66,292 research outputs found

    Environmental Forensic Chemistry and Sound Science in the Courtroom

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    A New Ammonium Smart Sensor with Interference Rejection

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    [EN] In many water samples, it is important to determine the ammonium concentration in order to obtain an overall picture of the environmental impact of pollutants and human actions, as well as to detect the stage of eutrophization. Ion selective electrodes (ISEs) have been commonly utilized for this purpose, although the presence of interfering ions (potassium and sodium in the case of NH4+-ISE) represents a handicap in terms of the measurement quality. Furthermore, random malfunctions may give rise to incorrect measurements. Bearing all of that in mind, a smart ammonium sensor with enhanced features has been developed and tested in water samples, as demonstrated and commented on in detail following the presentation of the complete set of experimental measurements that have been successfully carried out. This has been achieved through the implementation of an expert system that supervises a set of ISEs in order to (a) avoid random failures and (b) reject interferences. Our approach may also be suitable for in-line monitoring of the water quality through the implementation of wireless sensor networks.This research was supported by the Spanish Ministerio de Economia y Competitividad, grant number DPI2016-80303-C2-1-P.Capella Hernández, JV.; Bonastre Pina, AM.; Campelo Rivadulla, JC.; Ors Carot, R.; Peris Tortajada, M. (2020). A New Ammonium Smart Sensor with Interference Rejection. Sensors. 20(24):1-17. https://doi.org/10.3390/s20247102S1172024Molins-Legua, C., Meseguer-Lloret, S., Moliner-Martinez, Y., & Campíns-Falcó, P. (2006). A guide for selecting the most appropriate method for ammonium determination in water analysis. TrAC Trends in Analytical Chemistry, 25(3), 282-290. doi:10.1016/j.trac.2005.12.002Zhu, Y., Chen, J., Yuan, D., Yang, Z., Shi, X., Li, H., … Ran, L. (2019). Development of analytical methods for ammonium determination in seawater over the last two decades. TrAC Trends in Analytical Chemistry, 119, 115627. doi:10.1016/j.trac.2019.115627Liu, J. (2020). New directions in sensor technology. TrAC Trends in Analytical Chemistry, 124, 115818. doi:10.1016/j.trac.2020.115818Yaroshenko, I., Kirsanov, D., Marjanovic, M., Lieberzeit, P. A., Korostynska, O., Mason, A., … Legin, A. (2020). Real-Time Water Quality Monitoring with Chemical Sensors. Sensors, 20(12), 3432. doi:10.3390/s20123432Martı́nez-Máñez, R., Soto, J., Garcia-Breijo, E., Gil, L., Ibáñez, J., & Llobet, E. (2005). An «electronic tongue» design for the qualitative analysis of natural waters. Sensors and Actuators B: Chemical, 104(2), 302-307. doi:10.1016/j.snb.2004.05.022Legin, A. ., Rudnitskaya, A. ., Vlasov, Y. ., Di Natale, C., & D’Amico, A. (1999). The features of the electronic tongue in comparison with the characteristics of the discrete ion-selective sensors. Sensors and Actuators B: Chemical, 58(1-3), 464-468. doi:10.1016/s0925-4005(99)00127-6Mueller, A. V., & Hemond, H. F. (2013). Extended artificial neural networks: Incorporation of a priori chemical knowledge enables use of ion selective electrodes for in-situ measurement of ions at environmentally relevant levels. Talanta, 117, 112-118. doi:10.1016/j.talanta.2013.08.045Wen, Y., Mao, Y., Kang, Z., & Luo, Q. (2019). Application of an ammonium ion-selective electrode for the real-time measurement of ammonia nitrogen based on pH and temperature compensation. Measurement, 137, 98-101. doi:10.1016/j.measurement.2019.01.031Handbook of Electrochemistry. (2007). doi:10.1016/b978-0-444-51958-0.x5000-9Umezawa, Y., Bühlmann, P., Umezawa, K., Tohda, K., & Amemiya, S. (2000). Potentiometric Selectivity Coefficients of Ion-Selective Electrodes. Part I. Inorganic Cations (Technical Report). Pure and Applied Chemistry, 72(10), 1851-2082. doi:10.1351/pac200072101851Capella, J. V., Bonastre, A., Ors, R., & Peris, M. (2015). An interference-tolerant nitrate smart sensor for Wireless Sensor Network applications. Sensors and Actuators B: Chemical, 213, 534-540. doi:10.1016/j.snb.2015.02.125Choudhary, J., Balasubramanian, P., Varghese, D., Singh, D., & Maskell, D. (2019). Generalized Majority Voter Design Method for N-Modular Redundant Systems Used in Mission- and Safety-Critical Applications. Computers, 8(1), 10. doi:10.3390/computers8010010Capella, J. V., Bonastre, A., Ors, R., & Peris, M. (2014). A step forward in the in-line river monitoring of nitrate by means of a wireless sensor network. Sensors and Actuators B: Chemical, 195, 396-403. doi:10.1016/j.snb.2014.01.039Cuartero, M., Colozza, N., Fernández-Pérez, B. M., & Crespo, G. A. (2020). Why ammonium detection is particularly challenging but insightful with ionophore-based potentiometric sensors – an overview of the progress in the last 20 years. The Analyst, 145(9), 3188-3210. doi:10.1039/d0an00327aBembe, M., Abu-Mahfouz, A., Masonta, M., & Ngqondi, T. (2019). A survey on low-power wide area networks for IoT applications. Telecommunication Systems, 71(2), 249-274. doi:10.1007/s11235-019-00557-9Freiser, H. (Ed.). (1980). Ion-Selective Electrodes in Analytical Chemistry. doi:10.1007/978-1-4684-3776-8Peris, M., Bonastre, A., & Ors, R. (1998). Distributed expert system for the monitoring and control of chemical processes. Laboratory Robotics and Automation, 10(3), 163-168. doi:10.1002/(sici)1098-2728(1998)10:33.0.co;2-2Carminati, M., Turolla, A., Mezzera, L., Di Mauro, M., Tizzoni, M., Pani, G., … Antonelli, M. (2020). A Self-Powered Wireless Water Quality Sensing Network Enabling Smart Monitoring of Biological and Chemical Stability in Supply Systems. Sensors, 20(4), 1125. doi:10.3390/s20041125Nakas, C., Kandris, D., & Visvardis, G. (2020). Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey. Algorithms, 13(3), 72. doi:10.3390/a13030072Capella, J. V., Bonastre, A., Campelo, J. C., Ors, R., & Peris, M. (2020). IoT & environmental analytical chemistry: Towards a profitable symbiosis. Trends in Environmental Analytical Chemistry, 27, e00095. doi:10.1016/j.teac.2020.e00095Pretsch, E. (2007). The new wave of ion-selective electrodes. TrAC Trends in Analytical Chemistry, 26(1), 46-51. doi:10.1016/j.trac.2006.10.006STM Microelectronics https://www.st.com/content/st_com/en/products/microcontrollers-microprocessors/stm32-32-bit-arm-cortex-mcus/stm32-ultra-low-power-mcus/stm32l4-series/stm32l4x2/stm32l422cb.htmlAnalog Devices https://www.analog.com/media/en/technical-documentation/data-sheets/AD524.pdfCapella, J. V., Bonastre, A., Ors, R., & Peris, M. (2010). A Wireless Sensor Network approach for distributed in-line chemical analysis of water. Talanta, 80(5), 1789-1798. doi:10.1016/j.talanta.2009.10.025Bonastre, A., Capella, J. V., Ors, R., & Peris, M. (2012). In-line monitoring of chemical-analysis processes using Wireless Sensor Networks. 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    In vitro determination of hemoglobin A1c for diabetes diagnosis and management: technology update

