Geometric and Signal Strength Dilution of Precision (DoP)Wi-Fi

The democratization of wireless networks combined to the emergence of mobile devices increasingly autonomous and efficient lead to new services. Positioning services become overcrowded. Accuracy is the main quality criteria in positioning. But to better appreciate this one a coefficient is needed. In this paper we present Geometric and Signal Strength Dilution of Precision (DOP) for positioning systems based on Wi-Fi and Signal Strength measurements.Comment: International Journal of Computer Science Issues (IJCSI), Volume 3, pp35-44, August 200

Understanding the effect of emulsifiers on bread aeration during breadmaking

[EN] BACKGROUNDMuch research has been done to explain the action of emulsifiers during breadmaking, but there is still plenty unknown to elucidate their functionality despite their diverse chemical structure. The aim of the present study was to provide some light on the role of emulsifiers on air incorporation into the dough and gas bubbles progress during baking and their relationship with bread features. Emulsifiers like diacetyl tartaric acid ester of monoglycerides (DATEM), sodium stearoyl lactylate (SSL), distilled monoglyceride (DMG-45 and DMG-75), lecithin and polyglycerol esters of fatty acids (PGEF) were tested in very hydrated doughs. RESULTSEmulsifiers increase the maximum dough volume during proofing. Emulsifiers increase the number of bubbles incorporated during mixing, observing higher number of bubbles, particularly with PGEF. Major changes in dough occurred at 70K when bubble size augmented, becoming more heterogeneous. DMG-75 produced the biggest bubbles. As a consequence, emulsifiers tend to increase the number of gas cells with lower size in the bread crumb, but led to greater crumb firmness, which suggested different interactions between emulsifiers and gluten, affecting protein polymerization during baking. CONCLUSIONThe progress of the bubbles during baking allowed the differentiation of emulsifiers, which could explain their performance in breadmaking. (c) 2018 Society of Chemical IndustryAuthors acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness (Project AGL2014-52928-C2-1-R), the European Regional Development Fund (FEDER) and Generalitat Valenciana (Project Prometeo 2017/189).Garzon, R.; Hernando Hernando, MI.; Llorca Martínez, ME.; Molina Rosell, MC. (2018). Understanding the effect of emulsifiers on bread aeration during breadmaking. Journal of the Science of Food and Agriculture. 98(14):5494-5502. https://doi.org/10.1002/jsfa.9094S549455029814Rosell, C. M., & Garzon, R. (2015). Chemical Composition of Bakery Products. Handbook of Food Chemistry, 191-224. doi:10.1007/978-3-642-36605-5_22Chin, N. L., & Campbell, G. M. (2005). Dough aeration and rheology: Part 1. Effects of mixing speed and headspace pressure on mechanical development of bread dough. Journal of the Science of Food and Agriculture, 85(13), 2184-2193. doi:10.1002/jsfa.2236Trinh, L., Lowe, T., Campbell, G. M., Withers, P. J., & Martin, P. J. (2015). Effect of sugar on bread dough aeration during mixing. Journal of Food Engineering, 150, 9-18. doi:10.1016/j.jfoodeng.2014.10.020Peighambardoust, S. H., Fallah, E., Hamer, R. J., & van der Goot, A. J. (2010). Aeration of bread dough influenced by different way of processing. Journal of Cereal Science, 51(1), 89-95. doi:10.1016/j.jcs.2009.10.002Chin, N. L., Campbell, G. M., & Thompson, F. (2005). Characterisation of bread doughs with different densities, salt contents and water levels using microwave power transmission measurements. Journal of Food Engineering, 70(2), 211-217. doi:10.1016/j.jfoodeng.2004.09.024Mehta, K. L., Scanlon, M. G., Sapirstein, H. D., & Page, J. H. (2009). Ultrasonic Investigation of the Effect of Vegetable Shortening and Mixing Time on the Mechanical Properties of Bread Dough. Journal of Food Science, 74(9), E455-E461. doi:10.1111/j.1750-3841.2009.01346.xBellido, G. G., Scanlon, M. G., & Page, J. H. (2009). Measurement of dough specific volume in chemically leavened dough systems. Journal of Cereal Science, 49(2), 