RESUMEN

The concentrations, spatial distributions, pollution level, and health risks of heavy metals in sediments of the Sharm Obhur, Northern Jeddah, Saudi Arabia were evaluated. Average concentrations were found to be: Cr > Zn > Ni > Cu > As>Pb with the highest concentrations found near the head of the sharm, and decreasing towards the mouth. Environmental indices, together with the statistical analyses, showed that the sharm experiences a low to moderate degree of pollution. Sampling sites with heavy metal contamination are concentrated near the head and the southern coast of the sharm, where intensive human activities associated with a boat dock, construction, and recreation are common. The mean carcinogenic risk (CR) values of As, Cr and Ni are at permissible level suggesting unlikely adverse impacts of heavy metals on human health. Despite acceptable CR values; however, serious non-carcinogenic and carcinogenic health threats from metals may yet be an issue particularly for sensitive populations such as children.

Asunto(s)

RESUMEN

Elemental ratios (δ13C, δ15N and C/N) and carbon and nitrogen concentrations in macrophytes, sediments and sponges of the hypersaline Al-Kharrar Lagoon (KL), central eastern Red Sea coast, were measured to distinguish their sources, pathways and see how they have been influenced by biogeochemical processes and terrestrial inputs. The mangroves and halophytes showed the most depleted δ13C values of -27.07±0.2 ‰ and -28.34±0.4 ‰, respectively, indicating their preferential 12C uptake, similar to C3-photosynthetic plants, except for the halophytes Atriplex sp. and Suaeda vermiculata which showed δ13C of -14.31±0.6 ‰, similar to C4-plants. Macroalgae were divided into A and B groups based on their δ13C values. The δ13C of macroalgae A averaged -15.41±0.4 ‰, whereas macroalgae B and seagrasses showed values of -7.41±0.8 ‰ and -7.98 ‰, suggesting uptake of HCO3- as a source for CO2 during photosynthesis. The δ13C of sponges was -10.7±0.3 ‰, suggesting that macroalgae and seagrasses are their main favoured diets. Substrates of all these taxa showed δ13C of -15.52±0.8 ‰, suggesting the KL is at present a macroalgae-dominated lagoon. The δ15N in taxa/sediments averaged 1.68 ‰, suggesting that atmospheric N2-fixation is the main source of nitrogen in/around the lagoon. The heaviest δ15N (10.58 ‰) in halophytes growing in algal mats and sabkha is possibly due to denitrification and ammonia evaporation. The macrophytes in the KL showed high C %, N %, and C/N ratios, but this is not indicated in their substrates due possibly to a rapid turnover of dense, hypersaline waters carrying most of the detached organic materials out into the Red Sea. The δ13C allowed separation of subaerial from aquatic macrophytes, a proxy that could be used when interpreting paleo-sea level or paleoclimatic changes from the coastal marine sediments.

Asunto(s)

RESUMEN

Statistical modeling and forecasting of time-to-events data are crucial in every applied sector. For the modeling and forecasting of such data sets, several statistical methods have been introduced and implemented. This paper has two aims, i.e., (i) statistical modeling and (ii) forecasting. For modeling time-to-events data, we introduce a new statistical model by combining the flexible Weibull model with the Z-family approach. The new model is called the Z flexible Weibull extension (Z-FWE) model, where the characterizations of the Z-FWE model are obtained. The maximum likelihood estimators of the Z-FWE distribution are obtained. The evaluation of the estimators of the Z-FWE model is assessed in a simulation study. The Z-FWE distribution is applied to analyze the mortality rate of COVID-19 patients. Finally, for forecasting the COVID-19 data set, we use machine learning (ML) techniques i.e., artificial neural network (ANN) and group method of data handling (GMDH) with the autoregressive integrated moving average model (ARIMA). Based on our findings, it is observed that ML techniques are more robust in terms of forecasting than the ARIMA model.

Asunto(s)

RESUMEN

BACKGROUND: Monkeypox virus is gaining attention due to its severity and spread among people. This study sheds light on the modeling and forecasting of new monkeypox cases. Knowledge about the future situation of the virus using a more accurate time series and stochastic models is required for future actions and plans to cope with the challenge. METHODS: We conduct a side-by-side comparison of the machine learning approach with the traditional time series model. The multilayer perceptron model (MLP), a machine learning technique, and the Box-Jenkins methodology, also known as the ARIMA model, are used for classical modeling. Both methods are applied to the Monkeypox cumulative data set and compared using different model selection criteria such as root mean square error, mean square error, mean absolute error, and mean absolute percentage error. RESULTS: With a root mean square error of 150.78, the monkeypox series follows the ARIMA (7,1,7) model among the other potential models. Comparatively, we use the multilayer perceptron (MLP) model, which employs the sigmoid activation function and has a different number of hidden neurons in a single hidden layer. The root mean square error of the MLP model, which uses a single input and ten hidden neurons, is 54.40, significantly lower than that of the ARIMA model. The actual confirmed cases versus estimated or fitted plots also demonstrate that the multilayer perceptron model has a better fit for the monkeypox data than the ARIMA model. CONCLUSIONS AND RECOMMENDATION: When it comes to predicting monkeypox, the machine learning method outperforms the traditional time series. A better match can be achieved in future studies by applying the extreme learning machine model (ELM), support vector machine (SVM), and some other methods with various activation functions. It is thus concluded that the selected data provide a real picture of the virus. If the situations remain the same, governments and other stockholders should ensure the follow-up of Standard Operating Procedures (SOPs) among the masses, as the trends will continue rising in the upcoming 10 days. However, governments should take some serious interventions to cope with the virus. LIMITATION: In the ARIMA models selected for forecasting, we did not incorporate the effect of covariates such as the effect of net migration of monkeypox virus patients, government interventions, etc.

