the statistical two paired, \"t\" test, and the amount of the predicted risk in the different regions. It is believed that this modified model will prove beneficial to the decision makers in future.

{"references": ["", "M. Meinshausen, N. Meinshausen, W. Hare, S. C. B. Raper, K. Frieler, R.\nKnutti, D. J. Frame, M. R. Allen, \"Greenhouse-gas Emission Targets for\nLimiting Global Warming to 2\u00b0C\" Nature J., vol. 458, pp. 1158-1162,\n2009.", "P. Luis, T. Van Gerven, and B. Van der Bruggen, \"Recent Developments\nin Membrane-based Technologies for CO2 Capture\" Prog. Energy\nCombust. Sci. J., vol. 38, pp. 419-448, 2012.", "W. Ye, J. Lin, J. Shen, P. Luis, B. Van der Bruggen, \"Membrane\nCrystallization of Sodium Carbonate for Carbon Dioxide Recovery:\nEffect of Impurities on the Crystal Morphology\" Cryst. Growth Des.J.,\nvol. 13, pp. 2362\u20132372, 2013.", "P. Luis, D Van Aubel, B Van der Bruggen, \"Technical viability and\nexergy analysis of membrane crystallization: Closing the loop of CO2\nsequestration\" Int. J. Greenh. Gas Con. J., vol. 12, pp. 450-459, 2013.", "T. Ring, \"Kinetic effects on particle morphology and size distribution\nduring batch precipitation\" Powder Technol. J., vol. 65, pp. 195-206,\n1991.", "M. A. Bialik, H. Theliander, P. Sedin, C. L. Verrill, N. DeMartini,\n\" Solubility and solid-phase composition in Na2CO3-Na2SO4 solutions at\nboiling temperature: A modeling approach\" Ind. Eng. Chem. Res. J., vol.\n47, pp. 3233-3238, 2008.", "D. Weckesser, A. K\u00f6nig, \"Particle shape and purity in membrane based\ncrystallization\" Chem. Eng. Technol. J. vol. 31, pp. 157-162, 2008.", "B. Shi, W. J. Frederick Jr., R. W. Rousseau, \"Nucleation, growth, and\ncomposition of crystals obtained from solutions of Na2CO3 and Na2SO4\"\nInd. Eng. Chem. Res. J., vol. 42, pp. 6343-6347, 2003.", "A. P. Soemardji, C. L. Verrill, W. J. Jr. Frederick, H. Theliander,\n\"Prediction of Crystal Species Transition in Aqueous Solutions of\nNa2CO3 and Na2SO4 and Kraft Black Liquor\" Tappi J. vol. 3, pp. 27-32 ,\n2004."]} This study addresses the effect of impurities on the crystallization of Na2CO3 produced within a strategy for capturing CO2 from flue gases by alkaline absorption. A novel technology - membrane assisted crystallization - is proposed for Na2CO3 crystallization from mother liquors containing impurities. High purity of Na2CO3•10H2O crystals was obtained without impacting the performance of the mass transfer of water vapor through membranes during crystallization.

Buildings constructed in Japan during a period of high economic growth, 1954-1973, have deteriorated, and many need to be demolished. In the course of that demolition work, accidents have occurred, but the causes and circumstances of many of them are largely unknown. This study investigates accidents that occurred during building demolition work in Japan between 2010 and 2014, with the goal of preventing future accidents. The results show that fatal accidents while demolishing buildings accounted for 6.5% of all fatal accidents on construction sites. The greatest number of fatal accidents can be attributed to falls and collapsing walls. The causes of falls were found to be two-fold. First, in the process of demolishing a building, frequently, a hole is cut in the floor and scrap materials are thrown into it to dispose of them; however, workers have been known to fall into this hole and be injured fatally. It was also found that many workers do not use full harnesses. The cause of collapsing walls was identified as follows. When a wall is demolished, its lower part is often cut through to weaken it, at which point the wall can collapse, crushing workers. Analysis of the study results shows that greater attention must be paid to creating a system that promotes fall prevention and safe wall-cutting on demolition sites.

The 14th International Offshore and Polar Engineering Conference, Toulon, France, May 23-28, 2004.

