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Biomass burning (BB) is a major source of reactive organic carbon into the atmosphere. Once in the atmosphere, these organic BB emissions, in both the gas and particle phases, are subject to atmospheric oxidation, though the nature and impact of the chemical transformations are not currently well constrained. Here we describe experiments carried out as part of the FIREX FireLab campaign, in which smoke from the combustion of fuels typical of the Western US was sampled into an environmental chamber and exposed to high concentrations of OH, to simulate the equivalent of up to two days of atmospheric oxidation. The evolution of the organic mixture was monitored using three real-time time-of-flight mass spectrometric instruments (a proton transfer reaction mass spectrometer, an iodide chemical ionization mass spectrometer, and an aerosol mass spectrometer), providing measurements of both individual species and ensemble properties of the mixture. The combined measurements from these instruments achieve a reasonable degree of carbon closure (within 15–35 %), indicating that most of the reactive organic carbon is measured by these instruments. Consistent with our previous studies of the oxidation of individual organic species, atmospheric oxidation of the complex organic mixture leads to the formation of species that on average are smaller and more oxidized than those in the unoxidized emissions. In addition, comparison of mass spectra from the different fuels indicates that the oxidative evolution of BB emissions proceeds largely independent of fuel type, with different fresh smoke mixtures ultimately converging into a common, aged distribution of gas-phase compounds. This distribution is characterized by high concentrations of several small, volatile oxygenates, formed from fragmentation reactions, as well as a complex pool of many minor oxidized species and secondary organic aerosol, likely formed via functionalization processes.

The latest operational National Air Quality Forecast Capability (NAQFC) has been advanced to use the Community Multiscale Air Quality (CMAQ) model (version 5.3.1) with the CB6r3 (Carbon Bond 6 revision 3) AERO7 (version 7 of the aerosol module) chemical mechanism and is driven by the Finite-Volume Cubed-Sphere (FV3) Global Forecast System, version 16 (GFSv16). This update has been accomplished via the development of the meteorological preprocessor, NOAA-EPA Atmosphere–Chemistry Coupler (NACC), adapted from the existing Meteorology–Chemistry Interface Processor (MCIP). Differing from the typically used Weather Research and Forecasting (WRF) CMAQ system in the air quality research community, the interpolation-based NACC can use various meteorological outputs to drive the CMAQ model (e.g., FV3-GFSv16), even though they are on different grids. In this study, we compare and evaluate GFSv16-CMAQ and WRFv4.0.3-CMAQ using observations over the contiguous United States (CONUS) in summer 2019 that have been verified with surface meteorological and AIRNow observations. During this period, the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign was performed, and we compare the two models with airborne measurements from the NASA DC-8 aircraft. The GFS-CMAQ and WRF-CMAQ systems show similar performance overall with some differences for certain events, species and regions. The GFSv16 meteorology tends to have a stronger diurnal variability in the planetary boundary layer height (higher during daytime and lower at night) than WRF over the US Pacific coast, and it also predicted lower nighttime 10 m winds. In summer 2019, the GFS-CMAQ system showed better surface ozone (O3) than WRF-CMAQ at night over the CONUS domain; however, the models' fine particulate matter (PM2.5) predictions showed mixed verification results: GFS-CMAQ yielded better mean biases but poorer correlations over the Pacific coast. These results indicate that using global GFSv16 meteorology with NACC to directly drive CMAQ via interpolation is feasible and yields reasonable results compared to the commonly used WRF approach.

Introducción: La obstrucción intestinal provocada por un coágulo de sangre o hemobezoar es un cuadro poco frecuente. Presentación del caso: Mujer de 63 años que tras intervenirse de un ileo biliar mediante enterotomía tuvo un postoperatorio tórpido con clínica de obstrucción intestinal, siendo necesaria la reintervención donde se objetivó un coágulo de sangre en la sutura intestinal que estaba provocando el cuadro obstructivo. Conclusión: El hemobezoar es una complicación postquirúrgica que debemos tener presente en el diagnostico diferencial de la obstrucción intestinal en el postoperatorio.

