An analytical solution for two identical 2-level atoms interacting with a single-mode quantized
radiation field in the presence of the Stark shift is obtained. Both atoms are prepared initially
in the excited state and the field in a coherent state. The phase distribution, phase variance and
Wigner function are investigated. The influence of the Stark shift on the Wigner function and
the phase properties is analysed.

Kojic acid is a wonderful fungal secondary metabolite that has several applications in the food, medical, and agriculture sectors. Many human diseases become resistant to normal antibiotics and normal treatments. We need to search for alternative treatment sources and understand their mode of action. Aspergillus flavus ASU45 (OL314748) was isolated from the caraway rhizosphere as a non-aflatoxin producer and identified genetically using 18S rRNA gene sequencing. After applying the Box-Behnken statistical design to maximize KA production, the production raised from 39.96 to 81.59 g/l utilizing (g/l) glucose 150, yeast extract 5, KH₂PO₄ 1, MgSO₄.7H₂O 2, and medium pH 3 with a coefficient (R²) of 98.45%. Extracted KA was characterized using FTIR, XRD, and a scanning electron microscope. Crystalized KA was an effective antibacterial agent against six human pathogenic bacteria (Bacillus cereus, Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, Serratia marcescens, and Serratia plymuthica). KA achieves high inhibition activity against Bacillus cereus, K. pneumonia, and S. plymuthica at 100 μg/ml concentration by 2.75, 2.85, and 2.85 compared with chloramphenicol which gives inhibition zones 1, 1.1, and 1.6, respectively. Crystalized KA had anticancer activity versus three types of cancer cell lines (Mcf-7, HepG2, and Huh7) and demonstrated high cytotoxic capabilities on HepG-2 cells that propose strong antitumor potent of KA versus hepatocellular carcinoma. The antibacterial and anticancer modes of action were illustrated using the molecular docking technique. Crystalized kojic acid from a biological source represented a promising microbial metabolite that could be utilized as an alternative antibacterial and anticancer agent effectively.

This research aimed to identify the main sources of groundwater pollution and assess the non-carcinogenic human health
risk resulting from nitrate and fluoride contamination. These goals were achieved by employing unsupervised and supervised
machine algorithms, including principal component analysis (PCA) and multilayer perceptron artificial neural networks
(MLP-ANN). Thirty-seven groundwater samples were analyzed for twelve physical and chemical parameters, including pH,
EC, TDS, TH, Cl, F, SO4,
NO3,
Ca, Mg, Na, and HCO3,
and the initial investigation indicated that except for Cl, F, Ca, and
Mg, all the parameters are above the guidelines of the World Health Organization (WHO). PCA indicated that mineral dissolution
is the main source of F, while high NO3
concentration primarily resulted from agricultural operation due to extensive
use of nitrogen and calcium-based fertilizers. Consequently, the non-carcinogenic human health risk (HHR) for children and
adults is evaluated based on NO3
and F. The conventional approach for assessing HHR is time-consuming and often associated
with errors in calculating hazard quotients (HQ) and hazard indices (HI). In this research, MLP-ANN is suggested to
overcome these limitations. In the MLP-ANN modeling, the data were divided into two parts training (80%) and validation
(20%), with NO3
and F concentration as inputs and HQ and HI as outputs. The performance of the resulting models was
tested using root mean square error (RMSE) and coefficient of determination (
R2). The model provided a satisfactory result
with a maximum RMSE of 4% and R2
higher than 97% for training and validation. As a result, obtained HIs suggested that
97.3% of the groundwater samples in the study area are suitable for human consumption. The non-carcinogenic HHR is successfully
assessed using machine learning algorithms, and the results have led to the conclusion that this approach is highly
recommended for effectively managing groundwater resources.

