Efecto de la fertilización P y S sobre la producción y calidad de alfalfa (Medicago sativa L.) irrigada y el estado orgánico del suelo en el valle inferior del río Negro

Tesis de Grado para optar al título de Magister en Ciencias Agrarias de la Universidad Nacional del Sur, 2017En el Valle Inferior del río Negro aproximadamente el 20% de la superficie total bajo riego se destina al cultivo de alfalfa (Medicago sativa L.). Esto contribuye a mantener la oferta anual de forraje con heno de calidad, principal fuente de alimentación de los sistemas de invernada.EEA Valle InferiorFil: Gallego, Juan José. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Valle Inferior; Argentin

Verdeos invernales: su nuevo rol en los sistemas ganaderos de los valles norpatagonícos

En Patagonia, los sistemas ganaderos de los valles regados requieren de un proceso de intensificación de la producción de carne y leche que responda a las demandas regionales de estos productos, que actualmente no son satisfechasEEA Valle InferiorFil: Gallego, Juan José. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Valle Inferior; Argenitn

An overview of ensemble and feature learning in few-shot image classification using siamese networks

Siamese Neural Networks (SNNs) constitute one of the most representative approaches for addressing Few-Shot Image Classification. These schemes comprise a set of Convolutional Neural Network (CNN) models whose weights are shared across the network, which results in fewer parameters to train and less tendency to overfit. This fact eventually leads to better convergence capabilities than standard neural models when considering scarce amounts of data. Based on a contrastive principle, the SNN scheme jointly trains these inner CNN models to map the input image data to an embedded representation that may be later exploited for the recognition process. However, in spite of their extensive use in the related literature, the representation capabilities of SNN schemes have neither been thoroughly assessed nor combined with other strategies for boosting their classification performance. Within this context, this work experimentally studies the capabilities of SNN architectures for obtaining a suitable embedded representation in scenarios with a severe data scarcity, assesses the use of train data augmentation for improving the feature learning process, introduces the use of transfer learning techniques for further exploiting the embedded representations obtained by the model, and uses test data augmentation for boosting the performance capabilities of the SNN scheme by mimicking an ensemble learning process. The results obtained with different image corpora report that the combination of the commented techniques achieves classification rates ranging from 69% to 78% with just 5 to 20 prototypes per class whereas the CNN baseline considered is unable to converge. Furthermore, upon the convergence of the baseline model with the sufficient amount of data, still the adequate use of the studied techniques improves the accuracy in figures from 4% to 9%.First author is supported by the “Programa I+D+i de la Generalitat Valenciana” through grant APOSTD/2020/256. This research work was partially funded by the Spanish “Ministerio de Ciencia e Innovación” and the European Union “NextGenerationEU/PRTR” programmes through project DOREMI (TED2021-132103A-I00). Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

Statistical semi-supervised system for grading multiple peer-reviewed open-ended works

In the education context, open-ended works generally entail a series of benefits as the possibility of develop original ideas and a more productive learning process to the student rather than closed-answer activities. Nevertheless, such works suppose a significant correction workload to the teacher in contrast to the latter ones that can be self-corrected. Furthermore, such workload turns to be intractable with large groups of students. In order to maintain the advantages of open-ended works with a reasonable amount of correction effort, this article proposes a novel methodology: students perform the corrections using a rubric (closed Likert scale) as a guideline in a peer-review fashion; then, their markings are automatically analyzed with statistical tools to detect possible biased scorings; finally, in the event the statistical analysis detects a biased case, the teacher is required to intervene to manually correct the assignment. This methodology has been tested on two different assignments with two heterogeneous groups of people to assess the robustness and reliability of the proposal. As a result, we obtain values over 95% in the confidence of the intra-class correlation test (ICC) between the grades computed by our proposal and those directly resulting from the manual correction of the teacher. These figures confirm that the evaluation obtained with the proposed methodology is statistically similar to that of the manual correction of the teacher with a remarkable decrease in terms of effort.This work has been supported by the Vicerrectorado de Calidad e Innovación Educativa-Instituto de Ciencias de la Educación of the Universidad de Alicante (2016-17 edition) through the Programa de Redes-I3CE de investigación en docencia universitaria (ref. 3690)

