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-In any productive sector, predictive tools are crucial for optimal management and decision-making. In the health sector, it is especially important to have information available in advance, as this not only means optimizing resources, but also improving patient care. This work focuses on the management of healthcare resources in primary care centres. The main objective of this work is to develop a model capable of predicting the number of patients who will demand health care in a primary care centre on a daily basis. This model is integrated into a decision support system that is accessible and easy to use by the manager through a web application. In this case, data from a primary care centre in the city of Jaén, Spain, were used. The model was estimated using spatial-temporal training data, the daily health demand data in that centre for five years, and a series of meteorological data. Different regression algorithms have been employed. The workflow requires selecting the parameters that influence the health demand prediction and discarding those that distort the model. The main contribution of this research is the daily prediction of the number of patients attending the health centre with absolute errors better than 3%, which is crucial for decision-making on the sizing of health resources in a primary care health centre

Multispectral mapping on 3D models and multi-temporal monitoring for individual characterization of olive trees

3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction of vegetation. In this paper, we propose a novel application for the fusion of multispectral images and high-resolution point clouds of an olive orchard. Our methodology is based on a multi-temporal approach to study the evolution of olive trees. This process is fully automated and no human intervention is required to characterize the point cloud with the reflectance captured by multiple multispectral images. The main objective of this work is twofold: (1) the multispectral image mapping on a high-resolution point cloud and (2) the multi-temporal analysis of morphological and spectral traits in two flight campaigns. Initially, the study area is modeled by taking multiple overlapping RGB images with a high-resolution camera from an unmanned aerial vehicle (UAV). In addition, a UAV-based multispectral sensor is used to capture the reflectance for some narrow-bands (green, near-infrared, red, and red-edge). Then, the RGB point cloud with a high detailed geometry of olive trees is enriched by mapping the reflectance maps, which are generated for every multispectral image. Therefore, each 3D point is related to its corresponding pixel of the multispectral image, in which it is visible. As a result, the 3D models of olive trees are characterized by the observed reflectance in the plant canopy. These reflectance values are also combined to calculate several vegetation indices (NDVI, RVI, GRVI, and NDRE). According to the spectral and spatial relationships in the olive plantation, segmentation of individual olive trees is performed. On the one hand, plant morphology is studied by a voxel-based decomposition of its 3D structure to estimate the height and volume. On the other hand, the plant health is studied by the detection of meaningful spectral traits of olive trees. Moreover, the proposed methodology also allows the processing of multi-temporal data to study the variability of the studied features. Consequently, some relevant changes are detected and the development of each olive tree is analyzed by a visual-based and statistical approach. The interactive visualization and analysis of the enriched 3D plant structure with different spectral layers is an innovative method to inspect the plant health and ensure adequate plantation sustainability

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-Predictive systems are a crucial tool in management and decision-making in any productive sector. In the case of agriculture, it is especially interesting to have advance information on the profitability of a farm. In this sense, depending on the time of the year when this information is available, important decisions can be made that affect the economic balance of the farm. The aim of this study is to develop an effective model for predicting crop yields in advance that is accessible and easy to use by the farmer or farm manager from a web-based application. In this case, an olive orchard in the Andalusia region of southern Spain was used. The model was estimated using spatio-temporal training data, such as yield data from eight consecutive years, and more than twenty meteorological parameters data, automatically charged from public web services, belonging to a weather station located near the sample farm. The workflow requires selecting the parameters that influence the crop prediction and discarding those that introduce noise into the model. The main contribution of this research is the early prediction of crop yield with absolute errors better than 20%, which is crucial for making decisions on tillage investments and crop marketing.This research has been partially funded through the research projects PYC20-RE-005-UJA 1381202-GEU and IEG-2021 y PREDIC_I-GOPO-JA-20-0006, which are co-financed with the European Union FEDER, Instituto de Estudios Gienneses, and the Junta de Andalucía funds. We are also grateful for the support provided by the Ministry for Ecological Transition and the Demographic Challenge, Spanish Government (AEMET, Agencia Estatal de Meteorología)

Tumor Necrosis Factor Receptor 1 Expression Is Upregulated in Dendritic Cells in Patients with Chronic HCV Who Respond to Therapy

