Initialization Methods for Multiple Seasonal Holt-Winters Forecasting Models

[EN] The Holt-Winters models are one of the most popular forecasting algorithms. As well-known, these models are recursive and thus, an initialization value is needed to feed the model, being that a proper initialization of the Holt-Winters models is crucial for obtaining a good accuracy of the predictions. Moreover, the introduction of multiple seasonal Holt-Winters models requires a new development of methods for seed initialization and obtaining initial values. This work proposes new initialization methods based on the adaptation of the traditional methods developed for a single seasonality in order to include multiple seasonalities. Thus, new methods to initialize the level, trend, and seasonality in multiple seasonal Holt-Winters models are presented. These new methods are tested with an application for electricity demand in Spain and analyzed for their impact on the accuracy of forecasts. As a consequence of the analysis carried out, which initialization method to use for the level, trend, and seasonality in multiple seasonal Holt-Winters models with an additive and multiplicative trend is provided.Trull, O.; García-Díaz, JC.; Troncoso, A. (2020). Initialization Methods for Multiple Seasonal Holt-Winters Forecasting Models. Mathematics. 8(2):1-17. https://doi.org/10.3390/math8020268S11782Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030-1081. doi:10.1016/j.ijforecast.2014.08.008Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54(8), 799-805. doi:10.1057/palgrave.jors.2601589Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research, 204(1), 139-152. doi:10.1016/j.ejor.2009.10.003Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5-10. doi:10.1016/j.ijforecast.2003.09.015Bowerman, B. L., Koehler, A., & Pack, D. J. (1990). Forecasting time series with increasing seasonal variation. Journal of Forecasting, 9(5), 419-436. doi:10.1002/for.3980090502Initializing the Holt–Winters Methodhttps://robjhyndman.com/hyndsight/hw-initialization/Rasmussen, R. (2004). On time series data and optimal parameters. Omega, 32(2), 111-120. doi:10.1016/j.omega.2003.09.013Trull, Ó., García-Díaz, J., & Troncoso, A. (2019). Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. Energies, 12(6), 1083. doi:10.3390/en12061083Segura, J. V., & Vercher, E. (2001). A spreadsheet modeling approach to the Holt–Winters optimal forecasting. European Journal of Operational Research, 131(2), 375-388. doi:10.1016/s0377-2217(00)00062-xMakridakis, S., & Hibon, M. (1991). Exponential smoothing: The effect of initial values and loss functions on post-sample forecasting accuracy. International Journal of Forecasting, 7(3), 317-330. doi:10.1016/0169-2070(91)90005-gWilliams, D. W., & Miller, D. (1999). Level-adjusted exponential smoothing for modeling planned discontinuities. International Journal of Forecasting, 15(3), 273-289. doi:10.1016/s0169-2070(98)00083-

Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain

[EN] Electricity management and production depend heavily on demand forecasts made. Any mismatch between the energy demanded with respect to that produced supposes enormous losses for the consumer. Transmission System Operators use time series-based tools to forecast accurately the future demand and set the production program. One of the most effective and highly used methods are Holt-Winters. Recently, the incorporation of the multiple seasonal Holt-Winters methods has improved the accuracy of the predictions. These forecasts, depend greatly on the parameters with which the model is constructed. The forecasters need to deal with these parameters values when operating the model. In this article, the parameters space of the multiple seasonal Holt-Winters models applied to electricity demand in Spain is analysed and discussed. The parameters stability analysis leads to forecasters better understanding the behaviour of the predictions and managing their exploitation efficiently. The analysis addresses different time windows, depending on the period of the year as well as different training set sizes. The results show the influence of the calendar effect on these parameters and if it is necessary or not to update them in order to obtain a good accuracy over time.The authors would like to thank the Spanish Ministry of Economy and Competitiveness for the support under project TIN2017-8888209C2-1-R.Trull, Ó.; García-Díaz, JC.; Troncoso, A. (2020). Stability of Multiple Seasonal Holt-Winters Models Applied to Hourly Electricity Demand in Spain. Applied Sciences. 10(7):1-16. https://doi.org/10.3390/app10072630S11610

Mejora de la calidad y de la productividad en una planta de galvanización en caliente y en continuo mediante detección y diagnóstico de fallos

