Emerging Technologies for Urban Traffic Management

Nowadays, the number of vehicles on the road and the need of transporting people grow fast. Road transportation has become the backbone of industrialized countries. Nevertheless, the road network system in cities is not sufficient to cope with the current demands due to the size of roads available. Building additional or extending existing roads do not solve the traffic congestion problem due to the high costs and the environmental and geographical limitations. As a consequence, the modern society is facing more traffic jams, higher fuel bills and high levels of CO2 emissions

Imaging the Kirkendall effect in pyrite (FeS2) thin films: cross-sectional microstructure and chemical features

This investigation provides novel data on the structure and chemical composition of pyrite thin films and new hints concerning their formation mechanism. From TEM-HAADF data, it has been found that the films are composed of two different layers: one is very compact and the other one is quite porous with many voids separating a few groups of grains. This porous layer is always in direct contact with the substrate, and its thickness is quite similar to that of the original Fe film. The average size of pyrite grains is equal in both layers, what suggests that the same process is responsible for their formation. Concentration profiles of sulfur, iron and some impurities (mainly sodium and oxygen from the glass substrate) through both layers are given in this work, and thus chemical inhomogeneities of the films are proved by the obtained stoichiometric ratios (S/Fe). Moreover, Na from sodalime glass substrates mainly accumulates at the pyrite grain boundaries and barely dopes them. The obtained results support the hypothesis that the iron sulfuration process essentially induces the diffusion of iron atoms, what leads to the porous layer formation as a manifestation of the Kirkendall Effect. Therefore, it seems that the same mechanisms that operate in the synthesis of surface hollow structures at the nanoscale are also active in the formation of pyrite thin films ranging from several tens to hundreds of nanometersMembers of MIRE Group acknowledge the financial support of the Spanish MICINN under project RTI2018-099794-B-I00. E. Flores acknowledges the intramural CSIC project 2D-MeSes funding and the service from the MiNa Laboratory at IMN, and funding from CM (project SpaceTec, S2013/ICE2822), MINECO (project CSIC13-4E1794) and EU (FEDER,FSE). Financial support through the project UMA18-FEDERJA-041 is gratefully acknowledge

Implementation of a Convolutional Neural Network to Distinguish between Radiological Patterns of COVID-19 and Pneumonia in Chest CT Images

En el año 2020, la Organización Mundial de la Salud (OMS) proclamó la existencia de una pandemia originada por el coronavirus (COVID-19), cuyo brote inicial tuvo lugar en Wuhan, China. Este virus ha tenido un impacto devastador, cobrando la vida de miles y afectando a millones en todo el mundo. Sus síntomas, que incluyen tos, fiebre, fatiga y disnea, se asemejan a los de una gripe común. La propagación del virus ocurre principalmente a través de partículas respiratorias emitidas por personas infectadas, las cuales pueden depositarse en los ojos, boca o nariz de otras personas. Para confirmar la infección, se utilizan dos tipos de pruebas: la prueba de reacción en cadena de la polimerasa con transcripción inversa (RT-PCR) y las pruebas de antígenos. Sin embargo, debido a sus procesamientos, estas pruebas pueden demorar en proporcionar resultados definitivos. Es en este contexto que la inteligencia artificial y las técnicas de Machine Learning (ML) se presentan como herramientas valiosas para mejorar la detección del virus en los pulmones de manera eficiente. En este trabajo, se propone la implementación de una Red Neuronal Convolucional (CNN) para la detección temprana de pacientes con COVID-19. Se utiliza un conjunto de datos compuesto por 3616 imágenes de rayos X de tórax, empleando una red neuronal preentrenada denominada VGG16. A través del entrenamiento, se logra una precisión óptima en la clasificación de las imágenes en las categorías de COVID y Neumonía.In 2020, the World Health Organization (WHO) proclaimed the existence of a pandemic originating from the coronavirus (COVID-19), the initial outbreak of which occurred in Wuhan, China. This virus has had a devastating impact, claiming the lives of thousands and affecting millions worldwide. Its symptoms, which include cough, fever, fatigue and dyspnea, resemble those of a common flu. Spread of the virus occurs primarily through respiratory particles emitted by infected people, which can be deposited in the eyes, mouth or nose of others. Two types of tests are used to confirm infection: reverse transcription-polymerase chain reaction (RT-PCR) and antigen testing. However, due to their processing, these tests can take time to provide definitive results. It is in this context that artificial intelligence and Machine Learning (ML) techniques are presented as valuable tools to improve virus detection in lungs in an efficient way. In this work, the implementation of a Convolutional Neural Network (CNN) for the early detection of patients with COVID-19 is proposed. A dataset composed of 3616 chest X-ray images is used, employing a pre-trained neural network named VGG16. Through training, optimal accuracy in classifying images into COVID and Pneumonia categories is achieved

Scaling up cancer care for children without medical insurance in developing countries: The case of Mexico

