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"Ahmed Farid"
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Advanced generalized machine learning models for predicting hydrogen–brine interfacial tension in underground hydrogen storage systems
The global transition to clean energy has highlighted hydrogen (H
2
) as a sustainable fuel, with underground hydrogen storage (UHS) in geological formations emerging as a key solution. Accurately predicting fluid interactions, particularly interfacial tension (IFT), is critical for ensuring reservoir integrity and storage security in UHS. IFT is key in fluid behavior, influencing structural and residual trapping capacities. However, measuring IFT for H
2
–brine systems is challenging due to H
2
’s volatility and the complexity of reservoir conditions. This study applies machine learning (ML) techniques to predict IFT between H
2
and brine across various salt types, concentrations, and gas compositions. A dataset was used with variables such as temperature, pressure, brine salinity, and gas composition (H
2
, CH
4
, CO
2
). Several ML models, including Random Forests (RF), Gradient Boosting Regressor (GBR), Extreme Gradient Boosting Regressor (XGBoost), Artificial Neural Networks (ANN), Decision Trees (DT), and Linear Regression (LR), were trained and evaluated. RF, GBR, and XGBoost achieved R
2
values over 0.99 in training, 0.97 in testing, and all exceeded 0.975 in validation. These top models achieved RMSE values below 1.3 mN/m and MAPE values under 1.5%, confirming their high predictive accuracy. Residual frequency analysis and APRE results further confirmed these ensemble models’ low bias and high reliability, with error distributions centered near zero. DT performed slightly lower, with R
2
values of 0.93, while LR struggled to model the non-linear behavior of IFT. A novel salt equivalency metric was introduced, transforming multiple salt variables into a single parameter and improving model generalization while maintaining high prediction accuracy (R
2
= 0.98). Sensitivity analysis and SHAP (Shapley Additive Explanations) analysis revealed temperature as the dominant factor influencing IFT, followed by CO
2
concentration and pressure, while divalent salts (CaCl
2
, MgCl
2
) exhibited a stronger impact than monovalent salts (NaCl, KCl). This study optimizes hydrogen storage by offering a generalized, high-accuracy ML model that captures nonlinear fluid interactions in H
2
–brine systems. Integrating real-world experimental data with ML-driven insights enhances reservoir simulation accuracy, improves hydrogen injection strategies, and supports the global transition toward sustainable energy storage solutions.
Journal Article
Machine learning application to predict in-situ stresses from logging data
Determination of in-situ stresses is essential for subsurface planning and modeling, such as horizontal well planning and hydraulic fracture design. In-situ stresses consist of overburden stress (σ
v
), minimum (σ
h
), and maximum (σ
H
) horizontal stresses. The σ
h
and σ
H
are difficult to determine, whereas the overburden stress can be determined directly from the density logs. The σ
h
and σ
H
can be estimated either from borehole injection tests or theoretical finite elements methods. However, these methods are complex, expensive, or need unavailable tectonic stress data. This study aims to apply different machine learning (ML) techniques, specifically, random forest (RF), functional network (FN), and adaptive neuro-fuzzy inference system (ANFIS), to predict the σ
h
and σ
H
using well-log data. The logging data includes gamma-ray (GR) log, formation bulk density (RHOB) log, compressional (DTC), and shear (DTS) wave transit-time log. A dataset of 2307 points from two wells (Well-1 and Well-2) was used to build the different ML models. The Well-1 data was used in training and testing the models, and the Well-2 data was used to validate the developed models. The obtained results show the capability of the three ML models to predict accurately the σh and σH using the well-log data. Comparing the results of RF, ANFIS, and FN models for minimum horizontal stress prediction showed that ANFIS outperforms the other two models with a correlation coefficient (R) for the validation dataset of 0.96 compared to 0.91 and 0.88 for RF, and FN, respectively. The three models showed similar results for predicting maximum horizontal stress with R values higher than 0.98 and an average absolute percentage error (AAPE) less than 0.3%. a
20
index for the actual versus the predicted data showed that the three ML techniques were able to predict the horizontal stresses with a deviation less than 20% from the actual data. For the validation dataset, the RF, ANFIS, and FN models were able to capture all changes in the σ
h
and σ
H
trends with depth and accurately predict the σ
h
and σ
H
values. The outcomes of this study confirm the robust capability of ML to predict σ
h
and σ
H
from readily available logging data with no need for additional costs or site investigation.
Journal Article
Beneficial effects of curcumin nano-emulsion on spermatogenesis and reproductive performance in male rats under protein deficient diet model: enhancement of sperm motility, conservancy of testicular tissue integrity, cell energy and seminal plasma amino acids content
Background
Malnutrition resulting from protein and calorie deficiency continues to be a major concern worldwide especially in developing countries. Specific deficiencies in the protein intake can adversely influence reproductive performance. The present study aimed to evaluate the effects of curcumin and curcumin nano-emulsion on protein deficient diet (PDD)-induced testicular atrophy, troubled spermatogenesis and decreased reproductive performance in male rats.