[ESP] El acero galvanizado es un producto de alto valor añadido, proporcionando el funcionamiento eficaz combinando la resistencia a la corrosión del zinc con la resistencia y la formabilidad del acero. La complejidad de la galvanización continua en caliente y las exigencias de calidad cada vez más rigurosas en la industria del automóvil también han exigido esfuerzos en el control y monitorización del proceso para hacerlo más robusto frente a causas asignables de variabilidad. La utilización de modelos estadísticos avanzados puede ayudar a las industrias para diseñar planes de inversión más económicos y racionales. Por ello, la detección y diagnostico de los fallos en el proceso de producción es un problema importante en la industria del automóvil dado que los requisitos de calidad cada vez más estrictos requieren esfuerzos respecto al control del proceso. Para ello, se utilizan datos reales procedentes de unja línea de galvanización en caliente cuyo destino es la producción de chapa para la industria del automóvil. Las técnicas de Control estadístico de procesos (SPC) constituyen un conjunto de herramientas orientadas a la mejora de la calidad y de la productividad ampliamente implantadas en la industria. Las herramientas más importantes del SPC son sin duda los conocidos Gráficos de Control, utilizados para la monitorización y control de características de calidad o de proceso críticas, con el objetivo de fabricar siempre desde el principio unidades dentro de especificaciones con la menor variabilidad posible y centradas en el nominal. El objetivo del control y monitorización de un proceso industrial es el aseguramiento del éxito de las operaciones planificadas detectando anomalías en su comportamiento. La monitorización del proceso consiste en a) la detección de la presencia de un fallo a partir de la información extraída de los datos del proceso, b) identificación del fallo a través de las variables más relevantes para diagnosticarlo y c) diagnosis del fallo: tipo, tamaño y localización

Forecasting Irregular Seasonal Power Consumption. An Application to a Hot-Dip Galvanizing Process

[EN] The method described in this document makes it possible to use the techniques usually applied to load prediction efficiently in those situations in which the series clearly presents seasonality but does not maintain a regular pattern. Distribution companies use time series to predict electricity consumption. Forecasting techniques based on statistical models or artificial intelligence are used. Reliable forecasts are required for efficient grid management in terms of both supply and capacity. One common underlying feature of most demand-related time series is a strong seasonality component. However, in some cases, the electricity demanded by a process presents an irregular seasonal component, which prevents any type of forecast. In this article, we evaluated forecasting methods based on the use of multiple seasonal models: ARIMA, Holt-Winters models with discrete interval moving seasonality, and neural networks. The models are explained and applied to a real situation, for a node that feeds a galvanizing factory. The zinc hot-dip galvanizing process is widely used in the automotive sector for the protection of steel against corrosion. It requires enormous energy consumption, and this has a direct impact on companies' income statements. In addition, it significantly affects energy distribution companies, as these companies must provide for instant consumption in their supply lines to ensure sufficient energy is distributed both for the process and for all the other consumers. The results show a substantial increase in the accuracy of predictions, which contributes to a better management of the electrical distribution.Trull, O.; García-Díaz, JC.; Peiró Signes, A. (2021). Forecasting Irregular Seasonal Power Consumption. An Application to a Hot-Dip Galvanizing Process. Applied Sciences. 11(1):1-24. https://doi.org/10.3390/app11010075S12411

Bi-objective optimization of a multihead weighing process

[EN] A multihead weighing process is a packaging technology that can be of strategic importance to a company, as it can be a key to competitive advantage in the modern food industry. The improvement in the process quality and sensory quality of food packaged in a multihead weighing process investigated in this paper is relevant to industrial engineering. A bi-objective ad hoc algorithm based on explicit enumeration for the packaging processes in multihead weighers with an unequal supply of the product to the weighing hoppers is developed. The algorithm uses an a priori strategy to generate Pareto-optimal solutions and select a subset of hoppers from the set of available ones in each packing operation. The relative importance of both aforementioned objectives is dynamically managed and adjusted. The numerical experiments are provided to illustrate the performance of the proposed algorithm and find the optimum operational conditions for the process.García-Díaz, JC.; Pulido-Rojano, A.; Giner-Bosch, V. (2017). Bi-objective optimization of a multihead weighing process. European J of Industrial Engineering. 11(3):403-423. doi:10.1504/EJIE.2017.084882S40342311

