RESUMEN

The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs.

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RESUMEN

Pressure is one of the essential variables to give information about engine condition and monitoring. Direct recording of this signal is complex and invasive, while angular velocity can be measured. Nonetheless, the challenge is to predict the cylinder pressure using the shaft kinematics accurately. In this paper, a time-delay neural network (TDNN), interpreted as a finite pulse response (FIR) filter, is proposed to estimate the in-cylinder pressure of a single-cylinder internal combustion engine (ICE) from fluctuations in shaft angular velocity. The experiments are conducted over data obtained from an ICE operating in 12 different states by changing the angular velocity and load. The TDNN's delay is adjusted to get the highest possible correlation-based score. Our methodology can predict pressure with an R2 >0.9, avoiding complicated pre-processing steps.

RESUMEN

The circadian system controls the daily rhythms of a variety of physiological processes. Most organisms show physiological, metabolic and behavioral rhythms that are coupled to environmental signals. In humans, the main synchronizer is the light/dark cycle, although non-photic cues such as food availability, noise, and work schedules are also involved. In a continuously operating hospital, the lack of rhythmicity in these elements can alter the patient's biological rhythms and resilience. This paper presents a Theory of Inpatient Circadian Care (TICC) grounded in circadian principles. We conducted a literature search on biological rhythms, chronobiology, nursing care, and middle-range theories in the databases PubMed, SciELO Public Health, and Google Scholar. The search was performed considering a period of 6 decades from 1950 to 2013. Information was analyzed to look for links between chronobiology concepts and characteristics of inpatient care. TICC aims to integrate multidisciplinary knowledge of biomedical sciences and apply it to clinical practice in a formal way. The conceptual points of this theory are supported by abundant literature related to disease and altered biological rhythms. Our theory will be able to enrich current and future professional practice.