Entrenamiento neuromuscular para la prevención de lesiones de rodilla en féminas adolescentes

El elevado número de lesiones de rodilla que se vienen presentando en las mujeres que empiezan su maduración ha sido tema de estudio en las investigacones realizadas en los últimos años; y se ha llegado a la conclusión que la transición entre la niñez y la adolescencia es un lapso de tiempo en el que las mujeres presentan cambios a nivel biológico, endocrino y neuromuscular, cambios que sumados a factores externos como la ejecución técnica del movimiento, la superficie de juego y la higiene deportiva, se manifiestan en un aumento en los factores de riesgo de sufrir lesiones en la articulación de la rodilla. Problema que se ha convertido prácticamente en un asunto de salud pública, ya que en Estados Unidos se realizan 9000 cirugías de rodilla en atletas de colegio por año; además, éstas lesiones afectan numerosos espacios en la vida de las mujeres como largos y costosos periodos de rehabilitación, los cuales al final de los mismos no garantizan un 100% de la recuperación funcional de la articulación, la interrupción parcial o total en la participación de programas deportivos, consecuencias que sumadas disminuyen la calidad de vida del paciente (Noyer, F; et al. 1983)

Programación de carrotanques de transporte de hidrocarburos mediante una búsqueda tabú

Como proyecto aplicativo de conocimientos de ingeniería, se evidenció una posible oportunidad de mejora de procesos en el sector de hidrocarburos de Colombia. Abordando el problema en específico, se observó que los métodos aplicados para la selección de las rutas del transporte terrestre de crudo no aplicaban un proceso óptimo, incurriendo en costos elevados en este rubro. Para poder entender el problema, se tiene en cuenta que en el sector petrolero existen puntos de extracción de crudo con el fin de ser transportado a refinerías u otras ubicaciones donde se procede a destilarlo o a enviarlo a otros puntos por otros medios de transporte. Al evidenciar esta oportunidad, se procedió a evaluar la posibilidad de implantar un sistema de ahorro de costos el cual beneficiará al sector, además de poder planear con antelación sus rutas y saber cómo se mueve su sistema. Luego de esta evaluación y búsqueda bibliográfica, se procedió a realizar una meta heurística para la asignación de rutas que ahorren costos y una simulación para medir el rendimiento de los resultados que se obtienen de la meta heurística, encontrando resultados para el problema. Finalmente se procede a evaluar el modelo frente a los objetivos planteados de ahorro dando como resultado un modelo de ahorro de costos factible.As an application project of knowledge in engineering, it was evidenced a possible process improvement opportunity in the Colombian hydrocarbons sector. Considering that in the Oil field there are locations where the crude oil is extracted in order to be transported to refineries or other places where it is distilled or sent to different locations by other transport means. Therefore, the problem that is going to be solved is a variant of the well know multi-depot vehicle routing problem, with a homogenous fleet, under the constraints of minimum amount of tank-trucks hired from available companies. After searching for bibliographical sources regarding the observed situation, it was proceeded to define a mathematical model then, design and elaborate a hybrid Tabu Search to determine how many tank-trucks to hire and the respective routes for each one. Finally, to be able to measure the performance of the result obtained with the application, in contrast with the current method, a simulation of the routes network for the tankers was made. The feasibility of the solution under random scenarios was set as a performance indicator. The method proposed in this project achieved to improve the current process in an 18.77%, reducing the transportation cost by $ 3.465.065.575.Ingeniero (a) IndustrialPregrad

Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM). This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio

GENERALIZED URTICARIA AS A VASCULITIC MANIFESTATION IN A PATIENT WITH SARS-CoV-2 INFECTION: A CASE REPORT IN COLOMBIA

Skin manifestations have been reported in up to 20% of cases of SARS-CoV-2 infection, including morbilliform rash (22%), pernio-like acral lesions (18%), urticaria (16%), and macular erythema (13%). It is believed that in the case of SARS-CoV-2 infection, the mechanism involved is an inflammatory response that generates immune dysregulation, vascular congestion, vasculitis, vascular thrombosis, or neoangiogenesis. This case study, present the case of a patient with no previous history of urticarial reactions, autoimmune diseases, or exposure to medications who develops generalized urticaria lasting more than 24 hours and who was diagnosed with SARS-CoV-2 infection by RT-PCR with a nasopharyngeal swab. We suggest in this patient vasculitic urticaria as a manifestation of SARS-CoV-2 infection

Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia

The emergence of COVID-19 as a global pandemic forced researchers worldwide in various disciplines to investigate and propose efficient strategies and/or technologies to prevent COVID-19 from further spreading. One of the main challenges to be overcome is the fast and efficient detection of COVID-19 using deep learning approaches and medical images such as Chest Computed Tomography (CT) and Chest X-ray images. In order to contribute to this challenge, a new dataset was collected in collaboration with “S.E.S Hospital Universitario de Caldas” (https://hospitaldecaldas.com/) from Colombia and organized following the Medical Imaging Data Structure (MIDS) format. The dataset contains 7,307 chest X-ray images divided into 3,077 and 4,230 COVID-19 positive and negative images. Images were subjected to a selection and anonymization process to allow the scientific community to use them freely. Finally, different convolutional neural networks were used to perform technical validation. This dataset contributes to the scientific community by tackling significant limitations regarding data quality and availability for the detection of COVID-19. © 2022, The Author(s)

Machine diagnostics based on blind signal extraction and novelty detection using non-stationary vibration signals

Hoy en día el monitoreo de condición de maquinaria rotativa ha comenzado a ser un tema importante para la industria porque permite al mismo tiempo reducir daños accidentales y mejorar el rendimiento de las maquinas. Esta herramienta, conocida también como Mantenimiento basado en condición se basa en la evaluación adecuada de la salud de la maquinaria, empleando una serie de mediciones como vibraciones mecánicas. No obstante, la gran mayoría de máquinas en ambientes industriales reales utilizan piezas únicas, por lo cual no es posible inducir o simular fallas, haciendo infactible coleccionar datos útiles de la maquina bajo condiciones de daño. Además, en muchos casos, las condiciones de operación de la maquina se rigen dependiendo de los cambios de la velocidad o carga, lo cual incrementa la dificultad del análisis tradicional basado en normas ISO, y oculta información relevante de la salud de máquina. Bajo esa perspectiva, este documento presenta una metodología de diagnóstico de maquinaria basada en el análisis de señales de vibración no estacionarias, incluyendo las etapas de detección, separación e identificación de las posibles fallas. Particularmente, la metodología propuesta está compuesta por las mismas etapas que cualquier procedimiento de diagnóstico de fallas pero en un orden diferente. Primero, se propone un modelo de seguimiento de orden (Order Tracking - OT en inglés) para descomponer la señal en un conjunto de componentes espectrales de banda angosta, los cuales capturan la información asociada con las condiciones de operación. En ese sentido, el modelo OT propuesto brinda la posibilidad de extraer tanto la velocidad del eje de referencia cuando ´esta medida no es disponible. Segundo, se propone una novedosa metodología para detección de fallas, llamada detección de fallas con localización en frecuencia, la cual se basa en técnicas de detección de atípicos (Novelty Detection en inglés) y usa clasificadores de una clase para describir el rendimiento normal de la máquina. La metodología propuesta utiliza los componentes de orden, obtenidos usando el modelo OT, como nuevas pseudo-observaciones de la señal de vibración, y se emplea un esquema de clasificación, como etapa posterior, con el fin de determinar si cualquiera de las nuevas observaciones puede ser catalogada como un atípico. En consecuencia, a cada componente de orden se le asigna una etiqueta que puede tomar dos valores: normal o atípico. La ventaja de esta metodología se centra en el hecho que permite determinar el rango de frecuencia donde se encuentra una falla, reduciendo el tiempo de búsqueda y brindando información útil al personal de mantenimiento, que en muchos casos no tiene conocimientos especializados para este tipo de análisis. Finalmente, se analizan las propiedades cicloestacionarias de los componentes de orden y, mediante inspección visual, se identifican distintos tipos de falla relacionados con defectos en rodamientos. Con el uso de la metodología propuesta es posible detectar de una forma efectiva que y cuales fallas puede estar experimentando la máquina, considerando escenarios complejos donde las condiciones de operación son cambiantes a través del tiempo. Varios experimentos son discutidos, desde bancos de prueba de laboratorio hasta estudios de caso tales como la línea de propulsión de un barco, turbinas de viento, sistemas de engranajes y motores de combustión interna, donde el modelo OT propuesto fue probado para estimar la velocidad instantánea. Otro hallazgo significativo se basa en la definición de las propiedades cíclicas que tienen los componentes de orden, ya que esto abre la posibilidad de emplear el modelo propuesto como una técnica de descomposición para separar componentes estacionarias y cicloestacionarios cuando las condiciones de operación de la máquina son constantes. En conclusión, la metodología propuesta es una herramienta prometedora en el área de monitoreo de condición de máquinas rotativasAbstract : Nowadays, condition monitoring of rotating machinery is becoming increasingly important for the industry because it allows reducing accidental damages and improving the machine performance at the same time. This tool, also called Condition-based Maintenance relies on the adequate evaluation of the machine health or state, employing a set of measurements as mechanical vibration signals. Nevertheless, most of the real-world machinery operates unique pieces, which are not suitable for inducing faults, making unfeasible to collect useful data on damaged conditions. Furthermore, in many cases, the operating conditions of the machine are governed by speed or load changes, which makes difficult the traditional analysis based on the ISO standards, and hides relevant information of the machine health. In that sense, this document present a machine diagnostic methodology, based on the analysis of non-stationary vibration signals, which includes the detection, isolation, and identification of the possible faults. Particularly, the proposed methodology has the same stages but in an order different. Firstly, an order tracking (OT) model is proposed to decomposes the signal into a set of narrow-band spectral components that capture information associated with the operating conditions. Besides, the OT model provides the possibility also to extract the reference shaft speed when that measure is unavailable. Secondly, a novel methodology for fault detection, called frequency-located fault detection, based on novelty detection techniques that use one-class classifiers (OCC) to describe the normal machine performance. Here, the obtained order components, obtained using the OT model, are used as pseudo-observations of the vibration signal and a classification scheme is applied to determine if any new instance corresponds with an outlier. Therefore, this step makes a decision for each order component, assigning to each one a label either target or outlier. The advantage in this here is centered in the fact that allow determining the frequency range where the fault arises, reducing the search time and giving useful information to the machine operator. Finally, the cyclostationary properties of the order components are analyzed and inspected to identify the type of faults, which in this case are related with bearing failures. With the proposed methodologies to machine diagnostic, it is possible detecting efficiently that the fault exists, taking into account complex scenarios where the operating conditions are time-varying. Several experiments are discussed, lasting from laboratory test rigs to case studies such as ship driveline, wind turbine, gearbox and diesel engine, where the proposed OT model was tested estimating the instantaneous speed. Another significant finding is defined by the cyclic properties that the order components present because the model may be used as a preprocessing tool that contributes to separate stationary and cyclostationary processes whenever the operating condition of the machine be constant. In conclusion, the proposed methodology for machine diagnostic based on the OT model to extract blind components and to detect outlier behaviors is a promising tool in condition monitoringDoctorad

