Diseño estructural de una nave industrial para mejorar estándares de almacenamiento de concentrados de mineral en la unidad minera Toma La Mano, Carhuaz - 2021

El presente informe se enfoca en el diseño estructural de una nave industrial para mejorar estándares de almacenamiento de concentrados en la unidad minera Toma La Mano, aplicando el Reglamento Nacional de Edificaciones (RNE) y las especificaciones del American Institute of Steel Construction (AISC). Así mismo, se incluye el análisis de la estructura empleando el programa Sap2000 y el plano de estructura general empleando el programa AutoCad. Sin embargo, no se elaborará el diseño de las otras especialidades tales como: instalaciones eléctricas o electrónicas, instalaciones contra incendio, sistema colector de polvo, etc

Propuesta de mejora en la partida de acarreo, durante la construcción de una antena de telecomunicación en el distrito de Cieneguilla para la empresa Andina de Ingeniería y Proyectos S.A.C. aplicando la teoría de restricciones

RESUMEN El presente trabajo tiene como título “Propuesta de mejora en la partida de acarreo, durante la construcción de una antena de telecomunicación en el distrito de Cieneguilla para la empresa ANDINA DE INGENIERIA Y PROYECTOS S.A.C. aplicando la Teoría de Restricciones” aplicando la teoría de restricciones, mediante el cual se propone mejorar la productividad en la construcción de una antena de telecomunicacionesen el distrito de Cieneguilla. A través de la aplicación de la metodología de la Teoría de Restricciones, se identificó que el acarreo de los materiales de construcción era la restricción del sistema. El plan de mejora estuvo orientado a la planificación del acarreo del material y el uso adecuado de recursos como el dinero y personal, incrementándose la eficiencia en esta actividad

A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks

Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many environmental and social applications. The increase in availability of RS data has led to the development of new techniques for digital pattern classification. Very recently, deep learning (DL) models have emerged as a powerful solution to approach many machine learning (ML) problems. In particular, convolutional neural networks (CNNs) are currently the state of the art for many image classification tasks. While there exist several promising proposals on the application of CNNs to LULC classification, the validation framework proposed for the comparison of different methods could be improved with the use of a standard validation procedure for ML based on cross-validation and its subsequent statistical analysis. In this paper, we propose a general CNN, with a fixed architecture and parametrization, to achieve high accuracy on LULC classification over RS data from different sources such as radar and hyperspectral. We also present a methodology to perform a rigorous experimental comparison between our proposed DL method and other ML algorithms such as support vector machines, random forests, and k-nearest-neighbors. The analysis carried out demonstrates that the CNN outperforms the rest of techniques, achieving a high level of performance for all the datasets studied, regardless of their different characteristics.Ministerio de Economía y Competitividad TIN2014-55894-C2-1-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-

On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data

Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance on their suitability when using them in a particular scenario such as autonomous driving. In this work, we aim to assess the performance of existing 2D detection systems on a multi-class problem (vehicles, pedestrians, and cyclists) with images obtained from the on-board camera sensors of a car. We evaluate several one-stage (RetinaNet, FCOS, and YOLOv3) and two-stage (Faster R-CNN) deep learning meta-architectures under different image resolutions and feature extractors (ResNet, ResNeXt, Res2Net, DarkNet, and MobileNet). These models are trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context. For the experimental study, we use theWaymo Open Dataset, which is the largest existing benchmark. Despite the rising popularity of one-stage detectors, our findings show that two-stage detectors still provide the most robust performance. Faster R-CNN models outperform one-stage detectors in accuracy, being also more reliable in the detection of minority classes. Faster R-CNN Res2Net-101 achieves the best speed/accuracy tradeoff but needs lower resolution images to reach real-time speed. Furthermore, the anchor-free FCOS detector is a slightly faster alternative to RetinaNet, with similar precision and lower memory usage.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-RJunta de Andalucía US-1263341Junta de Andalucía P18-RT-277

Asynchronous dual-pipeline deep learning framework for online data stream classification

Data streaming classification has become an essential task in many fields where real-time decisions have to be made based on incoming information. Neural networks are a particularly suitable technique for the streaming scenario due to their incremental learning nature. However, the high computation cost of deep architectures limits their applicability to high-velocity streams, hence they have not yet been fully explored in the literature. Therefore, in this work, we aim to evaluate the effectiveness of complex deep neural networks for supervised classification in the streaming context. We propose an asynchronous deep learning framework in which training and testing are performed simultaneously in two different processes. The data stream entering the system is dual fed into both layers in order to concurrently provide quick predictions and update the deep learning model. This separation reduces processing time while obtaining high accuracy on classification. Several time-series datasets from the UCR repository have been simulated as streams to evaluate our proposal, which has been compared to other methods such as Hoeffding trees, drift detectors, and ensemble models. The statistical analysis carried out verifies the improvement in performance achieved with our dual-pipeline deep learning framework, that is also competitive in terms of computation time.Ministerio de Economía y Competitividad TIN2017-88209-C2-2-