Methods
Juvenile rats were fed the protein deficient diet (PDD) for 75 days. Starting from day 60 the rats were divided into 4 groups and given the corresponding treatments for the last 15 days orally and daily as follows: 1st group; curcumin group (C) received 50 mg/kg curcumin p.o. 2
nd
group; curcumin nano-form low dose group (NCL) received 2.5 mg/kg nano-curcumin. 3rd group; curcumin nano-form high dose group (NCH) received 5 mg/kg nano-curcumin. 4th group served as malnutrition group (PDD group) receiving the protein deficient diet daily for 75 days and received distilled water ingestions (5 ml/kg p.o) daily for the last 15 days of the experiment. A normal control group was kept under the same conditions for the whole experiment and received normal diet according to nutrition requirement center daily for 75 days and received distilled water ingestions (5 ml/kg p.o) daily for the last 15 days of the experiment.
Results
PDD induced significant (
P
< 0.05) reduction in serum testosterone level, sperm motility, testicular GSH, CAT, SOD, testicular cell energy (ATP, ADP and AMP), essential and non-essential amino acids in seminal plasma, an increase in testicular MDA, NOx, GSSG and 8-OHDG. Data was confirmed by histological examination and revealed pathological alteration in the PDD group. Ingestion of curcumin (50 mg/kg) and curcumin nano-emulsion (2.5 and 5 mg/kg) showed significant (
P
< 0.05) amelioration effects against PDD-induced disrupted reproductive performance as well as biochemical and pathological alterations and the overall results of the nano-emulsion (5 mg/kg) were comparable to curcumin (50 mg/kg).
Conclusions
The present study suggests that administration of curcumin nano-emulsion as a daily supplement would be beneficial in malnutrition- induced troubled male reproductive performance and spermatogenesis cases.
Journal Article
Data-driven models to predict shale wettability for CO2 sequestration applications
The significance of CO
2
wetting behavior in shale formations has been emphasized in various CO
2
sequestration applications. Traditional laboratory experimental techniques used to assess shale wettability are complex and time-consuming. To overcome these limitations, the study proposes the use of machine learning (ML); artificial neural networks (ANN), support vector machines (SVM), and adaptive neuro-fuzzy inference systems (ANFIS) tools to estimate the contact angle, a key indicator of shale wettability, providing a more efficient alternative to conventional laboratory methods. A dataset comprising various shale samples under different conditions was collected to predict shale-water-CO
2
wettability by considering shale properties, operating pressure and temperature, and brine salinity. Pearson’s correlation coefficient (R) was utilized to assess the linearity between the contact angle (CA) value and other input parameters. Initial data analysis showed that the elements affecting the shale wettability are primarily reliant on the pressure and temperature at which it operates, the total organic content (TOC), and the mineral composition of the rock. Between the different ML models, the artificial neural network (ANN) model performed the best, achieving a training R
2
of 0.99, testing R
2
of 0.98 and a validation R
2
of 0.96, with an RMSE below 5. The adaptive neuro-fuzzy inference system (ANFIS) model also accurately predicted the contact angle, obtaining a training R
2
of 0.99, testing R
2
of 0.97 and a validation R
2
of 0.95. Conversely, the support vector machine (SVM) model displayed signs of overfitting, as it achieved R
2
values of 0.99 in the training dataset, which decreased to 0.94 in the testing dataset, and 0.88 in the validation dataset. To avoid rerunning the ML models, an empirical correlation was developed based on the optimized weights and biases obtained from the ANN model to predict contact angle values using input parameters and the validation data set revealed R
2
of 0.96. The parametric study showed that, among the factors influencing shale wettability at a constant TOC, pressure had the most significant impact, and the dependency of the contact angle on pressure increased when TOC values were high.
Journal Article
Phosphorus-doped T-graphene nanocapsule toward O3 and SO2 gas sensing: a DFT and QTAIM analysis
Tetragonal graphene nano-capsule (TGC), a novel stable carbon allotrope of sp
2
hybridization is designed and doped with phosphorus (P) to study the O
3
and SO
2
gas sensitivity via density functional theory calculation. Real frequencies verified the natural existence of both TGC and P-doped TGC (PTGC). Both TGC and PTGC suffer structural deformations due to interaction with O
3
and SO
2
gases. The amount of charge transfer from the adsorbent to the gas molecule is significantly greater for O
3
adsorption than SO
2
adsorption. The adsorption energies for TGC + O
3
and PTGC + O
3
complexes are − 3.46 and − 4.34 eV respectively, whereas for TGC + SO
2
and PTGC + SO
2
complexes the value decreased to − 0.29 and − 0.30 eV respectively. The dissociation of O
3
is observed via interaction with PTGC. A significant variation in electronic energy gap and conductivity results from gas adsorption which can provide efficient electrical responses via gas adsorption. The blue/red shift in the optical response proved to be a way of detecting the types of adsorbed gases. The adsorption of O
3
is exothermic and spontaneous whereas the adsorption of SO
2
is endothermic and non-spontaneous. The negative change in entropy verifies the thermodynamic stability of all the complexes. QTAIM analysis reveals strong covalent or partial covalent interactions between absorbent and adsorbate. The significant variation in electrical and optical response with optimal adsorbent-gas interaction strength makes both TGC and PTGC promising candidates for O
3
and SO
2
sensing.
