Development of biomass fast proximate analysis by thermogravimetric scale

[EN] EN norms set the methods for determining the ash and volatile content in biomass. These establish the use of a muffle to heat the samples at temperatures of 550 degrees C and 900 degrees C respectively, with a minimum analysis time of 4 h as standard method. The objective of this work was to reduce significantly the analysis times, making very short heating periods using a thermogravimetric scale (TGA), and to apply an equation to the residual weight to obtain the weight of ash, volatiles and fixed carbon in biomass samples. We analyzed the factors: the temperature ramp, atmosphere and airflow in the determination. In this work new validated methods were developed with an analysis time of 10-20 min. (C) 2018 Elsevier Ltd. All rights reserved.This research work has been carried out inside the cooperation framework funded by the ADSIDEO program of the Centro de Cooperacion al Desarrollo (CCD) of Universidad Politecnica de Valencia (Spain), in collaboration with the Centro de Estudios de la Biomasa (CEB), Universidad Estatal de Bolivar, Guaranda, Ecuador.Velázquez Martí, B.; Gaibor-Chavez, J.; Niño-Ruiz, Z.; Cortes-Rojas, E. (2018). Development of biomass fast proximate analysis by thermogravimetric scale. Renewable Energy. 126:954-959. https://doi.org/10.1016/j.renene.2018.04.021S95495912

Chaotic Synchronization in a Small Network of a Class of Power Systems via Contraction Analysis

This paper presents a synchronization analysis of networks of a class of power systems using the contraction theory for nonlinear systems. This analysis is characterized by not being based on Lyapunov's stability theory, that is, it is not required to determine a Lyapunov candidate function. Moreover, from the contraction conditions, robustness of the synchronization can be obtained, in this sense, the analysis method is robust. The analysis consists in identifying or proposing a virtual or auxiliary system which is contracting in a region of the state space. It is intended that in this region the trajectories of the systems on the network converge to those of the virtual system and then obtain the synchronization of the systems in the network. The contribution consists in applying this nontraditional analysis to the problem of chaotic synchronization of a network of a class of power systems

Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction

Diabetes Mellitus is a serious metabolic condition for global health associations. Recently, the number of adults, adolescents and children who have developed Type 1 Diabetes Mellitus (T1DM) has increased as well as the mortality statistics related to this disease. For this reason, the scientific community has directed research in developing technologies to reduce T1DM complications. This contribution is related to a feedback control strategy for blood glucose management in population samples of ten virtual adult subjects, adolescents and children. This scheme focuses on the development of an inverse optimal control (IOC) proposal which is integrated by neural identification, a multi-step prediction (MSP) strategy, and Takagi–Sugeno (T–S) fuzzy inference to shape the convenient insulin infusion in the treatment of T1DM patients. The MSP makes it possible to estimate the glucose dynamics 15 min in advance; therefore, this estimation allows the Neuro-Fuzzy-IOC (NF-IOC) controller to react in advance to prevent hypoglycemic and hyperglycemic events. The T–S fuzzy membership functions are defined in such a way that the respective inferences change basal infusion rates for each patient's condition. The results achieved for scenarios simulated in Uva/Padova virtual software illustrate that this proposal is suitable to maintain blood glucose levels within normoglycemic values (70–115 mg/dL); furthermore, this level remains less than 250 mg/dL during the postprandial event. A comparison between a simple neural IOC (NIOC) and the proposed NF-IOC is carried out using the analysis for control variability named CVGA chart included in the Uva/Padova software. This analysis highlights the improvement of the NF-IOC treatment, proposed in this article, on the NIOC approach because each subject is located inside safe zones for the entire duration of the simulatio

Fatal Human Infection with Rickettsia rickettsii, Yucatán, Mexico

The first fatal Rickettsia rickettsii infection was diagnosed in the southwest of Mexico. The patient had fever, erythematous rash, abdominal pain, and severe central nervous system involvement with convulsive crisis. The diagnosis of R. rickettsii infection was established by immunohistochemistry and specific polymerase chain reaction

