GWAS for Systemic Sclerosis Identifies Multiple Risk Loci and Highlights Fibrotic and Vasculopathy Pathways

Systemic sclerosis (SSc) is an autoimmune disease that shows one of the highest mortality rates among rheumatic diseases. We perform a large genome-wide association study (GWAS), and meta-analysis with previous GWASs, in 26,679 individuals and identify 27 independent genome-wide associated signals, including 13 new risk loci. The novel associations nearly double the number of genome-wide hits reported for SSc thus far. We define 95% credible sets of less than 5 likely causal variants in 12 loci. Additionally, we identify specific SSc subtype-associated signals. Functional analysis of high-priority variants shows the potential function of SSc signals, with the identification of 43 robust target genes through HiChIP. Our results point towards molecular pathways potentially involved in vasculopathy and fibrosis, two main hallmarks in SSc, and highlight the spectrum of critical cell types for the disease. This work supports a better understanding of the genetic basis of SSc and provides directions for future functional experiments.Funding: This work was supported by Spanish Ministry of Economy and Competitiveness (grant ref. SAF2015-66761-P), Consejeria de Innovacion, Ciencia y Tecnologia, Junta de Andalucía (P12-BIO-1395), Ministerio de Educación, Cultura y Deporte through the program FPU, Juan de la Cierva fellowship (FJCI-2015-24028), Red de Investigación en Inflamación y Enfermadades Reumaticas (RIER) from Instituto de Salud Carlos III (RD16/0012/0013), and Scleroderma Research Foundation and NIH P50-HG007735 (to H.Y.C.). H.Y.C. is an Investigator of the Howard Hughes Medical Institute. PopGen 2.0 is supported by a grant from the German Ministry for Education and Research (01EY1103). M.D.M and S.A. are supported by grant DoD W81XWH-18-1-0423 and DoD W81XWH-16-1-0296, respectively

Cross-disease Meta-analysis of Genome-wide Association Studies for Systemic Sclerosis and Rheumatoid Arthritis Reveals IRF4 as a New Common Susceptibility Locus

Objectives: Systemic sclerosis (SSc) and rheumatoid arthritis (RA) are autoimmune diseases that share clinical and immunological characteristics. To date, several shared SSc- RA loci have been identified independently. In this study, we aimed to systematically search for new common SSc-RA loci through an inter-disease meta-GWAS strategy. Methods: We performed a meta-analysis combining GWAS datasets of SSc and RA using a strategy that allowed identification of loci with both same-direction and opposingdirection allelic effects. The top single-nucleotide polymorphisms (SNPs) were followed-up in independent SSc and RA case-control cohorts. This allowed us to increase the sample size to a total of 8,830 SSc patients, 16,870 RA patients and 43,393 controls. Results: The cross-disease meta-analysis of the GWAS datasets identified several loci with nominal association signals (P-value < 5 x 10-6), which also showed evidence of association in the disease-specific GWAS scan. These loci included several genomic regions not previously reported as shared loci, besides risk factors associated with both diseases in previous studies. The follow-up of the putatively new SSc-RA loci identified IRF4 as a shared risk factor for these two diseases (Pcombined = 3.29 x 10-12). In addition, the analysis of the biological relevance of the known SSc-RA shared loci pointed to the type I interferon and the interleukin 12 signaling pathways as the main common etiopathogenic factors. Conclusions: Our study has identified a novel shared locus, IRF4, for SSc and RA and highlighted the usefulness of cross-disease GWAS meta-analysis in the identification of common risk loci

Complement component C4 structural variation and quantitative traits contribute to sex-biased vulnerability in systemic sclerosis

Altres ajuts: Fondo Europeo de Desarrollo Regional (FEDER), "A way of making Europe".Copy number (CN) polymorphisms of complement C4 play distinct roles in many conditions, including immune-mediated diseases. We investigated the association of C4 CN with systemic sclerosis (SSc) risk. Imputed total C4, C4A, C4B, and HERV-K CN were analyzed in 26,633 individuals and validated in an independent cohort. Our results showed that higher C4 CN confers protection to SSc, and deviations from CN parity of C4A and C4B augmented risk. The protection contributed per copy of C4A and C4B differed by sex. Stronger protection was afforded by C4A in men and by C4B in women. C4 CN correlated well with its gene expression and serum protein levels, and less C4 was detected for both in SSc patients. Conditioned analysis suggests that C4 genetics strongly contributes to the SSc association within the major histocompatibility complex locus and highlights classical alleles and amino acid variants of HLA-DRB1 and HLA-DPB1 as C4-independent signals

