A Methodology Based on Machine Learning and Soft Computing to Design More Sustainable Agriculture Systems

©2023. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/ This document is the Published, version of a Published Work that appeared in final form in Sensors. To access the final edited and published work see https://doi.org/10.3390/s23063038Advances in new technologies are allowing any field of real life to benefit from using these ones. Among of them, we can highlight the IoT ecosystem making available large amounts of information, cloud computing allowing large computational capacities, and Machine Learning techniques together with the Soft Computing framework to incorporate intelligence. They constitute a powerful set of tools that allow us to define Decision Support Systems that improve decisions in a wide range of real-life problems. In this paper, we focus on the agricultural sector and the issue of sustainability. We propose a methodology that, starting from times series data provided by the IoT ecosystem, a preprocessing and modelling of the data based on machine learning techniques is carried out within the framework of Soft Computing. The obtained model will be able to carry out inferences in a given prediction horizon that allow the development of Decision Support Systems that can help the farmer. By way of illustration, the proposed methodology is applied to the specific problem of early frost prediction. With some specific scenarios validated by expert farmers in an agricultural cooperative, the benefits of the methodology are illustrated. The evaluation and validation show the effectiveness of the proposal

Evaporation Forecasting through Interpretable Data Analysis Techniques

©2022. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/ This document is the Published, version of a Published Work that appeared in final form in Electronics. To access the final edited and published work see https://doi.org/10.3390/electronics11040536Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the evaporation from water reservoirs, which implies a considerable amount of water lost during warmer periods of the year. Indeed, evaporation rate forecasting can help farmers grow crops more sustainably by managing water resources more efficiently in the context of precision agriculture. In this work, we expose an interpretable machine learning approach, based on a multivariate decision tree, to forecast the evaporation rate on a daily basis using data from an Internet of Things (IoT) infrastructure, which is deployed on a real irrigated plot located in Murcia (southeastern Spain). The climate data collected feed the models that provide a forecast of evaporation and a summary of the parameters involved in this process. Finally, the results of the interpretable presented model are validated with the best literature models for evaporation rate prediction, i.e., Artificial Neural Networks, obtaining results very similar to those obtained for them, reaching up to 0.85R2 and 0.6MAE. Therefore, in this work, a double objective is faced: to maintain the performance obtained by the models most frequently used in the problem while maintaining the interpretability of the knowledge captured in it, which allows better understanding the problem and carrying out appropriate actions

Making decisions for frost prediction in agricultural crops in a softcomputing framework

© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Computers and Electronics in Agriculture. To access the final edited and published work see https://doi.org/10.1016/j.compag.2020.105587Nowadays, there are many areas of daily life that can obtain benefit from technological advances and the large amounts of information stored. One of these areas is agriculture, giving place to precision agriculture. Frosts in crops are among the problems that precision agriculture tries to solve because produce great economic losses to farmers. The problem of early detection of frost is a process that involves a large amount of wheather data. However, the use of these data, both for the classification and regression task, must be carried out in an adequate way to obtain an inference with quality. A preprocessing of them is carried out in order to obtain a dataset grouping attributes that refer to the same measure in a single attribute expressed by a fuzzy value. From these fuzzy time series data we must use techniques for data analysis that are capable of manipulating them. Therefore, first a regression technique based on k-nearest neighbors in a Soft Computing framework is proposed that can deal with fuzzy data, and second, this technique and others to classification are used for the early detection of a frost from data obtained from different weather stations in the Region of Murcia (south-east Spain) with the aim of decrease the damages that these frosts can cause in crops. From the models obtained, an interpretation of the provided information is performed and the most relevant set of attributes is obtained for the anticipated prediction of a frost and of the temperature value. Several experiments are carried out on the datasets to obtain the models with the best performance in the prediction validating the results by means of a statistical analysis

