Search

Future climate change is expected to increase the risk and severity of wildland fires in tropical regions. Synoptic-scale fire weather conditions in Mexico were carefully analyzed using 20 years of satellite hotspot and rainfall data, hourly weather data, and various climate data. Fire analysis results showed that eighty-four percent of all fires in Mexico occurred south of 22° N. Southwest Mexico (SWM, N < 22°, 94–106° W) and Southeast Mexico (SEM, N < 22°, 86–94° W), account for 50% and 34% of all fires in Mexico. Synoptic-scale analysis results using hourly data showed that westerly wind sea breezes from the Pacific Ocean blow toward the coastal land areas of the SWM while easterly wind sea breezes from the Caribbean blow into the SEM. The most sensitive weather parameters were "relative humidity" for the SWM and "temperature" for the SEM. The fire-related indices selected were "precipitable water vapor anomaly" for the SWM and "temperature anomaly" for the SEM. The SWM fire index suggests that future fires will depend on dryness, while the SEM fire index suggests that future fires will depend on temperature trends. I do hope that this paper will improve local fire forecasts and help analyze future fire trends under global warming in Mexico. [ABSTRACT FROM AUTHOR]

In this paper, four probability functions are compared with the purpose of establishing a methodology to improve the accuracy of wind energy estimations in a desert city in Northwestern Mexico. Three time series of wind speed data corresponding to 2017, 2018, and 2019 were used for statistical modeling. These series were recorded with a sonic anemometer at a sampling frequency of 10 Hz. Analyses based on these data were performed at different stationarity periods (5, 30, 60, and 600 s). The estimation of the parameters characterizing the probability density functions (PDFs) was carried out using different methods; the statistical models were evaluated by the coefficient of determination and Nash–Sutcliffe efficiency coefficient, and their accuracy was estimated by the measured quadratic error, mean square error, mean absolute error, and mean absolute percentage error. Weibull, using the energy pattern factor method, and Gamma, using the Method of Moments, were the probability density functions that best described the statistical behavior of wind speed and were better at estimating the generated energy. We conclude that the proposed methodology will increase the confidence of both wind speed estimation and the energy supplied by small-scale wind installations. [ABSTRACT FROM AUTHOR]

Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival at these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors, such as inadequate prevention education, limited disaster resources, and insufficient coordination between public and private emergency services, can complicate these situations. In large-scale emergencies, multiple demand points (DPs) are generally observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as a multi-objective optimization problem, focusing on the location-allocation problem of resources in Mass Casualty Incidents (MCIs). The proposed case study is Mexico City in a earthquake post-disaster scenario, using voluntary geographic information, open government data, and historical data from the 19 September 2017 earthquake. It is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions. [ABSTRACT FROM AUTHOR]

This paper tackles the challenge of time series forecasting in the presence of missing data. Traditional methods often struggle with such data, which leads to inaccurate predictions. We propose a novel framework that combines the strengths of Generative Adversarial Networks (GANs) and Bayesian inference. The framework utilizes a Conditional GAN (C-GAN) to realistically impute missing values in the time series data. Subsequently, Bayesian inference is employed to quantify the uncertainty associated with the forecasts due to the missing data. This combined approach improves the robustness and reliability of forecasting compared to traditional methods. The effectiveness of our proposed method is evaluated on a real-world dataset of air pollution data from Mexico City. The results demonstrate the framework's capability to handle missing data and achieve improved forecasting accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the morphology and columnar microphysical properties of atmospheric aerosols in Mexico City (MC) for the period 2022–2023. The morphological study focused on the structure description of soot particles and tar balls (TB). By transmission electron microscope (TEM) and scanning electrode microscope (SEM), voluminous soot aggregates mixed with TBs were observed. The chemistry shows that both soot and TBs are mostly carbonaceous species with well-defined morphologies. On the other hand, the columnar aerosol microphysical properties recovered from AERONET show that the particles have a bimodal aerosol size distribution (ASD) with two modes: fine and coarse. The ASD remains constant without showing significant seasonal changes, only with some variability for coarse particles. The aerosol optical depth (AOD) value is significantly high, typical of urban areas. The real (n) and imaginary (k) parts of the complex refractive index (CRI) were obtained from the photometric measurements. The CRI values show seasonal variations, with spring being the season with the highest values for n, while the highest values for k were measured in winter. [ABSTRACT FROM AUTHOR]