Analysis of the common genetic component of large-vessel vasculitides through a meta- Immunochip strategy

Giant cell arteritis (GCA) and Takayasu's arteritis (TAK) are major forms of large-vessel vasculitis (LVV) that share clinical features. To evaluate their genetic similarities, we analysed Immunochip genotyping data from 1,434 LVV patients and 3,814 unaffected controls. Genetic pleiotropy was also estimated. The HLA region harboured the main disease-specific associations. GCA was mostly associated with class II genes (HLA-DRB1/HLA-DQA1) whereas TAK was mostly associated with class I genes (HLA-B/MICA). Both the statistical significance and effect size of the HLA signals were considerably reduced in the cross-disease meta-analysis in comparison with the analysis of GCA and TAK separately. Consequently, no significant genetic correlation between these two diseases was observed when HLA variants were tested. Outside the HLA region, only one polymorphism located nearby the IL12B gene surpassed the study-wide significance threshold in the meta-analysis of the discovery datasets (rs755374, P?=?7.54E-07; ORGCA?=?1.19, ORTAK?=?1.50). This marker was confirmed as novel GCA risk factor using four additional cohorts (PGCA?=?5.52E-04, ORGCA?=?1.16). Taken together, our results provide evidence of strong genetic differences between GCA and TAK in the HLA. Outside this region, common susceptibility factors were suggested, especially within the IL12B locus

A cross-disease meta-GWAS identifies four new susceptibility loci shared between systemic sclerosis and Crohn’s disease

Abstract: Genome-wide association studies (GWASs) have identified a number of genetic risk loci associated with systemic sclerosis (SSc) and Crohn’s disease (CD), some of which confer susceptibility to both diseases. In order to identify new risk loci shared between these two immune-mediated disorders, we performed a cross-disease meta-analysis including GWAS data from 5,734 SSc patients, 4,588 CD patients and 14,568 controls of European origin. We identified 4 new loci shared between SSc and CD, IL12RB2, IRF1/SLC22A5, STAT3 and an intergenic locus at 6p21.31. Pleiotropic variants within these loci showed opposite allelic effects in the two analysed diseases and all of them showed a significant effect on gene expression. In addition, an enrichment in the IL-12 family and type I interferon signaling pathways was observed among the set of SSc-CD common genetic risk loci. In conclusion, through the first cross-disease meta-analysis of SSc and CD, we identified genetic variants with pleiotropic effects on two clinically distinct immune-mediated disorders. The fact that all these pleiotropic SNPs have opposite allelic effects in SSc and CD reveals the complexity of the molecular mechanisms by which polymorphisms affect diseases

A Large-Scale Genetic Analysis Reveals a Strong Contribution of the HLA Class II Region to Giant Cell Arteritis Susceptibility

We conducted a large-scale genetic analysis on giant cell arteritis (GCA), a polygenic immune-mediated vasculitis. A case-control cohort, comprising 1,651 case subjects with GCA and 15,306 unrelated control subjects from six different countries of European ancestry, was genotyped by the Immunochip array. We also imputed HLA data with a previously validated imputation method to perform a more comprehensive analysis of this genomic region. The strongest association signals were observed in the HLA region, with rs477515 representing the highest peak (p = 4.05 × 10−40, OR = 1.73). A multivariate model including class II amino acids of HLA-DRβ1 and HLA-DQα1 and one class I amino acid of HLA-B explained most of the HLA association with GCA, consistent with previously reported associations of classical HLA alleles like HLA-DRB1∗04. An omnibus test on polymorphic amino acid positions highlighted DRβ1 13 (p = 4.08 × 10−43) and HLA-DQα1 47 (p = 4.02 × 10−46), 56, and 76 (both p = 1.84 × 10−45) as relevant positions for disease susceptibility. Outside the HLA region, the most significant loci included PTPN22 (rs2476601, p = 1.73 × 10−6, OR = 1.38), LRRC32 (rs10160518, p = 4.39 × 10−6, OR = 1.20), and REL (rs115674477, p = 1.10 × 10−5, OR = 1.63). Our study provides evidence of a strong contribution of HLA class I and II molecules to susceptibility to GCA. In the non-HLA region, we confirmed a key role for the functional PTPN22 rs2476601 variant and proposed other putative risk loci for GCA involved in Th1, Th17, and Treg cell function

Algorithms for reactive production scheduling: An application in the ceramic industry