The present studies assessed the level of tumor necrosis factor receptor (TNFR) expression in peripheral blood mononuclear cells (PBMCs) subsets from patients with chronic HCV undergoing interferon α/ribavirin-based therapy (Ifn/R). Methods. TNFR family member mRNA expression was determined using quantitative real-time PCR assays (RTPCRs) in PBMC from 39 HCV+ patients and 21 control HCV− patients. Further subset analysis of HCV + patients (untreated (U), sustained virological responders (SVR), and nonresponders (NR)/relapsers (Rel)) PBMC was performed via staining with anti-CD123, anti-CD33, anti-TNFR1 or via RTPCR for TNFR1 mRNA. Results. A similar level of TNFR1 mRNA in PBMC from untreated HCV+ genotype 1 patients and controls was noted. TNFR1 and TNFR2 mRNA levels in PBMC from HCV+ patients with SVR were statistically different than levels in HCV(−) patients. A significant difference was noted between the peak values of TNFR1 of the CD123+ PBMC isolated from SVR and the NR/Rel. Conclusion. Upregulation of TNFR1 expression, occurring in a specific subset of CD123+ dendritic cells, appeared in HCV+ patients with SVR

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-Currently, customer relationship management (CRM) tools are very important in our society because they provide a comunication channel to the healthcare system for patients. Salud Responde is a CRM that provides many health services for the entire population of Andalusia, in southern Spain. The number and frequenzy of phone calls received change along the year. They depend on many factors, such as weekdays, seasons, vaccination campaigns, environmental factors, pandemic periods, etc. All these are the main reasons number of health calls changes along the year. This variability makes that the current management of resources for offering emergency services based on historical data is inefficient. The factors, which influence the phone calls along the year, are different from one period to another. Therefore, it is clear to demand an improved in the current management system. In this context, the main goal for this research is to develop an expert system able to identify and analyze, using different data mining algorithms, the most relevant factors to predict the variability of health service demand. Thus, here, it is proposed a methodology in which using reasons calls received in the CRM as input data, it is possible to predict in advance the healthcare resources demand.

Predicting crystal growth via a unified kinetic three-dimensional partition model

Understanding and predicting crystal growth is fundamental to the control of functionality in modern materials. Despite investigations for more than one hundred years1, 2, 3, 4, 5, it is only recently that the molecular intricacies of these processes have been revealed by scanning probe microscopy6, 7, 8. To organize and understand this large amount of new information, new rules for crystal growth need to be developed and tested. However, because of the complexity and variety of different crystal systems, attempts to understand crystal growth in detail have so far relied on developing models that are usually applicable to only one system9, 10, 11. Such models cannot be used to achieve the wide scope of understanding that is required to create a unified model across crystal types and crystal structures. Here we describe a general approach to understanding and, in theory, predicting the growth of a wide range of crystal types, including the incorporation of defect structures, by simultaneous molecular-scale simulation of crystal habit and surface topology using a unified kinetic three-dimensional partition model. This entails dividing the structure into ‘natural tiles’ or Voronoi polyhedra that are metastable and, consequently, temporally persistent. As such, these units are then suitable for re-construction of the crystal via a Monte Carlo algorithm. We demonstrate our approach by predicting the crystal growth of a diverse set of crystal types, including zeolites, metal–organic frameworks, calcite, urea and L-cystine

<i>CrystalGrower</i>: a generic computer program for Monte Carlo modelling of crystal growth.

From Europe PMC via Jisc Publications RouterHistory: ppub 2020-11-01, epub 2020-11-18Publication status: PublishedA Monte Carlo crystal growth simulation tool, CrystalGrower, is described which is able to simultaneously model both the crystal habit and nanoscopic surface topography of any crystal structure under conditions of variable supersaturation or at equilibrium. This tool has been developed in order to permit the rapid simulation of crystal surface maps generated by scanning probe microscopies in combination with overall crystal habit. As the simulation is based upon a coarse graining at the nanoscopic level features such as crystal rounding at low supersaturation or undersaturation conditions are also faithfully reproduced. CrystalGrower permits the incorporation of screw dislocations with arbitrary Burgers vectors and also the investigation of internal point defects in crystals. The effect of growth modifiers can be addressed by selective poisoning of specific growth sites. The tool is designed for those interested in understanding and controlling the outcome of crystal growth through a deeper comprehension of the key controlling experimental parameters

Numerical investigation of nanostructured silica PCFs for sensing applications.

Photonic crystal fibers (PCFs) developed using nanostructured composite materials provide special optical properties. PCF light propagation and modal characteristics can be tailored by modifying their structural and material parameters. Structuring and infusion of liquid crystal materials enhances the capabilities of all silica PCFs, facilitating their operation in different spectral regimes. The wavelength tunability feature of nanostructured PCFs can be utilized for many advanced sensing applications. This paper discusses a new approach to modify the optical properties of PCFs by periodic nanostructuring and composite material (liquid crystal-silica) infiltration. PCF characteristics like confinement wavelength, confinement loss, mode field diameter (MFD) and bandwidth are investigated by varying the structural parameters and material infiltrations. Theoretical study revealed that composite material infusion resulted in a spectral band shift accompanied by an improvement in PCF bandwidth. Moreover, nanostructured PCFs also achieved reduced confinement losses and improved MFD which is very important in long-distance remote sensing applications