212-218. doi:10.1016/j.jcs.2008.10.002Moayedallaie, S., Mirzaei, M., & Paterson, J. (2010). Bread improvers: Comparison of a range of lipases with a traditional emulsifier. Food Chemistry, 122(3), 495-499. doi:10.1016/j.foodchem.2009.10.033Van Steertegem, B., Pareyt, B., Brijs, K., & Delcour, J. A. (2013). Impact of mixing time and sodium stearoyl lactylate on gluten polymerization during baking of wheat flour dough. Food Chemistry, 141(4), 4179-4185. doi:10.1016/j.foodchem.2013.07.017Gómez, A. V., Buchner, D., Tadini, C. C., Añón, M. C., & Puppo, M. C. (2012). Emulsifiers: Effects on Quality of Fibre-Enriched Wheat Bread. Food and Bioprocess Technology, 6(5), 1228-1239. doi:10.1007/s11947-011-0772-7Aamodt, A., Magnus, E. M., & FAERGESTAD, E. M. (2003). Effect of Flour Quality, Ascorbic Acid, and DATEM on Dough Rheological Parameters and Hearth Loaves Characteristics. Journal of Food Science, 68(7), 2201-2210. doi:10.1111/j.1365-2621.2003.tb05747.xFarvili, N., Walker, C. E., & Qarooni, J. (1995). Effects of Emulsifiers on Pita Bread Quality. Journal of Cereal Science, 21(3), 301-308. doi:10.1006/jcrs.1995.0033Gómez, M., del Real, S., Rosell, C. M., Ronda, F., Blanco, C. A., & Caballero., P. A. (2004). Functionality of different emulsifiers on the performance of breadmaking and wheat bread quality. European Food Research and Technology, 219(2), 145-150. doi:10.1007/s00217-004-0937-yRavi, R., Manohar, R. S., & Rao, P. H. (2000). Influence of additives on the rheological characteristics and baking quality of wheat flours. European Food Research and Technology, 210(3), 202-208. doi:10.1007/pl00005512Rodríguez-García, J., Salvador, A., & Hernando, I. (2013). Replacing Fat and Sugar with Inulin in Cakes: Bubble Size Distribution, Physical and Sensory Properties. Food and Bioprocess Technology, 7(4), 964-974. doi:10.1007/s11947-013-1066-zGarzón, R., Rosell, C. M., Malvar, R. A., & Revilla, P. (2017). Diversity among maize populations from Spain and the United States for dough rheology and gluten-free breadmaking performance. International Journal of Food Science & Technology, 52(4), 1000-1008. doi:10.1111/ijfs.13364Gómez, A. V., Ferrer, E., Añón, M. C., & Puppo, M. C. (2012). Analysis of soluble proteins/aggregates derived from gluten-emulsifiers systems. Food Research International, 46(1), 62-68. doi:10.1016/j.foodres.2011.12.007Ferrer, E. G., Gómez, A. V., Añón, M. C., & Puppo, M. C. (2011). Structural changes in gluten protein structure after addition of emulsifier. A Raman spectroscopy study. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 79(1), 278-281. doi:10.1016/j.saa.2011.02.022Turbin-Orger, A., Boller, E., Chaunier, L., Chiron, H., Della Valle, G., & Réguerre, A.-L. (2012). Kinetics of bubble growth in wheat flour dough during proofing studied by computed X-ray micro-tomography. Journal of Cereal Science, 56(3), 676-683. doi:10.1016/j.jcs.2012.08.008Babin, P., Della Valle, G., Chiron, H., Cloetens, P., Hoszowska, J., Pernot, P., … Dendievel, R. (2006). Fast X-ray tomography analysis of bubble growth and foam setting during breadmaking. Journal of Cereal Science, 43(3), 393-397. doi:10.1016/j.jcs.2005.12.002Kokelaar, J. J., Garritsen, J. A., & Prins, A. (1995). Surface rheological properties of sodium stearoyl-2-lactylate (SSL) and diacetyl tartaric esters of mono (and di) glyceride (DATEM) surfactants after a mechanical surface treatment in relation to their bread improving abilities. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 95(1), 69-77. doi:10.1016/0927-7757(94)03009-oChakrabarti-Bell, S., Wang, S., & Siddique, K. H. M. (2014). Flour quality and disproportionation of bubbles in bread doughs. Food Research International, 64, 587-597. doi:10.1016/j.foodres.2014.07.025McClements, D. J. (2015). Food Emulsions. doi:10.1201/b18868AZIZI, M. H., & RAO, G. V. (2005). Effect of Surfactant Gels on Dough Rheological Characteristics and Quality of Bread. Critical Reviews in Food Science and Nutrition, 44(7-8), 545-552. doi:10.1080/10408690490489288Gomes-Ruffi, C. R., Cunha, R. H. da, Almeida, E. L., Chang, Y. K., & Steel, C. J. (2012). Effect of the emulsifier sodium stearoyl lactylate and of the enzyme maltogenic amylase on the quality of pan bread during storage. LWT, 49(1), 96-101. doi:10.1016/j.lwt.2012.04.014Upadhyay, R., Ghosal, D., & Mehra, A. (2012). Characterization of bread dough: Rheological properties and microstructure. Journal of Food Engineering, 109(1), 104-113. doi:10.1016/j.jfoodeng.2011.09.02

Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork

[EN] The need of organizations to ensure service levels that impact on customer satisfaction has required the design of collaborative processes among stakeholders involved in inventory decision making. The increase of quantity and variety of items, on the one hand, and demand and customer expectations, on the other hand, are transformed into a greater complexity in inventory management, requiring effective communication and agreements between the leaders of the logistics processes. Traditionally, decision making in inventory management was based on approaches conditioned only by cost or sales volume. These approaches must be overcome by others that consider multiple criteria, involving several areas of the companies and taking into account the opinions of the stakeholders involved in these decisions. Inventory management becomes part of a complex system that involves stakeholders from different areas of the company, where each agent has limited information and where the cooperation between such agents is key for the system's performance. In this paper, a distributed inventory control approach was used with the decisions allowing communication between the stakeholders and with a multicriteria group decision-making perspective. This work proposes a methodology that combines the analysis of the value chain and the AHP technique, in order to improve communication and the performance of the areas related to inventory management decision making. This methodology uses the areas of the value chain as a theoretical framework to identify the criteria necessary for the application of the AHP multicriteria group decision-making technique. These criteria were defined as indicators that measure the performance of the areas of the value chain related to inventory management and were used to classify ABC inventory of the products according to these selected criteria. Therefore, the methodology allows us to solve inventory management DDM based on multicriteria ABC classification and was validated in a Colombian company belonging to the graphic arts sector.Pérez Vergara, IG.; Arias Sánchez, JA.; Poveda Bautista, R.; Diego-Mas, JA. (2020). Improving Distributed Decision Making in Inventory Management: A Combined ABC-AHP Approach Supported by Teamwork. Complexity. 2020:1-13. https://doi.org/10.1155/2020/6758108S1132020Poveda-Bautista, R., Baptista, D. C., & García-Melón, M. (2012). Setting competitiveness indicators using BSC and ANP. International Journal of Production Research, 50(17), 4738-4752. doi:10.1080/00207543.2012.657964Castro Zuluaga, C. A., Velez Gallego, M. C., & Catro Urrego, J. A. (2011). Clasificación ABC Multicriterio: Tipos de Criterios y efectos en la asignación de pesos. ITECKNE, 8(2). doi:10.15332/iteckne.v8i2.35Morash, E. A., & Clinton, S. R. (1998). Supply Chain Integration: Customer Value through Collaborative Closeness versus Operational Excellence. Journal of Marketing Theory and Practice, 6(4), 104-120. doi:10.1080/10696679.1998.11501814Fabbe-Costes, N. (2015). Évaluer la création de valeurdu Supply Chain Management. Logistique & Management, 23(4), 41-50. doi:10.1080/12507970.2015.11758621Flores, B. E., & Clay Whybark, D. (1986). Multiple Criteria ABC Analysis. International Journal of Operations & Production Management, 6(3), 38-46. doi:10.1108/eb054765Partovi, F. Y., & Burton, J. (1993). Using the Analytic Hierarchy Process for ABC Analysis. International Journal of Operations & Production Management, 13(9), 29-44. doi:10.1108/01443579310043619Balaji, K., & Kumar, V. S. S. (2014). Multicriteria Inventory ABC Classification in an Automobile Rubber Components Manufacturing Industry. Procedia CIRP, 17, 463-468. doi:10.1016/j.procir.2014.02.044Ramanathan, R. (2006). ABC inventory