A Fuzzy k-Nearest Neighbors Classifier to Deal with Imperfect Data

© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Soft Computing. To access the final edited and published work see https://doi.org/10.1007/s00500-017-2567-xThe k-nearest neighbors method (kNN) is a nonparametric, instance-based method used for regression and classification. To classify a new instance, the kNN method computes its k nearest neighbors and generates a class value from them. Usually, this method requires that the information available in the datasets be precise and accurate, except for the existence of missing values. However, data imperfection is inevitable when dealing with real-world scenarios. In this paper, we present the kNNimp classifier, a k-nearest neighbors method to perform classification from datasets with imperfect value. The importance of each neighbor in the output decision is based on relative distance and its degree of imperfection. Furthermore, by using external parameters, the classifier enables us to define the maximum allowed imperfection, and to decide if the final output could be derived solely from the greatest weight class (the best class) or from the best class and a weighted combination of the closest classes to the best one. To test the proposed method, we performed several experiments with both synthetic and realworld datasets with imperfect data. The results, validated through statistical tests, show that the kNNimp classifier is robust when working with imperfect data and maintains a good performance when compared with other methods in the literature, applied to datasets with or without imperfection

Readout electronics for the SiPM tracking plane in the NEXT-1 prototype

NEXT is a new experiment to search for neutrinoless double beta decay using a 100 kg radio-pure high-pressure gaseous xenon TPC with electroluminescence readout. A large-scale prototype with a SiPM tracking plane has been built. The primary electron paths can be reconstructed from time-resolved measurements of the light that arrives to the SiPM plane. Our approach is to measure how many photons have reached each SiPM sensor each microsecond with a gated integrator. We have designed and tested a 16-channel front-end board that includes the analog paths and a digital section. Each analog path consists of three different stages: a transimpedance amplifier, a gated integrator and an offset and gain control stage. Measurements show good linearity and the ability to detect single photoelectrons. © 2011 Elsevier B.V.The authors would like to acknowledge the support of the NEXT Collaboration, the DATE team at CERN PH-AID and the CONSOLIDER-INGENIO2010 grant CSD2008-0037 (Canfranc Underground Physics) from the Spanish Ministry of Science and Innovation.Herrero Bosch, V.; Toledo Alarcón, JF.; Català Pérez, JM.; Esteve Bosch, R.; Gil Ortiz, A.; Lorca, D.; Monzó Ferrer, JM.... (2012). Readout electronics for the SiPM tracking plane in the NEXT-1 prototype. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 695:229-232. https://doi.org/10.1016/j.nima.2011.12.057S22923269

Higher socioeconomic status is related to healthier levels of fatness and fitness already at 3 to 5 years of age: The PREFIT project: Relation between socioeconomic status, fatness and fitness in preschoolers

This study aimed to analyse the association between socioeconomic status (SES) and fatness and fitness in preschoolers. 2, 638 preschoolers (3–5 years old; 47.2% girls) participated. SES was estimated from the parental educational and occupational levels, and the marital status. Fatness was assessed by body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR). Physical fitness components were assessed using the PREFIT battery. Preschoolers whose parents had higher educational levels had lower fatness (P < 0.05). BMI significantly differed across occupational levels of each parent (P < 0.05) and WHtR across paternal levels (P = 0.004). Musculoskeletal fitness was different across any SES factor (P < 0.05), except handgrip across paternal occupational levels (P = 0.05). Preschoolers with high paternal occupation had higher speed/agility (P = 0.005), and those with high or low maternal education had higher VO2max (P = 0.046). Odds of being obese and having low musculoskeletal fitness was lower as SES was higher (P < 0.05). Those with married parents had higher cardiorespiratory fitness than single-parent ones (P = 0.010). School-based interventions should be aware of that children with low SES are at a higher risk of obesity and low fitness already in the first years of life

Low-diffusion Xe-He gas mixtures for rare-event detection: electroluminescence yield