Electricity Forecasting Improvement in a Destination Using Tourism Indicators

[EN] The forecast of electricity consumption plays a fundamental role in the environmental impact of a tourist destination. Poor forecasting, under certain circumstances, can lead to huge economic losses and air pollution, as prediction errors usually have a large impact on the utilisation of fossil fuel-generation plants. Due to the seasonality of tourism, consumption in areas where the industry represents a big part of the economic activity follows a different pattern than in areas with a more regular economic distribution. The high economic impact and seasonality of the tourist activity suggests the use of variables specific to it to improve the electricity demand forecast. This article presents a Holt¿Winters model with a tourism indicator to improve the effectiveness on the electricity demand forecast in the Balearic Islands (Spain). Results indicate that the presented model improves the accuracy of the prediction by 0.3%. We recommend the use of this type of model and indicator in tourist destinations where tourism accounts for a substantial amount of the Gross Domestic Product (GDP), we can control a significant amount of the flow of tourists and the electrical balance is controlled mainly by fossil fuel power plants.Trull Domínguez, O.; Peiró Signes, A.; García-Díaz, JC. (2019). Electricity Forecasting Improvement in a Destination Using Tourism Indicators. Sustainability. 11(13). https://doi.org/10.3390/su1113365636561113Zhang, M., Li, J., Pan, B., & Zhang, G. (2018). Weekly Hotel Occupancy Forecasting of a Tourism Destination. Sustainability, 10(12), 4351. doi:10.3390/su10124351Bakhat, M., & Rosselló, J. (2011). Estimation of tourism-induced electricity consumption: The case study of Balearics Islands, Spain. Energy Economics, 33(3), 437-444. doi:10.1016/j.eneco.2010.12.009Gössling, S. (2000). Sustainable Tourism Development in Developing Countries: Some Aspects of Energy Use. 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Investigating the influence of tourism on economic growth and carbon emissions: Evidence from panel analysis of the European Union. Tourism Management, 38, 69-76. doi:10.1016/j.tourman.2013.02.016Paramati, S. R., Alam, M. S., & Chen, C.-F. (2016). The Effects of Tourism on Economic Growth and CO2 Emissions: A Comparison between Developed and Developing Economies. Journal of Travel Research, 56(6), 712-724. doi:10.1177/0047287516667848Fortuny, M., Soler, R., Cánovas, C., & Sánchez, A. (2008). Technical approach for a sustainable tourism development. Case study in the Balearic Islands. Journal of Cleaner Production, 16(7), 860-869. doi:10.1016/j.jclepro.2007.05.003Becken, S. (2002). Analysing International Tourist Flows to Estimate Energy Use Associated with Air Travel. Journal of Sustainable Tourism, 10(2), 114-131. doi:10.1080/09669580208667157Becken, S., Simmons, D. G., & Frampton, C. (2003). Energy use associated with different travel choices. Tourism Management, 24(3), 267-277. doi:10.1016/s0261-5177(02)00066-3Tourism and Transport in New Zealand: Implications for Energy Usehttps://core.ac.uk/download/pdf/35459093.pdfTabatchnaia-Tamirisa, N., Loke, M. K., Leung, P., & Tucker, K. A. (1997). Energy and tourism in Hawaii. Annals of Tourism Research, 24(2), 390-401. doi:10.1016/s0160-7383(97)80008-4Becken, S., Frampton, C., & Simmons, D. (2001). Energy consumption patterns in the accommodation sector—the New Zealand case. Ecological Economics, 39(3), 371-386. doi:10.1016/s0921-8009(01)00229-4Katircioglu, S. T. (2014). International tourism, energy consumption, and environmental pollution: The case of Turkey. Renewable and Sustainable Energy Reviews, 36, 180-187. doi:10.1016/j.rser.2014.04.058Katircioglu, S. T., Feridun, M., & Kilinc, C. (2014). Estimating tourism-induced energy consumption and CO2 emissions: The case of Cyprus. Renewable and Sustainable Energy Reviews, 29, 634-640. doi:10.1016/j.rser.2013.09.004Zaman, K., Shahbaz, M., Loganathan, N., & Raza, S. A. (2016). Tourism development, energy consumption and Environmental Kuznets Curve: Trivariate analysis in the panel of developed and developing countries. Tourism Management, 54, 275-283. doi:10.1016/j.tourman.2015.12.001Tsai, K.-T., Lin, T.-P., Hwang, R.-L., & Huang, Y.-J. (2014). Carbon dioxide emissions generated by energy consumption of hotels and homestay facilities in Taiwan. Tourism Management, 42, 13-21. doi:10.1016/j.tourman.2013.08.017,, M. del P. P.