Background: In 2006, the Mexican government launched the Fund for Protection Against Catastrophic Expenditures (FPGC) to support financially healthcare of high cost illnesses. This study aimed at answering the question whether FPGC improved coverage for cancer care and to measure survival of FPGC affiliated children with cancer. Procedure A retrospective cohort study (2006–2009) was conducted in 47 public hospitals. Information of children and adolescents with cancer was analyzed. The coverage was estimated in accordance with expected number of incident cases and those registered at FPGC. The survival was analyzed by using Kaplan–Meier survival curves and Cox proportional hazards regression modeling. Results: The study included 3,821 patients. From 2006 to 2009, coverage of new cancer cases increased from 3.3% to 55.3%. Principal diagnoses were acute lymphoblastic leukemia (ALL, 46.4%), central nervous system (CNS) tumors (8.2%), and acute myeloid leukemia (AML, 7.4%). The survival rates at 36 months were ALL (50%), AML (30.5%), Hodgkin lymphoma (74.5%), Non-Hodgkin lymphoma (40.1%), CNS tumors (32.8%), renal tumors (58.4%), bone tumors (33.4%), retinoblastoma (59.2%), and other solid tumors (52.6%). The 3-year overall survival rates varied among the regions; children between the east and south-southeast had the higher risks (hazard ratio 3.0; 95% CI: 2.3–3.9) and 2.4; 95% CI: 2.0–2.8) of death from disease when compared with those from the central region. Conclusion: FPGC has increased coverage of cancer cases. Survival rates were different throughout the country. It is necessary to evaluate the effectiveness of this policy to increase access and identify opportunities to reduce the differences in survival

Extracción secuencial de metales pesados en dos suelos contaminados (Andisol y Vertisol) enmendados con ácidos húmicos

En suelos Andisoles y Vertisoles, bajo condiciones controladas, se evaluó el efecto de ácidos húmicos purificados en concentraciones de 0%, 2.5% y 5% (peso/peso)) sobre la extracción secuencial de metales pesados después de incubación a 60 y 90 días.  El fraccionamiento de los metales (Ni, Cu, Zn, Cd y Pb) en suelos contaminados y enmendados con ácidos húmicos se realizó mediante extracción secuencial de Tessier.  La movilidad de los metales se redujo con la adición de dichos ácidos, con mayor retención de Ni, Cu, Zn y Cd en la matriz del suelo (fracción residual).  El Pb en ambos suelos y el Zn en Vertisol experimentaron incremento significativo en su movilidad, mayor biodisponibilidad y potencial de afectación de diferentes componentes del medio-ambiente.  El incremento del tiempo de incubación permitió la interacción de los metales con los componentes de los suelos, generando disminución de su movilidad por mecanismos como formación de complejos estables y/o incremento de la capacidad de intercambio catiónico (CIC) de los suelos.  Los ácidos húmicos pueden ser utilizados, en general, como enmienda orgánica para la recuperación de suelos contaminados con metales pesados

Adsorción de metales pesados en Andisoles, Vertisoles y ácidos húmicos

La presente investigación estudió la adsorción de cinco metales pesados (Cd, Cu, Ni, Pb y Zn) en tres adsorbentes, dos suelos agrícolas colombianos (Typic Melanudand y Epiaquert ústico arcilloso fino isohipertérmico 1%) y ácidos húmicos (AH) extraídos de leonardita, de España, mediante la metodología descrita por Mosquera et al. (2007). A los suelos y a los AH se les determinaron propiedades químicas como pH, Capacidad de Intercambio Catiónico (CIC), carbono orgánico (%CO), bases intercambiables y contenido total de metales. La composición química de los AH se determinó empleando técnicas espectrométricas como ICP_MS, FTIR, UV-Vis, CPMAS 13C NMR y Py-GC/MS-THMA. Los resultados de la adsorción de metales se ajustaron al modelo de Freundlich, y muestran un comportamiento disímil de los absorbentes en relación a los metales estudiados, es así como la máxima capacidad de adsorción (K) y la fuerza de retención (n) de los metales es significativamente diferente (pPbCuNiZn, Andisol: PbCuCdZnNi, y Vertisol: CdPbCuNiZn; y para n, Ácidos Húmicos: PbZnCdCuNi, Andisol: CuNiZnPbCd, y Vertisol: ZnNiCuPbCd.This research studied the adsorption of five heavy metals (Cd, Cu, Ni, Pb and Zn) in three adsorbents, two Colombian agricultural soils (Typic Melanudand and fine clay 1% isohyperthermic-Ustic Epiaquert) and humic acids (HA) extracted from leonardite, of Spain. In both, HA and soils, the chemical properties determined were: pH, cation exchange capacity (CEC), organic carbon (% OC), exchangeable bases and total content of metals. The hemical composition of HA was determined using spectrometric techniques as ICP_MS, FTIR, UV-Vis, and CPMAS 13C NMR and Py-GC/MS-THMA. The results from the adsorption of metals in the three adsorbents were adjusted to Freundlich model, and these show a different behavior of the absorbers relative to the metals studied, in the same way the maximum adsorption capacity (K) and the retention force (n) of metals is significantly different (p 0.05). According to K and n of each adsorbent, the adsorption selectivity sequences of the metals has the following order of preference for K: In Humic Acids: Cd Pb Cu Ni Zn, Andisol: Pb Cu Cd Zn Ni, and Vertisol: Cd Pb Cu Ni Zn. For n, Humic Acids: Pb Zn Cd Cu Ni, Andisol: Cu Ni Zn Pb Cd, and Vertisol: Zn Ni Cu Pb C