classification with multiple-criteria using weighted linear optimization. Computers & Operations Research, 33(3), 695-700. doi:10.1016/j.cor.2004.07.014Van Kampen, T. J., Akkerman, R., & Pieter van Donk, D. (2012). SKU classification: a literature review and conceptual framework. International Journal of Operations & Production Management, 32(7), 850-876. doi:10.1108/01443571211250112Flores, B. E., Olson, D. L., & Dorai, V. K. (1992). Management of multicriteria inventory classification. Mathematical and Computer Modelling, 16(12), 71-82. doi:10.1016/0895-7177(92)90021-cGajpal, P. P., Ganesh, L. S., & Rajendran, C. (1994). Criticality analysis of spare parts using the analytic hierarchy process. International Journal of Production Economics, 35(1-3), 293-297. doi:10.1016/0925-5273(94)90095-7Scala, N. M., Rajgopal, J., & Needy, K. L. (2014). Managing Nuclear Spare Parts Inventories: A Data Driven Methodology. IEEE Transactions on Engineering Management, 61(1), 28-37. doi:10.1109/tem.2013.2283170Hadad, Y., & Keren, B. (2013). ABC inventory classification via linear discriminant analysis and ranking methods. International Journal of Logistics Systems and Management, 14(4), 387. doi:10.1504/ijlsm.2013.052744Altay Guvenir, H., & Erel, E. (1998). Multicriteria inventory classification using a genetic algorithm. European Journal of Operational Research, 105(1), 29-37. doi:10.1016/s0377-2217(97)00039-8Rezaei, J., & Dowlatshahi, S. (2010). A rule-based multi-criteria approach to inventory classification. International Journal of Production Research, 48(23), 7107-7126. doi:10.1080/00207540903348361Hatefi, S. M., Torabi, S. A., & Bagheri, P. (2013). Multi-criteria ABC inventory classification with mixed quantitative and qualitative criteria. International Journal of Production Research, 52(3), 776-786. doi:10.1080/00207543.2013.838328Ishizaka, A., Pearman, C., & Nemery, P. (2012). AHPSort: an AHP-based method for sorting problems. International Journal of Production Research, 50(17), 4767-4784. doi:10.1080/00207543.2012.657966Yu, M.-C. (2011). Multi-criteria ABC analysis using artificial-intelligence-based classification techniques. Expert Systems with Applications, 38(4), 3416-3421. doi:10.1016/j.eswa.2010.08.127Tsai, C.-Y., & Yeh, S.-W. (2008). A multiple objective particle swarm optimization approach for inventory classification. International Journal of Production Economics, 114(2), 656-666. doi:10.1016/j.ijpe.2008.02.017Aydin Keskin, G., & Ozkan, C. (2013). Multiple Criteria ABC Analysis with FCM Clustering. Journal of Industrial Engineering, 2013, 1-7. doi:10.1155/2013/827274Lolli, F., Ishizaka, A., & Gamberini, R. (2014). New AHP-based approaches for multi-criteria inventory classification. International Journal of Production Economics, 156, 62-74. doi:10.1016/j.ijpe.2014.05.015Raja, A. M. L., Ai, T. J., & Astanti, R. D. (2016). A Clustering Classification of Spare Parts for Improving Inventory Policies. IOP Conference Series: Materials Science and Engineering, 114, 012075. doi:10.1088/1757-899x/114/1/012075Zowid, F. M., Babai, M. Z., Douissa, M. R., & Ducq, Y. (2019). Multi-criteria inventory ABC classification using Gaussian Mixture Model. IFAC-PapersOnLine, 52(13), 1925-1930. doi:10.1016/j.ifacol.2019.11.484Babai, M. Z., Ladhari, T., & Lajili, I. (2014). On the inventory performance of multi-criteria classification methods: empirical investigation. International Journal of Production Research, 53(1), 279-290. doi:10.1080/00207543.2014.952791Schneeweiss, C. (2003). Distributed decision making––a unified approach. European Journal of Operational Research, 150(2), 237-252. doi:10.1016/s0377-2217(02)00501-5Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83. doi:10.1504/ijssci.2008.017590Cakir, O., & Canbolat, M. S. (2008). A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology. Expert Systems with Applications, 35(3), 1367-1378. doi:10.1016/j.eswa.2007.08.041Liu, J., Liao, X., Zhao, W., & Yang, N. (2016). A classification approach based on the outranking model for multiple criteria ABC analysis. Omega, 61, 19-34. doi:10.1016/j.omega.2015.07.004Douissa, M. R., & Jabeur, K. (2016). A