Statistical model of the decontamination of wastewater containing Carbendazim through photo-Fenton

RESUMEN La fotocatálisis homogénea foto-Fenton es uno de los procesos de oxidación avanzada más utilizados en el tratamiento de aguas residuales con contenido de pesticidas, donde la optimización de la dosis de catalizador y el agente oxidante, teniendo como respuesta la mineralización en términos de carbón orgánico total (COT) o la eliminación del contaminante específico, son el objetivo de cualquier diseño experimental. El desarrollo experimental en los procesos de tratamiento requiere la ejecución de una cantidad significativa de condiciones experimentales que necesitan el uso de reactivos, energía y tiempo de ejecución, por lo tanto, el modelamiento de este tipo de fenómenos surge como una alternativa a esta limitante en los tratamientos de aguas residuales. En esta investigación, se evaluó la influencia de los factores FeSO4 y H2O2, cada uno en tres niveles, en la mineralización de una solución sintética del insecticida Carbendazim (50 mg.L-1), en términos de COT, mediante un modelo de regresión lineal múltiple y optimizado por una superficie de respuesta. Los principales resultados establecieron que el mejor ajuste del modelo se da teniendo en cuenta la interacción entre el FeSO4 y el H2O2 (X1*X2) y los términos cuadráticos de cada una de ellas con p-values <0,05 y que la validación del modelo, mediante la técnica Leave-One-Out Cross Validation (LOOCV), así como la exactitud y la precisión, mediante el análisis de residuos y el supuesto de mínimos cuadrados ordinarios, establecen que las conclusiones que se deriven de él son válidas.

ABSTRACT Homogeneous photo-Fenton photocatalysis is one of the most widely used advanced oxidation processes in treating wastewater containing pesticides, where optimizing the catalyst dosage and oxidizing agent, with the response being mineralization in terms of total organic carbon (TOC) or removal of the specific contaminant, is the goal of any experimental design. Experimental development in treatment processes necessitates executing a significant number of experimental conditions that require the use of reagents, energy, and execution time. Therefore, modeling such phenomena emerges as an alternative to these limitations in wastewater treatment. In this research, the influence of factors FeSO4 and H2O2, each at three levels, on the mineralization of a synthetic solution of the insecticide Carbendazim (50 mg.L-1) in terms of TOC was evaluated using a multiple linear regression model optimized by response surface methodology. The main results established that the best model fit considers the interaction between FeSO4 and H2O2 (X 1 *X 2 ) and the quadratic terms of each with p-values<0.05. The validation of the model using the Leave-One-Out Cross Validation (LOOCV) technique, as well as accuracy and precision through residual analysis and ordinary least squares assumptions, confirms the validity of the conclusions derived from it.

The generalized odd log-logistic-G regression with interval-censored survival data.

The article proposes a new regression based on the generalized odd log-logistic family for interval-censored data. The survival times are not observed for this type of data, and the event of interest occurs at some random interval. This family can be used in interval modeling since it generalizes some popular lifetime distributions in addition to its ability to present various forms of the risk function. The estimation of the parameters is addressed by the classical and Bayesian methods. We examine the behavior of the estimates for some sample sizes and censorship percentages. Selection criteria, likelihood ratio tests, residual analysis, and graphical techniques assess the goodness of fit of the fitted models. The usefulness of the proposed models is red shown by means of two real data sets.

Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador.

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

A model for bimodal rates and proportions.

The beta model is the most important distribution for fitting data with the unit interval. However, the beta distribution is not suitable to model bimodal unit interval data. In this paper, we propose a bimodal beta distribution constructed by using an approach based on the alpha-skew-normal model. We discuss several properties of this distribution, such as bimodality, real moments, entropies and identifiability. Furthermore, we propose a new regression model based on the proposed model and discuss residuals. Estimation is performed by maximum likelihood. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the results. An application is provided to show the modelling competence of the proposed distribution when the data sets show bimodality.

K-Bessel regression model for speckled data.