En el presente trabajo se aplica el enfoque predictivo-reactivo para la generación de programas productivos y se sugiere su aplicación en las empresas cerámicas que, bajo el tradicional enfoque estático, necesiten mantener esta información como base de su proceso decisional, para coordinar las entregas de materiales por parte de los proveedores, realizar un ajuste fino de la capacidad, planificar el transporte, etc., pero que, a su vez, necesiten hacer frente a los cambios inesperados adaptando el programa en curso. La incertidumbre es un aspecto poco considerado en la literatura a pesar de estar presente en la mayoría de los procesos de fabricación. Con el objetivo de facilitar a las empresas cerámicas un nuevo marco de trabajo que permita abordar la incertidumbre, se presenta una propuesta alineada con la visión predictivo-reactiva y que puede constituir una herramienta útil para este tipo de empresas. Una vez establecido el marco de trabajo, la contribución se centra en los métodos para abordar la fase reactiva, dado su menor desarrollo en el ámbito científico respecto a la fase predictiva. Se incluyen aportaciones específicas que consideran los tiempos de cambio de partida característicos del sector cerámico, junto con otras empleadas en otros ámbitos. Se obtienen resultados que permiten pronosticar una mejora de la productividad aplicando métodos sencillos.[EN] In this work a predictive-reactive approach is applied for generating productive schedules and its application is suggested in ceramic companies that, following a traditional static approach, they need to keep this information as a beginning of its decisional process, in order to coordinate material delivering from providers, to carry out a close/fine capacity adjustment, transport planning, etc. but they also need to face unexpected changes adapting the initial schedule. Uncertainty is an aspect few times considered in literature despite of being present in most of manufacturing processes. In order to facilitate companies a new framework that allows tackling uncertainty, a new proposal aligned with a predictivereactive view and which can constitute a useful tool for this kind of companies is presented. Once the framework established, this contribution is focus on methods to deal with reactive phase, which is less developed in the scientific field than predictive. Specific contributions which consider characteristic setups from ceramic industry and others related to different environments are included. Obtained results illustrate a productivity improvement by applying simple methods.Gómez Gasquet, P.; Díaz Madroñero, M. (2014). Algoritmos para la programación reactiva de la producción: Aplicación a la industria cerámica. Boletín de la Sociedad Española de Cerámica y Vidrio. 53(4):1-4. doi:10.3989/cyv.2014.v53.i4.12921453

A system dynamics model for the supply chain procurement transport problem: comparing spreadsheets, fuzzy programming and simulation approaches