Bursera fagaroides, popularly used in México, possesses bioactive lignans. These compounds are low in the bark, and its extraction endangers the life of the trees. The aim of the present investigation was to search for alternative sources of cytotoxic compounds in B. fagaroides prepared as leaves and in vitro callus cultures. The friable callus of B. fagaroides was established using a combination of plant growth regulators: 4 mgL−1 of 2,4-dichlorophenoxyacetic acid (2,4-D), 1 mgL−1 Naphthaleneacetic Acid (NAA) and 1 mgL−1 Zeatin. The maximum cell growth was at day 28 with a specific growth rate of μ = 0.059 days−1 and duplication time td = 11.8 days. HPLC quantification of the dichloromethane callus biomass extract showed that Scopoletin, with a concentration of 10.7 µg g−1 dry weight, was the main compound inducible as a phytoalexin by the addition of high concentrations of 2,4-D, as well as by the absence of nutrients in the culture medium. In this same extract, the compounds γ-sitosterol and stigmasterol were also identified by GC-MS analysis. Open column chromatography was used to separate and identify yatein, acetyl podophyllotoxin and 7′,8′-dehydropodophyllotoxin in the leaves of the wild plant. Cytotoxic activity on four cancer cell lines was tested, with PC-3 prostate carcinoma (IC50 of 12.6 ± 4.6 µgmL−1) being the most sensitive to the wild-type plant extract and HeLa cervical carcinoma (IC50 of 72 ± 5 µgmL−1) being the most sensitive to the callus culture extract. [ABSTRACT FROM AUTHOR]

Background: Refractive errors, including myopia, hyperopia, and astigmatism, are the leading causes of visual impairment in school-aged children and can significantly impact their academic performance and quality of life. This study aimed to assess the prevalence of refractive errors among school children from economically disadvantaged areas in Northwest México, using a consistent methodology to facilitate comparison with global data. Methods: We adopted the Refractive Error Study in Children (RESC) protocol by the World Health Organization to examine the prevalence of myopia, hyperopia, and astigmatism. The study comprised a systematic sampling of children aged 6 to 18 years from diverse schools in Northwest México. Trained optometrists conducted visual acuity testing and autorefraction, while ophthalmologists performed cycloplegic refraction to ensure accuracy. Results: The study found a myopia (SE ≤−1.50 D at least one eye) prevalence of 14.55% (95% CI: 13.27–15.91), with a higher incidence in females (6.92%) compared to males (6.00%) in at least one eye. Hyperopia (SE ≥ +1.00 D at least one eye) was less common, at 3.23% (95% CI: 2.61–3.95), with a slightly higher occurrence in males in at least one eye. Astigmatism (Cylinder ≥ 0.75 D at least one eye) was present in 18.63% (95% CI: 17.21–20.12) of the students in at least one eye, with no significant difference between genders. These findings are consistent with other studies in regions such as Puerto Rico and Iran, indicating widespread refractive error issues among schoolchildren. Conclusions: The high prevalence of refractive errors, particularly myopia and astigmatism, highlights the critical need for regular vision screenings in schools and the implementation of public health interventions to provide corrective eyewear. Our study confirms the importance of utilizing standardized methodologies like the RESC protocol to compare refractive error prevalence across different geographical and socio-economic contexts, thereby informing global public health strategies. [ABSTRACT FROM AUTHOR]