Synthetic aperture radar (SAR) provides an efficient way to monitor the Earth's surface. But the speckle noise that the SAR system generates when acquiring images makes it difficult to understand and interpret SAR intensity features. To automatically analyze SAR images, this paper presents a K-Bessel regression (KBR) model in which a function of the mean intensity response is explained by other features (or covariates) determined in parallel. Some mathematical properties of this regression are derived and discussed in the context of the physical origin of the SAR image. A maximum likelihood estimation procedure is planned and its performance is quantified by Monte Carlo experiments. An application to real data obtained from a polarimetric SAR image of San Francisco Bay is realized. Results show that both the KBR-based processing is more informative than the unconditional approach to describe SAR intensity and that our proposal can outperform the normal and gamma regression models. Finally, it is shown that the KBR model is useful to reproduce the relief signal of one channel from the intensity values of the other.

Longitudinal genetic dynamics of weaning index and implications for cow-calf production efficiency.

In beef cattle, the selection for higher weights at young ages has been questioned with the argument that this criterion may increase the adult weight of cows, resulting in higher costs. Therefore, selection criteria should be employed to increase weights at young ages with minimal impact on the adult weight of cows. Additionally, the relationship between measures of cow production efficiency and other well-established selection criteria in breeding programs remains poorly understood. The objective of this study was to longitudinally evaluate the relationship between the weaning index (WIndex) as a measure of efficiency and growth traits of the cows. Possible changes over time in WIndex due to selection applied for yearling weight (YW) were also investigated. The WIndex was proposed to maximize genetic response in the weaning weight of the calf while maintaining genetic gain in BW of the cow at zero. A random regression model was adopted to estimate correlations between WIndex, BW, hip height (HH), and body condition score (BCS) using records of Nelore cows from three lines. Genetic trends were calculated for the control line (NeC) and lines selected for greater YW (NeS and NeT). The age of 3 years was the most critical for the weaning efficiency of the cows. At this stage, young cows are still growing and wean lighter calves than their adult counterparts. The genetic correlation estimates between WIndex and BW (-0.58 to 0.04), HH (-0.05 to -0.34), and BCS (-0.51 to -0.17) were close to zero or negative. BW and HH were strongly correlated genetically across all ages (0.73-0.76). In general, HH exhibited a weak and negative genetic relationship with BCS. The genetic correlation between BW and BCS was stronger for advanced ages (0.45-0.68). In lines selected for YW, important increases in WIndex were observed. However, NeS has been selected since the 1980s until the present for YW, and thus, it showed a more pronounced trend of increasing BW and, consequently, a more modest trend of increasing WIndex compared to NeT. In contrast, WIndex exhibited a trend close to zero for NeC. In this context, monitoring HH and BCS can be useful to avoid losses in the weaning efficiency of cows. Furthermore, we suggest that one way to mitigate efficiency losses in calf production could involve stabilizing the BW of cows and increasing the weaning weight of calves using the WIndex.

Candidate genes for longitudinal traits under sequential sampling in beef cattle.

Both the measurement age of a longitudinal trait and the common pre-sampling procedures used in beef cattle herds may affect the identification of a functional candidate gene (FCG) that is potentially associated with a trait. To identify the FCG that takes part in the genetic control of body weight at five different ages in a beef cattle population with and without sequential sampling, the animals were weighed at different measurement events, around 330, 385, 440, 495 and 550 days old. Genetic parameters were estimated for body weight at each age using a single trait (STM) and a random regression model (RRM). In addition, two different databases were used to estimate the genetic parameters: the first (DB100) was formed by all animals that were weighed in the five measurement events, and the second (DB70) has records of the same population, considering that 70% of the heaviest animals were selected after each measurement event. For DB100, genome-wide association studies (GWAS) were performed with 21,667 SNP markers to identify genomic windows that explained at least 1% of the genetic variance. Additionally, prioritization analyses were performed and FCGs were selected. We associated seven different FCGs with body weight at different ages. Among them, the gene DUSP10 was suggested as FCG in all five ages evaluated. Genetic parameters estimated for body weight using DB100 were similar when STM and RRM were applied. However, when DB70 was used as phenotypic data, there were differences between the two models. When the STM was applied, there were differences between the genetic parameters estimated for body weight when DB100 or DB70 were used as sources of phenotypes, but not for the estimates obtained with RRM. The importance of each gene for animal growth can change at different ages, and different genes may be more relevant to body weight at each different growth stage for beef cattle. Besides, sequential sampling can affect the GWAS results of a longitudinal trait. The age of the animal when a longitudinal trait is measured and pre-sampling can also contribute to inconsistencies in GWAS results for body weight in beef cattle, depending on the time when that data were collected, and consequently on the identification of FCG between studies, even when models that consider a covariance structure are used.

