Leveraging the self-heating effect of ntc thermistor to detect its minimum coverage by a dispensed paste in the mass production process of temperature sensors

Saúl Alejandro Rodríguez Jiménez LEONOR ADRIANA CARDENAS ROBLEDO (2023, [Artículo])

This paper develops a device capable of confirming the minimum coverage area on a thermistor by a thermal paste dispensed and cured in the manufacturing process of Exhaust Gas Recirculation (EGR) temperature sensors to provide the required fixation in the presence of mechanical shock conditions. Such a device leverages the thermistor’s self-heating effect and the thermal conductivity of the paste to read the voltage drop from the sensor, which translates into paste coverage area. The methodology follows a synthesized procedure to develop equipment and processes, considering an early phase for concept confirmation to demonstrate the feasibility of the device development. In a later phase, the optimal parameters are calculated and set to the device for delivering the corresponding classification of the sensors during the test. Once launched in production, the device demonstrates high effectiveness in screening out the sensors with rejectable paste coverage area on the thermistor.

Self-heating effect Thermistor Paste Temperature sensor Heat dissipation INGENIERÍA Y TECNOLOGÍA CIENCIAS TECNOLÓGICAS OTRAS ESPECIALIDADES TECNOLÓGICAS OTRAS OTRAS

Integrative transcriptome and metabolome analysis reveals the mechanisms of light-induced pigmentation in purple waxy maize

XUECAI ZHANG Yunbi Xu Prasanna Boddupalli (2023, [Artículo])

Pigment Accumulation Light Response CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ANTHOCYANINS LIGHT METABOLOMICS TRANSCRIPTOMICS WAXY MAIZE

Optimizing nitrogen fertilizer and planting density levels for maize production under current climate conditions in Northwest Ethiopian midlands

Kindie Tesfaye Dereje Ademe Enyew Adgo (2023, [Artículo])

This study determined the most effective plating density (PD) and nitrogen (N) fertilizer rate for well-adapted BH540 medium-maturing maize cultivars for current climate condition in north west Ethiopia midlands. The Decision Support System for Agrotechnology Transfer (DSSAT)-Crop Environment Resource Synthesis (CERES)-Maize model has been utilized to determine the appropriate PD and N-fertilizer rate. An experimental study of PD (55,555, 62500, and 76,900 plants ha−1) and N (138, 207, and 276 kg N ha−1) levels was conducted for 3 years at 4 distinct sites. The DSSAT-CERES-Maize model was calibrated using climate data from 1987 to 2018, physicochemical soil profiling data (wilting point, field capacity, saturation, saturated hydraulic conductivity, root growth factor, bulk density, soil texture, organic carbon, total nitrogen; and soil pH), and agronomic management data from the experiment. After calibration, the DSSAT-CERES-Maize model was able to simulate the phenology and growth parameters of maize in the evaluation data set. The results from analysis of variance revealed that the maximum observed and simulated grain yield, biomass, and leaf area index were recorded from 276 kg N ha−1 and 76,900 plants ha−1 for the BH540 maize variety under the current climate condition. The application of 76,900 plants ha−1 combined with 276 kg N ha−1 significantly increased observed and simulated yield by 25% and 15%, respectively, compared with recommendation. Finally, future research on different N and PD levels in various agroecological zones with different varieties of mature maize types could be conducted for the current and future climate periods.

Maize Model Planting Density CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE MODELS SPACING NITROGEN FERTILIZERS YIELDS

Offshore wind energy climate projection using UPSCALE climate data under the RCP8.5 emission scenario

MARKUS SEBASTIAN GROSS (2016, [Artículo])

