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. The research investigates the role of internal and external filter cakes on sand column pressure distribution and heavy ion absorption. Results indicate that the mobility of colloids and heavy ions is influenced by the ionic strength and pH of the carrying fluid. Colloidal clay suspensions show a higher affinity for Pb (II) absorption, while Cd (II) exhibits increased mobility in both clean sand and colloidal environments. Notably, the formation of an external filter cake significantly delays the breakthrough of heavy ions, up to four times longer than in clean sand, and reduces Cd (II) and Pb (II) outlet concentrations by 86% and 93%, respectively. This cake also limits clay concentration and particle size passage. High clay concentrations or injections under high ionic conditions induce clay bridging in pore throats, enhancing internal filtration and heavy ion retention. Conversely, low clay fluxes allow freer particle passage, increasing heavy ion loads and outlet concentrations.

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. Afterward, dynamic filtration has been conducted in a filtration cell equipped with an agitating system with a disk-type impeller to investigate the hydrodynamic and formation of a filter cake under shear flow conditions. Fluid flow velocity and wall shear stress (WSS) distribution over the filter cake were analyzed using an exact 3D computational fluid dynamic (CFD) simulation. A transparent filtration cell with a camera was used to accurately record the fluid flow field inside the filter press and validate the CFD results. The obtained results indicated that adding silica NPs at a concentration of less than 2 wt % increases the fluid loss due to reducing rheological properties such as yield point. While silica NPs could not significantly change the mud properties, the experimental results showed that, under both LPLT and HPHT conditions, the PAM-S NC could reduce the total filtration loss by 70% at a low concentration of 0.75 wt %. Moreover, during dynamic filtration, the results indicated that there is a linear relationship between the cake thickness and the inverse of WSS at different operating pressures. However, no correlation could be found between predeposited mud cake erosion and WSS. At a rotating disk speed of 1000 rpm, more than 60% of the predeposited mud cake was eroded after 30 min for a saline mud sample while for the NC-treated mud sample cake erosion is considerably reduced and reaches up to 20% at 1.5 wt % PAM-S.

The Impact of the Double Cross-Phase Diffusion Mechanism on Oil Recovery during CO2 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 CO2 injection in chalk fractured rocks in the presence of initial water saturation. The results revealed a significant difference in the oil recovery predicted by the model when the double cross-phase diffusion mechanism is considered. The transient behavior of produced oil composition, predicted by the simulation model, is matched well with the experimental data. The portion of active oil recovery mechanisms in the system has been evaluated for the first time and it was observed that the molecular diffusion mechanism induced 75.4% of the total oil transfer rate in the initial time oil recovery, in which 23.1% of this value was supplied by the double cross-phase diffusion mechanism, which is an interesting finding. Results of sensitivity analysis showed that by increasing the initial water saturation, the impact of the double cross-phase diffusion mechanism on oil recovery increases. In contrast, the transferred rate by the diffusion mechanism decreases from 85.4% to 60.8% when matrix permeability increases from 0.1 to 10 mD. The results of this work illustrate that the double cross-phase diffusion mechanism introduced in this study plays a significant role in the simulation results since the water is responsible for accelerating the diffusivity of CO2 into the crude oil and, in consequence, increasing the oil recovery.

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. A Gamma test (GT) was used to find the most important determinant input for optimizing each output factor. GEP had a higher prediction accuracy than M5' model tree. GT results showed that BA (Γ = 4.0178), Mesos (Γ = 0.5482), Mesos (Γ = 184.0100), Micros (Γ = 136.6100) and Mesos (Γ = 1.1146), for PR, SL, STN, Vitri and QI respectively, were the most important factors in culturing OHF 69, while for Pyrodwarf culture, BA (Γ = 10.2920), Micros (Γ = 0.7874), NH4NO3 (Γ = 166.410), KNO3 (Γ = 168.4400), and Mesos (Γ = 1.4860) were the most important influences on PR, SL, STN, Vitri and QI respectively. The PSO optimized GEP models produced the best outputs for both rootstocks.

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. Tregs play a key role in the induction of peripheral tolerance that can prevent autoimmunity, by protecting self-reactive lymphocytes from the immune reaction. In contrast to autoimmune responses, tumor cells exploit Tregs in order to prevent immune cell recognition and anti-tumor immune response during the carcinogenesis process. Recently, numerous studies have focused on unraveling the biological functions and principles of Tregs and their primary suppressive mechanisms. Due to the promising and outstanding results, Tregs have been widely investigated as an alternative tool in preventing graft rejection and treating autoimmune diseases. On the other hand, targeting Tregs for the purpose of improving cancer immunotherapy is being intensively evaluated as a desirable and effective method. The purpose of this review is to point out the characteristic function and therapeutic potential of Tregs in regulatory immune mechanisms in transplantation tolerance, autoimmune diseases, cancer therapy, and also to discuss that how the manipulation of these mechanisms may increase the therapeutic options.

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). Here, a novel algorithm, i.e. GEP which has not been previously applied in plant tissue culture researches was compared to RBFNN and MLR for the first time. Pear rootstocks (Pyrodwarf and OHF) were used as case studies on predicting the effect of minerals and some hormones in the culture medium on proliferation indices. RESULTS: Generally, RBFNN and GEP showed extremely higher performance accuracy than the MLR. Moreover, GEP models as the most accurate models were optimized using genetic algorithm (GA). The improvement was mainly due to the RBFNN and GEP strong estimation capability and their superior tolerance to experimental noises or improbability. CONCLUSIONS: GEP as the most robust and accurate prospecting procedure to achieve the highest proliferation quality and quantity has also the benefit of being easy to use.