Black hole feedback and the evolution of massive early-type galaxies

Observationally, constraining the baryonic cycle within massive galaxies has proven to be quite difficult. In particular, the role of black hole feedback in regulating star formation, a key process in our theoretical understanding of galaxy formation, remains highly debated. We present here observational evidence showing that, at fixed stellar velocity dispersion, the temperature of the hot gas is higher for those galaxies hosting more massive black holes in their centers. Analyzed in the context of well-established scaling relations, particularly the mass-size plane, the relation between the mass of the black hole and the temperature of the hot gas around massive galaxies provides further observational support to the idea that baryonic processes within massive galaxies are regulated by the combined effects of the galaxy halo virial temperature and black hole feedback, in agreement with the expectations from the EAGLE cosmological numerical simulation.Comment: 10 pages, 5 figure, accepted for publication in MNRAS

Formulation of “questions – answers” in teaching-learning process as a way of improving learning of students at university level

One of the main things in a process of teaching-learning consists of making hierarchical contents of the programs and to determine specific and clearly which are the objectives of the course. It is not always easy for a teacher to discriminate the relative importance of the program contents. If we consider that the questions the students do are a reflection of what the teachers consider important, the exams could be considered as an adequate source to know their opinion about the contents that a student on a topic should know. Nevertheless, more important than the content itself, is the form in which is evaluated, that is to say the task demanded

Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem

[EN] Mathematical programming models are invaluable tools at decision making, assisting managers to uncover otherwise unattainable means to optimize their processes. However, the value they provide is only as good as their capacity to capture the process domain. This information can only be obtained from stakeholders, i.e., clients or users, who can hardly communicate the requirements clearly and completely. Besides, existing conceptual models of mathematical programming models are not standardized, nor is the process of deriving the mathematical programming model from the concept model, which remains ad hoc. In this paper, we propose an agile methodology to construct mathematical programming models based on two techniques from requirements engineering that have been proven effective at requirements elicitation: the language extended lexicon (LEL) and scenarios. Using the pair of LEL + scenarios allows to create a conceptual model that is clear and complete enough to derive a mathematical programming model that effectively captures the business domain. We also define an ontology to describe the pair LEL + scenarios, which has been implemented with a semantic mediawiki and allows the collaborative construction of the conceptual model and the semi-automatic derivation of mathematical programming model elements. The process is applied and validated in a known fresh tomato packing optimization problem. This proposal can be of high relevance for the development and implementation of mathematical programming models for optimizing agriculture and supply chain management related processes in order to fill the current gap between mathematical programming models in the theory and the practice.This work was supported by the European Commission, project RUC-APS, grant number 691249, funded by the European Union's research and innovation programme under the H2020 Marie SklodowskaCurie Actions; and the Argentinian National Agency for Scientific and Technical Promotion (ANPCyT), grant number PICT-2015-3000.Garrido, A.; Antonelli, L.; Martin, J.; Alemany Díaz, MDM.; Mula, J. (2020). Using LEL and scenarios to derive mathematical programming models. Application in a fresh tomato packing problem. Computers and Electronics in Agriculture. 170:1-14. https://doi.org/10.1016/j.compag.2020.105242S114170Alemany, M., Ortiz, A., & Fuertes-Miquel, V. S. (2018). A decision support tool for the order promising process with product homogeneity requirements in hybrid Make-To-Stock and Make-To-Order environments. Application to a ceramic tile company. Computers & Industrial Engineering, 122, 219-234. doi:10.1016/j.cie.2018.05.040Alemany, M. M. E., Alarcón, F., Lario, F.-C., & Boj, J. J. (2011). An application to support the temporal and spatial distributed decision-making process in supply chain collaborative planning. Computers in Industry, 62(5), 519-540. doi:10.1016/j.compind.2011.02.002Alemany, M. M. E., Lario, F.-C., Ortiz, A., & Gómez, F. (2013). Available-To-Promise modeling for multi-plant manufacturing characterized by lack of homogeneity in the product: An illustration of a ceramic case. Applied Mathematical Modelling, 37(5), 3380-3398. doi:10.1016/j.apm.2012.07.022Alexander, I., & Maiden, N. (2004). Scenarios, stories, and use cases: the modern basis for system development. Computing and Control Engineering, 15(5), 24-29. doi:10.1049/cce:20040505Armengol, Á., Mula, J., Díaz-Madroñero, M., & Pelkonen, J. (2015). Conceptual Model for Associated Costs of the Internationalisation of Operations. Enhancing Synergies in a Collaborative Environment, 181-188. doi:10.1007/978-3-319-14078-0_21Baraniuk, R. G., Burrus, C. S., Johnson, D. H., & Jones, D. L. (2004). Signal processing education - Sharing knowledge and building communities in Signal Processing. IEEE Signal Processing Magazine, 21(5), 10-16. doi:10.1109/msp.2004.1328080Cid-Garcia, N. M., & Ibarra-Rojas, O. J. (2019). An integrated approach for the rectangular delineation of management zones and the crop planning problems. Computers and Electronics in Agriculture, 164, 104925. doi:10.1016/j.compag.2019.104925Dominguez-Ballesteros, B., Mitra, G., Lucas, C., & Koutsoukis, N.-S. (2002). Modelling and solving environments for mathematical programming (MP): a status review and new directions. Journal of the Operational Research Society, 53(10), 1072-1092. doi:10.1057/palgrave.jors.2601361Esteso, A., Alemany, M. M. E., Ortiz, Á., & Peidro, D. (2018). A multi-objective model for inventory and planned production reassignment to committed orders with homogeneity requirements. Computers & Industrial Engineering, 124, 180-194. doi:10.1016/j.cie.2018.07.025Esteso, A., Alemany, M. M. E., & Ortiz, A. (2018). Conceptual framework for designing agri-food supply chains under uncertainty by mathematical programming models. International Journal of Production Research, 56(13), 4418-4446. doi:10.1080/00207543.2018.1447706Grillo, H., Alemany, M. M. E., Ortiz, A., & Fuertes-Miquel, V. S. (2017). Mathematical modelling of the order-promising process for fruit supply chains considering the perishability and subtypes of products. Applied Mathematical Modelling, 49, 255-278. doi:10.1016/j.apm.2017.04.037Grossmann, I. (2005). Enterprise-wide optimization: A new frontier in process systems engineering. AIChE Journal, 51(7), 1846-1857. doi:10.1002/aic.10617Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199-220. doi:10.1006/knac.1993.1008Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing? International Journal of Human-Computer Studies, 43(5-6), 907-928. doi:10.1006/ijhc.1995.1081Hernández, 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.2008.06.004Laporti, V., Borges, M. R. S., & Braganholo, V. (2009). Athena: A collaborative approach to requirements elicitation. Computers in Industry, 60(6), 367-380. doi:10.1016/j.compind.2009.02.011Do Prado Leite, J. C. S., Hadad, G. D. S., Doorn, J. H., & Kaplan, G. N. (2000). A Scenario Construction Process. Requirements Engineering, 5(1), 38-61. doi:10.1007/pl00010342Lenat, D. B. (1995). CYC. Communications of the ACM, 38(11), 33-38. doi:10.1145/219717.219745Lesh, R. (1981). Applied mathematical problem solving. Educational Studies in Mathematics, 12(2), 235-264. doi:10.1007/bf00305624Lezoche, M., Yahia, E., Aubry, A., Panetto, H., & Zdravković, M. (2012). Conceptualising and structuring semantics in cooperative enterprise information systems models. Computers in Industry, 63(8), 775-787. doi:10.1016/j.compind.2012.08.006Liu, L., Wang, H., & Xing, S. (2019). Optimization of distribution planning for agricultural products in logistics based on degree of maturity. Computers and Electronics in Agriculture, 160, 1-7. doi:10.1016/j.compag.2019.02.030Miller, G. A. (1995). WordNet. Communications of the ACM, 38(11), 39-41. doi:10.1145/219717.219748Miller, W. A., Leung, L. C., Azhar, T. M., & Sargent, S. (1997). Fuzzy production planning model for fresh tomato packing. International Journal of Production Economics, 53(3), 227-238. doi:10.1016/s0925-5273(97)00110-2Moskaliuk, J., Kimmerle, J., & Cress, U. (2009). Wiki-supported learning and knowledge building: effects of incongruity between knowledge and information. Journal of Computer Assisted Learning, 25(6), 549-561. doi:10.1111/j.1365-2729.2009.00331.xMula, J., Poler, R., García-Sabater, J. P., & Lario, F. C. (2006). Models for production planning under uncertainty: A review. International Journal of Production Economics, 103(1), 271-285. doi:10.1016/j.ijpe.2005.09.001Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204(3), 377-390. doi:10.1016/j.ejor.2009.09.008MUNDI, I., ALEMANY, M. M. E., BOZA, A., & POLER, R. (2013). A Model-Driven Decision Support System for the Master Planning of Ceramic Supply Chains with Non-uniformity of Finished Goods. Studies in Informatics and Control, 22(2). doi:10.24846/v22i2y201305Munir, K., & Sheraz Anjum, M. (2018). The use of ontologies for effective knowledge modelling and information retrieval. Applied Computing and Informatics, 14(2), 116-126. doi:10.1016/j.aci.2017.07.003Perales, D. D. P., Esteban, F.-C. L., Díaz, M. M. E. A., & Hernández, J. E. (2012). Framework for Modelling the Decision. International Journal of Decision Support System Technology, 4(2), 59-77. doi:10.4018/jdsst.2012040104Raghunathan, S. (1996). A structured modeling based methodology to design decision support systems. Decision Support Systems, 17(4), 299-312. doi:10.1016/0167-9236(96)00006-1Schneeweiss, C. (2003). Distributed decision making in supply chain management. International Journal of Production Economics, 84(1), 71-83. doi:10.1016/s0925-5273(02)00381-xSchneeweiss, C. (2003). Distributed decision making––a unified approach. European Journal of Operational Research, 150(2), 237-252. doi:10.1016/s0377-2217(02)00501-5Schön, E.-M., Thomaschewski, J., & Escalona, M. J. (2017). Agile Requirements Engineering: A systematic literature review. Computer Standards & Interfaces, 49, 79-91. doi:10.1016/j.csi.2016.08.011Shapiro, J. F. (1993). Chapter 8 Mathematical programming models and methods for production planning and scheduling. Handbooks in Operations Research and Management Science, 371-443. doi:10.1016/s0927-0507(05)80188-4Udias, A., Pastori, M., Dondeynaz, C., Carmona Moreno, C., Ali, A., Cattaneo, L., & Cano, J. (2018). A decision support tool to enhance agricultural growth in the Mékrou river basin (West Africa). Computers and Electronics in Agriculture, 154, 467-481. doi:10.1016/j.compag.2018.09.03