This article proposes a simulation approach based on system dynamics for operational procurement and transport planning in a two-level, multi-product and multi-period supply chain. This work uses the Vensim((R)) simulation tool to highlight the potential of system dynamics for supply chain simulation. A real continuous simulation application is presented in an automobile supply chain. The effectiveness of the proposed model is validated through the comparison of the results provided by spreadsheet-based simulation, fuzzy multi-objective programming and system dynamics-based simulation models. The fundamental point of this paper is that the simulation model is the most effective approach in quantifying the trade-off between number of truck shipments and average inventory level. In this case, the number of truck shipments is to be minimised, resulting in a higher inventory level. If the average inventory level were minimised, then there would be more truck shipments. Here, it is shown the benefit of this type of simulation model in reducing inventory by about 10%.This work has been funded by the Spanish Ministry of Science and Technology project: Production technology based on the feedback from production, transport and unload planning and the redesign of warehouses decisions in the supply chain (Ref. DPI2010-19,977).Mula, J.; Campuzano Bolarín, F.; Díaz-Madroñero Boluda, FM.; Carpio, KM. (2013). A system dynamics model for the supply chain procurement transport problem: comparing spreadsheets, fuzzy programming and simulation approaches. International Journal of Production Research. 51(13):4087-4104. doi:10.1080/00207543.2013.774487S408741045113Archibald, G., N. Karabakal, and P. Karlsson. 1999. Supply Chain vs. Supply Chain: Using Simulation to Compete beyond the Four Walls.Proceedings of the 31st conference on Winter Simulation: Simulation – A Bridge to the Future - Volume 2. New York, USA: ACM, 1207–1214.Barnabè, F. (2011). A «system dynamics‐based Balanced Scorecard» to support strategic decision making. International Journal of Productivity and Performance Management, 60(5), 446-473. doi:10.1108/17410401111140383Baumol, W. J., & Vinod, H. D. (1970). An Inventory Theoretic Model of Freight Transport Demand. Management Science, 16(7), 413-421. doi:10.1287/mnsc.16.7.413Beamon, B. M. (1998). Supply chain design and analysis: International Journal of Production Economics, 55(3), 281-294. doi:10.1016/s0925-5273(98)00079-6Biswas, S., & Narahari, Y. (2004). Object oriented modeling and decision support for supply chains. European Journal of Operational Research, 153(3), 704-726. doi:10.1016/s0377-2217(02)00806-8Bose, S., & Pekny, J. F. (2000). A model predictive framework for planning and scheduling problems: a case study of consumer goods supply chain. Computers & Chemical Engineering, 24(2-7), 329-335. doi:10.1016/s0098-1354(00)00469-5Bottani, E., & Montanari, R. (2009). Supply chain design and cost analysis through simulation. International Journal of Production Research, 48(10), 2859-2886. doi:10.1080/00207540902960299Byrne, M. ., & Bakir, M. . (1999). Production planning using a hybrid simulation – analytical approach. International Journal of Production Economics, 59(1-3), 305-311. doi:10.1016/s0925-5273(98)00104-2Byrne, M. D., & Hossain, M. M. (2005). Production planning: An improved hybrid approach. International Journal of Production Economics, 93-94, 225-229. doi:10.1016/j.ijpe.2004.06.021Cachon, G. P. (1999). Managing Supply Chain Demand Variability with Scheduled Ordering Policies. Management Science, 45(6), 843-856. doi:10.1287/mnsc.45.6.843Campuzano, F., & Mula, J. (2011). Supply Chain Simulation. doi:10.1007/978-0-85729-719-8Campuzano, F., Mula, J., & Peidro, D. (2010). Fuzzy estimations and system dynamics for improving supply chains. Fuzzy Sets and Systems, 161(11), 1530-1542. doi:10.1016/j.fss.2009.12.002Chandra, P., & Fisher, M. L. (1994). Coordination of production and distribution planning. European Journal of Operational Research, 72(3), 503-517. doi:10.1016/0377-2217(94)90419-7Chatfield, D. C. (2013). Underestimating the bullwhip effect: a simulation study of the decomposability assumption. International Journal of Production Research, 51(1), 230-244. doi:10.1080/00207543.2012.660576Choi, K., Narasimhan, R., & Kim, S. W. (2012). Postponement strategy for international transfer of products in a global supply chain: A system dynamics examination. Journal of Operations Management, 30(3), 167-179. doi:10.1016/j.jom.2012.01.003Cid Yáñez, F., Frayret, J.-M., Léger, F., & Rousseau, A. (2009). Agent-based simulation and analysis of demand-driven production strategies in the timber industry. International Journal of Production Research, 47(22), 6295-6319. doi:10.1080/00207540802158283Erengüç, Ş. S., Simpson, N. C., & Vakharia, A. J. (1999). Integrated production/distribution planning in supply chains: An invited review. European Journal of Operational Research, 115(2), 219-236. doi:10.1016/s0377-2217(98)90299-5Ferreira, L., & Borenstein, D. (2011). Normative agent-based simulation for supply chain planning. Journal of the Operational Research Society, 62(3), 501-514. doi:10.1057/jors.2010.144Gen, M., & Syarif, A. (2005). Hybrid genetic algorithm for multi-time period production/distribution planning. Computers & Industrial Engineering, 48(4), 799-809. doi:10.1016/j.cie.2004.12.012Giannoccaro, I. and P. Pontrandolfo. 