This research aimed to identify the main sources of groundwater pollution and assess the non-carcinogenic human health
risk resulting from nitrate and fluoride contamination. These goals were achieved by employing unsupervised and supervised
machine algorithms, including principal component analysis (PCA) and multilayer perceptron artificial neural networks
(MLP-ANN). Thirty-seven groundwater samples were analyzed for twelve physical and chemical parameters, including pH,
EC, TDS, TH, Cl, F, SO4,
NO3,
Ca, Mg, Na, and HCO3,
and the initial investigation indicated that except for Cl, F, Ca, and
Mg, all the parameters are above the guidelines of the World Health Organization (WHO). PCA indicated that mineral dissolution
is the main source of F, while high NO3
concentration primarily resulted from agricultural operation due to extensive
use of nitrogen and calcium-based fertilizers. Consequently, the non-carcinogenic human health risk (HHR) for children and
adults is evaluated based on NO3
and F. The conventional approach for assessing HHR is time-consuming and often associated
with errors in calculating hazard quotients (HQ) and hazard indices (HI). In this research, MLP-ANN is suggested to
overcome these limitations. In the MLP-ANN modeling, the data were divided into two parts training (80%) and validation
(20%), with NO3
and F concentration as inputs and HQ and HI as outputs. The performance of the resulting models was
tested using root mean square error (RMSE) and coefficient of determination (
R2). The model provided a satisfactory result
with a maximum RMSE of 4% and R2
higher than 97% for training and validation. As a result, obtained HIs suggested that
97.3% of the groundwater samples in the study area are suitable for human consumption. The non-carcinogenic HHR is successfully
assessed using machine learning algorithms, and the results have led to the conclusion that this approach is highly
recommended for effectively managing groundwater resources

The present work used the electrical resistivity approach to conduct a three-dimensional modeling and initial volume estimation of the limestone layer in the Mintom region located in southern Cameroon. In order to achieve the objectives of the study, a total of 21 electrical soundings spaced 250 m were first collected in the field using the Schlumberger array. These soundings were conducted along three profiles oriented in an east–west direction, spaced 500 m. Additionally, a geological survey was conducted to identify and emphasize the presence of limestone formations within the designated study region. The interpretation of the sounding data was conducted based on the analysis of the sounding curves. The interpretation outcomes, specifically resistivity and thickness, were compared with the geological field data, resulting in the development of lithostratigraphic logs for each sounding. The geological sections were constructed using the logs of the designated profile. The lithological logs were utilized to establish a lithological interface model and calculate the volume of the limestone layer at 260 ± 13 × 10⁶ m³, utilizing the inverse distance method built into RockWorks software. A resistivity value is assigned to each geological layer in a sounding curve, allowing for the development of a resistivity variation model specific to the limestone layer. The proposed model facilitates the categorization of limestone layers based on their resistivity variations, thus serving as a fundamental reference for prospective exploratory activities within the designated study region. Our integrated approach provides a replicable model for a better understanding of the limestone reserve and effective management of this valuable resource.

This research aimed to identify the main sources of groundwater pollution and assess the non-carcinogenic human health risk resulting from nitrate and fluoride contamination. These goals were achieved by employing unsupervised and supervised machine algorithms, including principal component analysis (PCA) and multilayer perceptron artificial neural networks (MLP-ANN). Thirty-seven groundwater samples were analyzed for twelve physical and chemical parameters, including pH, EC, TDS, TH, Cl, F, SO₄, NO₃, Ca, Mg, Na, and HCO_3, and the initial investigation indicated that except for Cl, F, Ca, and Mg, all the parameters are above the guidelines of the World Health Organization (WHO). PCA indicated that mineral dissolution is the main source of F, while high NO₃ concentration primarily resulted from agricultural operation due to extensive use of nitrogen and calcium-based fertilizers. Consequently, the non-carcinogenic human health risk (HHR) for children and adults is evaluated based on NO₃ and F. The conventional approach for assessing HHR is time-consuming and often associated with errors in calculating hazard quotients (HQ) and hazard indices (HI). In this research, MLP-ANN is suggested to overcome these limitations. In the MLP-ANN modeling, the data were divided into two parts training (80%) and validation (20%), with NO₃ and F concentration as inputs and HQ and HI as outputs. The performance of the resulting models was tested using root mean square error (RMSE) and coefficient of determination (R²). The model provided a satisfactory result with a maximum RMSE of 4% and R² higher than 97% for training and validation. As a result, obtained HIs suggested that 97.3% of the groundwater samples in the study area are suitable for human consumption. The non-carcinogenic HHR is successfully assessed using machine learning algorithms, and the results have led to the conclusion that this approach is highly recommended for effectively managing groundwater resources.

