Solving water scarcity challenges in arid regions: A novel approach employing human-based meta-heuristics and machine learning algorithm for groundwater potential mapping.

Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents a comprehensive evaluation of advanced modeling techniques to enhance the precision of GPM. This study, conducted in the Zayandeh Rood watershed, Iran, employed a spatial database comprising 16 influential factors on groundwater potential and data from 175 wells. This study introduced an innovative approach to GPM by enhancing the Random Forest (RF) algorithm. This enhancement involved integrating three metaheuristic algorithms inspired by human behavior: ICA (Imperialist Competitive Algorithm), TLBO (Teaching-Learning-Based Optimization), and SBO (Student Psychology Based Optimization). The modeling process used 70% training data and 30% evaluation data. Data preprocessing was performed using the multicollinearity test method and frequency ratio (FR) technique to refine the dataset. Subsequently, the GPM was generated using four distinct models, demonstrating the combined power of machine learning and human-inspired metaheuristic algorithms. The performance of the models was systematically assessed through extensive statistical analyses, including root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC) for the receiver operating characteristic curve (ROC), Friedman tests, chi-squared tests, and Wilcoxon signed-rank tests. RF-ICA and RF-SPBO emerged as frontrunners, displaying statistically comparable accuracy and significantly outperforming RF-TLBO and the non-optimized RF model. The results of the GPM revealed the exceptional accuracy of RF-ICA, which exhibited a commanding AUC score of 0.865, underscoring its superiority in discriminating between different groundwater potential classes. RF-SPBO also displayed strong performance with an AUC of 0.842, highlighting its effectiveness in inaccurate classification. RF-TLBO and the non-optimized RF model achieved AUC values of 0.813 and 0.810, respectively, indicating comparable performance. The outcomes of this study provide valuable insights for policymakers, offering a robust framework for tackling water scarcity challenges in arid regions through precise and reliable groundwater potential assessments.

Risk stratification by sex and menopausal status in the multivariable apnea prediction index.

STUDY OBJECTIVES: To determine the sensitivity of the Multivariable Apnea Prediction (MAP) index for obstructive sleep apnea (OSA) in pre- and post-menopausal women with the goal of developing a tailored scoring classification approach. METHODS: Data from two studies (N = 386); the diabetes sleep treatment trial (N = 236) and EMPOWER (N = 150) were used to assess the sensitivity and specificity of the MAP index by comparing men (n = 129) to women (n = 257), and premenopausal (n = 100) to post-menopausal women (n = 136). We evaluated participants at two cut points, apnea-hypopnea index (AHI) values of ≥ 5 and ≥ 10, using 0.5 as a predicted probability cut point to establish baseline sensitivity and specificity. Contingency tables and receiver operating characteristic (ROC) analysis were conducted to evaluate the accuracy of the MAP index in predicting OSA in men versus women, and in pre-versus post-menopausal women. To select optimal predicted probabilities for classification by sex and menopausal status, Youden's J statistic was generated from ROC coordinates. RESULTS: The MAP index was more sensitive to women in the AHI ≥ 5 group (76%) compared to AHI ≥ 10 group (30%). Among post-menopausal women with AHI ≥ 5, sensitivity was similar to men (98%), but less than men when AHI ≥ 10 (32%). Suggested probability cut points for women with an AHI ≥ 10 are 0.24 overall; 0.15 for premenopausal, and 0.38 for postmenopausal women. CONCLUSIONS: Because women's risk for OSA (AHI ≥ 10) was underestimated by the MAP index, we suggest the use of tailored cut points based on sex and menopausal status or assessing for OSA risk with an AHI of ≥ 5.

Mapping the geogenic radon potential and radon risk by using Empirical Bayesian Kriging regression: A case study from a volcanic area of central Italy.