2001.Models for supply chain management: A taxonomy. In: Proceedings of the Production and Operations Management. Pom-2001 Conference: Pom Mastery in the New Millennium, 30 March to April 2, Orlando, Florida, 2001.Gnoni, M. G., Iavagnilio, R., Mossa, G., Mummolo, G., & Di Leva, A. (2003). Production planning of a multi-site manufacturing system by hybrid modelling: A case study from the automotive industry. International Journal of Production Economics, 85(2), 251-262. doi:10.1016/s0925-5273(03)00113-0Helo, P. T. (2000). Dynamic modelling of surge effect and capacity limitation in supply chains. International Journal of Production Research, 38(17), 4521-4533. doi:10.1080/00207540050205271Hernández, J. E., Mula, J., Ferriols, F. J., & Poler, R. (2008). A conceptual model for the production and transport planning process: An application to the automobile sector. Computers in Industry, 59(8), 842-852. doi:10.1016/j.compind.2008.06.004Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L. K., & Young, T. (2010). Simulation in manufacturing and business: A review. European Journal of Operational Research, 203(1), 1-13. doi:10.1016/j.ejor.2009.06.004June-Young Bang, & Yeong-Dae Kim. (2010). Hierarchical Production Planning for Semiconductor Wafer Fabrication Based on Linear Programming and Discrete-Event Simulation. IEEE Transactions on Automation Science and Engineering, 7(2), 326-336. doi:10.1109/tase.2009.2021462Jung, J. Y., Blau, G., Pekny, J. F., Reklaitis, G. V., & Eversdyk, D. (2004). A simulation based optimization approach to supply chain management under demand uncertainty. Computers & Chemical Engineering, 28(10), 2087-2106. doi:10.1016/j.compchemeng.2004.06.006Katsaliaki, K., Mustafee, N., Taylor, S. J. E., & Brailsford, S. (2009). Comparing conventional and distributed approaches to simulation in a complex supply-chain health system. Journal of the Operational Research Society, 60(1), 43-51. doi:10.1057/palgrave.jors.2602531Khaji, M. R., & Shafaei, R. (2011). A system dynamics approach for strategic partnering in supply networks. International Journal of Computer Integrated Manufacturing, 24(2), 106-125. doi:10.1080/0951192x.2010.531288Kim, B., & Kim, S. (2001). Extended model for a hybrid production planning approach. International Journal of Production Economics, 73(2), 165-173. doi:10.1016/s0925-5273(00)00172-9Klug, F. (2013). The internal bullwhip effect in car manufacturing. International Journal of Production Research, 51(1), 303-322. doi:10.1080/00207543.2012.677551Lai, Y.-J., & Hwang, C.-L. (1992). A new approach to some possibilistic linear programming problems. Fuzzy Sets and Systems, 49(2), 121-133. doi:10.1016/0165-0114(92)90318-xLee, Y. H., & Kim, S. H. (2002). Production–distribution planning in supply chain considering capacity constraints. Computers & Industrial Engineering, 43(1-2), 169-190. doi:10.1016/s0360-8352(02)00063-3Lee, Y. H., Kim, S. H., & Moon, C. (2002). Production-distribution planning in supply chain using a hybrid approach. Production Planning & Control, 13(1), 35-46. doi:10.1080/09537280110061566Liang, T.-F. (2006). Distribution planning decisions using interactive fuzzy multi-objective linear programming. Fuzzy Sets and Systems, 157(10), 1303-1316. doi:10.1016/j.fss.2006.01.014Lim, S. J., Jeong, S. J., Kim, K. S., & Park, M. W. (2005). A simulation approach for production-distribution planning with consideration given to replenishment policies. The International Journal of Advanced Manufacturing Technology, 27(5-6), 593-603. doi:10.1007/s00170-004-2208-2Lim, S. J., Jeong, S. J., Kim, K. S., & Park, M. W. (2005). Hybrid approach to distribution planning reflecting a stochastic supply chain. The International Journal of Advanced Manufacturing Technology, 28(5-6), 618-625. doi:10.1007/s00170-004-2398-7(s. f.). doi:10.1021/ie960901Mikati, N. (2009). Dependence of lead time on batch size studied by a system dynamics model. International Journal of Production Research, 48(18), 5523-5532. doi:10.1080/00207540903164628Min, H., & Zhou, G. (2002). Supply chain modeling: past, present and future. Computers & Industrial Engineering, 43(1-2), 231-249. doi:10.1016/s0360-8352(02)00066-9Minegishi, S., & Thiel, D. (2000). System dynamics modeling and simulation of a particular food supply chain. Simulation Practice and Theory, 8(5), 321-339. doi:10.1016/s0928-4869(00)00026-4Othman, S. N. and N. H. Mustaffa. 2012. Supply Chain Simulation and Optimization Methods: An Overview. InProceedings of Third International Conference on Intelligent Systems, Modelling and Simulation(ISMS), Sabah, Malaysia, 161 –167.Padhi, S. S., S. M. Wagner, T. T. Niranjan, and V. Aggarwal. 