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(IEE Conf. Publ. No. 473) 09/17-21/2000,\nEdinburgh, UK, pp 324-329.", "N. Yadaiah and Nagireddy Ravi, \"Fault Detection Techniques for\nPower Transformers,\" Industrial & Commercial Power Systems\nTechnical Conference, 2007. ICPS 2007. IEEE/IAS Vol.6 , Issue ,11\nMay 2007, pp:1 - 9.", "Diego Roberto Moaris and Jacqueline Gisete Rolim, \"An artificial\nNeural Network Approach to Transformer Fault Diagnosis,\" IEEE\nTransactions on Power Delivery , Vol. 11, No. 4, April 1996, pp 1836-\n1841.", "IS 10593:2006 Indian Standard \"Mineral Oil-impregnated Electrical\nEquipment in service-Guide to interpretation of dissolved and free gas\nanalysis.\" ( Second Revision).", "C. F Lin, J. M. Ling and C. L. Huang \"An Expert System for\nTransformers Fault Diagnosis Using Dissolved Gas Analysis\", IEEE\nTransactions on Power Delivery, Vol. 8, No. 1, January 1993, pp 231-\n238.\n[10] K. Tomsovic, M. Tapper and T. Ingvarsson, \"A Fuzzy Information\nApproach to Integrate Different Transformer Diagnostic Methods,\"\nIEEE Transactions on Power Systems, Vol. 08, No. 3, July 1993, pp\n1638-1646.\n[11] Yann-Chang Huang, Hong-Tzer Yang and Ching Lien Huang,\n\"Developing a new transformer Fault diagnostic System through\nEvolutionary Fuzzy Logic,\" IEEE Transactions on Power Delivery, Vol.\n12, No. 2, April 1997, pp 761-767.\n[12] Q. Su, C. Mi, L. L. Lai and P Austin, \"A Fuzzy Dissolved Gas Analysis\nMethod for Diagnosis of Multiple Incipient Faults in a Transformer,\"\nIEEE Transactions on Power Systems, Vol. 15, No. 2, May 2000, pp\n593-598.\n[13] N.A Muhamad, B. T Phung and T R Blackburn, \"Comparative study\nand analysis of DGA methods for Mineral Oil Using Fuzzy Logic,\"\nProceedings of 8th International Power Engineering Conference\n(IPEC2007), pp 1301-1306.\n[14] Hong-Tzer Yang, Chiung Chou Liou and Jeng Hong Chow, \"Fuzzy\nLearning Vector Quantization Networks for Power Transformer\nCondition Assessment, \" IEEE Transactions on Dielectrics and Electrical\nInsulation, Vol. 08, No. 1, February 2001, pp 143-149.\n[15] O. Vanegas, Y. Mizuno, K. Naito , and T Kamiya, \" Diagnosis of Oil\nInsulated Power Apparatus using Neural Network Simulation,\" IEEE\nTransactions on Dielectrics and Electrical Insulations, Vol 4, No3, June\n1997, pp 290-299.\n[16] Hong- Tzer Yang and Yann-Chang Huang, \"Intelligent Decision\nSupport for Diagnosis of Incipient Transformer Faults Using Self-\nOrganizing Polynomial Networks,\" IEEE Transactions on Power\nSystems Vol 13, No: 3, August 1998, pp 946-952.\n[17] J. L. Guardado, J. L. Naredo, P. Moreno and C. R. Fuerte, \"A\ncomparative study of Neural Network Efficiency in Power Transformer\nDiagnosis Using Dissolved Gas Analysis,\" IEEE Transactions on Power\nDelivery, Vol. 16, No. 4, October 2001, pp 643-647.\n[18] A. S. Farag, M. Mohandes and Ali Al Shaikh, \"Diagnosing Failed\nDistribution Transformers using Neural Networks,\" IEEE Transactions\non Power Delivery, Vol. 16, No. 4, October 2001, pp 631-636.\n[19] Y. C. Huang, H.T. Yang, and K. Y. Huang, \"Abductive Network Modelbased\nDiagnostic System of Power Transformer Incipient Fault\nDetection,\" IEE Proceedings on Generation Transmission Distribution,\nVol. 149, No 3, May 2002.\n[20] M. H. Wang, \"Extension Neural Network for Power Transformer\nIncipient Fault Diagnosis,\" IEE Proceeding Generation Transmission\nDistribution, Vol 150, No. 6, Nov 2003, pp 679-656.\n[21] Zhan Wang, JiWei guo, Jing Dong Xie, and Guoqing Tang, \"An\nIntroduction of a Condition Monitoring System of Electrical\nEquipment.\" Proceedings of 2001 International Symposium on\nElectrical Insulating Materials, 2001. (ISEIM 2001). Himeji, Japan\n11/19/2001-11/22/2001 pp 221-224.\n[22] D.V.S.S Siva Sarma and G.N.S Kalyani, \"ANN approach for Condition\nMonitoring of Power Transformers Using DGA,\" (0-1803-\n8560/04\u252c\u00ae2004 IEEE) pp 444-447.\n[23] James J. Dukaram, \"Transformer oil diagnosis using Fuzzy Logic and\nNeural Networks,\" Canadian Conference on Electrical & Computer\nEngineering, Vol. 1, pp 329-332.