-R., Pozo-Barajas, R., & Sánchez-Rivas, J. (2017). Relationships between Tourism and Hospitality Sector Electricity Consumption in Spanish Provinces (1999–2013). Sustainability, 9(4), 480. doi:10.3390/su9040480Pablo-Romero, M., Sánchez-Braza, A., & Sánchez-Rivas, J. (2017). Relationships between Hotel and Restaurant Electricity Consumption and Tourism in 11 European Union Countries. Sustainability, 9(11), 2109. doi:10.3390/su9112109Wang, J. C. (2016). A study on the energy performance of school buildings in Taiwan. Energy and Buildings, 133, 810-822. doi:10.1016/j.enbuild.2016.10.036Warnken, J., Bradley, M., & Guilding, C. (2005). Eco-resorts vs. mainstream accommodation providers: an investigation of the viability of benchmarking environmental performance. Tourism Management, 26(3), 367-379. doi:10.1016/j.tourman.2003.11.017Financing Europe’s Low Carbon, Climate Resilient Futurehttps://www.eea.europa.eu/themes/climate/financing-europe2019s-low-carbon-climatePace, L. A. (2016). How do tourism firms innovate for sustainable energy consumption? A capabilities perspective on the adoption of energy efficiency in tourism accommodation establishments. Journal of Cleaner Production, 111, 409-420. doi:10.1016/j.jclepro.2015.01.095Sozer, H. (2010). Improving energy efficiency through the design of the building envelope. Building and Environment, 45(12), 2581-2593. doi:10.1016/j.buildenv.2010.05.004Hobbs, B. F., Jitprapaikulsarn, S., Konda, S., Chankong, V., Loparo, K. A., & Maratukulam, D. J. (1999). Analysis of the value for unit commitment of improved load forecasts. IEEE Transactions on Power Systems, 14(4), 1342-1348. doi:10.1109/59.801894Bilan, Y., Streimikiene, D., Vasylieva, T., Lyulyov, O., Pimonenko, T., & Pavlyk, A. (2019). Linking between Renewable Energy, CO2 Emissions, and Economic Growth: Challenges for Candidates and Potential Candidates for the EU Membership. Sustainability, 11(6), 1528. doi:10.3390/su11061528Pfenninger, S., & Keirstead, J. (2015). Renewables, nuclear, or fossil fuels? Scenarios for Great Britain’s power system considering costs, emissions and energy security. Applied Energy, 152, 83-93. doi:10.1016/j.apenergy.2015.04.102Aguiló, E., Alegre, J., & Sard, M. (2005). The persistence of the sun and sand tourism model. Tourism Management, 26(2), 219-231. doi:10.1016/j.tourman.2003.11.004Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4), 1030-1081. doi:10.1016/j.ijforecast.2014.08.008Cancelo, J. R., Espasa, A., & Grafe, R. (2008). Forecasting the electricity load from one day to one week ahead for the Spanish system operator. International Journal of Forecasting, 24(4), 588-602. doi:10.1016/j.ijforecast.2008.07.005Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and Sustainable Energy Reviews, 16(2), 1223-1240. doi:10.1016/j.rser.2011.08.014Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413-1421. doi:10.1016/j.energy.2009.06.034De Felice, M., Alessandri, A., & Ruti, P. M. (2013). Electricity demand forecasting over Italy: Potential benefits using numerical weather prediction models. Electric Power Systems Research, 104, 71-79. doi:10.1016/j.epsr.2013.06.004Taylor, J. W. (2008). An evaluation of methods for very short-term load forecasting using minute-by-minute British data. International Journal of Forecasting, 24(4), 645-658. doi:10.1016/j.ijforecast.2008.07.007Gardner, E. S., & Mckenzie, E. (1985). Forecasting Trends in Time Series. Management Science, 31(10), 1237-1246. doi:10.1287/mnsc.31.10.1237Taylor, J. W. (2003). Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting, 19(4), 715-725. doi:10.1016/s0169-2070(03)00003-7Williams, D. W., & Miller, D. (1999). Level-adjusted exponential smoothing for modeling planned discontinuities. International Journal of Forecasting, 15(3), 273-289. doi:10.1016/s0169-2070(98)00083-1Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54(8), 799-805. doi:10.1057/palgrave.jors.2601589Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research, 204(1), 139-152. doi:10.1016/j.ejor.2009.10.003Gardner, E. S. (2006). Exponential smoothing: The state of the art—Part II. International Journal of Forecasting, 22(4), 637-666. doi:10.1016/j.ijforecast.2006.03.005Trull, Ó., García-Díaz, J., & Troncoso, A. (2019). Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. Energies, 12(6), 1083. doi:10.3390/en12061083Pardo, A., Meneu, V., & Valor, E. (2002). Temperature and seasonality influences on Spanish electricity load. Energy Economics, 24(1), 55-70. doi:10.1016/s0140-9883(01)00082-2Taylor, J. W., & McSharry, P. E. (2007). 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Municipal water demand forecasting: Tools for intervention time series