Control neuro-fuzzy para páncreas artificial: Desarrollo y validación in-silico

Type 1 Diabetes Mellitus (DMT1) is currently one of the most harmful diseases that affect people of any age, including children from birth. Exogenous insulin injections remain the most common treatment for these patients, however, it is not the optimal one. The scientific community has endeavored to optimize i nsulin administration using electronic devices and thus improve the diabetics life expectancy. There are numerous limitations for this biomedical evolution to become a reality such as the control algorithms validation, experimentation with electronic devices, and applicability in patients age transcendence, among others. This work presents the prototyping of a neuro-fuzzy intelligent controller on the Texas Instruments LAUNCHXL-F28069M development board to form a hardware in the loop (HIL) scheme. That is, the embedded controller sends the insulin delivery rate data to the computer where it is captured by the Uva/Padova software and integrated into the metabolic simulation of virtual diabetic patients treated with an insulin pump. The main task of the embedded intelligent algorithm is to determine the optimal insulin infusion rate for each of the 30 virtual patients who follow a meal protocol. The novelty of this work focuses on overcoming current limitations through a first intelligent control algorithm a pproach applicable to artificial pancreas (A P) and an alyzing the feasibility of this proposal in age transcendence since the results correspond to in-silico tests in populations of 10 adults, 10 adolescents and 10 children. © 2020 Universitat Politecnica de Valencia. All rights reserved

Neuro-fuzzy control for artificial pancreas: in silico development and validation