RESUMEN

In this article, the "truncated-composed" scheme was applied to the Burr X distribution to motivate a new family of univariate continuous-type distributions, called the truncated Burr X generated family. It is mathematically simple and provides more modeling freedom for any parental distribution. Additional functionality is conferred on the probability density and hazard rate functions, improving their peak, asymmetry, tail, and flatness levels. These characteristics are represented analytically and graphically with three special distributions of the family derived from the exponential, Rayleigh, and Lindley distributions. Subsequently, we conducted asymptotic, first-order stochastic dominance, series expansion, Tsallis entropy, and moment studies. Useful risk measures were also investigated. The remainder of the study was devoted to the statistical use of the associated models. In particular, we developed an adapted maximum likelihood methodology aiming to efficiently estimate the model parameters. The special distribution extending the exponential distribution was applied as a statistical model to fit two sets of actuarial and financial data. It performed better than a wide variety of selected competing non-nested models. Numerical applications for risk measures are also given.

RESUMEN

Radiation leakage is a serious problem in various technological applications. In this paper, radiation shielding characteristics of some natural rocks are elucidated. Mass attenuation coefficients (µ/ρ) of these rocks are obtained at different photon energies with the help of the EPICS2017 library. The obtained µ/ρ values are confirmed via the theoretical XCOM program by determining the correlation factor and relative deviation between both of these methods. Then, effective atomic number (Zeff), absorption length (MFP), and half value layer (HVL) are evaluated by applying the µ/ρ values. The maximum µ/ρ values of the natural rocks were observed at 0.37 MeV. At this energy, the Zeff values of the natural rocks were 16.23, 16.97, 17.28, 10.43, and 16.65 for olivine basalt, jet black granite, limestone, sandstone, and dolerite, respectively. It is noted that the radiation shielding features of the selected natural rocks are higher than that of conventional concrete and comparable with those of commercial glasses. Therefore, the present rocks can be used in various radiation shielding applications, and they have many advantages for being clean and low-cost products. In addition, we found that the EPICS2017 library is useful in determining the radiation shielding parameters for the rocks and may be used for further calculations for other rocks and construction building materials.

RESUMEN

The purpose of this study is to discriminate sunflower seeds with the help of a dataset having spectral and textural features. The production of crop based on seed purity and quality other hand sunflower seed used for oil content worldwide. In this regard, the foundation of a dataset categorizes sunflower seed varieties (Syngenta CG, HS360, S278, HS30, Armani, and High Sun 33), which were acquired from the agricultural farms of The Islamia University of Bahawalpur, Pakistan, into six classes. For preprocessing, a new region-oriented seed-based segmentation was deployed for the automatic selection of regions and extraction of 53 multi-features from each region, while 11 optimized fused multi-features were selected using the chi-square feature selection technique. For discrimination, four supervised classifiers, namely, deep learning J4, support vector machine, random committee, and Bayes net, were employed to optimize the multi-feature dataset. We observe very promising accuracies of 98.2%, 97.5%, 96.6%, and 94.8%, respectively, when the size of a region is (180 × 180).

Asunto(s)

RESUMEN

The inverse Lomax distribution has been widely used in many applied fields such as reliability, geophysics, economics and engineering sciences. In this paper, an unexplored practical problem involving the inverse Lomax distribution is investigated: the estimation of its entropy when multiple censored data are observed. To reach this goal, the entropy is defined through the Rényi and q-entropies, and we estimate them by combining the maximum likelihood and plugin methods. Then, numerical results are provided to show the behavior of the estimates at various sample sizes, with the determination of the mean squared errors, two-sided approximate confidence intervals and the corresponding average lengths. Our numerical investigations show that, when the sample size increases, the values of the mean squared errors and average lengths decrease. Also, when the censoring level decreases, the considered of Rényi and q-entropies estimates approach the true value. The obtained results validate the usefulness and efficiency of the method. An application to two real life data sets is given.

RESUMEN

The Southern Corniche of Jeddah (SCJ) is located on the centre of the eastern Red Sea coast, Saudi Arabia and is increasingly affected by many anthropogenic activities, making it vulnerable to pollution. Sixty-three sediments and water samples were examined in regard to environmental parameters (temperature, salinity, dissolved oxygen (DO) and pH), grain size, organic matter (OM) and carbonate content and metals concentrations (Fe, Mn, Cu, Pb, Cr, Zn, Ni, and Co) in order to assess the level of contamination in SCJ's bottom sediment. The results showed that the highest concentrations of heavy metals in the shoreline and Lagoon areas are mainly due to the influx of domestic and industrial wastewater into the area where they were correlated with mud, OM, salinity and pH. The contamination factor (CF) for Fe and Co in the bottom sediments exhibited higher values than the threshold 3 value, particularly in the Lagoon and Al-Budhai area. The pollution load index (PLI) values of sediment samples could be classified as contaminated samples, especially in the nearshore samples indicating an increase of metals accumulation with decreasing distance from the source area. PCA has shown that Fe and Mn are positively correlated with all heavy metals, probably due to their high adsorption capacity in the presence of DO. The metals were Normalized with Fe, it was found that the bottom sediments of the SCJ could be reported as metal contaminated and mainly affected by natural and human sources.

Asunto(s)