Functional traits and performance of woody species on oil-affected soils of the Ecuadorian Amazon / Rasgos funcionales y comportamiento de especies leñosas en suelos afectados por petróleo de la Amazonía ecuatoriana

Abstract Introduction: Plant functional traits are widely used to predict community productivity. However, they are rarely used to predict the performance (in terms of growth diameter, growth height, survival, and integral response index) of woody species planted in degraded soils. Objective: To evaluate the relationship between the functional traits and the performance of 25 woody species planted in disturbed soils affected by oil extraction activities in Ecuadorian Amazon. Methods: Eighteen permanent sampling plots were established and five 6-month-old seedlings of each 25 species were randomly planted in each plot (125 individuals per plot), at a distance of 4×4 m. Eight quantitative functional traits (leaf size, specific leaf area, leaf nitrogen concentration, leaf phosphorus concentration, leaf minimum unit, leaf dry matter content, stem specific density and leaf tensile strength) were determined for each species. Results: The woody species with high performance shows greater leaf size, specific leaf area and Stem Specific Density than those showing low performance. Leaf nitrogen concentration and stem specific density had a direct relationship with the integral response index. The leaf size, leaf phosphorus concentration, leaf dry matter content and leaf tensile strength showed a negative relationship with the integral response index. Conclusions: Our study demonstrated that the performance of woody species o disturbed soils can be predicted satisfyingly by leaf and stem functional traits, presumably because these traits capture most of environmental and neighborhood conditions.

Resumen Introducción: Los rasgos funcionales de las plantas han sido ampliamente utilizados para predecir la productividad (en términos de crecimiento en diámetro, crecimiento en altura, sobrevivencia e índice de respuesta integral) de las comunidades vegetales. Sin embargo, rara vez han sido utilizados para predecir el desempeño de las especies leñosas plantadas en suelos degradados. Objetivo: Evaluar la relación entre el desempeño y los rasgos funcionales de 25 especies leñosas plantadas en suelos afectados por actividades de extracción de petróleo en la Amazonía ecuatoriana. Métodos: Se establecieron 18 parcelas permanentes de muestreo y en cada parcela se sembraron aleatoriamente cinco plántulas de 6 meses de las 25 especies (125 individuos por parcela), a una distancia de 4×4 m. Se determinaron ocho rasgos funcionales (área foliar, área foliar específica, concentración de nitrógeno foliar, concentración de fósforo foliar, unidad mínima foliar, contenido foliar de materia seca, densidad específica del fuste y fuerza tensil foliar) de cada especie. Resultados: Las especies leñosas con alto desempeño presentaron mayor área foliar, área foliar específica y densidad específica del fuste que las especies de bajo desempeño. La concentración de nitrógeno foliar y la densidad específica del fuste mostraron una relación directa. El área foliar, la concentración de fósforo foliar, el contenido de materia seca foliar y la fuerza tensil foliar presentaron una relación inversa con el Índice de Respuesta Integral. Conclusión: Se demostró que el desempeño de las especies leñosas plantadas en suelos alterados puede predecirse satisfactoriamente por rasgos funcionales de hoja y de tallo, debido posiblemente a que los rasgos influyen en el crecimiento y supervivencia de las especies, y reflejan la mayoría de las condiciones ambientales.

Mapping the risk of respiratory infections using suburban district areas in a large city in Colombia.