A detailed geochemical study on radon related to local geology was carried out in the municipality of Celleno, a little settlement located in the eastern border of the Quaternary Vulsini volcanic district (central Italy). This study included soil-gas and terrestrial gamma dose rate survey, laboratory analyses of natural radionuclides (238U, 226Ra, 232Th, 40K) activity in rocks and soil samples, and indoor radon measurements carried out in selected private and public dwellings. Soil-gas radon and carbon dioxide concentrations range from 6 to 253 kBq/m3 and from 0.3 to11% v/v, respectively. Samples collected from outcropping volcanic and sedimentary rocks highlight: significant concentrations of 238U, 226Ra and 40K for lavas (151, 150 and 1587 Bq/kg, respectively), low concentrations for tuffs (126, 123 and 987 Bq/kg, respectively), and relatively low for sedimentary rocks (108, 109 and 662 Bq/kg, respectively). Terrestrial gamma dose rate values range between 0.130 and 0.417 µSv/h, being in good accordance with the different bedrock types. Indoor radon activity ranges from 162 to 1044 Bq/m3; the calculated values of the annual effective dose varied from 4.08 and 26.31 mSv/y. Empirical Bayesian Kriging Regression (EBKR) was used to develop the Geogenic Radon Potential (GRP) map. EBKR provided accurate predictions of data on a local scale developing a spatial regression model in which soil-gas radon concentrations were considered as the response variable; several proxy variables, derived from geological, topographic and geochemical data, were used as predictors. Risk prediction map for indoor radon was tentatively produced using the Gaussian Geostatistical Simulation and a soil-indoor transfer factor was defined for a 'standard' dwelling (i.e., a dwelling with well-defined construction properties). This approach could be successfully used in the case of homogeneous building characteristics and territory with uniform geological characteristics.

Determination and Visualization of Peimine and Peiminine Content in Fritillaria thunbergii Bulbi Treated by Sulfur Fumigation Using Hyperspectral Imaging with Chemometrics.

Rapid, non-destructive, and accurate quantitative determination of the effective components in traditional Chinese medicine (TCM) is required by industries, planters, and regulators. In this study, near-infrared hyperspectral imaging was applied for determining the peimine and peiminine content in Fritillaria thunbergii bulbi under sulfur fumigation. Spectral data were extracted from the hyperspectral images. High-performance liquid chromatography (HPLC) was conducted to determine the reference peimine and peiminine content. The successive projection algorithm (SPA), weighted regression coefficient (Bw), competitive adaptive reweighted sampling (CARS), and random frog (RF) were used to select optimal wavelengths, while the partial least squares (PLS), least-square support vector machine (LS-SVM) and extreme learning machine (ELM) were used to build regression models. Regression models using the full spectra and optimal wavelengths obtained satisfactory results with the correlation coefficient of calibration (rc), cross-validation (rcv) and prediction (rp) of most models being over 0.8. Prediction maps of peimine and peiminine content in Fritillaria thunbergii bulbi were formed by applying regression models to the hyperspectral images. The overall results indicated that hyperspectral imaging combined with regression models and optimal wavelength selection methods were effective in determining peimine and peiminine content in Fritillaria thunbergii bulbi, which will help in the development of an online detection system for real-world quality control of Fritillaria thunbergii bulbi under sulfur fumigation.

Modelling the spatial distribution of Fasciola hepatica in bovines using decision tree, logistic regression and GIS query approaches for Brazil.

Fascioliasis is a condition caused by the trematode Fasciola hepatica. In this paper, the spatial distribution of F. hepatica in bovines in Brazil was modelled using a decision tree approach and a logistic regression, combined with a geographic information system (GIS) query. In the decision tree and the logistic model, isothermality had the strongest influence on disease prevalence. Also, the 50-year average precipitation in the warmest quarter of the year was included as a risk factor, having a negative influence on the parasite prevalence. The risk maps developed using both techniques, showed a predicted higher prevalence mainly in the South of Brazil. The prediction performance seemed to be high, but both techniques failed to reach a high accuracy in predicting the medium and high prevalence classes to the entire country. The GIS query map, based on the range of isothermality, minimum temperature of coldest month, precipitation of warmest quarter of the year, altitude and the average dailyland surface temperature, showed a possibility of presence of F. hepatica in a very large area. The risk maps produced using these methods can be used to focus activities of animal and public health programmes, even on non-evaluated F. hepatica areas.