Time-Frequency analysis of mechanic vibration signals for fault detection in rotating machines

La presente tesis pretende desarrollar un conjunto de metodologías que permitan caracterizar señales de vibraciones mecánicas empleando la variabilidad estocástica para la identificación y tipificación de distintos tipos de fallos en rodamientos, cajas reductoras y ejes (desbalanceo, desalineación, soltura mecánica y lubricación). Los estados transitorios y regímenes variables de carga y velocidad como el arranque, parada y distintas velocidades constantes, son analizados a profundidad permitiendo asociar la calidad de las señales y la identificación de fallos a varios puntos de medición estudiados. Los resultados de clasificación muestran que las metodologías aplicadas son bastantes significativas, debido a que, en general, las tasas de rendimiento se encuentran por encima de un 90% de eficiencia. Finalmente, las diversas técnicas de caracterización y clasificación empleadas, así como el análisis de transitorios, permiten diferenciar de manera clara distintos tipos de fallos y mostrar que es necesario un análisis tiempo-frecuencia si se quieren obtener los mejores resultados / Abstract: This thesis aims to develop a set of methodologies to characterize mechanical vibration signals using stochastic variability in the identification and classification of different types of faults in bearings, gearboxes and axles (imbalance, misalignment, mechanical looseness and poor lubrication). Transient states and varying load and speed regimes as the starting, stopping and different constant speeds are analyzed in depth allowing to associate the signal quality and identification of failures at several measuring points studied. The classification results show that the methodologies used are quite significant, because, in general, the performance rates are higher than 90% efficiency. Finally, the various techniques of characterization and classification employed, as well transient analysis, allows to clearly distinguish different between the types of failures and show that we need a time-frequency analysis in order to obtain the best results.Maestrí