Utilization of Porcine Livers through the Formation of Zn-Protoporphyrin Pigment Optimized by a Response Surface Methodology

There is a growing demand for clean-label products. This study aimed to obtain a food-grade coloring ingredient for meat products based on the formation of Zn-protoporphyrin from porcine livers, thus contributing to the development of nitrite-free products. First, the effects of sodium disulfite and acetic, ascorbic, and lactic acids on the formation of Zn-protoporphyrin and the total microbial count were studied. The combination of ascorbic and acetic acids resulted in a higher Zn-protoporphyrin content than acetic acid alone, and microbial levels were maintained (ca. 3 log CFU/mL). Second, a response surface methodology was used to maximize Zn-protoporphyrin while maintaining microbiological food standards. To that end, the effects of pH (4.2–5.4), incubation time (3–30 h), and temperature (25–50 °C) were studied. The selected conditions for Zn-protoporphyrin formation involved anaerobic incubation at pH 4.8 and 45 °C for 24 h. The safety was validated through challenge testing for relevant pathogens (Listeria monocytogenes, Salmonella spp., and Clostridium perfringens). A significant reduction (>6 log units) was observed in the selected conditions for L. monocytogenes and Salmonella, whereas C. perfringens spores remained at the inoculated levels. The optimized procedure is proven to be microbiologically safe, and may improve the color of nitrite-free meat products.info:eu-repo/semantics/publishedVersio

The state of multiple sclerosis: current insight into the patient/health care provider relationship, treatment challenges, and satisfaction

