Fate and cotransport of Pb(II) and Cd(II) heavy ions with bentonite colloidal flow in saturated porous media: The role of filter cake, counter ions, colloid concentration, and fluid velocity.

This study examines the transport and retention of colloidal particles and heavy ions in porous sand, focusing on the environmental risks associated with waste from oil and gas drilling. Experimental and numerical models assess the influence of flow rate, external filter cake layer, and ionic strength on bentonite clay particles and heavy ions, such as cadmium (Cd) and lead (Pb), in near-wellbore (high-flux) and far-field (low-flux) scenarios. Colloidal filtration theory and the one-dimensional convection-dispersion equation with two-site kinetic model for attachment and detachment were utilized to calibrate and predict the transport of colloidal suspension in porous media.

Hydrodynamic Analysis and Cake Erosion Properties of a Modified Water-Based Drilling Fluid by a Polyacrylamide/Silica Nanocomposite during Rotating-Disk Dynamic Filtration.

In this study, the potential of using a polyacrylamide-silica nanocomposite (PAM-S) to control the filtration properties of bentonite water-based drilling muds under different salinity conditions was evaluated. Static filtration tests under low-pressure/low-temperature (LPLT) conditions accompanied by rheological measurements have been carried out to analyze the role of silica nanoparticles (NPs) and nanocomposites (NCs) in the base fluid properties. Moreover, high-pressure/high-temperature (HPHT) static filtration was also investigated to evaluate the thermal stability of PAM-S.

The Impact of the Double Cross-Phase Diffusion Mechanism on Oil Recovery during CO Injection into Fractured Rocks: A Simulation Study.

In this research, a new diffusion mechanism called "double cross-phase diffusion" is introduced and applied to simulate the non-equilibrium gas injection process into fractured rocks. This new mechanism represents additional multicomponent gas diffusion into the crude oil through the water phase, existing in porous media as initial water saturation. Therefore, a lab-scale simulator, by implementing the generalized Fick's law of multicomponent diffusion, is developed and used for predicting the experimental data of oil recovery during CO injection in chalk fractured rocks in the presence of initial water saturation.

The Therapeutic Potential of Regulatory T Cells: Challenges and Opportunities.

Regulatory T cells (Tregs) are an immunosuppressive subgroup of CD4 T cells which are identified by the expression of forkhead box protein P3 (Foxp3). The modulation capacity of these immune cells holds an important role in both transplantation and the development of autoimmune diseases. These cells are the main mediators of self-tolerance and are essential for avoiding excessive immune reactions.

High throughput mathematical modeling and multi-objective evolutionary algorithms for plant tissue culture media formulation: Case study of pear rootstocks.

Simplified prediction of the interactions of plant tissue culture media components is of critical importance to efficient development and optimization of new media. We applied two algorithms, gene expression programming (GEP) and M5' model tree, to predict the effects of media components on in vitro proliferation rate (PR), shoot length (SL), shoot tip necrosis (STN), vitrification (Vitri) and quality index (QI) in pear rootstocks (Pyrodwarf and OHF 69). In order to optimize the selected prediction models, as well as achieving a precise multi-optimization method, multi-objective evolutionary optimization algorithms using genetic algorithm (GA) and particle swarm optimization (PSO) techniques were compared to the mono-objective GA optimization technique.

Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation.

Background: Predicting impact of plant tissue culture media components on explant proliferation is important especially in commercial scale for optimizing efficient culture media. Previous studies have focused on predicting the impact of media components on explant growth via conventional multi-layer perceptron neural networks (MLPNN) and Multiple Linear Regression (MLR) methods. So, there is an opportunity to find more efficient algorithms such as Radial Basis Function Neural Network (RBFNN) and Gene Expression Programming (GEP).