[ES] La Diabetes Mellitus Tipo 1 (DMT1) es una de las enfermedades actuales más dañinas que afectan a personas de cualquier edad incluyendo niños desde el nacimiento. Las inyecciones de insulina exógena siguen siendo el tratamiento más común para estos pacientes, sin embargo, no es el óptimo. La comunidad científica se ha esforzado en optimizar el suministro de insulina usando dispositivos electrónicos y de esta manera mejorar la esperanza de vida de los diabéticos. Existen numerosas limitaciones para que esta evolución biomédica sea realidad tales como la validación de algoritmos controladores, experimentación con dispositivos electrónicos, aplicabilidad en pacientes de diferentes edades, entre otras. Este trabajo presenta el prototipado de un controlador inteligente neuro-fuzzy en la tarjeta LAUNCHXL-F28069M de Texas Instruments para formar un esquema de hardware en el lazo (HIL). Esto es, el controlador embebido manda los datos de la tasa de suministro de insulina al computador donde se capturan por el software Uva/Padova y se integran a la simulación metabólica de pacientes diabéticos virtuales tratados con bomba de insulina. Una tarea principal del algoritmo inteligente embebido es determinar la tasa óptima de infusión insulínica para cada uno de los 30 pacientes virtuales disponibles, los cuales llevan un protocolo de comida. La novedad de este trabajo se centra en superar las limitaciones actuales a través de un primer enfoque de algoritmo de control inteligente aplicable al páncreas artificial (PA) y analizar la factibilidad de esta propuesta en la trascendencia con la edad ya que los resultados corresponden a pruebas in-silico en poblaciones de 10 adultos, 10 adolescentes y 10 niños.[EN] Type 1 Diabetes Mellitus (DMT1) is currently one of the most harmful diseases that aect people of any age, including children from birth. Exogenous insulin injections remain the most common treatment for these patients, however, it is not the optimal one. The scientific community has endeavored to optimize insulin administration using electronic devices and thus improve the diabetics life expectancy. There are numerous limitations for this biomedical evolution to become a reality such as the control algorithms validation, experimentation with electronic devices, and applicability in patients age transcendence, among others. This work presents the prototyping of a neuro-fuzzy intelligent controller on the Texas Instruments LAUNCHXL-F28069M development board to form a hardware in the loop (HIL) scheme. That is, the embedded controller sends the insulin delivery rate data to the computer where it is captured by the Uva/Padova software and integrated into the metabolic simulation of virtual diabetic patients treated with an insulin pump. The main task of the embedded intelligent algorithm is to determine the optimal insulin infusion rate for each of the 30 virtual patients who follow a meal protocol. The novelty of this work focuses on overcoming current limitations through a first intelligent control algorithm approach applicable to artificial pancreas (AP) and analyzing the feasibility of this proposal in age transcendence since the results correspond to in-silico tests in populations of 10 adults, 10 adolescents and 10 children.Rios, Y.; García-Rodríguez, J.; Sánchez, E.; Alanis, A.; Ruiz-Velázquez, E.; Pardo, A. (2020). Control neuro-fuzzy para páncreas artificial: desarrollo y validación in-silico. Revista Iberoamericana de Automática e Informática industrial. 17(4):390-400. https://doi.org/10.4995/riai.2020.13035OJS390400174Alanis, A. Y., Sanchez, E. N., Loukianov, A. G., 2007. Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks. IEEE Transactions on Neural Networks 18 (4), 1185-1195. https://doi.org/10.1109/TNN.2007.899170American Diabetes Association, 2013. Economic costs of diabetes in the U.S. in 2012. Diabetes Care 36 (4), 1033-1046. https://doi.org/10.2337/dc12-2625Brown, J. B., Pedula, K. L., Bakst, A. W., 09 1999. The Progressive Cost of Complications in Type 2 Diabetes Mellitus. JAMA Internal Medicine 159 (16), 1873-1880.https://doi.org/10.1001/archinte.159.16.1873Centers for Disease Control and Prevention, 2017. National Diabetes Statistics Report, 2017. Estimates of Diabetes and Its Burden in the United States. National Center for Chronic Disease Prevention and Health Promotion. USA. 1 (1), 1-20.Chang, F. J., Chiang, Y. M., Chang, L. C., 2010. Multi-step-ahead neural networks for flood forecasting. Hydrological Sciences Journal 52 (1), 114-130. https://doi.org/10.1623/hysj.52.1.114Chen, P. A., Chang, L. C., Chang, F. J., 2013. Reinforced recurrent neural networks for multi-step-ahead flood forecasts. Journal of Hydrology 497 (2013), 71-79. https://doi.org/10.1016/j.jhydrol.2013.05.038Cinar, A., 2018. Artificial Pancreas Systems: An Introduction to the Special Issue. IEEE Control Systems 38 (1), 26-29. https://doi.org/10.1109/MCS.2017.2766321Control, T. D., Group, C. T. R., 1993. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. New England Journal of Medicine 329 (14), 977-986, pMID: 8366922. https://doi.org/10.1056/NEJM199309303291401Dalla Man, C., Micheletto, F., Lv, D., Breton, M., Kovatchev, B., Cobelli, C., jan 2014. The UVA/PADOVA Type 1 Diabetes Simulator. Journal of Diabetes Science and Technology 8 (1), 26-34. https://doi.org/10.1177/1932296813514502Freeman, R. A., Kokotovic, P., 2009. Robust Nonlinear Control Design, springer s Edition. Birkhäuser Boston, Boston. https://doi.org/10.1007/978-0-8176-4759-9Geman, O., Chiuchisan, I., Toderean, R., 2017. Application of adaptive neuro-fuzzy inference system for diabetes classification and prediction. In: 2017 E-Health and Bioengineering Conference (EHB). Sinaia, pp. 639-642. https://doi.org/10.1109/EHB.2017.7995505Institute of Medicine, 2005. Summary Tables, Dietary Reference Intakes. In: Press, T. N. A. (Ed.), Dietary Reference Intakes for Energy, the nation Edition. Elsevier, Washington D.C, U.S., Ch. Summary Ta, pp. 1319-1331. https://doi.org/10.17226/10490Karahoca, A., Karahoca, D., Kara, A., sep 2009. Diagnosis of diabetes by using adaptive neuro fuzzy inference systems. In: 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control. Famagusta, pp. 1-4. https://doi.org/10.1109/ICSCCW.2009.5379497Kim, S., 2007. Burden of hospitalizations primarily due to uncontrolled diabetes. Diabetes Care 30 (5), 1281-1282. http://care.diabetesjournals.org/content/30/5/1281 , https://doi.org/10.2337/dc06-2070Kovatchev, B., Raimondo, D., Breton, M., Patek, S., Cobelli, C., jan 2008. In Silico Testing and in Vivo Experiments with Closed-Loop Control of Blood Glucose in Diabetes. IFAC Proceedings Volumes 41 (2), 4234-4239. https://doi.org/10.3182/20080706-5-KR-1001.00712Kovatchev, B. P., Breton, M., Dalla Man, C., Cobelli, C., 2009. In silico preclinical trials: A proof of concept in closed-loop control of type 1 diabetes. Journal of Diabetes Science and Technology 3 (1), 44-55. https://doi.org/10.1177/193229680900300106Kropff, J., et al., December 2015. 2 month evening and night closed-loop glucose control in patients with type 1 diabetes under free-living conditions: a randomised crossover trial. The Lancet Diabetes & Endocrinology 3 (2), 939-947. https://doi.org/10.1016/S2213-8587(15)00335-6Kux, L., 2012. Guidance for Industry and Food and Drug Administration Staff; The Content of Investigational Device Exemption and Premarket Approval Applications for Artificial Pancreas Device Systems; Availability. Federal Register 77 (226), 1-63.Lekkas, S., Mikhailov, L., 2010. Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases. Artificial Intelligence in Medicine 50 (2), 117-126. https://doi.org/10.1016/j.artmed.2010.05.007Leon, B. S., Alanis, A. Y., Sanchez, E. N., Ornelas-Tellez, F., Ruiz-Velazquez, E., 2013. Neural inverse optimal control applied to type 1 diabetes mellitus patients. Analog Integrated Circuits and Signal Processing 76 (3), 343-352. https://doi.org/10.1007/s10470-013-0109-8Li, W., Todorov, E., Liu, D., 2011. Inverse optimality design for biological movement systems. In: IFAC Proceedings Volumes (IFAC-PapersOnline). Vol. 44. Elsevier, Milano, pp. 9662-9667. https://doi.org/10.3182/20110828-6-IT-1002.00877Nath, A., Dey, R., Balas, V. E., 2018. Closed Loop Blood Glucose Regulation of Type 1 Diabetic Patient Using Takagi-Sugeno Fuzzy Logic Control. In: Advances in Intelligent Systems and Computing. Springer, Cham, Switzerland, pp. 286-296. https://doi.org/10.1007/978-3-319-62524-9_23Ornelas, F., Sanchez, E. N., Loukianov, A. G., 2011. Discrete-time nonlinear systems inverse optimal control: A control Lyapunov function approach. In: Proceedings of the IEEE International Conference on Control Applications. IEEE, Denver, pp. 1431-1436. https://doi.org/10.1109/CCA.2011.6044461Ornelas-Tellez, F., Sanchez, E. N., Loukianov, A. G., Navarro-Lopez, E. M., 2011. Speed-gradient inverse optimal control for discrete-time nonlinear systems. In: Proceedings of the IEEE Conference on Decision and Control. IEEE, Orlando, pp. 290-295. https://doi.org/10.1109/CDC.2011.6160374Pesl, P., Herrero, P., Reddy, M., Xenou, M., Oliver, N., Johnston, D., Toumazou, C., Georgiou, P., Jan 2016. An advanced bolus calculator for type 1 diabetes: System architecture and usability results. IEEE Journal of Biomedical and Health Informatics 20 (1), 11-17. https://doi.org/10.1109/JBHI.2015.2464088Rios, Y. Y., Garcia-Rodriguez, J., Sanchez, E. N., Alanis, A. Y., Ruiz-Velazquez, E., 2018a. Rapid Prototyping of Neuro-Fuzzy Inverse Optimal Control as Applied to T1DM Patients. In: 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI). IEEE, Guadalajara, pp. 1-5. https://doi.org/10.1109/LA-CCI.2018.8625241Rios, Y. Y., García-Rodríguez, J. A., Sanchez, E. N., Alanis, A. Y., Ruiz-Velázquez, E., Durán, C., 2018b. Treatment for T1DM patients using neuro-fuzzy inverse optimal control algorithm: a rapid prototyping implementation. In: Revista Colombiana de Tecnologías de Avanzada. Colombia, pp. 26-33.Rios, Y. Y., García-Rodríguez, J. A., Sánchez, O. D., Sanchez, E. N., Alanis, A. Y., Ruiz-Velázquez, E., Arana-Daniel, N., 2018c. Inverse Optimal Control Using A Neural Multi-Step Predictor for T1DM Treatment. In: Proceedings of the International Joint Conference on Neural Networks. Rio de Janeiro, pp. 1-8. https://doi.org/10.1109/IJCNN.2018.8489197Rovithakis, G. A., Christodoulou, M. A., 2000. Adaptive Control with Recurrent High-order Neural Networks : Theory and Industrial Applications. Springer London, London, U.K. https://doi.org/10.1007/978-1-4471-0785-9Sanchez, E. N., Ornelas-Tellez, F., 2013. Discrete-time inverse optimal control for nonlinear systems, taylor & f Edition. CRC Press, Boca Raton, Florida, U.S. https://doi.org/10.1201/b14779Takagi, T., Sugeno, M., 1985. Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Transactions on Systems, Man and Cybernetics SMC-15 (1), 116 - 132. https://doi.org/10.1109/TSMC.1985.6313399Thabit, H., Hovorka, R., Sep. 2016. Coming of age: the artificial pancreas for type 1 diabetes. Diabetologia 59 (9), 1795-1805. https://doi.org/10.1007/s00125-016-4022-4Trevitt, S., Simpson, S., Wood, A., 2016. Artificial Pancreas Device Systems for the Closed-Loop Control of Type 1 Diabetes. Journal of Diabetes Science and Technology 10 (3), 714-723. https://doi.org/10.1177/1932296815617968Turksoy, K., Samadi, S., Feng, J., Littlejohn, E., Quinn, L., Cinar, A., Jan 2016. Meal detection in patients with type 1 diabetes: A new module for the multivariable adaptive artificial pancreas control system. IEEE Journal of Biomedical and Health Informatics 20 (1), 47-54. https://doi.org/10.1109/JBHI.2015.2446413van Bon, A. C., Luijf, Y. M., Koebrugge, R., Koops, R., Hoekstra, J. B. L., DeVries, J. H., 2014. Feasibility of a Portable Bihormonal Closed-Loop System to Control Glucose Excursions at Home Under Free-Living Conditions for 48 Hours. Diabetes Technology & Therapeutics 16 (3), 131-136, pMID: 24224750. https://doi.org/10.1089/dia.2013.0166Yeh, H., et al., 2012. Comparative effectiveness and safety of methods of insulin delivery and glucose monitoring for diabetes mellitus: A systematic review and meta-analysis. Annals of Internal Medicine 157 (5), 336-347. https://doi.org/10.7326/0003-4819-157-5-201209040-0050