BACKGROUND: Acute respiratory infections (ARI) in Cúcuta -Colombia, have a comparatively high burden of disease associated with high public health costs. However, little is known about the epidemiology of these diseases in the city and its distribution within suburban areas. This study addresses this gap by estimating and mapping the risk of ARI in Cúcuta and identifying the most relevant risk factors. METHODS: A spatial epidemiological analysis was designed to investigate the association of sociodemographic and environmental risk factors with the rate of ambulatory consultations of ARI in urban sections of Cúcuta, 2018. The ARI rate was calculated using a method for spatial estimation of disease rates. A Bayesian spatial model was implemented using the Integrated Nested Laplace Approximation approach and the Besag-York-Mollié specification. The risk of ARI per urban section and the hotspots of higher risk were also estimated and mapped. RESULTS: A higher risk of IRA was found in central, south, north and west areas of Cúcuta after adjusting for sociodemographic and environmental factors, and taking into consideration the spatial distribution of the city's urban sections. An increase of one unit in the percentage of population younger than 15 years; the Index of Multidimensional Poverty and the rate of ARI in the migrant population was associated with a 1.08 (1.06-1.1); 1.04 (1.01-1.08) and 1.25 (1.22-1.27) increase of the ARI rate, respectively. Twenty-four urban sections were identified as hotspots of risk in central, south, north and west areas in Cucuta. CONCLUSION: Sociodemographic factors and their spatial patterns are determinants of acute respiratory infections in Cúcuta. Bayesian spatial hierarchical models can be used to estimate and map the risk of these infections in suburban areas of large cities in Colombia. The methods of this study can be used globally to identify suburban areas and or specific communities at risk to support the implementation of prevention strategies and decision-making in the public and private health sectors.

Quantification of pharmaceuticals in hospital effluent: Weighted ranking of environmental risk using a fuzzy hybrid multicriteria method.

An analytical method for quantification of seventeen pharmaceuticals and one metabolite was validated and applied in the analysis of hospital effluent samples. Two different sampling strategies were used: seasonal sampling, with 7 samples collected bimonthly; and hourly sampling, with 12 samples collected during 12 h. Thus, the variability was both seasonal and within the same day. High variability was observed in the measured concentrations of the pharmaceuticals and the metabolite. The quantification method, performed using weighted linear regression model, demonstrated results of average concentrations in seasonal samples ranged between 0.19 µgL-1 (carbamazepine) and higher than 61.56 µgL-1 (acetaminophen), while the hourly samples showed average concentrations between 0.07 µgL-1 (diazepam) and higher than 54.91 µgL-1 (acetaminophen). It is described as higher because the maximum concentration of the calibration curve took into account the dilution factor provided by DLLME. The diurnal results showed a trend towards higher concentrations in the first and last hours of sampling. The risk quotient (RQ) was calculated using organisms from three different trophic levels, for all the analytes quantified in the samples. Additionally, in order to understand the level of importance of each RQ, an expert panel was established, with contributions from 23 specialists in the area. The results were analyzed using a hybrid decision-making approach based on a Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, in order to rank the compounds by environmental risk priority. The compounds of greatest concern were losartan, acetaminophen, 4-aminoantipyrine, sulfamethoxazole, and metoclopramide. Comparison of the environmental risk priority ranking with the potential human health risk was performed by applying the same multicriteria approach, with the prediction of endpoints using in silico (Q)SAR models. The results obtained suggested that sulfamethoxazole and acetaminophen were the most important analytes to be considered for monitoring.

Physicochemical and Rheological Characteristics of Commercial and Monovarietal Wheat Flours from Peru.

In Peru, wheat (Triticum aestivum L.) is one of the main resources in the food industry; however, due to its low harvested area, it is the second most imported cereal. The quality of wheat flour was studied to verify that it has desirable characteristics for the preparation of bakery products. The quality of commercial and monovarietal wheat flours was assessed by measuring their physicochemical and rheological parameters, as well as the gluten content and wheat protein fractions. Eight commercial wheat flours and four monovarietal wheat flours (Barba negra, Candeal, Espelta, and Duro) from Peru were evaluated. Commercial wheat flours presented significantly higher levels of protein and gluten index compared to monovarietal wheat flours (p < 0.05). Between both groups, no significant differences were observed in the content of wet and dry gluten. Interestingly, monovarietal wheat flours presented a higher percentage of gliadins and albumins/globulins, as well as lower levels of glutenin, compared to commercial wheat flours (p < 0.05). According to the logistic regression models, the baking strength (W) was the most important parameter to evaluate the quality of commercial and monovarietal wheat flours. Our results show that monovarietal wheat flours show a lower quality compared to commercial wheat flours.