Outlier detection in rotating machinery under non-stationary operating conditions using dynamic features and one-class classifiers

The main goal of condition-based maintenance is to describe the machine state under current operating regimes, which can be non-stationary depending of load/speed changes. Besides, damaged machine data are not always available in real-world applications. This paper proposes a methodology of outlier detection in time-varying mechanical systems based on dynamic features and data description classifiers. Dynamic features set is formed by spectral sub-band centroids and linear frequency cepstral coefficients extracted from time-frequency representations. One-class classification is carried out to validate performance of the dynamic features as descriptors of machine behavior. The methodology is tested with a data set coming from a test-rig including different machine states with variable speed conditions. The proposed approach is validated on real recordings acquired from a ship driveline. The results outperform other time-frequency features in terms of classification performance. The methodology is robust to minimal changes in the machine state and/or time-varying operational conditions

Metformin Enhances TKI-Afatinib Cytotoxic Effect, Causing Downregulation of Glycolysis, Epithelial–Mesenchymal Transition, and EGFR-Signaling Pathway Activation in Lung Cancer Cells

The combination of metformin and TKIs for non-small cell lung cancer has been proposed as a strategy to overcome resistance of neoplastic cells induced by several molecular mechanisms. This study sought to investigate the effects of a second generation TKI afatinib, metformin, or their combination on three adenocarcinoma lung cancer cell lines with different EGFRmutation status. A549, H1975, and HCC827 cell lines were treated with afatinib, metformin, and their combination for 72 h. Afterwards, several parameters were assessed including cytotoxicity, interactions, apoptosis, and EGFR protein levels at the cell membrane and several glycolytic, oxidative phosphorylation (OXPHOS), and EMT expression markers. All cell lines showed additive to synergic interactions for the induction of cytotoxicity caused by the tested combination, as well as an improved pro-apoptotic effect. This effect was accompanied by downregulation of glycolytic, EMT markers, a significant decrease in glucose uptake, extracellular lactate, and a tendency towards increased OXPHOS subunits expression. Interestingly, we observed a better response to the combined therapy in lung cancer cell lines A549 and H1975, which normally have low affinity for TKI treatment. Findings from this study suggest a sensitization to afatinib therapy by metformin in TKI-resistant lung cancer cells, as well as a reduction in cellular glycolytic phenotype

Will We Unlock the Benefit of Metformin for Patients with Lung Cancer? Lessons from Current Evidence and New Hypotheses

Metformin has been under basic and clinical study as an oncological repurposing pharmacological agent for several years, stemming from observational studies which consistently evidenced that subjects who were treated with metformin had a reduced risk for development of cancer throughout their lives, as well as improved survival outcomes when diagnosed with neoplastic diseases. As a result, several basic science studies have attempted to dissect the relationship between metformin’s metabolic mechanism of action and antineoplastic cellular signaling pathways. Evidence in this regard was compelling enough that a myriad of randomized clinical trials was planned and conducted in order to establish the effect of metformin treatment for patients with diverse neoplasms, including lung cancer. As with most novel antineoplastic agents, early results from these studies have been mostly discouraging, though a recent analysis that incorporated body mass index may provide significant information regarding which patient subgroups might derive the most benefit from the addition of metformin to their anticancer treatment. Much in line with the current pipeline for anticancer agents, it appears that the benefit of metformin may be circumscribed to a specific patient subgroup. If so, addition of metformin to antineoplastic agents could prove one of the most cost-effective interventions proposed in the context of precision oncology. Currently published reviews mostly rely on a widely questioned mechanism of action by metformin, which fails to consider the differential effects of the drug in lean vs. obese subjects. In this review, we analyze the pre-clinical and clinical information available to date regarding the use of metformin in various subtypes of lung cancer and, further, we present evidence as to the differential metabolic effects of metformin in lean and obese subjects where, paradoxically, the obese subjects have reported more benefit with the addition of metformin treatment. The novel mechanisms of action described for this biguanide may explain the different results observed in clinical trials published in the last decade. Lastly, we present novel hypothesis regarding potential biomarkers to identify who might reap benefit from this intervention, including the role of prolyl hydroxylase domain 3 (PHD3) expression to modify metabolic phenotypes in malignant diseases