Contains fulltext : 283099.pdf (Publisher’s version ) (Open Access) BACKGROUND: Young children living with HIV have few treatment options. We aimed to assess the efficacy and safety of dolutegravir-based antiretroviral therapy (ART) in children weighing between 3 kg and less than 14 kg. METHODS: ODYSSEY is an open-label, randomised, non-inferiority trial (10% margin) comparing dolutegravir-based ART with standard of care and comprises two cohorts (children weighing ≥14 kg and

Contains fulltext : 282959.pdf (Publisher’s version ) (Open Access) BACKGROUND: Children with HIV-associated tuberculosis (TB) have few antiretroviral therapy (ART) options. We aimed to evaluate the safety and pharmacokinetics of dolutegravir twice-daily dosing in children receiving rifampicin for HIV-associated TB. METHODS: We nested a two-period, fixed-order pharmacokinetic substudy within the open-label, multicentre, randomised, controlled, non-inferiority ODYSSEY trial at research centres in South Africa, Uganda, and Zimbabwe. Children (aged 4 weeks to

In this paper, a robust analysis of SO 2 concentration measurements taken at the Belisario air quality monitoring station, Quito, Ecuador is carried out. The analyzed data contain 12 years of measurements, from January 2008 to December 2019. In addition, this set of measurements was decomposed into variables that represent each year, month, day of the week, and hour of the day in groups of two hours. For the analysis, classic, nonparametric and robust statistical methods were used, and the data were classified based on criteria established by the Quiteno Air Quality Index, taking confidence intervals into account. The results showed that the level of air pollution at the Belisario station due to the SO 2 concentration is acceptable. In addition, the trend in the level of SO 2 concentration decreased over the years studied, with a sharp drop from 2008 to 2012, then a small rise in 2013 and another fall until 2019, presenting decreasing oscillations that tend toward a desirable level of pollution. In this paper, it was shown that the air pollution at the Belisario station due to the concentration of SO 2 in the last 12 years is not harmful to humans, with the measurement precision provided by robust statistical methods. Therefore, it can be concluded that the measures that have been taken by the Quito city council over the last few years are yielding good results.

Measurements of CO

In their daily business, bank branches should register their operations with several systems in order to share information with other branches and to have a central repository of records. In this way, information can be analysed and processed according to different requisites: fraud detection, accounting or legal requirements. Within this context, there is increasing use of big data and artificial intelligence techniques to improve customer experience. Our research focuses on detecting matches between bank operation records by means of applied intelligence techniques in a big data environment and business intelligence analytics. The business analytics function allows relationships to be established and comparisons to be made between variables from the bank's daily business. Finally, the results obtained show that the framework is able to detect relationships between banking operation records, starting from not homogeneous information and taking into account the large volume of data involved in the process.

Modern companies generate value by digitalizing their services and products. Knowing what customers are saying about the firm through reviews in social media content constitutes a key factor to succeed in the big data era. However, social media data analysis is a complex discipline due to the subjectivity in text review and the additional features in raw data. Some frameworks proposed in the existing literature involve many steps that thereby increase their complexity. A two-stage framework to tackle this problem is proposed: the first stage is focused on data preparation and finding an optimal machine learning model for this data; the second stage relies on established layers of big data architectures focused on getting an outcome of data by taking most of the machine learning model of stage one. Thus, a first stage is proposed to analyze big and small datasets in a non-big data environment, whereas the second stage analyzes big datasets by applying the first stage machine learning model of. Then, a study case is presented for the first stage of the framework to analyze reviews of hotel-related businesses. Several machine learning algorithms were trained for two, three and five classes, with the best results being found for binary classification.