Development of efficient recirculation system for Tilapia (Oreochromis niloticus) culture using low cost materials

In the current experiment, a recirculation system was built using low cost materials that are available locally and its performance was tested. The performance evaluation procedure was carried out in an aquaculture system in greenhouse with sex-reversed male Nile tilapia (Oreochromis niloticus) production in Querétaro State, Mexico. The recirculation system had four sections (sediment collector, gravel and sand filters, biofilter and clarification) in order to eliminate the organic matter produced by the fish excretion material and uneaten food, as well as, the nitrogenous compounds undesirable in the water tanks, such as, total ammonia nitrogen (TAN). The monitored variables include: temperature, dissolved oxygen, pH, visibility, TAN, nitrites and nitrates. The obtained data were compared with previous studies to evaluate the achieved state of the system. This research clearly demonstrated that it is feasible to use the proposed configuration in aquaculture systems in areas where water source is limited. Consequently, the obtained results represent an environmental standpoint for the conservation of water use in the aquaculture industry and also constitute an important contribution to the aquaculture and farmers who receive minimal economic support.Key words: Water recirculation, aquaculture, sustainability, low cost, water use efficiency

Tracking Control Based on Recurrent Neural Networks for Nonlinear Systems with Multiple Inputs and Unknown Deadzone

This paper deals with the problem of trajectory tracking for a broad class of uncertain nonlinear systems with multiple inputs each one subject to an unknown symmetric deadzone. On the basis of a model of the deadzone as a combination of a linear term and a disturbance-like term, a continuous-time recurrent neural network is directly employed in order to identify the uncertain dynamics. By using a Lyapunov analysis, the exponential convergence of the identification error to a bounded zone is demonstrated. Subsequently, by a proper control law, the state of the neural network is compelled to follow a bounded reference trajectory. This control law is designed in such a way that the singularity problem is conveniently avoided and the exponential convergence to a bounded zone of the difference between the state of the neural identifier and the reference trajectory can be proven. Thus, the exponential convergence of the tracking error to a bounded zone and the boundedness of all closed-loop signals can be guaranteed. One of the main advantages of the proposed strategy is that the controller can work satisfactorily without any specific knowledge of an upper bound for the unmodeled dynamics and/or the disturbance term

Administration of royal jelly in estrus synchronization protocols for wool and hair sheep

Objetive: To know the most representative results of the use of royal jelly in reproductive protocols in wool and hair sheep,Desing/methodology/approach: A review of studies referenced in scientific databases published in the livestock sector was carried out. Results: In sheep, the administration of royal jelly in conjunction with reproductive management protocols improves the response to oestrus timing, oestrus onset time and duration, number of large follicles, ovulatory rate, and gestation rate. Study limitations/implications: Royal jelly is a substance that has beneficial effects on sheep reproductive variables, however the cost can be a limitation for its incorporation into the synchronization protocols, in addition to being necessary to elucidate the active metabolites that exert the action and the most effective route of administration.Findings/conclusions: Royal jelly can be an alternative to be incorporated in the estrous synchronization programs in sheep in order to replace some hormonal and without reducing reproductive variables.Objective: To assess the most relevant results on the usage of royal jelly in reproductive protocols of wool and hair sheep. Design/methodology/approach: A review of studies referenced and published in scientific databases regard the livestock sector. Results: In ewes, administration of “royal jelly” in addition to reproductive management protocols improves the response to estrus synchronization, time of onset and duration of estrus, number of large follicles, ovulatory rate and gestation rate. Study limitations/implications: Royal jelly is a substance with beneficial effects on reproductive variables in ewes; however, the cost may be a limitation for its incorporation in synchronization protocols. Additionally, it is necessary to clarify the active metabolites that exert the action and the most effective route of administration. Findings/conclusions: Royal jelly can be an alternative incorporated to estrus synchronization programs in ewes to substitute some hormones without decreasing reproductive variables

Consecuencias del estrés calórico sobre la reproducción del ganado vacuno

Heat stress represents one of the major environmental factors that adversely affect the reproductive performance of cattle. In this paper the behavioral adjustments, physical mechanisms and physiological responses to heat loss are described; bos indicus adaptive advantages with respect to bos Taurus, pathophysiology of heat stress and heat stress effects in animal reproduction, both the male and the female.El estrés calórico representa unos de los principales factores del medio ambiente que repercuten negativamente en el desempeño reproductivo del ganado vacuno. En este trabajo se describen los ajustes conductuales, mecanismos físicos y respuestas fisiológicas para la pérdida calórica; ventajas adaptativas del bos indicus con respecto al bos Taurus, fisiopatología del estrés calórico y repercusiones del estrés calórico en la reproducción animal, tanto en el macho como en la hembra