Water scarcity is becoming a growing problem in the Middle East due to urbanization, industrialization, and population growth. Saudi Arabia is one of the region’s largest consumers of water, so it is important to take immediate action to address this issue. This study used data from the Gravity Recovery and Climate Experiment (GRACE) to assess changes in groundwater storage in Wadi Fatimah and its surrounding areas. The results showed that the average annual rainfall (AAR) in Wadi Fatimah was 131 mm, while the AAR for the entire Makah province was 99.3 mm. The AAR in Makah province can be divided into three climatic periods: Period I (April 2002-December 2011): AAR of 92.8 mm; Period II (January 2012-December 2016): AAR of 101.8 mm and Period III (January 2017-December 2021): AAR of 116.4 mm. The GRACE-derived ΔTWS (time-variable gravity) variations were −0.18 ± 0.023 cm/yr in Wadi Fatimah and −0.38 ± 0.018 cm/yr in the entire Makah Province. The soil moisture storage (ΔSMS) variations were +0.039 ± 0.025 mm/yr in Wadi Fatimah and −0.008 ± 0.002 mm/yr in the entire Makah Province. The average groundwater storage (ΔGWS) variation in Wadi Fatimah was −0.18 ± 0.022 cm/yr, which indicates a slight decrease. The ΔGWS variation in the entire Makah region was −0.38 ± 0.017 cm/yr, which indicates a negative trend. The study also found that surface runoff from rainfall in the eastern section of Wadi Fatimah flows westward to join other streams that flow into the Wadi’s central and downstream areas. This runoff replenishes the shallow alluvium deposits and aquifers. Wadi Fatimah is able to partially compensate for the impact of its groundwater extraction with a recharge rate of +0.22 ± 0.22 mm/yr. The integrated method used in this study is a helpful and economical way to evaluate groundwater resource variability over Wadi Fatimah region and its surrounding province.

griculture is considered one of the primary elements for socioeconomic stability in most parts of Sudan. Consequently, the irrigation water should be properly managed to achieve sustainable crop yield and soil fertility. This research aims to predict the irrigation indices of sodium adsorption ratio (SAR), sodium percentage (Na%), permeability index (PI), and potential salinity (PS) using innovative machine learning (ML) techniques, including K-nearest neighbor (KNN), random forest (RF), support vector regression (SVR), and Gaussian process regression (GPR). Thirty-seven groundwater samples are collected and analyzed for twelve physiochemical parameters (TDS, pH, EC, TH, Ca⁺², Mg⁺², Na⁺, HCO₃⁻, Cl, SO₄⁻², and NO₃⁻) to assess the hydrochemical characteristics of groundwater and its suitability for irrigation purposes. The primary investigation indicated that the samples are dominated by Ca-Mg-HCO₃ and Na-HCO₃ water types resulted from groundwater recharge and ion exchange reactions. The observed irrigation indices of SAR, Na%, PI, and PS showed average values of 7, 42.5%, 64.7%, and 0.5, respectively. The ML modeling is based on the ion’s concentration as input and the observed values of the indices as output. The data is divided into two sets for training (70%) and validation (30%), and the models are validated using a 10-fold cross-validation technique. The models are tested with three statistical criteria, including mean square error (MSE), root means square error (RMSE), and correlation coefficient (R²). The SVR algorithm showed the best performance in predicting the irrigation indices, with the lowest RMSE value of 1.45 for SAR. The RMSE values for the other indices, Na%, PI, and PS, were 6.70, 7.10, and 0.55, respectively. The models were applied to digital predictive data in the Nile River area of Khartoum state, and the uncertainty of the maps was estimated by running the models 10 times iteratively. The standard deviation maps were generated to assess the model’s sensitivity to the data, and the uncertainty of the model can be used to identify areas where a denser sampling is needed to improve the accuracy of the irrigation indices estimates.

A simple, time saving and accurate method for the simultaneous determination of Fe2O3, Al2O3 and TiO2 in Portland cement relies on the use of zero crossing and derivative ratio techniques has been examined. Alizarin Complexone (Alizarin-3-methylamine-N,N-diaceticacid , AC ) was used as a complexing agent. The complexes formed at pH 3.8 allow precise and accurate determination of iron (1.6 – 6.14 mgL-1), aluminum (0.63 – 1.7 mgL-1) and titanium (0.47 – 1.9 mgL-1). The validity of the method was checked by analyzing several synthetic mixtures containing different ratios of metal ions. The reproducibility was checked by analyzing a series of seven solutions each having an Fe(III), Al(III) and Ti(IV) concentration of 2.79,1.35 and 0.47 mgL-1 respectively . The relative standard deviations (RSD) are 0.70 % for iron, 0.98% for aluminum and 0.88% for titanium. The major merit of this method is the absence of matrix effect.