Globally, there is growing concern over the presence of lead (Pb) in foods because it is a heavy metal with several toxic effects on human health. However, monitoring studies have not been conducted in Mexico. In this study, we estimated the concentrations of Pb in the most consumed foods and identified those that exceeded the maximum limits (MLs) for Pb in foods established by the International Standards. Based on the Mexican National Health and Nutrition Survey, 103 foods and beverages were selected and purchased in Mexico City retail stores and markets. Samples were analyzed twice using atomic absorption spectrophotometry. Values above the limit of quantification (0.0025 mg/kg) were considered to be detected. The percentage of detected values was 18%. The highest concentration was found in infant rice cereal (1.005 mg/kg), whole wheat bread (0.447 mg/kg), pre-cooked rice (0.276 mg/kg), black pepper (0.239 mg/kg), and turmeric (0.176 mg/kg). Among the foods with detected Pb, the levels in infant rice cereal, whole wheat bread, pre-cooked rice, and soy infant formula exceeded the MLs. The food groups with the highest percentages of exceeded MLs were baby foods (18%) and cereals (11%). Monitoring the concentration of contaminants in foods is essential for implementing food safety policies and protecting consumer health. [ABSTRACT FROM AUTHOR]

Leptospirosis is a neglected zoonotic disease that commonly affects cattle, pigs, horses, and dogs in many countries. Infection in dogs is usually subclinical, but acute cases of leptospirosis may occur along with systemic failure, which may become fatal. After recovery from an acute infection, dogs may become asymptomatic carriers and shed pathogenic leptospires through urine for long periods of time. Here, a study of ten different cases of leptospirosis is presented, showing the relevance of dogs as asymptomatic carriers of pathogenic Leptospira. The diagnosis was confirmed via isolation and further serological and genetic identification. Four Leptospira isolates (LOCaS28, 31, 34, and 46) were obtained from the kidneys and urine samples of 58 dogs destined for destruction (6.89%) at a Canine Control Center in Mexico City. No spirochetes were observed in the urine samples of those Leptospira-positive dogs examined under dark-field microscopy, and no clinical signs of disease were observed either. Six additional isolates were obtained: two came from asymptomatic carrier dogs (CEL60 and UADY22); another isolate came from an asymptomatic dog that was a pack companion of a clinically ill dog with fatal leptospirosis (AGFA24); and finally, three isolates were taken from dogs that died of leptospirosis (LOCaS59, Citlalli, and Nayar1). Nine out of the ten isolates were identified as being from the serogroup Canicola via cross-absorption MAT using reference strains and specific antisera, and their identity was genetically confirmed as Canicola ST34 via multi-locus sequencing typing (MLST). In contrast, the isolate Nayar1 was identified as serovar Copenhageni ST2. Interestingly, the asymptomatic dogs from which Leptospira isolates were recovered consistently showed high antibody titers in the microscopic agglutination test (MAT), revealing values of at least 1:3200 against serogroup Canicola and lower titer values against other serogroups. Isolates showed different virulence levels in the hamster model. Taken as a whole, all these findings confirmed that dogs may act as asymptomatic carriers of pathogenic leptospires and possibly spread them out to the environment, thus representing an active public health risk. The results also showed that the Canicola ST34 clone is the most prevalent Leptospira serovar in dogs in Mexico, and finally that the old-fashioned MAT is a good alternative for the detection of presumptive Leptospira asymptomatic carrier dogs. [ABSTRACT FROM AUTHOR]