Real-time portable system for fabric defect detection using an ARM processor

Modern textile industry seeks to produce textiles as little defective as possible since the presence of defects can decrease the final price of products from 45% to 65%. Automated visual inspection (AVI) systems, based on image analysis, have become an important alternative for replacing traditional inspections methods that involve human tasks. An AVI system gives the advantage of repeatability when implemented within defined constrains, offering more objective and reliable results for particular tasks than human inspection. Costs of automated inspection systems development can be reduced using modular solutions with embedded systems, in which an important advantage is the low energy consumption. Among the possibilities for developing embedded systems, the ARM processor has been explored for acquisition, monitoring and simple signal processing tasks. In a recent approach we have explored the use of the ARM processor for defects detection by implementing the wavelet transform. However, the computation speed of the preprocessing was not yet sufficient for real time applications. In this approach we significantly improve the preprocessing speed of the algorithm, by optimizing matrix operations, such that it is adequate for a real time application. The system was tested for defect detection using different defect types. The paper is focused in giving a detailed description of the basis of the algorithm implementation, such that other algorithms may use of the ARM operations for fast implementations

Biomechanical Loading as an Alternative Treatment for Tremor: A Review of Two Approaches

Background: Tremor is the most common movement disorder and strongly increases in incidence and prevalence with aging. Although not life threatening, upper-limb tremors hamper the independence of 65% of people suffering from them affected persons, greatly impacting their quality of life. Current treatments include pharmacotherapy and surgery (thalamotomy and deep brain stimulation). However, these options are not sufficient for approximately 25% of patients. Therefore, further research and new therapeutic options are required to effectively manage pathological tremor. Methods: This paper presents findings of two research projects in which two different wearable robots for tremor management were developed based on force loading and validated. The first consisted of a robotic exoskeleton that applied forces to tremulous limbs and consistently attenuated mild and severe tremors. The second was a neuroprosthesis based on transcutaneous neurostimulation. A total of 22 patients suffering from parkinsonian or essential tremor (ET) of different severities were recruited for experimental validation, and both systems were evaluated using standard tasks employed for neurological examination. The inclusion criterion was a postural and/or kinetic pathological upper-limb tremor resistant to medication. Results: The results demonstrate that both approaches effectively suppressed tremor in most patients, although further research is required. The work presented here is based on clinical evidence from a small number of patients (n = 10 for robotic exoskeleton and n = 12 for the neuroprosthesis), but most had a positive response to the approaches. In summary, biomechanical loading is non-invasive and painless. It may be effective in patients who are insufficiently responsive (or have adverse reactions) to drugs or in whom surgery is contraindicated. Discussion: This paper identifies and evaluates biomechanical loading approaches to tremor management and discusses their potential

La producción de la alfalfa en el Valle Inferior del Río negro frente al cultivo en otros ambientes de la Argentina: resultados de una comparación

Se comparó el comportamiento de dos grupos de cultivares de alfalfa con distinto grado de reposo CRIM 5-6-7 y SR 8-9 en el Valle Inferior del Río Negro (Viedma) frente al comportamiento de los mismos grupos en otros ambientes productivos de la región pampeana. Las localidades participantes fueron: Santiago del Estero, Reconquista y Rafaela (Santa Fe), Manfredi y Marcos Juárez (Córdoba), Paraná y Concepción del Uruguay (Entre Ríos), General Villegas, Bordenave y Hilario Ascasubi (Buenos Aires), Villa Mercedes (San Luis), Anguil (La Pampa) y Viedma (Río Negro). Se utilizó información de la red de evaluación de cultivares de alfalfa durante 4 años consecutivos en esas localidades. Los experimentos se realizaron de acuerdo al protocolo de la mencionada red del INTA (Spada, 2011). Las variables estudiadas fueron acumulación de forraje (Mg MS·ha -1), persistencia (% de suelo cubierto), duración del período de aprovechamiento (días) y la distribución estacional de forraje (%). Las variables fueron divididas en cuatro Clases: Alta, Intermedia-Alta, intermedia-Baja y Baja. En Viedma las alfalfas CRIM y SR se mostraron entre las más productivas y persistentes ocupando de manera consistente la Clase Alta. La duración del período de aprovechamiento fue similar para CRIM y SR que ocuparon de manera consistente la clase intermedia-alta y la producción de forraje de CRIM y SR fue principalmente estival. El Valle Inferior del Río Negro presenta condiciones muy favorables para la producción de alfalfa, destacándose a nivel nacional entre las distintas localidades que integraron la red.EEA Valle InferiorFil: Gallego, Juan Jose. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Valle Inferior; Argentina. Universidad Nacional del Comahue. CURZA; ArgentinaFil: Miñon, Daniel Pedro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Valle Inferior; Argentina. Universidad Nacional de Río Negro; Argentin