New Model for Multi-criteria ABC Inventory Classification: PROAFTN Method. Procedia Computer Science, 96, 550-559. doi:10.1016/j.procs.2016.08.233Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Rimini, B., & Regattieri, A. (2018). Machine learning for multi-criteria inventory classification applied to intermittent demand. Production Planning & Control, 30(1), 76-89. doi:10.1080/09537287.2018.1525506Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers & Industrial Engineering, 101, 599-613. doi:10.1016/j.cie.2016.06.004López-Soto, D., Angel-Bello, F., Yacout, S., & Alvarez, A. (2017). A multi-start algorithm to design a multi-class classifier for a multi-criteria ABC inventory classification problem. Expert Systems with Applications, 81, 12-21. doi:10.1016/j.eswa.2017.02.048Dweiri, F., Kumar, S., Khan, S. A., & Jain, V. (2016). Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Systems with Applications, 62, 273-283. doi:10.1016/j.eswa.2016.06.030Bruno, G., Esposito, E., Genovese, A., & Simpson, M. (2016). Applying supplier selection methodologies in a multi-stakeholder environment: A case study and a critical assessment. Expert Systems with Applications, 43, 271-285. doi:10.1016/j.eswa.2015.07.016Poza, C. (2020). A Conceptual Model to Measure Football Player’s Market Value. A Proposal by means of an Analytic Hierarchy Process. [Un modelo conceptual para medir el valor de mercado de los futbolistas. Una propuesta a través de un proceso analítico jerárquico]. RICYDE. Revista internacional de ciencias del deporte, 16(59), 24-42. doi:10.5232/ricyde2020.05903Guarnieri, P., Sobreiro, V. A., Nagano, M. S., & Marques Serrano, A. L. (2015). The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: a Brazilian case. Journal of Cleaner Production, 96, 209-219. doi:10.1016/j.jclepro.2014.05.040Ishizaka, A., & Labib, A. (2011). Selection of new production facilities with the Group Analytic Hierarchy Process Ordering method. Expert Systems with Applications, 38(6), 7317-7325. doi:10.1016/j.eswa.2010.12.004Partovi, F. Y., & Anandarajan, M. (2002). Classifying inventory using an artificial neural network approach. Computers & Industrial Engineering, 41(4), 389-404. doi:10.1016/s0360-8352(01)00064-xAlonso-Manzanedo, M., De-la -Fuente-Aragon, M. V., & Ros-McDonnell, L. (2013). A Proposed Collaborative Network Enterprise Model in the Fruit-and-Vegetable Sector Using Maturity Models. Annals of Industrial Engineering 2012, 359-366. doi:10.1007/978-1-4471-5349-8_42Augusto, M., Lisboa, J., Yasin, M., & Figueira, J. R. (2008). Benchmarking in a multiple criteria performance context: An application and a conceptual framework. 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Evaluating how agent methodologies support the specification of the normative environment through the development process

[EN] Due to the increase in collaborative work and the decentralization of processes in many domains, there is an expanding demand for large-scale, flexible and adaptive software systems to support the interactions of people and institutions distributed in heterogeneous environments. Commonly, these software applications should follow specific regulations meaning the actors using them are bound by rights, duties and restrictions. Since this normative environment determines the final design of the software system, it should be considered as an important issue during the design of the system. Some agent-oriented software engineering methodologies deal with the development of normative systems (systems that have a normative environment) by integrating the analysis of the normative environment of a system in the development process. This paper analyses to what extent these methodologies support the analysis and formalisation of the normative environment and highlights some open issues of the topic.This work is partially supported by the PROMETEOII/2013/019, TIN2012-36586-C03-01, FP7-29493, TIN2011-27652-C03-00, CSD2007-00022 projects, and the CASES project within the 7th European Community Framework Program under the grant agreement No 294931.Garcia Marques, ME.; Miles, S.; Luck, M.; Giret Boggino, AS. (2014). Evaluating how agent methodologies support the specification of the normative environment through the development process. Autonomous Agents and Multi-Agent Systems. 