This paper analyzes 12 years of tropospheric ozone (O3) concentration measurements using robust techniques. The measurements were taken at an air quality monitoring station called Belisario, which is in Quito, Ecuador, the data collection time period was 1 January 2008 to 31 December 2019, and the measurements were carried out using photometric O3 analyzers. Here, the measurement results were used to build variables that represented hours, days, months, and years, and were then classified and categorized. The index of air quality (IAQ) of the city was used to make the classifications, and robust and nonrobust confidence intervals were used to make the categorizations. Furthermore, robust analysis methods were compared with classical methods, nonparametric methods, and bootstrap-based methods. The results showed that the analysis using robust methods is better than the analysis using nonrobust methods, which are not immune to the influence of extreme observations. Using all of the aforementioned methods, confidence intervals were used to both establish and quantify differences between categories of the groups of variables under study. In addition, the central tendency and variability of the O3 concentration at Belisario station were exhaustively analyzed, concluding that said concentration was stable for years, highly variable for months and hours, and slightly changing between the days of the week. Additionally, according to the criteria established by the IAQ, it was shown that in Quito, the O3 concentration levels during the study period were not harmful to human health.

Using a new approach that constrains thermodynamic modeling of aerosol composition with measured gas-to-particle partitioning of inorganic nitrate, we estimate the acidity levels for aerosol sampled in the South Korean planetary boundary layer during the NASA/NIER KORUS-AQ field campaign. The pH (mean ± 1σ = 2.43 ± 0.68) and aerosol liquid water content determined were then used to determine the chemical regime of the inorganic fraction of particulate matter (PM) sensitivity to ammonia and nitrate availability. We found that the aerosol formation is always sensitive to HNO3 levels, especially in highly polluted regions, while it is only exclusively sensitive to NH3 in some rural/remote regions. Nitrate levels are further promoted because dry deposition velocity is low and allows its accumulation in the boundary layer. Because of this, HNO3 reductions achieved by NOx controls prove to be the most effective approach for all conditions examined, and that NH3 emissions can only partially affect PM reduction for the specific season and region. Despite the benefits of controlling PM formation to reduce ammonium-nitrate aerosol and PM mass, changes in the acidity domain can significantly affect other processes and sources of aerosol toxicity (such as e.g., solubilization of Fe, Cu and other metals) as well as the deposition patterns of these trace species and reactive nitrate.

In this paper, a practical comparison is performed between the use of the principal component analysis method (PCA) and the multidimensional scaling method (MDS) for image classification applications, where the images under study are contaminated using different tools. Here, characteristics of these images are obtained and, according to criteria for measuring the distance between the images, their classification is carried out. For the case under study, it can be said that the classification using PCA performed better than the classification using MDS.

Single-particle mass spectrometry (SPMS) instruments characterize the composition of individual aerosol particles in real time. Their fundamental ability to differentiate the externally mixed particle types that constitute the atmospheric aerosol population enables a unique perspective into sources and transformation. However, quantitative measurements by SPMS systems are inherently problematic. We introduce a new technique that combines collocated measurements of aerosol composition by SPMS and size-resolved absolute particle concentrations on aircraft platforms. Quantitative number, surface area, volume, and mass concentrations are derived for climate-relevant particle types such as mineral dust, sea salt, and biomass burning smoke. Additionally, relative ion signals are calibrated to derive mass concentrations of internally mixed sulfate and organic material that are distributed across multiple particle types. The NOAA Particle Analysis by Laser Mass Spectrometry (PALMS) instrument measures size-resolved aerosol chemical composition from aircraft. We describe the identification and quantification of nine major atmospheric particle classes, including sulfate–organic–nitrate mixtures, biomass burning, elemental carbon, sea salt, mineral dust, meteoric material, alkali salts, heavy fuel oil combustion, and a remainder class. Classes can be sub-divided as necessary based on chemical heterogeneity, accumulated secondary material during aging, or other atmospheric processing. Concentrations are derived for sizes that encompass the accumulation and coarse size modes. A statistical error analysis indicates that particle class concentrations can be determined within a few minutes for abundances above ∼10 ng m−3. Rare particle types require longer sampling times. We explore the instrumentation requirements and the limitations of the method for airborne measurements. Reducing the size resolution of the particle data increases time resolution with only a modest increase in uncertainty. The principal limiting factor to fast time response concentration measurements is statistically relevant sampling across the size range of interest, in particular, sizes D µm for accumulation-mode studies and D > 2 µm for coarse-mode analysis. Performance is compared to other airborne and ground-based composition measurements, and examples of atmospheric mineral dust concentrations are given. The wealth of information afforded by composition-resolved size distributions for all major aerosol types represents a new and powerful tool to characterize atmospheric aerosol properties in a quantitative fashion.

