We investigate the death of entanglement and the purity loss of a two qubits–field system in the
dispersive regime with a reservoir. For an alternative entanglement measure, we calculate the negativity
of the eigenvalues of a partially transposed density matrix and compare it with the mutual entropy. A
new measure related to the mutual entropy, namely, the index of entropy, is proposed to measure the
degree of entanglement, and this agrees well with the negativity. We found that the entanglement has
a strong sensitivity to the phase damping. The asymptotic behavior of the field states, the two qubits,
and the total system fall into a mixed state. We treat the phenomena of death of entanglement and
purity as they arise from the effect of phase damping.

In this paper, we have presented an analytic solution for two 2-level
identical atoms interacting with a single-mode quantized radiation field, taking into
account the level shifts produced by Stark shift. We assume that the two atoms are
initially prepared in the exited state and the field in a coherent state.We investigate the
purity loss of the system and bipartite partitions of the system. The effects of the Stark
shift on the purity loss of the system and different bipartite partitions of the system
(field-two atoms, atom-(field+atom)) through the tangles are considered. In particular,
the effect of the Stark shift on the amount of entanglement between atoms and field is
evaluated by the negativity.

We investigate the quantum entanglement in a system of two moving atoms interacting with a
single mode field. An analytical solution for this system is obtained when both atoms are
initially in the excited state and the field is in a coherent state. We study the effects of atomic
motion and other parameters on the entanglement of the system and different bipartite
partitions of the system (field–two atoms, atom–(field+atom)) through the tangles. The effect
of atomic motion on the amount of entanglement between atoms and the field is also evaluated
through the negativity. The results show that atomic motion leads to the periodic death and
anabiosis of the entanglement between two moving atoms, and the time of the death and the
amplitude of the anabiosis of the entanglement between two moving atoms depend on the
coupling constant of two moving atoms and the parameter of the mode field.

The interaction of two 2-level atoms inside a resonant microcavity under stimulated
emission via multi-photon-transition is considered. An analytical solution for this
system when both atoms are initially in the exited state and the field in a coherent state is
obtained. Entanglement dynamics between the two atoms taking into account the effect of
the stimulated emission is studied by using various measures of entanglement. We compare
the results for these various measures, and discuss the entanglement induced due to the
stimulated emission.

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.