1-20. https://doi.org/10.1007/s10458-014-9275-zS120Cossentino, M., Hilaire, V., Molesini, A., & Seidita, V. (Eds.). (2014). Handbook on agent-oriented design processes (Vol. VIII, 569 p. 508 illus.). Berlin: Springer.Akbari, O. (2010). A survey of agent-oriented software engineering paradigm: Towards its industrial acceptance. 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Verhagen (Eds.), Normative multi-agent systems, number 09121 in Dagstuhl seminar proceedings.Boella, G., Torre, L., & Verhagen, H. (2006). Introduction to normative multiagent systems. Computational and Mathematical Organization Theory, 12(2–3), 71–79.Bogdanovych, A., Esteva, M., Simoff, S., Sierra, C., & Berger, H. (2008). A methodology for developing multiagent systems as 3d electronic institutions. In M. Luck & L. Padgham (Eds.), Agent-Oriented Software Engineering VIII (Vol. 4951, pp. 103–117). Lecture Notes in Computer Science. Berlin: Springer.Boissier, O., Padget, J., Dignum, V., Lindemann, G., Matson, E., Ossowski, S., Sichman, J., & Vazquez-Salceda, J. (2006). Coordination, organizations, institutions and norms in multi-agent systems. LNCS (LNAI) (Vol. 3913).Bordini, R. H., Fisher, M., Visser, W., & Wooldridge, M. (2006). Verifying multi-agent programs by model checking. In Autonomous agents and multi-agent systems (Vol. 12, pp. 239–256). 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A review of mobile robots: Concepts, methods, theoretical framework, and applications

[EN] Humanoid robots, unmanned rovers, entertainment pets, drones, and so on are great examples of mobile robots. They can be distinguished from other robots by their ability to move autonomously, with enough intelligence to react and make decisions based on the perception they receive from the environment. Mobile robots must have some source of input data, some way of decoding that input, and a way of taking actions (including its own motion) to respond to a changing world. The need to sense and adapt to an unknown environment requires a powerful cognition system. Nowadays, there are mobile robots that can walk, run, jump, and so on like their biological counterparts. Several fields of robotics have arisen, such as wheeled mobile robots, legged robots, flying robots, robot vision, artificial intelligence, and so on, which involve different technological areas such as mechanics, electronics, and computer science. In this article, the world of mobile robots is explored including the new trends. These new trends are led by artificial intelligence, autonomous driving, network communication, cooperative work, nanorobotics, friendly human-robot interfaces, safe human-robot interaction, and emotion expression and perception. Furthermore, these news trends are applied to different fields such as medicine, health care, sports, ergonomics, industry, distribution of goods, and service robotics. These tendencies will keep going their evolution in the coming years.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy and Competitiveness, which has funded the DPI2013-44227-R project.Rubio Montoya, FJ.; Valero Chuliá, FJ.; Llopis Albert, C. (2019). A review of mobile robots: Concepts, methods, theoretical framework, and applications. 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On environment difficulty and discriminating power

The final publication is available at Springer via http://dx.doi.org/10.1007/s10458-014-9257-1This paper presents a way to estimate the difficulty and discriminating power of any task instance. We focus on a very general setting for tasks: interactive (possibly multiagent) environments where an agent acts upon observations and rewards. Instead of analysing the complexity of the environment, the state space or the actions that are performed by the agent, we analyse the performance of a population of agent policies against the task, leading to a distribution that is examined in terms of policy complexity. This distribution