Empleo del silaje de grano húmedo de maíz en la terminación de vaquillona con destino a faena, en el Valle inferior del Río Negro

EEA Valle InferiorFil: Gallego Juan José. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Valle Inferior; ArgentinaFil: Barbarossa, Raúl Antonio. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Valle Inferior; Argentin

Comportamiento de cultivares diploides y tetraploides de Chloris gayana Kunt (grama Rhodes) al sur del paralelo 40º en condiciones de riego

Las especies megatérmicas o C4 han experimentado un proceso de expansión en el país, incluyendo NOA, NEA y regiones pampeana y semiárida central. En la norpatagonia trabajos exploratorios mostraron un comportamiento promisorio de la grama Rhodes. En el Valle Inferior de Río Negro (40º48´LS) se evaluó la adaptación de cultivares diploides y tetraploides en condiciones de riego, corte mecánico y fertilización. Los cultivares fueron Callide, Epica Peman INTA y Toro (tetraploides), Fine Cut, Top Cut, Katambora, Tolga y Santana INTA Peman (diploides). En el primer ciclo se efectuaron dos cortes y los más productivos fueron un conjunto integrado por diploides (Fine Cut y Top Cut) y tetraploides (Callide y Épica). En primavera del segundo ciclo Callide, Épica y Toro no rebrotaron. Fine Cut y Top Cut resultaron las más productivas acumulando alrededor de 10 t MS ha-1 durante dos ciclos de crecimiento, mientras que Santana y Tolga produjeron entre 6,5 y 7,5. Ninguno de los cultivares sobrevivió a las heladas del segundo invierno. Se considera que la grama Rhodes no se adaptó a las condiciones de los valles templados fríos norpatagónicos por su corta persistencia y período de aprovechamiento.The C4 species are undergoing a process of expansion in the different regions of the country, including NOA, NEA, pampean and central semi-arid region. In the northern patagonia exploratory works showed promising behavior of Rhodes grass. In the Lower Black River Valley (40º48’LS) the performance of diploid and tetraploid cultivars of Rhodes grass under conditions of irrigation, mechanical cutting and fertilization were evaluated. The cultivars were Callide, Epica Peman INTA and Toro (tetraploids), Fine Cut, Top Cut, Katambora, Tolga and Santana INTA Peman (diploids). In the first cycle, two cuts were made and the most productive cultivars were composed of diploids (Fine Cut and Top Cut) and tetraploids (Callide and Epic). In spring of the second cycle Callide, Epic and Toro did not regrow. Fine Cut and Top Cut were the most productive accumulating around 10 t DM ha-1 during two growth cycles, Santana and Tolga produced between 6.5 and 7.5 t. None of the varieties survived the frost of the second winter. It is considered that the Rhodes grass did not adapt to the northern patagonia conditions of cold temperate valleys due to their short persistence and harvest period.EEA Valle InferiorFil: Miñon, Daniel Pedro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Valle Inferior; Argentina. Universidad Nacional de Río Negro; ArgentinaFil: Gallego, Juan Jose. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Valle Inferior; Argentina. Universidad Nacional del Comahue. CURZA; Argentin

Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence

Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an educational context such information may be deemed insufficient, it would be beneficial for both the student and the instructor to receive additional feedback about the overall development of the task. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor. More precisely, we consider the use of learning-based schemes—particularly, Multi-Instance Learning and classical Machine Learning formulations—to model student behaviour. Besides, Explainable Artificial Intelligence is contemplated to provide human-understandable feedback. The proposal has been evaluated considering a case of study comprising 2,500 submissions from roughly 90 different students from a programming-related course in a Computer Science degree. The results obtained validate the proposal: the model is capable of significantly predicting the user outcome (either passing or failing the assignment) solely based on the behavioural pattern inferred by the submissions provided to the OJ. Moreover, the proposal is able to identify prone-to-fail student groups and profiles as well as other relevant information, which eventually serves as feedback to both the student and the instructor.This work has been partially funded by the “Programa Redes-I3CE de investigacion en docencia universitaria del Instituto de Ciencias de la Educacion (REDES-I3CE-2020-5069)” of the University of Alicante. The third author is supported by grant APOSTD/2020/256 from “Programa I+D+I de la Generalitat Valenciana”