Esclerosi múltiple; Relació pacient-proveïdor d’atenció mèdica; Satisfacció amb el tractamentEsclerosis múltiple; Relación paciente-proveedor de atención médica; Satisfacción con el tratamientoMultiple sclerosis; Patient-health care provider relationship; Treatment satisfactionBackground: Managing multiple sclerosis (MS) treatment presents challenges for both patients and health care professionals. Effective communication between patients with MS and their neurologist is important for improving clinical outcomes and quality of life. Methods: A closed-ended online market research survey was used to assess the current state of MS care from the perspective of both patients with MS (≥18 years of age) and neurologists who treat MS from Europe and the US and to gain insight into perceptions of treatment expectations/goals, treatment decisions, treatment challenges, communication, and satisfaction with care, based on current clinical practice. Results: A total of 900 neurologists and 982 patients completed the survey, of whom 46% self-identified as having remitting-relapsing MS, 29% secondary progressive MS, and 11% primary progressive MS. Overall, patients felt satisfied with their disease-modifying therapy (DMT); satisfaction related to comfort in speaking with their neurologist and participation in their DMT decision-making process. Patients who self-identified as having relapsing-remitting MS were more likely to be very satisfied with their treatment. Top challenges identified by patients in managing their DMT were cost, side effects/tolerability of treatment, and uncertainty if treatment was working. Half of the patients reported skipping doses, but only 68% told their health care provider that they did so. Conclusion: Several important differences in perception were identified between patients and neurologists concerning treatment selection, satisfaction, expectations, goals, and comfort discussing symptoms, as well as treatment challenges and skipped doses. The study results emphasize that patient/neurologist communication and patient input into the treatment decision-making process likely influence patient satisfaction with treatment

uning and validation of an analytical method for the determination of petroleum hydrocarbons in water using gas chromatography and FID detector.

Total oil hydrocarbons (TPHs) are released into the environment mainly by polluting the water on the surface, especially in areas close to production and storage places, but also during the handling, transport and processing of those products. The assessment of this contamination is carried out by measuring the concentrations of petroleum products in water [1]. The most commonly used methods for determining TPHs are infrared absorption (IR) and gas chromatography (GC). However, the cancellation of the IR absorption method in Europe due to the ban on the use of freons (necessary for the extraction of hydrocarbons in the sample) resulted in the GC being the most commonly used, in addition to its high sensitivity, selectivity and wide range of hydrocarbons capable of detecting [2]. This study aims to fine-tune and validate a method capable of identifying and quantifying chain hydrocarbons between C7-C40 using gaseous chromatography with a flame ionization detector (GC-FID) in all types of water, in the one that everybody consumes and in non-drinking water. After an exhaustive analysis of the available bibliography, different methods that are currently being used for the quantification of TPHs have been compared and analyzed, finally selecting a method described by UNE-EN ISO 9377-22:2001 [3]. This regulation includes the extraction and chromatographic method. Regarding the first, the extraction method is carried out thanks to a purification column with Florisil and using dichloromethane as a solvent, as well as evaporation techniques for its concentration. For the chromatographic method, a chromatograph adjustment and a calibration line shall be made with pattern solutions formed by a mixture of n-alkane mineral oils. This is ultimately intended to validate the method so that the analytical laboratory can offer its customers reliable and reproducible results

Dynamics of Microbial Communities in Nitrite-Free and Nutritionally Improved Dry Fermented Sausages

Dry fermented sausage innovation trends are linked to consumer preferences for clean label and sodium-reduced foods. This study aims to evaluate the effect of the formulation and production process temperature on the dynamics of bacterial communities in fuet-type dry fermented sausages using metataxonomics. Six fuet batches were manufactured, including formulations without and with the addition of nitrifying salts (replaced or not by pork liver auto-hydrolysate as a colouring agent), processed at 3 to 12 °C, and a partial replacement of NaCl by KCl, processed at 12 °C. Fermentation was performed spontaneously or by a starter culture. Physicochemical characterisation and culture-dependent and independent bacterial analyses were performed at day 0, 4 and 12, at the end of ripening (aw < 0.90) and after storage. Temperature was the most important factor determining the change in pH, aw and lactic acid bacteria levels while the presence of a starter culture promoted a pH decrease. Metataxonomic analysis showed that low temperature processes and the absence of nitrifying salts allowed the growth of spoilage-related species, while sausages submitted to a mild temperature containing a starter culture and nitrifying salts showed less bacterial diversity. Liver auto-hydrolysate added putative probiotic species to the product. This study provides valuable information to manufacturers who want to innovate safely.info:eu-repo/semantics/publishedVersio