\n[24] Jingen Wang, Lin Shang, Shifu, Chen and Yanfei Wang, \"Application of\nFuzzy Classification by Evolutionary Neural Network in Incipient Fault\nDetection of Power Transformer,\" Proceedings of IEEE International\nJoint Conference on Neural Networks, 2004, Date: 25-29 July 2004,\nVol. 3, pp 2279- 2283.\n[25] Diego Roberto Moaris and Jacqueline Gisete Rolim, \"A Hybrid Tool for\nDetection of Incipient Faults in Transformers Based on Dissolved Gas\nAnalysis of Insulating oil,\" IEEE Transactions on Power Delivery, Vol.\n21, No. 2, April 2006, pp 673-680.\n[26] M. A. Izzularab, G.E.M Aly, D.A Mansour, \"On line diagnosis of\nIncipient Faults and Cellulose Degradation Based on Artificial\nIntelligence Methods,\" Proceedings of the 2004 IEEE International\nConference on Solid Dielectrics, 2004, Vol. 2,, 7/5-9 2004 pp: 767-770.\n[27] Adriana Rosa, Garcez Castro, Valdimiro Miranda, \"Knowledge\nDiscovery in Neural Networks with Application to Transformer Failure\nDiagnosis,\" IEEE Transactions on Power Systems, Vol. 20, No 2. May\n2005, pp 717-724.\n[28] Wei-Song Lin, Chin-Pao Hung, Mang -Hui Wang, \"CMAC_ based\nFault Diagnosis of Power Transformers,\" Proceedings of the 2002\nInternational Joint Conference onNeural Networks 2002, Volume 3,\nIssue , 2002 pp 986-991.\n[29] Yann-Chang Huang, \"A New Data Mining Approach to Dissolved Gas\nAnalysis of Oil-Insulated Power Apparatus,\" IEEE Transactions on\nPower Delivery, Vol 18, No. 4, October 2003, pp 1257-1261.\n[30] Weigen Chen, Chong Pan, Yuxin Yun, and Yilu Liu, \"Wavelet Network\nin Power Transformers Diagnosis Using Dissolved Gas Analysis,\" IEEE\nTransactions on Power Delivery, Vol. 24, No: 1, January 2009, pp187-\n194.\n[31] Karen L. Butler-Purry and Mustafa Bagriyanik, \"Identifying\nTransformer Incipient Events for Maintaining Distribution System\nReliability,\" Proceedings of the 36th Annual Hawaii International\nConference on System Sciences (HICSS-03), 1/6-9/ 2003, pp-8.\n[32] Hang Wang and Karen L. Butler, \"Modeling Transformers with Internal\nIncipient Faults,\" IEEE Transactions on Power Systems Vol 17, No:2,\nApril 2002, pp 500-509.\n[33] S. Rajasekaran, & G.A. Vijayalakshmi Pai, \"Neural Networks, Fuzzy\nlogic and Genetic Algorithms Synthesis and Applications,\" Prentice-Hall\nof India Pvt,New Delhi. Ltd First edition, pp 297-300."]} The gases generated in oil filled transformers can be used for qualitative determination of incipient faults. The Dissolved Gas Analysis has been widely used by utilities throughout the world as the primarily diagnostic tool for transformer maintenance. In this paper, various Artificial Intelligence Techniques that have been used by the researchers in the past have been reviewed, some conclusions have been drawn and a sequential hybrid system has been proposed. The synergy of ANN and FIS can be a good solution for reliable results for predicting faults because one should not rely on a single technology when dealing with real–life applications.

Photovoltaic (PV) is one of the fastest growing electricity generation technologies in the world. Average annual growth rates of global PV-installations have reached around 45% for the last 15 years, which triggered a fast and ongoing reduction of production cost in PV industry. The presented work aims at consolidating historical price and cost information, deriving refined learning curves for PV modules and systems, and analysing the main factors of learning. For c-Si modules a valid learning rate of 17% is found based on a meta-analysis of various studies. In early years, even a learning rate of 30% is observed. As an example for thin-film PV, CdTe module cost reduce by 16% as the cumulated production output doubles. Interestingly, efficiency improvements contribute only in second order to the overall cost reduction for both technologies, emphasising the relevance of production excellence and economies of scale. On PV system level, a cost reduction of 14% per doubling of cumulated installed capacity is derived. Finally, a sensitivity analysis reveals that learning rate variations are only of minor influence on the overall global PV market potential. 26th European Photovoltaic Solar Energy Conference and Exhibition; 4697-4702