This article introduces some approaches to common issues arising in real cases of water demand prediction. Occurrences of negative data gathered by the network metering system and demand changes due to closure of valves or changes in consumer behavior are considered. Artificial neural networks (ANNs) have a principal role modeling both circumstances. First, we propose the use of ANNs as a tool to reconstruct any anomalous time series information. Next, we use what we call interrupted neural networks (I-NN) as an alternative to more classical intervention ARIMA models. Besides, the use of hybrid models that combine not only the modeling ability of ARIMA to cope with the time series linear part, but also to explain nonlinearities found in their residuals, is proposed. These models have shown promising results when tested on a real database and represent a boost to the use and the applicability of ANNs.This work has been supported by project IDAWAS, DPI2009-11591, of the Direccion General de Investigacion of the Ministerio de Ciencia e Innovacion of Spain, and ACOMP/2010/146 of the Conselleria de Educacion of the Generalitat Valenciana. As well, the authors are grateful to "Aguas de Murcia" for the collaboration in this work and for the availability of the data.This work has been supported by project IDAWAS, DPI2009-11591, of the Direccion General de Investigacion of the Ministerio de Ciencia e Innovacion of Spain, and ACOMP/2010/146 of the Conseller a de Educacion of the Generalitat Valenciana. As well, the authors are grateful to "Aguas de Murcia" for the collaboration in this work and for the availability of the data.Herrera Fernández, AM.; García-Díaz, JC.; Izquierdo Sebastián, J.; Pérez García, R. (2011). Municipal water demand forecasting: Tools for intervention time series. Stochastic Analysis and Applications. 29(6):998-1007. https://doi.org/10.1080/07362994.2011.610161S9981007296Zhou, S. ., McMahon, T. ., Walton, A., & Lewis, J. (2002). Forecasting operational demand for an urban water supply zone. Journal of Hydrology, 259(1-4), 189-202. doi:10.1016/s0022-1694(01)00582-0Bougadis, J., Adamowski, K., & Diduch, R. (2005). Short-term municipal water demand forecasting. Hydrological Processes, 19(1), 137-148. doi:10.1002/hyp.5763Jain, A., & Ormsbee, L. E. (2002). Short-term water demand forecast modeling techniques-CONVENTIONAL METHODS VERSUS AI. Journal - American Water Works Association, 94(7), 64-72. doi:10.1002/j.1551-8833.2002.tb09507.xPeña, D., Tiao, G. C., & Tsay, R. S. (Eds.). (2000). A Course in Time Series Analysis. Wiley Series in Probability and Statistics. doi:10.1002/9781118032978et al. 2000 . Mining Time Series of Meteorological Variables Using Rough Sets—A Case Study, Binding Environmental Sciences and Artificial Intelligent. BESAI 2000, Germany, 7:1–8.Herrera, M., Torgo, L., Izquierdo, J., & Pérez-García, R. (2010). Predictive models for forecasting hourly urban water demand. Journal of Hydrology, 387(1-2), 141-150. doi:10.1016/j.jhydrol.2010.04.005McLeod, A. I., & Vingilis, E. R. (2005). Power Computations for Intervention Analysis. Technometrics, 47(2), 174-181. doi:10.1198/004017005000000094Box, G. E. P., & Tiao, G. C. (1975). Intervention Analysis with Applications to Economic and Environmental Problems. Journal of the American Statistical Association, 70(349), 70-79. doi:10.1080/01621459.1975.10480264Zhang, G. P., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160(2), 501-514. doi:10.1016/j.ejor.2003.08.037Zealand, C. M., Burn, D. H., & Simonovic, S. P. (1999). Short term streamflow forecasting using artificial neural networks. Journal of Hydrology, 214(1-4), 32-48. doi:10.1016/s0022-1694(98)00242-xWang, W., Gelder, P. H. A. J. M. V., Vrijling, J. K., & Ma, J. (2006). Forecasting daily streamflow using hybrid ANN models. Journal of Hydrology, 324(1-4), 383-399. doi:10.1016/j.jhydrol.2005.09.032Kneale , P. , See , L. , and Smith , A. 2001 .Towards Defining Evaluation Measures for Neural Network Forecasting Models; Proceedings of the Sixth International Conference on GeoComputation, University of Queensland, Australia.Peña, D., & Rodríguez, J. (2002). A Powerful Portmanteau Test of Lack of Fit for Time Series. Journal of the American Statistical Association, 97(458), 601-610. doi:10.1198/016214502760047122Peña, D., & Rodríguez, J. (2006). The log of the determinant of the autocorrelation matrix for testing goodness of fit in time series. Journal of Statistical Planning and Inference, 136(8), 2706-2718. doi:10.1016/j.jspi.2004.10.026LJUNG, G. M., & BOX, G. E. P. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303. doi:10.1093/biomet/65.2.297MONTI, A. C. (1994). A proposal for a residual autocorrelation test in linear models. Biometrika, 81(4), 776-780. doi:10.1093/biomet/81.4.77

