Looking Beyond “Mow, Blow and Go”: A Case Study of Mexican Immigrant Gardeners in Los Angeles

Abstract: Recent research on Mexican immigrants focuses on the working conditions of farm workers, garment workers, janitors and day laborers. This coincides with successful efforts by organized labor and immigrant advocacy groups to organize these marginalized workforces. Little attention, however, has been given to Mexican paid gardeners. As part of the household service economy, paid gardeners represent a difficult labor sector to organize and research because they typically operate as independent contractors in the informal economy. This paper seeks to provide a more holistic picture of this dynamic, informal workforce. Drawing primarily upon ethnographic techniques, the paper documents how this informal sector operates and its social organization. Based on research conducted in Los Angeles, the paper also demonstrates how a select group of self-employed, Mexican gardeners function as petty-entrepreneurs, benefiting financially and socially in the informal economy by successfully utilizing their social capital and social networks

Packaging and testing of fiber Bragg gratings for use as strain sensor for rock specimens

This paper reports a packaging and calibration procedure for surface mounting of fiber Bragg grating (FBG) sensors to measure strain in rocks. The packaging of FBG sensors is performed with glass fiber and polyester resin, and then subjected to tensile loads in order to obtain strength and deformability parameters, necessaries to assess the mechanical performance of the sensor packaging. For a specific package, an optimal curing condition has been found, showing good repeatability and adaptability for non-planar surfaces, such as occurs in rock engineering. The successfully packaged sensors and electrical strain gages were attached to standard rock specimens of gabbro. Longitudinal and transversal strains under compression loads were measured with both techniques, showing that response of FBG sensors is linear and reliable. An analytical model is used to characterize the influences of rock substrate and FBG packaging in strain transmission. As a result, we obtained a sensor packaging for non-planar and complex natural material under acceptable sensitivity suitable for very small strains as occurs in hard rocks

Cooperativismo y Desarrollo Humano: Análisis comparativo entre socios y no socios de la Unión de Cooperativas Agropecuarias (UCA); La Dalia, Matagalpa durante el segundo semestre del 2008

La contribución de las cooperativas al desarrollo humano de sus integrantes es un problema poco estudiado en el ámbito internacional y Nicaragua en particular. El objetivo de la investigación fue conocer el grado de desarrollo humano alcanzado por los socios (as) de la Unión de Cooperativas Agropecuaria Bernardino Díaz Ochoa con presencia en once comunidades del municipio Tuma-La Dalia. La selección fue aleatoria, obteniendo 76 unidades muestrales, 53 varones y 23 mujeres. Se tomó como patrón de comparación el mismo número de no socios (as). Se estudiaron ocho variables: ingreso, educación, nutrición, vivienda, seguridad, participación, autoestima y calidad ambiental, que permitió la construcción del Índice Multidimensional del Desarrollo Humano (IMDH). Se encontró que los socios (as) de la Unión de Cooperativas Agropecuaria obtienen un IMDH de 0.52, en tanto que los no socios (as) 0.43, diferencia que es altamente significativa estadísticamente. Los atributos de la cooperativa para lograr un mayor IMDH se relacionan a sus capacidades individuales o colectivas que permiten la autogestión y la integración con otros niveles sociales, entre ellos la interacción con otros organismos gubernamentales y no gubernamentales. Palabras clave: cooperativismo, desarrollo humano, Índice Multidimensional de Desarrollo Human

Evaluación con la aplicación del método computacional Smith e Ichiyen al circuito de flotación zinc en la planta concentradora Sansil de la compañía minera San Valentin

En el presente trabajo tiene como finalidad realizar una evaluación con el método Smith e Ichiyen a todo el circuito de Flotación Zinc, se pretende obtener un balance metalúrgico con las leyes calculadas, para ello se realiza todo un esquema teniendo lo siguientes capítulos. En el capítulo primero se describe las generalidades, indicando los objetivos, planteamiento del problema, la justificación, hipótesis y describiendo las variables. En el capítulo segundo se describe la planta concentradora de una capacidad de 550 TMD, describiendo cada etapa del proceso metalúrgico. En el capítulo tercero se describe todo el fundamento teórico, las bases sobre el cual se desarrollan todo el método de Smith e Ichiyen, teniendo como conceptos básicos la teoría sobre matrices, que sirvió para el desarrollo del presente estudio referente al balance de materia. En el capítulo cuarto se describe el desarrollo experimental, empezando desde la preparación de las muestras, metodología del muestreo, la identificación de los puntos a muestrear en cada etapa del circuito de Flotación Zinc hasta la obtención de datos experimentales de los análisis químicos de las muestras. En el capítulo quinto se describe todo el diseño del método computacional Smith e Ichiyen, teniendo como punto de partida el reconocimiento de todo el Flow Sheet de Flotación Zinc, también se realiza cálculos metalúrgicos como: el modelamiento del circuito de flotación, el planteamiento de ecuaciones en cada etapa del proceso de flotación, la determinación de las masas de los concentrados, con la finalidad de poder realizar la construcción de todo el método Smith e Ichiyen. En el capítulo sexto se describe el análisis y evaluación de los resultados, donde se realiza el análisis del muestreo sistemático realizado, además se realiza la evaluación comparando las leyes experimentales con las leyes calculadas, obteniendo de esta forma un balance metalúrgico en función de las leyes ajustadas. Finalmente se presenta las conclusiones, recomendaciones, bibliografía y los anexos; con los cuales se permitirá una evaluación con la aplicación del método computacional Smith e Ichiyen

A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs

[EN] A broad variety of algorithms for detection and classification of rhythm and morphology abnormalities in ECG recordings have been proposed in the last years. Although some of them have reported very promising results, they have been mostly validated on short and non-public datasets, thus making their comparison extremely difficult. PhysioNet/CinC Challenge 2020 provides an interesting opportunity to compare these and other algorithms on a wide set of ECG recordings. The present model was created by ¿ELBIT¿ team. The algorithm is based on deep learning, and the segmentation of all beats in the 12-lead ECG recording, generating a new signal for each one by concatenating sequentially the information found in each lead. The resulting signal is then transformed into a 2- D image through a continuous Wavelet transform and inputted to a convolutional neural network. According to the competition guidelines, classification results were evaluated in terms of a class-weighted F-score (Fß) and a generalization of the Jaccard measure (Gß). In average for all training signals, these metrics were 0.933 and 0.811, respectively. Regarding validation on the testing set from the first phase of the challenge, mean values for both performance indices were 0.654 and 0.372, respectivelyThis research has been supported by the grants DPI2017¿83952¿C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha, AICO/2019/036 from Generalitat Valenciana and FEDER 2018/11744Huerta, A.; Martinez-Rodrigo, A.; Rieta, JJ.; Alcaraz, R. (2020). A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.305S1

Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings

[EN] In the last years, atrial fibrillation (AF) has become one of the most remarkable health problems in the developed world. This arrhythmia is associated with an increased risk of cardiovascular events, being its early detection an unresolved challenge. To palliate this issue, long-term wearable electrocardiogram (ECG) recording systems are used, because most of AF episodes are asymptomatic and very short in their initial stages. Unfortunately, portable equipments are very susceptible to be contaminated with different kind of noises, since they work in highly dynamics and ever-changing environments. Within this scenario, the correct identification of free-noise ECG segments results critical for an accurate and robust AF detection. Hence, this work presents a deep learning-based algorithm to identify high-quality intervals in single-lead ECG recordings obtained from patients with paroxysmal AF. The obtained results have provided a remarkable ability to classify between high- and low-quality ECG segments about 92%, only misclassifying around 7% of clean AF intervals as noisy segments. These outcomes have overcome most previous ECG quality assessment algorithms also dealing with AF signals by more than 20%.This research has been supported by the grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha, AICO/2019/036 from Generalitat Valenciana and FEDER 2018/11744.Huerta, A.; Martinez-Rodrigo, A.; Arias, MA.; Langley, P.; Rieta, JJ.; Alcaraz, R. (2020). Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.367S1

Comparative Study of Convolutional Neural Networks for ECG Quality Assessment

[EN] In the last years, convolutional neural networks (CNNs) have become popular in ECG analysis, since they do not require pre-processing stages, nor specific pre-training. However, their ability for ECG quality assessment has still not been thoroughly assessed. Hence, this work introduces a comparison about the ability of several CNN algorithms to classify between high and low-quality ECGs. Taking advantage of the concept of transfer learning, five common pre-trained CNNs were analyzed, such as AlexNet, GoogLeNet, VGG16, ResNet18 and InceptionV3. They were fed with 2-D images obtained by turning 5 second-length ECG segments into scalograms through a continuous Wavelet transform. To train and validate the algorithms, 1,168 noisy ECG intervals, along with other 1,200 ECG excerpts with sufficient quality for their further interpretation, were extracted from a public database. The obtained results showed that all CNNs provided mean values of accuracy between 89 and 91%, but notable difference in terms of computational load were noticed. Thus, AlexNet was the fastest algorithm, requiring notably less CPU usage and memory than the remaining methods. Consequently, this CNN exhibited the best trade-off between high-quality ECG identification accuracy and computational load, and it could be considered as the most convenient algorithm for ECG quality assessment.This research has been supported by the grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2019/036 from Generalitat Valenciana.Huerta, A.; Martinez-Rodrigo, A.; Puchol, A.; Pachon, MI.; Rieta, JJ.; Alcaraz, R. (2020). Comparative Study of Convolutional Neural Networks for ECG Quality Assessment. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.370S1

Single-lead electrocardiogram quality assessment in the context of paroxysmal atrial fibrillation through phase space plots

[EN] Current wearable electrocardiogram (ECG) recording systems have great potential to revolutionize early diagnosis of paroxysmal atrial fibrillation (AF). They are able to continuously acquire an ECG signal for long weeks and then increase the probability of detecting first brief, intermittent signs of the arrhythmia. However, the recorded signal is often broadly corrupted by noise and artifacts, and accurate assessment of its quality to avoid automated misdiagnosis and false alarms of AF is still an unsolved challenge. In this context, the present work is pioneer in exploring the usefulness of transforming the single-lead ECG signal into two common phase space (PS) representations, such as the Poincare plot and the first order difference graph, for evaluation of its quality. Several machine and deep learning models fed with features and images derived from these PS portraits reported a better performance than well-known previous methods, even when they were trained and validated on two separate databases. Indeed, in binary classification of high- and low-quality ECG excerpts, the generated PS-based algorithms reported a discriminant power greater than 85%, misclassifying less than 20% of high-quality AF episodes and non -normal rhythms as noisy excerpts. Moreover, because both PS reconstructions do not require any mathematical transformation, these algorithms also spent much less time in classifying each ECG excerpt in validation and testing stages than previous methods. As a consequence, ECG transformation to both PS portraits enables novel, simple, effective, and computational low-cost techniques, based both on machine and deep learning classifiers, for ECG quality assessment.This research has received financial support from Daiichi Sankyo SLU and from public grants PID2021-00X128525-IV0, PID2021-12380 4OB-I00, and TED2021-130935B-I00 of the Spanish Government 10.13039/501100011033 jointly with the European Regional Development Fund (EU) , SBPLY/21/180501/000186 from Junta de Comunidades de Castilla-La Mancha, Spain, and AICO/2021/286 from Generalitat Valenciana.Huerta, A.; Martínez-Rodrigo, A.; Bertomeu-González, V.; Ayo-Martin, O.; Rieta, JJ.; Alcaraz, R. (2024). Single-lead electrocardiogram quality assessment in the context of paroxysmal atrial fibrillation through phase space plots. Biomedical Signal Processing and Control. 91. https://doi.org/10.1016/j.bspc.2023.1059209

Valorización de Cementos Pacasmayo

Cementos Pacasmayo S.A.A. y sus subsidiarias son los productores, distribuidores y comercializadores de cemento más importante en el norte del Perú, cuenta con 3 plantas de producción en Pacasmayo, Rioja y Piura, con una capacidad de producción total anual de 4,9 millones de TM. Su participación de mercado es aproximadamente el 95% y tienen más de 200 puntos de venta. La valorización por el método de flujos de caja descontados (DCF) tiene como fecha base el 30 de diciembre del 2016. Entre los principales supuestos empleados se encuentra un periodo de proyección de 10 años, un crecimiento promedio de los ingresos de 4,20% en dicho periodo y una inversión en el aumento de la capacidad de planta por S/ 1.057 millones que se realizaría posterior al 2023. Además de los supuestos señalados se trabajó con un WACC en soles equivalente a 8,04% y con un crecimiento de los flujos de la perpetuidad (g) de 3,12%. El valor obtenido de la firma fue de S/ 3.592 millones, obteniéndose un valor fundamental de la acción común a diciembre 2016 era de S/ 6,65, que estaba 5,50% por encima del precio de mercado al cierre del 31 de diciembre de 2016 (acción común a S/ 6,30). Con el segundo método se valoriza sobre la base de la comparación con múltiplos de compañías similares a Cementos Pacasmayo. Se tomaron en cuenta los ratios P/E y EV/EBITDA, y se obtuvo como resultado los valores de S/ 6,63 y S/ 6,68, respectivamente. Estos valores se encuentran cerca del valor de S/ 6,65 obtenido a través del método de valorización de flujos. De acuerdo a los resultados obtenidos y al precio actual de la acción de Cementos Pacasmayo y subsidiarias, nuestra recomendación es mantener/comprar