Serum albumin level as a risk factor for mortality in burn patients

OBJECTIVE: Hypoalbuminemia is a common clinical deficiency in burn patients and is associated with complications related to increased extravascular fluid, including edema, abnormal healing, and susceptibility to sepsis. Some prognostic scales do not include biochemical parameters, whereas others consider them together with comorbidities. The purpose of this study was to determine whether serum albumin can predict mortality in burn patients. METHODS: We studied burn patients ≥16 years of age who had complete clinical documentation, including the Abbreviated Burn Severity Index, serum albumin, globulin, and lipids. Sensitivity and specificity analyses were performed to determine the cut-off level of albumin that predicts mortality. RESULTS: In our analysis of 486 patients, we found that mortality was higher for burns caused by flame (p = 0.000), full-thickness burns (p = 0.004), inhalation injuries (p = 0.000), burns affecting >;30% of the body surface area (p = 0.001), and burns associated with infection (p = 0.008). Protein and lipid levels were lower in the patients who died (

DPYD pathogenic variants associated with fluoropyrimidines toxicity

Background: Genetic variants in dihydropyrimidine dehydrogenase gene (DPYD) coding for the key enzyme (DPD) of fluoropyrimidines (FPs) catabolism. DPYD contributes to the development of severe FPs-related toxicity, and pathogenic DPYD variants detection reduces side effects and complications associated with FP-toxicity. The allelic frequency of these variants in the Mexican population is currently unknown. Methods: The study was carried out at the Centro Universitario Contra el Cáncer (CUCC) of the Universidad Autónoma de Nuevo León (UANL) in Monterrey México. Genomic DNA was isolated from 154 subjects using the QIAamp DNA Blood Midi kit (QIAGEN) following the manufacturer\u27s recommendations. We analyze the variants c.1156G-\u3eT, c.2846A-\u3eT, and c.1129-5923C-\u3eG by qPCR using predesigned probes. For the remaining genomic variants (c.1905+1G-\u3eA, c.1679T-\u3eG, c.1898delC and c.299_302delTCAT), we design sequencing oligos using the software Oligo Primer v.7®. The allele frequency was calculated for each variant. Results: We analyzed a total of 154 samples to detect the seven variants analyzed. So far, only 2 samples have been found that presented the variant c.1129-5923C\u3eG in a state of heterozygosis, representing 1.2987% of the total of our population. Conclusions: The allele frequency for the variant c.1129-5923C-\u3eG was higher than reported in other populations. So this allele is more common in our population, which could attribute to the large percentage of side effects in our patients. However, more studies and increasing the number of samples are needed to establish DPYD the allele frequency more precisely

Initial CONNECT Architecture

Interoperability remains a fundamental challenge when connecting heterogeneous systems which encounter and spontaneously communicate with one another in pervasive computing environments. This challenge is exasperated by the highly heterogeneous technologies employed by each of the interacting parties, i.e., in terms of hardware, operating system, middleware protocols, and application protocols. The key aim of the CONNECT project is to drop this heterogeneity barrier and achieve universal interoperability. Here we report on the development of the overall CONNECT architecture that will underpin this solution; in this respect, we present the following contributions: i) an elicitation of interoperability requirements from a set of pervasive computing scenarios, ii) a survey of existing solutions to interoperability, iii) an initial view of the CONNECT architecture, and iv) a series of experiments to provide initial validation of the architecture

Mejoramiento del parque Juan Aldama - Autoconstrucción de techo verde de bambú en vivienda social, Puerto Vallarta, Jalisco

El proyecto Regeneración socioambiental del río Pitillal en Puerto Vallarta, ha sido desarrollado por los equipos del PAP desde la primavera de 2021 y contempla una metodología que combina la producción social del hábitat (Ortiz. E, 2012) con la autoconstrucción con materiales locales (Comunal, 2019). Se han desarrollado investigaciones para generar diagnósticos, posteriormente se han desarrollados propuestas técnicas que se han validado con los habitantes de la zona. Se busca actualmente pasar a la etapa de la acción participativa, buscando motivar el involucramiento de los actores locales y beneficiarios de las zonas en las que se da el acompañamiento. Se presenta aquí el reporte del proceso de la realización de dos líneas de investigación que dan continuidad a procesos que se originaron en semestres anteriores del PAP en el escenario de Puerto Vallarta:AUTOCONSTRUCCIÓN DE CUBIERTA DE BAMBÚ Y FORESTACIÓN EN PARQUE JUAN ALDAMA y AUTOCONSTRUCCIÓN DE CUBIERTA DE BAMBÚ Y FORESTACIÓN EN AZOTEA DE VIVIENDA EN VALLARTA VILLAS.ITESO, A.C