In México, the utilization of fast-growing plant species for biomass production presents transportation-related issues such as freshness reduction, the transportation of large volumes with minimal weight due to low density, limited storage availability, and logistical challenges. To enhance understanding in this field, the research aimed to evaluate the densification potential of a variety of organic materials, with a specific emphasis on the biomass derived from eight species of rapidly growing plants, three animal species' manure, and concentrated feed. After conditioning, 100 g samples underwent particle size analysis and were categorized into seven sizes. Pellets and briquettes (40 g, 10% moisture, 15 MPa pressure, 5 min processing, 80 °C temperature) were produced and evaluated for bulk density (BD) and pellet (PD) and briquette durability (BDU). The predominant particle sizes were 0.850 mm and 0.425 mm. Original biomass bulk density (OBBD) varied notably, with ovine manure (0.50 g cm−3) and cattle feed (0.49 g cm−3) exhibiting the highest values. Caprine manure (0.83 g cm−3) and ovine manure (0.78 g cm−3) yielded the densest pellets at 1.76 and 1.84 g cm−3, respectively. Apple tree pellets achieved premium quality with the highest hardness (97.9%). Cattle manure (1.25 g cm−3) and cattle feed (1.25 g cm−3) had the densest briquettes, with notable BBD/OBBD ratios in pine sawdust (4.6) and corn (4.5). Caprine manure and Acacia biomass briquettes showed premium quality with the highest hardness (99.1%), emphasizing densification variations and the need for tailored approaches based on organic material characteristics. [ABSTRACT FROM AUTHOR]

The presence of deep convective clouds is directly related to potential convective hazards, such as lightning strikes, hail, severe storms, flash floods, and tornadoes. On the other hand, Mexico has a limited and heterogeneous network of instruments that allow for efficient and reliable monitoring and forecasting of such events. In this study, a quasi-real-time framework for deep convective cloud identification and modeling based on machine learning (ML) models was developed. Eight different ML models and model assembly approaches were fed with Interest Fields estimated from Advanced Baseline Imager (ABI) sensor data on the Geostationary Operational Environmental Satellite-R Series (GOES-R) for one region in central Mexico and another in northeastern Mexico, both selected for their intense convective activity and high levels of vulnerability to severe weather. The results indicate that a simple approach such as Logistic Regression (LR) or Random Forest (RF) can be a good alternative for the identification and simulation of deep convective clouds in both study areas, with a probability of detection of (POD) ≈ 0.84 for Los Mochis and POD of ≈ 0.72 for Mexico City. Similarly, the false alarm ratio (FAR) ≈ 0.2 and FAR ≈ 0.4 values were obtained for Los Mochis and Mexico City, respectively. Finally, a post-processing filter based on lightning incidence (Lightning Filter) was applied with data from the Geostationary Lightning Mapper (GLM) of the GOES-16 satellite, showed great potential to improve the probability of detection (POD) of the ML models. This work sets a precedent for the implementation of an early-warning system for hazards associated with intense convective activity in Mexico. [ABSTRACT FROM AUTHOR]

This study investigates the relationships between lexical development and inhibition, as well as morphosyntax and inhibition, in typically developing monolingual Spanish-speaking children. Recent studies of the relationship between lexical development and inhibition suggest that, as the size of the lexicon increases, so does inhibitory ability. However, the relationship between grammar and inhibition seems more controversial. The work distinguishing the relationships between inhibition and lexicon vs. grammar have been carried out in English, which has relatively impoverished inflectional morphology. Because the relationships considered in the literature are hypothetically not language-particular to English, but rather claims about cognition in general, we would expect to find that they also hold in other languages, including languages with richer morphology, such as Spanish. These considerations led us to ask the following: are expressive and receptive measures of the lexicon and morphosyntax predictive of typically developing monolingual child Spanish-speakers' inhibitory ability? A sample of 82 monolingual, typically developing Spanish-speaking children in Mexico City were tested with 5 lexical measures, 4 morphosyntax measures, and the Flanker Task measure of inhibition. Results showed that all lexical and morphosyntactic variables correlated significantly with Flanker (p < 0.01), except for Number of Different Words (NDW), calculated on the spontaneous production sample. Therefore, inhibition is predicted by lexical development in child Spanish. Additionally, an ever-increasing set of competitor morphological forms requires an ever-increasing inhibitory ability as well. [ABSTRACT FROM AUTHOR]