2012. A Simulation-Based Methodology to Analyse Production Line Disruptions.International Journal of Production Research51 (6): 1885–1897.Peidro, D., M. Díaz-Madroñero, and J. Mula. 2009. Operational Transport Planning in an Automobile Supply Chain: An Interactive Fuzzy Multi-Objective Approach.Proceedings of the 8th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics. Stevens Point, Wisconsin, USA: World Scientific and Engineering Academy and Society (WSEAS), 121–127.Petrovic, D., Roy, R., & Petrovic, R. (1998). Modelling and simulation of a supply chain in an uncertain environment. European Journal of Operational Research, 109(2), 299-309. doi:10.1016/s0377-2217(98)00058-7Qu, W. W., Bookbinder, J. H., & Iyogun, P. (1999). An integrated inventory–transportation system with modified periodic policy for multiple products. European Journal of Operational Research, 115(2), 254-269. doi:10.1016/s0377-2217(98)00301-4Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28(5), 581-598. doi:10.1016/s0305-0483(99)00080-8Safaei, A. S., Moattar Husseini, S. M., Z.-Farahani, R., Jolai, F., & Ghodsypour, S. H. (2009). Integrated multi-site production-distribution planning in supply chain by hybrid modelling. International Journal of Production Research, 48(14), 4043-4069. doi:10.1080/00207540902791777Santa-Eulalia, L. A., D’Amours, S., & Frayret, J.-M. (2012). Agent-based simulations for advanced supply chain planning and scheduling: The FAMASS methodological framework for requirements analysis. International Journal of Computer Integrated Manufacturing, 25(10), 963-980. doi:10.1080/0951192x.2011.652177Sharda, B., & Akiya, N. (2012). Selecting make-to-stock and postponement policies for different products in a chemical plant: A case study using discrete event simulation. International Journal of Production Economics, 136(1), 161-171. doi:10.1016/j.ijpe.2011.10.001Tako, A. A., & Robinson, S. (2010). Model development in discrete-event simulation and system dynamics: An empirical study of expert modellers. European Journal of Operational Research, 207(2), 784-794. doi:10.1016/j.ejor.2010.05.011Thürer, M., Silva, C., & Stevenson, M. (2010). Optimising workload norms: the influence of shop floor characteristics on setting workload norms for the workload control concept. International Journal of Production Research, 49(4), 1151-1171. doi:10.1080/00207541003604836Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214. doi:10.1016/j.fss.2007.08.010Van der Vorst, J. G. A. ., Beulens, A. J. ., & van Beek, P. (2000). Modelling and simulating multi-echelon food systems. European Journal of Operational Research, 122(2), 354-366. doi:10.1016/s0377-2217(99)00238-6Le Hoa Vo, T., & Thiel, D. (2011). Economic simulation of a poultry supply chain facing a sanitary crisis. British Food Journal, 113(8), 1011-1030. doi:10.1108/00070701111153760Volling, T., & Spengler, T. S. (2011). Modeling and simulation of order-driven planning policies in build-to-order automobile production. International Journal of Production Economics, 131(1), 183-193. doi:10.1016/j.ijpe.2011.01.008Wang, R.-C., & Liang, T.-F. (2005). Applying possibilistic linear programming to aggregate production planning. International Journal of Production Economics, 98(3), 328-341. doi:10.1016/j.ijpe.2004.09.011Wangphanich, P., Kara, S., & Kayis, B. (2009). Analysis of the bullwhip effect in multi-product, multi-stage supply chain systems–a simulation approach. International Journal of Production Research, 48(15), 4501-4517. doi:10.1080/00207540902950852Williams, J. F. (1981). Heuristic Techniques for Simultaneous Scheduling of Production and Distribution in Multi-Echelon Structures: Theory and Empirical Comparisons. Management Science, 27(3), 336-352. doi:10.1287/mnsc.27.3.33

Variation of the Seebeck coefficient with hydrogen content in carbon microfilaments

The Seebeck coefficient of vapour grown carbon fibres and carbon fibres made from polyacryolytrile precursor has been studied as a possible parameter to control hydrogen storage inside. The sign of the Seebeck coefficient gives the sign of the dominant charge carriers in the fibres, and when hydrogen is absorbed by the carbonaceous material, mainly as H +, it acts as a positive charge carrier. A simple two-band electronic model has been considered to explain the influence of hydrogenation on the Seebeck's coefficient of these carbon microfibres. The most favourable condition for hydrogen adsorption is a moderately low pressure of hydrogen. Furthermore, it was observed that outgassing is more pronounced than expected in some types of fibres, thereby supporting the proposed presence of hydrogen generated during the manufacturing process. © Springer Science+Business Media, LLC 2012.The authors would like to thank the Spanish Ministry of Science and Innovation for funding via the Consolider Program (Project NANOTHERM CSD2010-00044).Peer Reviewe