In previous work, the authors demonstrated how data from climate simulations can be utilized to estimate regional wind power densities. In particular, it was shown that the quality of wind power densities, estimated from the UPSCALE global dataset in offshore regions of Mexico, compared well with regional high resolution studies. Additionally, a link between surface temperature and moist air density in the estimates was presented. UPSCALE is an acronym for UK on PRACE (the Partnership for Advanced Computing in Europe)-weather-resolving Simulations of Climate for globAL Environmental risk. The UPSCALE experiment was performed in 2012 by NCAS (National Centre for Atmospheric Science)- Climate, at the University of Reading and the UK Met Office Hadley Centre. The study included a 25.6-year, five-member ensemble simulation of the HadGEM3 global atmosphere, at 25km resolution for present climate conditions. The initial conditions for the ensemble runs were taken from consecutive days of a test configuration. In the present paper, the emphasis is placed on the single climate run for a potential future climate scenario in the UPSCALE experiment dataset, using the Representation Concentrations Pathways (RCP) 8.5 climate change scenario. Firstly, some tests were performed to ensure that the results using only one instantiation of the current climate dataset are as robust as possible within the constraints of the available data. In order to achieve this, an artificial time series over a longer sampling period was created. Then, it was shown that these longer time series provided almost the same results than the short ones, thus leading to the argument that the short time series is sufficient to capture the climate. Finally, with the confidence that one instantiation is sufficient, the future climate dataset was analysed to provide, for the first time, a projection of future changes in wind power resources using the UPSCALE dataset. It is hoped that this, in turn, will provide some guidance for wind power developers and policy makers to prepare and adapt for climate change impacts on wind energy production. Although offshore locations around Mexico were used as a case study, the dataset is global and hence the methodology presented can be readily applied at any desired location. © Copyright 2016 Gross, Magar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reprod

atmosphere, climate change, Europe, Mexico, sampling, time series analysis, university, weather, wind power, climate, risk, theoretical model, wind, Climate, Models, Theoretical, Risk, Wind CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA CIENCIAS DE LA TIERRA Y DEL ESPACIO OCEANOGRAFÍA OCEANOGRAFÍA

Catching-up with genetic progress: Simulation of potential production for modern wheat cultivars in the Netherlands

João Vasco Silva Frits K. Van Evert Pytrik Reidsma (2023, [Artículo])

Context: Wheat crop growth models from all over the world have been calibrated on the Groot and Verberne (1991) data set, collected between 1982 and 1984 in the Netherlands, in at least 28 published studies to date including various recent ones. However, the recent use of this data set for calibration of potential yield is questionable as actual Dutch winter wheat yields increased by 3.1 Mg ha-1 over the period 1984 – 2015. A new comprehensive set of winter wheat experiments, suitable for crop model calibration, was conducted in Wageningen during the growing seasons of 2013–2014 and of 2014–2015. Objective: The present study aimed to quantify the change of winter wheat variety traits between 1984 and 2015 and to examine which of the identified traits explained the increase in wheat yield most. Methods: PCSE-LINTUL3 was calibrated on the Groot and Verberne data (1991) set. Next, it was evaluated on the 2013–2015 data set. The model was further recalibrated on the 2013–2015 data set. Parameter values of both calibrations were compared. Sensitivity analysis was used to assess to what extent climate change, elevated CO2, changes in sowing dates, and changes in cultivar traits could explain yield increases. Results: The estimated reference light use efficiency and the temperature sum from anthesis to maturity were higher in 2013–2015 than in 1982–1984. PCSE-LINTUL3, calibrated on the 1982–1984 data set, underestimated the yield potential of 2013–2015. Sensitivity analyses showed that about half of the simulated winter wheat yield increase between 1984 and 2015 in the Netherlands was explained by elevated CO2 and climate change. The remaining part was explained by the increased temperature sum from anthesis to maturity and, to a smaller extent, by changes in the reference light use efficiency. Changes in sowing dates, biomass partitioning fractions, thermal requirements for anthesis, and biomass reallocation did not explain the yield increase. Conclusion: Recalibration of PCSE-LINTUL3 was necessary to reproduce the high wheat yields currently obtained in the Netherlands. About half of the reported winter wheat yield increase was attributed to climate change and elevated CO2. The remaining part of the increase was attributed to changes in the temperature sum from anthesis to maturity and, to a lesser extent, the reference light use efficiency. Significance: This study systematically addressed to what extent changes in various cultivar traits, climate change, and elevated CO2 can explain the winter wheat yield increase observed in the Netherlands between 1984 and 2015.

Light Use Efficiency Potential Yield CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROP MODELLING LIGHT PHENOLOGY MAXIMUM SUSTAINABLE YIELD TRITICUM AESTIVUM WINTER WHEAT

La construcción fantástica y gótica en una pieza teatral, El fantasma del hotel Alsace. Los últimos días de Oscar Wilde de Vicente Quirarte

Andrea Carolina Godínez Rivera (2023, [Otro, Trabajo terminal, especialidad])

45 páginas. Especialización en Literatura Mexicana del Siglo XX.