Journal Article
Effects of Chronic Thermal Stress on Performance, Energy Metabolism, Antioxidant Activity, Brain Serotonin, and Blood Biochemical Indices of Broiler Chickens
The aim of this paper was to investigate the effects of chronic thermal stress on the performance, energy metabolism, liver CoQ10, brain serotonin, and blood parameters of broiler chickens. In total, 100 one-day-old chicks were divided into two equal groups of five replicates. At 22 days of age and thereafter, the first group (TN) was maintained at a thermoneutral condition (23 ± 1 °C), while the second group (TS) was subjected to 8 h of thermal stress (34 °C). The heat-stressed group showed significantly lower ADFI but higher FCR than the thermoneutral group (p = 0.030 and 0.041, respectively). The TS group showed significantly higher serum cholesterol, ALT, and AST (p = 0.033, 0.024, and 0.010, respectively). Meanwhile, the TS group showed lower serum total proteins, albumin, globulin, and Na+ than the TN group (p = 0.001, 0.025, 0.032, and 0.002, respectively). Furthermore, the TS group showed significantly lower SOD and catalase in heart tissues (p = 0.005 and 0.001, respectively). The TS group showed significantly lower liver ATP than the TN group (p = 0.005). Meanwhile, chronic thermal stress significantly increased the levels of ADP and AMP in the liver tissues of broiler chickens (p = 0.004 and 0.029, respectively). The TS group showed significantly lower brain serotonin (p = 0.004) and liver CoQ10 (p = 0.001) than the TN group. It could be concluded that thermal stress disturbed the antioxidant defense system and energy metabolism and exhausted ATP levels in the liver tissues of broiler chickens. Interestingly, chronic thermal stress reduced the level of brain serotonin and the activity of CoQ10 in liver tissues.
Journal Article
Melatonin Alleviated Potassium Dichromate-Induced Oxidative Stress and Reprotoxicity in Male Rats
Melatonin (ML) is a potent antioxidant that reduces oxidative stress. This study was designed to examine the protective effect of melatonin on potassium dichromate- (PDC-) induced male reproductive toxicity. Forty rats were divided into five groups: the control group, rats administered PDC orally (10 mg/kg body weight) for eight weeks, rats administered ML intraperitoneally at doses of either 2.5 or 5 mg/kg followed by the administration of PDC, and rats administered 5 mg/kg ML only. The treatment of rats with PDC led to a decrease in the levels of plasma sex hormones, glutathione, superoxide dismutase, catalase, carnitine, sperm count, and motility. Testicular malondialdehyde levels, nitric oxide concentrations, and abnormalities increased significantly in the PDC group. Melatonin administration to the PDC-treated rats reduced the increase of malondialdehyde and restored the activity of antioxidant enzymes (superoxide dismutase and catalase), glutathione, and sex hormone levels. Moreover, ML attenuated PDC-induced increase in levels of tumor necrosis factor-alpha or interleukin-6. ML alleviated histopathological changes and an increase of p53-positive immune reaction due to PDC. Furthermore, ML inhibited PDC-induced decrease in the DNA content of spermatogenic cells. This study proposed that melatonin may be useful in mitigating oxidative stress-induced testicular damage due to potassium dichromate toxicity.
Journal Article
Curcumin-resveratrol nano-formulation counteracting hyperammonemia in rats
Malnutrition and low dietary protein intake could be risk factors for developing peripheral and central hyperammonemia, especially in pediatrics. Both curcumin and resveratrol proved to be effective against several hepatic and cerebral injuries. They were reported to be beneficial in lowering circulating ammonia levels, yet both are known for their low bioavailability. The use of pharmaceutical nano-formulations as delivery systems for these two nutraceuticals could solve the aforementioned problem. Hence, the present study aimed to investigate the valuable outcome of using a combination of curcumin and resveratrol in a nanoemulsion formulation, to counteract protein-deficient diet (PDD)-induced hyperammonemia and the consequent complications in male albino rats. Results revealed that using a nanoemulsion containing both curcumin and resveratrol at a dose of (5 + 5 mg/kg) effectively reduced hepatic and brain ammonia levels, serum ALT and AST levels, hepatic and brain nitric oxide levels, oxidative DNA damage as well as disrupted cellular energy performance. In addition, there was a substantial increase in brain levels of monoamines, and a decrease in glutamate content. Therefore, it can be concluded that the use of combined curcumin and resveratrol nanoemulsion is an effective means of ameliorating the hepatic and cerebral adverse effects resulting from PDD-induced hyperammonemia in rats.
Journal Article