High pressure xenon Time Projection Chambers (TPC) based on secondary scintillation (electroluminescence) signal amplification are being proposed for rare event detection such as directional dark matter, double electron capture and double beta decay detection. The discrimination of the rare event through the topological signature of primary ionisation trails is a major asset for this type of TPC when compared to single liquid or double-phase TPCs, limited mainly by the high electron diffusion in pure xenon. Helium admixtures with xenon can be an attractive solution to reduce the electron diffu- sion significantly, improving the discrimination efficiency of these optical TPCs. We have measured the electroluminescence (EL) yield of Xe–He mixtures, in the range of 0 to 30% He and demonstrated the small impact on the EL yield of the addition of helium to pure xenon. For a typical reduced electric field of 2.5 kV/cm/bar in the EL region, the EL yield is lowered by ∼ 2%, 3%, 6% and 10% for 10%, 15%, 20% and 30% of helium concentration, respectively. This decrease is less than what has been obtained from the most recent simulation framework in the literature. The impact of the addition of helium on EL statistical fluctuations is negligible, within the experimental uncertainties. The present results are an important benchmark for the simulation tools to be applied to future optical TPCs based on Xe-He mixtures. [Figure not available: see fulltext.]

Energy calibration of the NEXT-White detector with 1% resolution near Q ββ of 136Xe

Excellent energy resolution is one of the primary advantages of electroluminescent high-pressure xenon TPCs. These detectors are promising tools in searching for rare physics events, such as neutrinoless double-beta decay (ββ0ν), which require precise energy measurements. Using the NEXT-White detector, developed by the NEXT (Neutrino Experiment with a Xenon TPC) collaboration, we show for the first time that an energy resolution of 1% FWHM can be achieved at 2.6 MeV, establishing the present technology as the one with the best energy resolution of all xenon detectors for ββ0ν searches. [Figure not available: see fulltext.

Thrombopoietin Receptor Agonists for Severe Thrombocytopenia after Allogeneic Stem Cell Transplantation : Experience of the Spanish Group of Hematopoietic Stem Cell Transplant

Persistent thrombocytopenia is a common complication after allogeneic hematopoietic stem cell transplantation (allo-SCT). Romiplostim and eltrombopag are the currently available thrombopoietin receptor agonists (TPO-RAs), and some studies with very small numbers of cases have reported their potential efficacy in the allo-SCT setting. The present retrospective study evaluated the safety and efficacy of TPO-RAs in 86 patients with persistent thrombocytopenia after allo-HSCT. Sixteen patients (19%) had isolated thrombocytopenia (PT), and 71 (82%) had secondary failure of platelet recovery (SFPR). TPO-RA therapy was started at a median of 127 days (range, 27 to 1177 days) after allo-SCT. The median initial and maximum administered doses were 50 mg/day (range, 25 to 150 mg/day) and 75 mg/day (range, 25 to 150 mg/day), respectively, for eltrombopag and 1 µg/kg (range, 1 to 7 µg/kg) and 5 µg/kg (range, 1 to 10 µg/kg), respectively, for romiplostin. The median platelet count before initiation of TPO-RA therapy was 14,000/µL (range, 1000 to 57,000/µL). Platelet recovery to ≥50,000/µL without transfusion support was achieved in 72% of patients at a median time of 66 days (range, 2 to 247 days). Eighty-one percent of the patients had a decreased number of megakaryocytes before treatment, showing a slower response to therapy (P =.011). The median duration of treatment was 62 days (range, 7 to 700 days). Grade 3-4 adverse events (hepatic and asthenia) were observed in only 2% of the patients. At last follow-up, 81% of patients had discontinued TPO-RAs and maintained response, and 71% were alive. To our knowledge, this is the largest series analyzing the use of TPO-RAs after allo-SCT reported to date. Our results support the efficacy and safety in this new setting. Further prospective trials are needed to increase the level of evidence and to identify predictors of response

Metagenes Associated with Survival in Non-Small Cell Lung Cancer

NSCLC (non-small cell lung cancer) comprises about 80% of all lung cancer cases worldwide. Surgery is most effective treatment for patients with early-stage disease. However, 30%–55% of these patients develop recurrence within 5 years. Therefore, markers that can be used to accurately classify early-stage NSCLC patients into different prognostic groups may be helpful in selecting patients who should receive specific therapies