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    It is fascinating to consider the analytical improvements that have occurred since glycated hemoglobin was first used in routine clinical laboratories for diabetes monitoring around 1977; at that time methods displayed poor precision, there were no calibrators or material with assayed values for quality control purposes. This review outlines the major improvements in hemoglobin A1c (HbA1c) measurement that have occurred since its introduction, and reflects on the increased importance of this hemoglobin fraction in the monitoring of glycemic control. The use of HbA1c as a diagnostic tool is discussed in addition to its use in monitoring the patient with diabetes; the biochemistry of HbA1c formation is described, and how these changes to the hemoglobin molecule have been used to develop methods to measure this fraction. Standardization of HbA1c is described in detail; the development of the IFCC Reference Measurement Procedure for HbA1c has enabled global standardization to be achieved which has allowed global targets to be set for glycemic control and diagnosis. The importance of factors that may interfere in the measurement of HbA1c are highlighted

    Accelerating scientific codes by performance and accuracy modeling

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    Scientific software is often driven by multiple parameters that affect both accuracy and performance. Since finding the optimal configuration of these parameters is a highly complex task, it extremely common that the software is used suboptimally. In a typical scenario, accuracy requirements are imposed, and attained through suboptimal performance. In this paper, we present a methodology for the automatic selection of parameters for simulation codes, and a corresponding prototype tool. To be amenable to our methodology, the target code must expose the parameters affecting accuracy and performance, and there must be formulas available for error bounds and computational complexity of the underlying methods. As a case study, we consider the particle-particle particle-mesh method (PPPM) from the LAMMPS suite for molecular dynamics, and use our tool to identify configurations of the input parameters that achieve a given accuracy in the shortest execution time. When compared with the configurations suggested by expert users, the parameters selected by our tool yield reductions in the time-to-solution ranging between 10% and 60%. In other words, for the typical scenario where a fixed number of core-hours are granted and simulations of a fixed number of timesteps are to be run, usage of our tool may allow up to twice as many simulations. While we develop our ideas using LAMMPS as computational framework and use the PPPM method for dispersion as case study, the methodology is general and valid for a range of software tools and methods

    Study on evaluation of International Science and Technology Cooperation Project (ISTCP) in China

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    This paper presents an overview of evaluation of ISTCP in China. We discuss briefly the history of evaluation and the strengths and weaknesses of different assessment systems. On this basis, with Analytical Hierarchy Process (AHP), we establish evaluation indicator system for ISTCP that includes research project establishment evaluation, mid-period evaluation system, effect evaluation system, and confirm the value of each indicator. At the same time, we established expert database, project database, research organization database, researcher database etc. We therefore establish an evaluation platform for international science and technology cooperation project. We use it to realize full process supervision from evaluation expert selection to project management

    AI and OR in management of operations: history and trends

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    The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

    Subject benchmark statement: forensic science

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    A rocket engine design expert system

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    The overall structure and capabilities of an expert system designed to evaluate rocket engine performance are described. The expert system incorporates a JANNAF standard reference computer code to determine rocket engine performance and a state-of-the-art finite element computer code to calculate the interactions between propellant injection, energy release in the combustion chamber, and regenerative cooling heat transfer. Rule-of-thumb heuristics were incorporated for the hydrogen-oxygen coaxial injector design, including a minimum gap size constraint on the total number of injector elements. One-dimensional equilibrium chemistry was employed in the energy release analysis of the combustion chamber and three-dimensional finite-difference analysis of the regenerative cooling channels was used to calculate the pressure drop along the channels and the coolant temperature as it exits the coolant circuit. Inputting values to describe the geometry and state properties of the entire system is done directly from the computer keyboard. Graphical display of all output results from the computer code analyses is facilitated by menu selection of up to five dependent variables per plot
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