The GoAmazon 2014/5 field campaign took place in Manaus (Brazil) and allowed the investigation the interaction between background level biogenic air masses and anthropogenic plumes. We present in this work a box model built to simulate the impact of urban chemistry on biogenic Secondary Organic Aerosol (SOA) formation and composition. An organic chemistry mechanism is generated with the Generator for Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to simulate the explicit oxidation of biogenic and anthropogenic compounds. A parameterization is also included to account for the reactive uptake of isoprene oxidation products on aqueous particles. After some reductions of biogenic emissions relative to existing emission inventories, the model is able to reproduce measured primary compounds, ozone and NOx for clean or polluted situations. The explicit model is able to reproduce background case SOA mass concentrations but is underestimating the enhancement observed in the urban plume. Oxidation of biogenic compounds is the major contributor to SOA mass. A Volatility Basis Set parameterization (VBS) applied to the same cases obtains better results than GECKO-A for predicting SOA mass in the box model. The explicit mechanism may be missing SOA formation processes related to the oxidation of monoterpenes that could be implicitly accounted for in the VBS parameterization.

In this paper, a method for image classification based on multidimensional scaling (MDS) is presented. Once the image has been fragmented into smaller cells and the wavelet decomposition has also been performed, up to a certain level, statistics are obtained from the new cells with the aim of constructing vectors of characteristics of the original image. Then, these vectors are grouped in a matrix and the MDS of said matrix is performed. Finally, the principal coordinates of the MDS indicate the proximity between vectors and, therefore, between images.

Social Media is playing a key role in today's society. Many of the events that are taking place in diverse human activities could be explained by the study of these data. Big Data is a relatively new parading in Computer Science that is gaining increasing interest by the scientific community. Big Data Predictive Analytics is a Big Data discipline that is mostly used to analyze what is in the huge amounts of data and then perform predictions based on such analysis using advanced mathematics and computing techniques. The study of Social Media Data involves disciplines like Natural Language Processing, by the integration of this area to academic studies, useful findings have been achieved. Social Network Rating Systems are online platforms that allow users to know about goods and services, the way in how users review and rate their experience is a field of evolving research. This paper presents a deep investigation in the state of the art of these areas to discover and analyze the current status of the research that has been developed so far by academics of diverse background.

In this paper, the bootstrap method was used to estimate the frequency of a signal. Here, the periodic signal was transformed into a rectangular wave and the interval of time between two consecutive positive edges of this rectangular wave was estimated. First, in order to estimate this interval of time, a counter was used to count the number of clock pulses that have occurred during every interval of this type. In this case, the clock frequency was the sampling frequency. Next, this process was repeated several times and a vector of estimated values of the period of the signal was created. After that, this vector was considered as a random sample and it was used to create thousands of resamples with the same size as the above-mentioned random sample and that were created by sampling with replacement from the original random sample. Then, for each resample, the sample mean was calculated and the inverse of the mean of these sample means was used as an estimation of the frequency of the signal. Finally, a prototype of this statistic method to frequency estimation was built. The results of the experiment were satisfactory. © 2016 The Author(s)

A robust algorithm for Japanese handwritten hiragana character classifier is proposed using a machine learning approach for minimal training data to reduce computational power and time consumption. The proposed algorithm utilizes image recognition techniques to process samples from a data set. Six different models involving convolutional neural networks are implemented using image templates that were previously processed, in order to achieve great results with the least possible amount of training data. Prediction results were evaluated separating the dataset in training and validation data at a ratio of 5:95 respectively, achieving 96.95% as the highest accuracy across different models, competing against state-of-the-art classifiers with 80:20 training ratio.