Concept Drift Detection to Improve Time Series Forecasting of Wind Energy Generation

Most of the current data sources generate large amounts of data over time. Renewable energy generation is one example of such data sources. Machine learning is often applied to forecast time series. Since data flows are usually large, trends in data may change and learned pat terns might not be optimal in the most recent data. In this paper, we analyse wind energy generation data extracted from the Sistema de Infor mación del Operador del Sistema (ESIOS) of the Spanish power grid. We perform a study to evaluate detecting concept drifts to retrain models and thus improve the quality of forecasting. To this end, we compare the performance of a linear regression model when it is retrained randomly and when a concept drift is detected, respectively. Our experiments show that a concept drift approach improves forecasting between a 7.88% and a 33.97% depending on the concept drift technique appliedMinisterio de Ciencia e Innovación PID2020-117954RB-C22Junta de Andalucía US-1263341Junta de Andalucía P18-RT-277

La coherencia cognitiva del constructo diagramático en el proyecto arquitectónico contemporáneo

Ponència presentada a: Session 6: Teorías cerebro-máquina. Teorías de diseño y metodologías del diseño. Teorías criticas socio-físicas / Brain/machine theories. Design theories and methods systems approac

Evaluación de escombreras de mármoles para su aprovechamiento como agregado en una estructura de pavimento, Córdoba, Argentina

In this contribution, 10,000 tonnes of marble waste dumps have been characterized in order to define the physico-mechanical and mineralogical parameters and determine their feasibility to be applied as main components in road bases and sub-bases. he Los Angeles abrasion values obtained (41-53%) exceeded those permitted for the standard technical specifications. However, particle-size distribution, plasticity and California Bearing Ratio (C.B.R.)(76, 83, 100% at 97, 98 and 100% of maximum density, respectively) satisfied, marginally, those specifications. Therefore, with a simple screening, these waste dumps would be applied as granular materials in roads. This is an alternative: a) environmentally sustainable, since future crushed stone exploitations would be reduced and removed a potential environmental passive; b) technically feasible, because the carrying capacity of the granular layers would not be affected; and c) economically profitable, because adds value to a currently non useful material and reduces the extraction and transportation costs.En este trabajo se caracterizaron 10.000 toneladas de escombreras de mármoles a fin de definir sus parámetros físico-mecánicos y mineralógicos y determinar su aptitud como componente principal en capas granulares de una estructura de pavimento. Aunque los coeficientes Los Ángeles (41-53%) exceden lo permitido en la especificación de referencia, la granulometría, plasticidad y California Bearing Ratio (C.B.R.) (76, 83, 100% al 97, 98 y 100% de la densidad máxima, respectivamente) satisfacen lo especificado, con condicionamientos. Por consiguiente, con un simple cribado, este material de escombrera podría ser utilizado en terraplenes, sub-bases y bases granulares de carreteras, constituyendo una alternativa: a) Ambientalmente sostenible: porque evita nuevas explotaciones y elimina un potencial pasivo ambiental; b) Técnicamente viable: ya que la capacidad portante de las capas granulares no se vería afectada; c) Económicamente conveniente: porque aporta un valor agregado a un sub-producto hoy desechado y reduce costos de explotación y transporte

Sensitivity of fishery resources to climate change in the warm-temperate Southwest Atlantic Ocean

Climate change impacts on fishery resources have been widely reported worldwide. Nevertheless, a knowledge gap remains for the warm-temperate Southwest Atlantic Ocean—a global warming hotspot that sustains important industrial and small-scale fisheries. By combining a trait-based framework and long-term landing records, we assessed species’ sensitivity to climate change and potential changes in the distribution of important fishery resources (n = 28; i.e., bony fishes, chondrichthyans, crustaceans, and mollusks) in Southern Brazil, Uruguay, and the northern shelf of Argentina. Most species showed moderate or high sensitivity, with mollusks (e.g., sedentary bivalves and snails) being the group with the highest sensitivity, followed by chondrichthyans. Bony fishes showed low and moderate sensitivities, while crustacean sensitivities were species-specific. The stock and/or conservation status overall contributed the most to higher sensitivity. Between 1989 and 2019, species with low and moderate sensitivity dominated regional landings, regardless of the jurisdiction analyzed. A considerable fraction of these landings consisted of species scoring high or very high on an indicator for potential to change their current distribution. These results suggest that although the bulk of past landings were from relatively climate-resilient species, future catches and even entire benthic fisheries may be jeopardized because (1) some exploited species showed high or very high sensitivities and (2) the increase in the relative representation of landings in species whose distribution may change. This paper provides novel results and insights relevant for fisheries management from a region where the effects of climate change have been overlooked, and which lacks a coordinated governance system for climate-resilient fisheriesOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. I.G., O.D., and G.J. acknowledge the support provided by the Inter-American Institute for Global Change Research (Grant SGP-HW017). O.D., D.L., E.C., L.O., G.J., and A.R. thank the Comisión Sectorial de Investigación Científica (CSIC Grupos ID 32) for additional support. M.H. thanks for the support from the Brazilian National Scientific and Technological Research Council (CNPq, grant 307994/2020–1)S