A random effect regression based on the odd log-logistic generalized inverse Gaussian distribution.

In recent decades, the use of regression models with random effects has made great progress. Among these models' attractions is the flexibility to analyze correlated data. In various situations, the distribution of the response variable presents asymmetry or bimodality. In these cases, it is possible to use the normal regression with random effect at the intercept. In light of these contexts, i.e. the desire to analyze correlated data in the presence of bimodality or asymmetry, in this paper we propose a regression model with random effect at the intercept based onthe generalized inverse Gaussian distribution model with correlated data. The maximum likelihood is adopted to estimate the parameters and various simulations are performed for correlated data. A type of residuals for the new regression is proposed whose empirical distribution is close to normal. The versatility of the new regression is demonstrated by estimating the average price per hectare of bare land in 10 municipalities in the state of São Paulo (Brazil). In this context, various databases are constantly emerging, requiring flexible modeling. Thus, it is likely to be of interest to data analysts, and can make a good contribution to the statistical literature.

Meteorological, PM2.5 and PM10 factors on SARS-COV-2 transmission: The case of southern regions in Chile.

There are several determinants of a population's health, including meteorological factors and air pollution. For example, it is well known that low temperatures and air pollution increase mortality rates in infant and elderly populations. With the emergence of SARS-COV-2, it is important to understand what factors contribute to its mitigation and control. There is some research in this area which shows scientific evidence on the virus's behavior in the face of these variables. This research aims to quantify the impact of climatic factors and environmental pollution on SARS-COV-2 specifically the effect on the number of new infections in different areas of Chile. At the local level, historical information available from the Department of Statistics and Health Information, the Chilean National Air Quality Information System, the Chilean Meteorological Directorate, and other databases will allow the generation of panel data suitable for the analysis. The results show the significant effect of pollution and climate variables measured in lags and will allow us to explain the behavior of the pandemic by identifying the relevant factors affecting health, using heteroskedastic models, which in turn will serve as a contribution to the generation of more effective and timely public policies for the control of the pandemic.

A novel differential diagnosis algorithm for chronic lymphocytic leukemia using immunophenotyping with flow cytometry.

INTRODUCTION: The availability of a clinical decision algorithm for diagnosis of chronic lymphocytic leukemia (CLL) may greatly contribute to the diagnosis of CLL, particularly in cases with ambiguous immunophenotypes. Herein we propose a novel differential diagnosis algorithm for the CLL diagnosis using immunophenotyping with flow cytometry. METHODS: The hierarchical logistic regression model (Backward LR) was used to build a predictive algorithm for the diagnosis of CLL, differentiated from other lymphoproliferative disorders (LPDs). RESULTS: A total of 302 patients, of whom 220 (72.8%) had CLL and 82 (27.2%), B-cell lymphoproliferative disorders other than CLL, were included in the study. The Backward LR model comprised the variables CD5, CD43, CD81, ROR1, CD23, CD79b, FMC7, sIg and CD200 in the model development process. The weak expression of CD81 and increased intensity of expression in markers CD5, CD23 and CD200 increased the probability of CLL diagnosis, (p < 0.05). The odd ratio for CD5, C23, CD200 and CD81 was 1.088 (1.050 - 1.126), 1.044 (1.012 - 1.077), 1.039 (1.007 - 1.072) and 0.946 (0.921 - 0.970) [95% C.I.], respectively. Our model provided a novel diagnostic algorithm with 95.27% of sensitivity and 91.46% of specificity. The model prediction for 97.3% (214) of 220 patients diagnosed with CLL, was CLL and for 91.5% (75) of 82 patients diagnosed with an LPD other than CLL, was others. The cases were correctly classified as CLL and others with a 95.7% correctness rate. CONCLUSIONS: Our model highlighting 4 markers (CD81, CD5, CD23 and CD200) provided high sensitivity and specificity in the CLL diagnosis and in distinguishing of CLL among other LPDs.