There is a wide variety of methods for the analysis of textures and the extraction of image characteristics based on wavelet decomposition. The objective of this paper is to find a vector of characteristics of each image and to determine a classification among them using principal component analysis. The procedures presented here are for classifying test images. After fragmenting the image, a wavelet decomposition of the fragmented images is performed both in scale and in orientation. To characterize the images, statistics of marginal and joint distributions based on local neighborhoods of spatial, orientation and scale type are used.

Single-particle mass spectrometer (SPMS) instruments characterize the composition of individual aerosol particles in real time. Their fundamental ability to differentiate the externally mixed particle types that constitute the atmospheric aerosol population enables a unique perspective into sources and transformation. However, quantitative measurements by SPMS systems are inherently problematic. We introduce a new technique that combines collocated measurements of aerosol composition by SPMS and size-resolved absolute particle concentrations on aircraft platforms. Quantitative number, surface area, volume, and mass concentrations are derived for climate-relevant particle types such as mineral dust, sea salt, and biomass burning smoke. Additionally, relative ion signals are calibrated to derive mass concentrations of internally mixed sulfate and organic material that are distributed across multiple particle types. The NOAA Particle Analysis by Laser Mass Spectrometry (PALMS) instrument measures size-resolved aerosol chemical composition from aircraft. We describe the identification and quantification of nine major atmospheric particle classes, including sulfate/organic/nitrate mixtures, biomass burning, elemental carbon, sea salt, mineral dust, meteoric material, alkali salts, heavy fuel oil combustion, and a remainder class. Classes can be sub-divided as necessary based on chemical heterogeneity, accumulated secondary material during aging, or other atmospheric processing. Concentrations are derived for sizes that encompass the accumulation and coarse size modes. A statistical error analysis indicates that particle class concentrations can be determined within a few minutes for abundances above ~ 10 ng m−3. Rare particle types require longer sampling times. We explore the instrumentation requirements and the limitations of the method for airborne measurements. Reducing the size resolution of the particle data increases time resolution with only a modest increase in uncertainty. The principal limiting factor to fast time response concentration measurements is statistically relevant sampling across the size range of interest, in particular, sizes D  2 μm for coarse mode analysis. We demonstrate the use of a virtual impactor to enhance sampling statistics for the inherently sparse coarse mode. Performance is compared to other airborne and ground-based composition measurements, and examples of atmospheric mineral dust concentrations are given. The wealth of information afforded by composition-resolved size distributions for all major aerosol types represents a new and powerful tool to characterize atmospheric aerosol properties in a quantitative fashion.

Acid-driven multiphase chemistry of isoprene epoxydiols (IEPOX), a key isoprene oxidation product, with inorganic sulfate aerosol yields substantial amounts of secondary organic aerosol (SOA) through the formation of organosulfur. The extent and implications of inorganic-to-organic sulfate conversion, however, are unknown. Herein, we reveal that extensive consumption of inorganic sulfate occurs, which increases with the IEPOX-to-inorganic sulfate ratio (IEPOX:Sulfinorg), as determined by laboratory and field measurements. We further demonstrate that organosulfur greatly modifies critical aerosol properties, such as acidity, morphology, viscosity, and phase state. These new mechanistic insights reveal that changes in SO2 emissions, especially in isoprene-dominated environments, will significantly alter biogenic SOA physicochemical properties. Consequently, IEPOX:Sulfinorg will play a central role in understanding historical climate and determining future impacts of biogenic SOA on global climate and air quality.

Negative impedance converters (NICs) have been proposed as structures to improve the performance of RF circuits and electrically small antennas. However, NICs suffer from stability problems. This paper presents a compact procedure to analyze the stability of NICs. Then, the required and sufficient conditions to predict the stability of a negative impedance converter are given. These conditions can be evaluated using standard computer-aided-design software. Finally, a NIC prototype is given to validate and illustrate the presented design procedure, it is also integrated with a printed, blade-type, electrically small monopole in the VHF band.

