Visible and infrared photocurrent enhancement in a graphene-silicon Schottky photodetector through surface-states and electric field engineering

The design of efficient graphene-silicon (GSi) Schottky junction photodetectors requires detailed understanding of the spatial origin of the photoresponse. Scanning-photocurrent-microscopy (SPM) studies have been carried out in the visible wavelengths regions only, in which the response due to silicon is dominant. Here we present comparative SPM studies in the visible ($\lambda$ = 633nm) and infrared ($\lambda$ = 1550nm) wavelength regions for a number of GSi Schottky junction photodetector architectures, revealing the photoresponse mechanisms for silicon and graphene dominated responses, respectively, and demonstrating the influence of electrostatics on the device performance. Local electric field enhancement at the graphene edges leads to a more than ten-fold increased photoresponse compared to the bulk of the graphene-silicon junction. Intentional design and patterning of such graphene edges is demonstrated as an efficient strategy to increase the overall photoresponse of the devices. Complementary simulations and modeling illuminate observed effects and highlight the importance of considering graphene's shape and pattern and device geometry in the device design

Numerical Simulation of the Deformation of Some MEMS

In this paper we present the numerical simulation of the deformation of two Micro-Electromechanical Systems (MEMS), a trampoline-type one i.e. a rectangular cantilever beam and an accelerometer that consists of a square plate with all edges simply supported. The deformation of these systems is modeled by fourth-order differential equations, ordinary and partial respectively. We find the approximate solutions by using the finite differences method programmed in Matlab, solving the system of linear equations associated with different methods to evaluate the efficiency of these. We obtained very good approximations with small errors compared to other articles that use other approaches

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. 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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

Characterization, selection and micro-assembly of nanowire laser systems

Semiconductor nanowire (NW) lasers are a promising technology for the realization of coherent optical sources with ultrasmall footprint. To fully realize their potential in on-chip photonic systems, scalable methods are required for dealing with large populations of inhomogeneous devices that are typically randomly distributed on host substrates. In this work two complementary, high-throughput techniques are combined: the characterization of nanowire laser populations using automated optical microscopy, and a high-accuracy transfer-printing process with automatic device spatial registration and transfer. Here, a population of NW lasers is characterized, binned by threshold energy density, and subsequently printed in arrays onto a secondary substrate. Statistical analysis of the transferred and control devices shows that the transfer process does not incur measurable laser damage, and the threshold binning can be maintained. Analysis on the threshold and mode spectra of the device populations proves the potential for using NW lasers for integrated systems fabrication

Determining the Impact of Government Intervention on Firm Decisions for Sustainable Production

We use a game theoretic approach to assess how the government can influence firms’ corporate social responsibility (CSR) investment and production decisions to enhance social welfare, considering the negative externalities of unsustainable production and positive externalities from CSR investments. Using a Stackelberg duopoly as a base model and lump-sum tax as the government’s decision variable, we find that when the government chooses not to intervene, it results in greater environmental damage as firms will underinvest in CSR and overproduce in quantity to achieve profit maximization. As such, the model extends to the assumption that the government acts as a benevolent dictator to model how firms will act under a regulated environment to achieve the optimal outcome. Ultimately, we show that firms have to be placed under a regulated environment to prevent them from exploiting resources and damaging the environment, thereby negatively affecting societal welfare

A Game Theoretic Study on CSR and Government Intervention for Sustainable Production

We use a game theoretic approach to assess how the government can influence firms’ CSR investment and production decisions to enhance social welfare, considering the negative externalities brought by unsustainable production and positive externalities brought by CSR investments. Using a Stackelberg duopoly as a base model and lump-sum tax as the government’s decision variable, we find that when the government chooses not to intervene, it results in greater environmental damage as firms will underinvest in CSR and overproduce in quantity to achieve profit maximization. As such, the model extends to the assumption that the government acts as a benevolent dictator to model how firms will act under a regulated environment to achieve the Pareto optimal outcome. Ultimately, we show that firms have to be placed under a regulated environment to prevent them from exploiting resources and damaging the environment, thereby negatively affecting societal welfare