Empirical impact of financial service access on farmers income in Ghana / Impacto empírico do acesso a serviços financeiros na renda dos agricultores em Gana

The impact of access to financial services (AFS) and access to informal financial services (AIFS) on farmer income is examined in this study. After a multi-stage random sampling procedure, the study used a sample size of 478 people from two regions in Ghana. The endogenous treatment regression (ETR) model was used to account for selection bias while the unconditional quantile regression (UQR) model was used for a heterogenous analysis. The findings showed that education, financial literacy, IT access, farm size, and distance were all factors of access to financial services. Similarly, the findings revealed a positive and statistically significant link between household income and access to formal financial services. Similarly, there was a positive and significant association between access to informal financial services and household income. The findings showed that access to formal and informal financial services has different effects on household income. As a result, the effects of access to financial services on income varied by quantile. Based on the findings of the study, we developed policies to boost financial services accessibility as a means of increasing household income.

O impacto do acesso a serviços financeiros (AFS) e acesso a serviços financeiros informais (AIFS) na renda do agricultor é examinado neste estudo. Após um procedimento de amostragem aleatória em vários estágios, o estudo utilizou uma amostra de 478 pessoas de duas regiões de Gana. O modelo de regressão de tratamento endógeno (ETR) foi usado para explicar o viés de seleção, enquanto o modelo de regressão quantílica incondicional (UQR) foi usado para uma análise heterogênea. Os resultados mostram que educação, alfabetização financeira, acesso a TI, tamanho da fazenda e distância foram fatores de acesso a serviços financeiros. Da mesma forma, os resultados revelaram uma ligação positiva e estatisticamente significativa entre a renda familiar e o acesso a serviços financeiros formais. Da mesma forma, houve associação positiva e significativa entre acesso a serviços financeiros informais e renda familiar. Os resultados mostram que o acesso a serviços financeiros formais e informais tem efeitos diferentes na renda familiar. Como resultado, os efeitos do acesso a serviços financeiros sobre a renda variaram por quantil. Com base nos resultados do estudo, desenvolvemos políticas para aumentar a acessibilidade dos serviços financeiros como forma de aumentar a renda familiar.

Predicting the final grade using a machine learning regression model: insights from fifty percent of total course grades in CS1 courses.

This article introduces a model for accurately predicting students' final grades in the CS1 course by utilizing their grades from the first half of the course. The methodology includes three phases: training, testing, and validation, employing four regression algorithms: AdaBoost, Random Forest, Support Vector Regression (SVR), and XGBoost. Notably, the SVR algorithm outperformed the others, achieving an impressive R-squared (R2) value ranging from 72% to 91%. The discussion section focuses on four crucial aspects: the selection of data features and the percentage of course grades used for training, the comparison between predicted and actual values to demonstrate reliability, and the model's performance compared to existing literature models, highlighting its effectiveness.

A novel differential diagnosis algorithm for chronic lymphocytic leukemia using immunophenotyping with flow cytometry

Abstract Introduction The availability of a clinical decision algorithm for diagnosis of chronic lymphocytic leukemia (CLL) may greatly contribute to the diagnosis of CLL, particularly in cases with ambiguous immunophenotypes. Herein we propose a novel differential diagnosis algorithm for the CLL diagnosis using immunophenotyping with flow cytometry. Methods The hierarchical logistic regression model (Backward LR) was used to build a predictive algorithm for the diagnosis of CLL, differentiated from other lymphoproliferative disorders (LPDs). Results A total of 302 patients, of whom 220 (72.8%) had CLL and 82 (27.2%), B-cell lymphoproliferative disorders other than CLL, were included in the study. The Backward LR model comprised the variables CD5, CD43, CD81, ROR1, CD23, CD79b, FMC7, sIg and CD200 in the model development process. The weak expression of CD81 and increased intensity of expression in markers CD5, CD23 and CD200 increased the probability of CLL diagnosis, (p < 0.05). The odd ratio for CD5, C23, CD200 and CD81 was 1.088 (1.050 - 1.126), 1.044 (1.012 - 1.077), 1.039 (1.007 - 1.072) and 0.946 (0.921 - 0.970) [95% C.I.], respectively. Our model provided a novel diagnostic algorithm with 95.27% of sensitivity and 91.46% of specificity. The model prediction for 97.3% (214) of 220 patients diagnosed with CLL, was CLL and for 91.5% (75) of 82 patients diagnosed with an LPD other than CLL, was others. The cases were correctly classified as CLL and others with a 95.7% correctness rate. Conclusions Our model highlighting 4 markers (CD81, CD5, CD23 and CD200) provided high sensitivity and specificity in the CLL diagnosis and in distinguishing of CLL among other LPDs.