is then sliced by the algorithmic complexity of the policy and analysed through several diagrams and indicators. The notion of environment response curve is also introduced, by inverting the performance results into an ability scale. We apply all these concepts, diagrams and indicators to two illustrative problems: a class of agent-populated elementary cellular automata, showing how the difficulty and discriminating power may vary for several environments, and a multiagent system, where agents can become predators or preys, and may need to coordinate. Finally, we discuss how these tools can be applied to characterise (interactive) tasks and (multi-agent) environments. These characterisations can then be used to get more insight about agent performance and to facilitate the development of adaptive tests for the evaluation of agent abilities.I thank the reviewers for their comments, especially those aiming at a clearer connection with the field of multi-agent systems and the suggestion of better approximations for the calculation of the response curves. The implementation of the elementary cellular automata used in the environments is based on the library 'CellularAutomaton' by John Hughes for R [58]. I am grateful to Fernando Soler-Toscano for letting me know about their work [65] on the complexity of 2D objects generated by elementary cellular automata. I would also like to thank David L. Dowe for his comments on a previous version of this paper. This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST - European Cooperation in the field of Scientific and Technical Research IC0801 AT, and the REFRAME project, granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA), and funded by the Ministerio de Economia y Competitividad in Spain (PCIN-2013-037).José Hernández-Orallo (2015). On environment difficulty and discriminating power. Autonomous Agents and Multi-Agent Systems. 29(3):402-454. https://doi.org/10.1007/s10458-014-9257-1S402454293Anderson, J., Baltes, J., & Cheng, C. T. (2011). 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Effect of Microwave Power Coupled with Hot Air Drying on Sorption Isotherms and Microstructure of Orange Pee

[EN] Drying is one of the most cost-effective methods of worthwhile by-product valorisation. This study had two main objectives. The first was to determine the effect of hot air drying (HAD) combined with microwave (MW) irradiation on the treatment kinetics and the macrostructural and microstructural properties of the dried product. The second aim was to develop engineering tools to predict the extent of dehydration. Drying was performed using hot air at 55 A degrees C and the combined (HAD + MW) treatment at different power intensities (2, 4, and 6 W/g). After 5, 15, 40, 60, and 120 min, the mass, surface, volume, water activity and moisture were measured in fresh and dried samples. Sorption isotherms were obtained and fitted to the GAB model, with high correlation coefficients. The macroscopic and microscopic analyses showed shrinkage and swelling in the peel tissue caused by the MW treatment. The HAD + MW methods not only resulted in increased moisture reduction but also induced microstructural changes that generated higher sorption capacity.The authors would like to thank the Basque Government for the financial support of the project (LasaiFood). They also acknowledge the financial support from the Spanish Ministerio de Economia, Industria y Competitividad, Programa Estatal de I+D+i orientada a los Retos de la Sociedad AGL2016-80643-R. This paper is contribution no. 777 from AZTI (Food Research Division). The authors would like to thank the Electronic Microscopy Service of the Universidad Politecnica de Valencia for its assistance in the use of Cryo-SEM.Talens Vila, C.; Castro Giraldez, M.; Fito Suñer, PJ. (2018). Effect of Microwave Power Coupled with Hot Air Drying on Sorption Isotherms and Microstructure of Orange Pee. Food and Bioprocess Technology. 11(4):723-734. https://doi.org/10.1007/s11947-017-2041-xS723734114Al-Muhtaseb, A. H., McMinn, W. A. M., & Magee, T. R. 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