16α‐Bromoepiandrosterone as a new candidate for experimental diabetes‐tuberculosis comorbidity treatment

Tuberculosis (TB) is the leading cause of death from a single bacterial infectious agent and is one of the most relevant issues of public health. Another pandemic disease is type II diabetes mellitus (T2D) that is estimated to affect half a billion people in the world. T2D is directly associated with obesity and a sedentary lifestyle and is frequently associated with immunosuppression. Immune dysfunction induced by hyperglycemia increases infection frequency and severity. Thus, in developing countries the T2D/TB co-morbidity is frequent and represents one of the most significant challenges for the health-care systems. Several immunoendocrine abnormalities are occurring during the chronic phase of both diseases, such as high extra-adrenal production of active glucocorticoids (GCs) by the activity of 11-β-hydroxysteroid dehydrogenase type 1 (11-βHSD1). 11-βHSD1 catalyzes the conversion of inactive cortisone to active cortisol or corticosterone in lungs and liver, while 11-β-hydroxysteroid dehydrogenase type 2 (11-βHSD2) has the opposite effect. Active GCs have been related to insulin resistance and suppression of Th1 responses, which are deleterious factors in both T2D and TB. The anabolic adrenal hormone dehydroepiandrosterone (DHEA) exerts antagonistic effects on GC signaling in immune cells and metabolic tissues; however, its anabolic effects prohibit its use to treat immunoendocrine diseases. 16α-bromoepiandrosterone (BEA) is a water miscible synthetic sterol related to DHEA that lacks an anabolic effect while amplifying the immune and metabolic properties with important potential therapeutic uses. In this work, we compared the expression of 11-βHSD1 and the therapeutic efficacy of BEA in diabetic mice infected with tuberculosis (TB) (T2D/TB) with respect to non-diabetic TB-infected mice (TB). T2D was induced by feeding mice with a high-fat diet and administering a single low-dose of streptozotocin. After 4 weeks of T2D establishment, mice were infected intratracheally with a high-dose of Mycobacterium tuberculosis strain H37Rv. Then, mice were treated with BEA three times a week by subcutaneous and intratracheal routes. Infection with TB increased the expression of 11-βHSD1 and corticosterone in the lungs and liver of both T2D/TB and TB mice; however, T2D/TB mice developed a more severe lung disease than TB mice. In comparison with untreated animals, BEA decreased GC and 11-βHSD1 expression while increasing 11-βHSD2 expression. These molecular effects of BEA were associated with a reduction in hyperglycemia and liver steatosis, lower lung bacillary loads and pneumonia. These results uphold BEA as a promising effective therapy for the T2D/TB co-morbidity.Fil: López Torres, Manuel Othoniel. Instituto Nacional de la Nutrición Salvador Zubiran; MéxicoFil: Marquina Castillo, Brenda. Instittuto de Ciencias Médicas y Nutrición; MéxicoFil: Ramos Espinosa, Octavio. Instittuto de Ciencias Médicas y Nutrición; MéxicoFil: Mata Espinosa, Dulce. Instittuto de Ciencias Médicas y Nutrición; MéxicoFil: Barrios Payan, Jorge A.. Instittuto de Ciencias Médicas y Nutrición; MéxicoFil: Baay Guzman, Guillermina. Instittuto de Ciencias Médicas y Nutrición; MéxicoFil: Huerta Yepez, Sara. Instittuto de Ciencias Médicas y Nutrición; MéxicoFil: Bini, Estela Isabel. Instittuto de Ciencias Médicas y Nutrición; MéxicoFil: Torre Villalvazo, Ivan. Instittuto de Ciencias Médicas y Nutrición; MéxicoFil: Torres, Nimbe. Instittuto de Ciencias Médicas y Nutrición; MéxicoFil: Tovar, Armando. Instittuto de Ciencias Médicas y Nutrición; MéxicoFil: Chamberlin, William. No especifíca;Fil: Ge, Yu. No especifíca;Fil: Carranza, Maria Andrea. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Houssay. Instituto Alberto C. Taquini de Investigaciones En Medicina Traslacional. - Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Cardiologicas "prof. Dr. Alberto C. Taquini". Instituto Alberto C. Taquini de Investigaciones En Medicina Traslacional.; ArgentinaFil: Hernández Pando, Rogelio. Instituto Nacional de Ciencias Medicas y Nutricion; Méxic