COVID-19: A Comparative Study of Contagions Peaks in Cities from Europe and the Americas.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a group of viruses that provoke illnesses ranging from the common cold to more serious illnesses such as pneumonia. COVID-19 started in China and spread rapidly from a single city to an entire country in just 30 days and to the rest of the world in no more than 3 months. Several studies have tried to model the behavior of COVID-19 in diverse regions, based on differential equations of the SIR and stochastic SIR type, and their extensions. In this article, a statistical analysis of daily confirmed COVID-19 cases reported in eleven different cities in Europe and America is conducted. Log-linear models are proposed to model the rise or drop in the number of positive cases reported daily. A classification analysis of the estimated slopes is performed, allowing a comparison of the eleven cities at different epidemic peaks. By rescaling the curves, similar behaviors among rises and drops in different cities are found, independent of socioeconomic conditions, type of quarantine measures taken, whether more or less restrictive. The log-linear model appears to be suitable for modeling the incidence of COVID-19 both in rises and drops.

Application of Support Vector Machine to Obtain the Dynamic Model of Proton-Exchange Membrane Fuel Cell.

An accurate model of a proton-exchange membrane fuel cell (PEMFC) is important for understanding this fuel cell's dynamic process and behavior. Among different large-scale energy storage systems, fuel cell technology does not have geographical requirements. To provide an effective operation estimation of PEMFC, this paper proposes a support vector machine (SVM) based model. The advantages of the SVM, such as the ability to model nonlinear systems and provide accurate estimations when nonlinearities and noise appear in the system, are the main motivations to use the SVM method. This model can capture the static and dynamic voltage-current characteristics of the PEMFC system in the three operating regions. The validity of the proposed SVM model has been verified by comparing the estimated voltage with the real measurements from the Ballard Nexa® 1.2 kW fuel cell (FC) power module. The obtained results have shown high accuracy between the proposed model and the experimental operation of the PEMFC. A statistical study is developed to evaluate the effectiveness and superiority of the proposed SVM model compared with the diffusive global (DG) model and the evolution strategy (ES)-based model.

Modeling Overdispersed Dengue Data via Poisson Inverse Gaussian Regression Model: A Case Study in the City of Campo Grande, MS, Brazil.

Dengue fever is a tropical disease transmitted mainly by the female Aedes aegypti mosquito that affects millions of people every year. As there is still no safe and effective vaccine, currently the best way to prevent the disease is to control the proliferation of the transmitting mosquito. Since the proliferation and life cycle of the mosquito depend on environmental variables such as temperature and water availability, among others, statistical models are needed to understand the existing relationships between environmental variables and the recorded number of dengue cases and predict the number of cases for some future time interval. This prediction is of paramount importance for the establishment of control policies. In general, dengue-fever datasets contain the number of cases recorded periodically (in days, weeks, months or years). Since many dengue-fever datasets tend to be of the overdispersed, long-tail type, some common models like the Poisson regression model or negative binomial regression model are not adequate to model it. For this reason, in this paper we propose modeling a dengue-fever dataset by using a Poisson-inverse-Gaussian regression model. The main advantage of this model is that it adequately models overdispersed long-tailed data because it has a wider skewness range than the negative binomial distribution. We illustrate the application of this model in a real dataset and compare its performance to that of a negative binomial regression model.