This paper shows the design of an active patch antenna. First, an analysis of the electric field inside the patch is performed to determine the compatibility of the modes of the patch with the desired feeding. The desired feeding is as a serial two-port resonator, so the suitable mode is the “Mode 1”. Then a linear analysis of the active antenna is performed. In this particular example, it is possible to use the classic Loop Gain Method, but its use is not guaranteed. So, the proposed NDF Method is used because it is independent of the oscillator topology and it is valid without any restriction or proviso. Then a Harmonic Balance analysis is performed and the results are compared with the measurements of a prototype.

Measurement techniques that provide molecular-level information are needed to elucidate the multi-phase processes that produce secondary organic aerosol (SOA) species in the atmosphere. Here we demonstrate the application of ion mobility spectrometry-mass spectrometry (IMS-MS) to the simultaneous characterization of the elemental composition and molecular structures of organic species in the gas and particulate phases. Molecular ions of gas-phase organic species are measured online with IMS-MS after ionization with a custom build nitrate chemical ionization (CI) source. This CI-IMS-MS technique is used to obtain time-resolved measurements (5 min) of highly oxidized organic molecules during the 2013 Southern Oxidant and Aerosol Study (SOAS) ambient field campaign in the forested SE US. The ambient IMS-MS signals are consistent with laboratory IMS-MS spectra obtained from single-component carboxylic acids and multicomponent mixtures of isoprene and monoterpene oxidation products. Mass-mobility correlations in the 2-dimensional IMS-MS space provide a means of identifying ions with similar molecular structures within complex mass spectra and are used to separate and identify monoterpene oxidation products in the ambient data that are produced from different chemical pathways. Water-soluble organic carbon (WSOC) constituents of fine aerosol particles that are not resolvable with standard analytical separation methods, such as liquid chromatography (LC), are shown to be separable with IMS-MS coupled to an electrospray ionization (ESI) source. The capability to use ion mobility to differentiate between isomers is demonstrated for organosulfates derived from the reactive uptake of isomers of isoprene epoxydiols (IEPOX) onto wet acidic sulfate aerosol. Controlled fragmentation of precursor ions by collisional dissociation (CID) in the transfer region between the IMS and the MS is used to validate MS peak assignments, elucidate structures of oligomers, and confirm the presence of the organosulfate functional group.

This paper presents a modification of the short-circuited ring patch antenna that operates in two bands: the lower band with linear polarization and the upper band with circular polarization. The new single-feed antenna has been manufactured and a comparison between measurements and numerical simulations is shown. Different characteristics such as radiation patterns, gain, and resonant frequencies have been analyzed. Moreover, a parametric study has been performed in both bands.

A low-cost, multiple-purpose, and high-precision timing test setup for the measurements of secondary Mode S radar transmission signal was proposed. The goal was to fully guarantee compliance of the proposed transmitter under test with the really hard International Civil Aircraft Organization requirements using traditional measurement equipment, which was difficult or even impossible to ensure up to now. The low-cost structure proposed in this paper allows the user to perform measurements independently of the measurements performed by the pieces of test equipment shelled by the manufacturer of radar, which is a very important aspect since the independence of the verifications is a mandatory requirement established by the safety standards of civil aviation. The proposed setup has been used to verify several transmitters with some defects that are not detected by monopulse secondary surveillance radar specific pieces of test equipment that are focused on more high-level functionalities. It also is valid and it has been used, as a general-purpose setup, for testing other radio navigation aids.

This paper shows one active patch antenna with a Negative Impedance Converters (NIC) and other one without it. The NIC works as negative admittance to increase the Q of the antenna. These circuits can be analysed using classic methods, but classic methods require the verification of the appropriated provisos. The chosen classic method depends on the oscillator topology, while the Normalized Determinant Function (NDF) is independent of topologies and it does not require any provisos. The NDF is suitable for Q estimation and noise optimization during linear analysis of any topology. The oscillators are designed using the NDF and the Admittance; then the phase noise is evaluated using the NDF. The Harmonic Balance (HB) analysis and the measurements of the prototypes confirm, in favour of NDF, the stability discrepancies between classic methods and NDF. The linear approximation of the improvements introduced by a NIC is questioned by the risk of instabilities and the introduction of new noise sources.