Esta investigación recibió apoyo del Sistema Nacional de Posgrados (SNP), del Consejo Nacional de Humanidades, Ciencias y Tecnologías (CONAHCYT)

El propósito de la presente tesina es plantear una guía general con los componentes más sobresalientes que colaboran para la construcción gótica y fantástica de la obra de teatro El fantasma del hotel Alsace de Vicente Quirarte. Se analizarán los recursos como la representación y la simbología del fantasma, el efecto de la ambigüedad, la construcción del espacio y el tiempo como productores de universos ficcionales. En el primer capítulo, “El fantasma literario”, se visualizará la construcción fantástica del fantasma por medio de la teatralidad y el recurso de la fantasmagoría con base en las teorías de Max Milner, David Roas y José Miguel Sardiñas, Anne Ubersfeld y José Luis García Barrientos con el fin de mostrar la subjetividad de los misterios del ser humano donde existen los miedos y las angustias, los terrores del humano y así, mostrar lo que en otro tipo de literaturas no se puede hacer: presentar lo inexpresable de los horrores del hombre. En el segundo capítulo, “La imaginería gótica en lo fantástico”, se desarrolla el efecto de la ambigüedad que parte de la imaginería mental del personaje Oscar Wilde como producto de la locura. Además, se explicará en qué consiste la configuración del espacio encarcelado y el tiempo estático y desdibujado como parte de la construcción de la atmósfera gótica que aparece a lo largo de las cuatro escenas de la obra de teatro.

Wilde, Oscar, 1854-1900--Criticism and interpretation. Ghosts in the theater. Art, Gothic. Fantasmas en la literatura. Arte gótico. PR5824 HUMANIDADES Y CIENCIAS DE LA CONDUCTA CIENCIAS DE LAS ARTES Y LAS LETRAS TEORÍA, ANÁLISIS Y CRÍTICA DE LAS BELLAS ARTES

Carbamazepine degradation by visible-light-driven photocatalyst Ag3PO4/GO: Mechanism and pathway

Guanhan Chen Wenyi Dong Hongjie Wang Zilong Zhao Feng Wang Feifei Wang César Nieto Delgado (2022, [Artículo])

"Carbamazepine (CBZ), as one of the most frequently detected pharmaceuticals, is of great concern due to its potential impact on the ecosystem and human health. This study provides an effective approach to remove CBZ by using photocatalyst silver phosphate combined with graphene oxide (Ag3PO4/GO) under visible irradiation. The morphology, composition, and optical properties of Ag3PO4/GO were characterized employing SEM, XRD, and DRS. Graphene oxide could improve the visible-light utilization and promote electron's charge to enhance the photocatalytic performance of Ag3PO4/GO. With the optimal reaction condition of 5.86 mW/cm(2) light intensity, 15-25 degrees C temperature, 5-7 pH, and 0.5 mg/L catalytic dosages, 5 mg/L CBZ could be completely degraded in 30 min, and the apparent rate constant could reach 0.12 min(-1). Additionally, the radical trapping experiments indicated center dot OH and O-2(-)center dot were the main reactive oxygen species employed to eliminate CBZ. The decay pathways of CBZ had been proposed accordingly, and the main product was the low-molecular products. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences, Harbin Institute of Technology, Chinese Research Academy of Environmental Sciences."

Carbamazepine Ag3PO4/GO Visible light Photocatalysis INGENIERÍA Y TECNOLOGÍA INGENIERÍA Y TECNOLOGÍA

Physiological and molecular aspects of seed longevity: exploring intra-species variation in eight Pisum sativum L. accessions

Maraeva Gianella Daniele Dondi Andreas Börner Anca Macovei Andrea Pagano Filippo Guzzon Alma Balestrazzi (2022, [Artículo])

Thermogravimetry CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CARBOHYDRATES PROLINE TOCOPHEROLS GENETICS PHYSIOLOGY PEAS SEEDS SUGARS DIFFERENTIAL SCANNING CALORIMETRY SEED LONGEVITY THERMOGRAVIMETRIC ANALYSIS

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Martin van Ittersum (2023, [Artículo])

Context: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective: The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods: A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results: Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion: Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance: The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

Model Accuracy Model Precision Linear Mixed Models CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MACHINE LEARNING SUSTAINABLE INTENSIFICATION BIG DATA YIELDS MODELS AGRONOMY

Using an incomplete block design to allocate lines to environments improves sparse genome-based prediction in plant breeding

Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])

Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to

the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding