Seis Sigma en pymes con bajo volumen de producción: una experiencia de éxito en aeronáutica

Six Sigma is currently one of the most powerful tools that exists for quality improvement. Designed for highly repetitive and high volume production manufacturing processes, it has been adopted by leading large organisations in many different sectors all around the world. Our goal is to study its applicability to SMEs with low production volumes and identify key success factors and obstacles to its implementation. The methodology followed is Action Research in an SME in the aeronautics sector using the DMAIC improvement cycle applied to a specific Six Sigma project. The results confirm Six Sigma’s applicability and suggest that success depends on key factors, such as the team’s commitment, the availability of resources and prior learning.Seis Sigma es actualmente una de las herramientas más potentes para la mejora de la calidad. Concebida para procesos productivos muy repetitivos y de gran volumen de producción, ha sido adoptada por las principales grandes organizaciones de todo el mundo en muchos sectores. Nuestro objetivo es estudiar su aplicabilidad en pymes, con bajos volúmenes de producción, e identificar los principales factores de éxito y obstáculos para su implementación. Se ha empleado la metodología “investigación en acción” en una pyme del sector aeronáutico, aplicando el ciclo de mejora DMAIC a un proyecto Seis Sigma concreto. Los resultados confirman su aplicabilidad y sugieren que el éxito depende de factores claves como el compromiso del equipo, la disponibilidad de recursos y la formación previa

On the design and performance evaluation of automatic traffic report generation systems with huge data volumes

In this paper, we analyze the performance issues involved in the generation of automated traffic reports for large IT infrastructures. Such reports allow the IT manager to proactively detect possible abnormal situations and roll out the corresponding corrective actions. With the ever‐increasing bandwidth of current networks, the design of automated traffic report generation systems is very challenging. In a first step, the huge volumes of collected traffic are transformed into enriched flow records obtained from diverse collectors and dissectors. Then, such flow records, along with time series obtained from the raw traffic, are further processed to produce a usable report. As will be shown, the data volume in flow records turns out to be very large as well and requires careful selection of the key performance indicators (KPIs) to be included in the report. In this regard, we discuss the use of high‐level languages versus low‐level approaches, in terms of speed and versatility. Furthermore, our design approach is targeted for rapid development in commodity hardware, which is essential to cost‐effectively tackle demanding traffic analysis scenarios. Actually, the paper shows feasibility of delivering a large number of KPIs, as will be detailed later, for several TBytes of traffic per day using a commodity hardware architecture and high‐level languagesThis work has been partially supported by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund under the projects TRÁFICA (MINECO/FEDER TEC2015‐69417‐C2‐1‐R) and Procesado Inteligente de Tráfico (MINECO/FEDER TEC2015‐69417‐C2‐2‐

Microcalcification Detection Applying Artificial Neural Networks and Mathematical Morphology in Digital Mammograms

Breast cancer is one of the leading causes to women mortality in the world and early detection is an important means to reduce the mortality rate. The presence of microcalcifications clusters has been considered as a very important indicator of malignant types of breast cancer and its detection is important to prevent and treat the disease. This paper presents an alternative and effective approach in order to detect microcalcifications clusters in digitized mammograms based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. A k-means algorithm is used to cluster the data based on the features vectors and finally an artificial neural network-based classifier is applied and the classification performance is evaluated by a ROC curve. Experimental results indicate that the percentage of correct classification was 99.72%, obtaining 100% true positive (sensitivity) and 99.67% false positive (specificity), with the best classifier proposed. In case of the best classifier, we obtained a performance evaluation of classification of Az = 0.987

Diseño de una granja para producción de huevo en el municipio de Meoqui, Chihuahua.

A pesar de que existen distribuidoras de huevo que distribuyen a grandes centros de población, en la región de estudio se tienen problemas de abasto. El presente trabajo se realizó para contribuir al abasto de huevo a través de un proyecto de inversión elaborado para una granja productora de huevo. El canal de distribución identificado lo conforman los minoristas locales que no son abastecidos. Se aplicó un cuestionario de nueve preguntas a 18 tiendas de abarrotes de laregion. El 78.6%, manifestó tener problemas de abasto, de los cuales en su totalidad estarían dispuestos a comprarle  a  la  empresa.  Además  se detectó  la  necesidad  de una  distribución  constante  del producto, con buena calidad y que el precio se encuentre entre los promedios del mercado. La viabilidad financiera del proyecto es aceptable al tener una VAN (10%)=239,865, una TIR=19% y una REL B/C=1.17

Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images

The image by Computed Tomography is a non-invasive alternative for observing soil structures, mainly pore space. The pore space correspond in soil data to empty or free space in the sense that no material is present there but only fluids, the fluid transport depend of pore spaces in soil, for this reason is important identify the regions that correspond to pore zones. In this paper we present a methodology in order to detect pore space and solid soil based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. In order to find pixels groups with a similar gray level intensity, or more or less homogeneous groups, a novel image sub-segmentation based on a Possibilistic Fuzzy c-Means (PFCM) clustering algorithm was used. The Artificial Neural Networks (ANNs) are very efficient for demanding large scale and generic pattern recognition applications for this reason finally a classifier based on artificial neural network is applied in order to classify soil images in two classes, pore space and solid soil respectively

Recursos para la Enseñanza de la Alimentación en la Formación Inicial de Maestros (PIMCD, nº 103, convocatoria 2015)

Actualización y mejora de recursos sobre la enseñanza-aprendizaje de la Alimentación para la Formación Inicial de Maestros puestos en practica en el desarrollo docente

Air pollution Analysis with a PFCM Clustering Algorithm Applied in a Real Database of Salamanca (Mexico)

Over the last ten years, Salamanca has been considered among the most polluted cities in México. Nowadays, there is an Automatic Environmental Monitoring Network (AEMN) which measures air pollutants (Sulphur Dioxide (SO2), Particular Matter (PM10), Ozone (O3), etc.), as well as environmental variables (wind speed, wind direction, temperature, and relative humidity), and it takes a sample of the variables every minute. The AEM Network is mainly based on three monitoring stations located at Cruz Roja, DIF, and Nativitas. In this work, we use the PFCM (Possibilistic Fuzzy c Means) clustering algorithm as a mean to get a combined measure, from the three stations, looking to provide a tool for better management of contingencies in the city, such that local or general action can be taken in the city according to the pollution level given by each station and the combined measure. Besides, we also performed an analysis of correlation between pollution and environmental variables. The results show a significative correlation between pollutant concentrations and some environmental variables. So, the combined measure and the correlations can be used for the establishment of general contingency thresholds

Prevalence of symptoms, comorbidities, and reinfections in individuals infected with Wild-Type SARS-CoV-2, Delta, or Omicron variants: a comparative study in western Mexico

IntroductionThe variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been classified into variants of interest (VOIs) or concern (VOCs) to prioritize global monitoring and research on variants with potential risks to public health. The SARS-CoV-2 high-rate mutation can directly impact the clinical disease progression, epidemiological behavior, immune evasion, vaccine efficacy, and transmission rates. Therefore, epidemiological surveillance is crucial for controlling the COVID-19 pandemic. In the present study, we aimed to describe the prevalence of wild-type (WT) SARS-CoV-2 and Delta and Omicron variants in Jalisco State, Mexico, from 2021 to 2022, and evaluate the possible association of these variants with clinical manifestations of COVID-19.MethodsFour thousand and ninety-eight patients diagnosed with COVID-19 by real-time PCR (COVIFLU, Genes2Life, Mexico) from nasopharyngeal samples from January 2021 to January 2022 were included. Variant identification was performed by the RT-qPCR Master Mut Kit (Genes2Life, Mexico). A study population follow-up was performed to identify patients who had experienced reinfection after being vaccinated.Results and DiscussionSamples were grouped into variants according to the identified mutations: 46.3% were Omicron, 27.9% were Delta, and 25.8% were WT. The proportions of dry cough, fatigue, headache, muscle pain, conjunctivitis, fast breathing, diarrhea, anosmia, and dysgeusia were significantly different among the abovementioned groups (p < 0.001). Anosmia and dysgeusia were mainly found in WT-infected patients, while rhinorrhea and sore throat were more prevalent in patients infected with the Omicron variant. For the reinfection follow-up, 836 patients answered, from which 85 cases of reinfection were identified (9.6%); Omicron was the VOC that caused all reported reinfection cases. In this study, we demonstrate that the Omicron variant caused the biggest outbreak in Jalisco during the pandemic from late December 2021 to mid-February 2022 but with a less severe form than the one demonstrated by Delta and WT. The co-analysis of mutations and clinical outcomes is a public health strategy with the potential to infer mutations or variants that could increase disease severity and even be an indicator of long-term sequelae of COVID-19

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detectio