Bicriteria food packaging process optimization in double-layered upright and diagonal multihead weighers

[EN] Double-layered multihead weighing machines contain twice the number of hoppers as present in a simple machine with the same number of heads, which enables additional objective optimization possibilities considering the increased number of combinations among hoppers. This research study deals with bicriteria optimization for double-layered upright and diagonal machines using brute force as the optimization criteria. One of the optimization objectives is related to the target weight; the target weight must be at least and as close as possible to the weight to pack. Furthermore, this study also aims to minimize the time for which a certain portion of a product remains in the hopper while waiting to be selected for package formation. This time is known as priority and is measured based on the number of iterations or the number of packages produced by the machine while the hopper waits to be discharged. For these purposes, Different strategies were tested for both machines, which simultaneously optimize the target weight and the priority of the hoppers, showing the reduction of the extraction of the process in addition to reducing the costs of excess product and its reprocessing.We express our gratitude for the support from Universidad Simon Bolivar, Colombia, and Universitat Politecnica de Valencia.Garcia-Jimenez, R.; García-Díaz, JC.; Pulido-Rojano, AD. (2023). Bicriteria food packaging process optimization in double-layered upright and diagonal multihead weighers. Journal of Computational and Applied Mathematics. 428:1-10. https://doi.org/10.1016/j.cam.2023.11516811042

Anxiety towards Statistics and Its Relationship with Students' Attitudes and Learning Approach

[EN] Many university students have difficulties when facing statistics related tasks, leading to an increase in their levels of anxiety and poor performance. Researchers have identified negative attitudes towards statistics, which have been shaped through students' secondary education experience, as a major driver for their failure. In this study we want to uncover the causal recipes of attitudes leading to high and low levels of anxiety in secondary education students, and the role that the learning approach plays in these relationships. We used fuzzy sets comparative qualitative analysis (fsQCA) in a sample of 325 students surveyed on the multifactorial scale of attitudes toward statistics (MSATS) and the revised two factor study process questionnaire (R-SPQ-2F). The results indicate that, respectively, a high or a low level of self-confidence is the most important and a sufficient condition by itself for achieving a low or a high level of anxiety, while the learning approaches and other attitudes are only present in other causal combinations that represent a small number of cases.Peiró Signes, A.; Trull, O.; Segarra-Oña, M.; García-Díaz, JC. (2021). Anxiety towards Statistics and Its Relationship with Students' Attitudes and Learning Approach. Behavioral Sciences. 11(3):1-13. https://doi.org/10.3390/bs11030032S11311