Optimization of oil industry wastewater treatment system and proposing empirical correlations for chemical oxygen demand removal using electrocoagulation and predicting the system's performance by artificial neural network.

The alarming pace of environmental degradation necessitates the treatment of wastewater from the oil industry in order to ensure the long-term sustainability of human civilization. Electrocoagulation has emerged as a promising method for optimizing the removal of chemical oxygen demand (COD) from wastewater obtained from oil refineries. Therefore, in this study, electrocoagulation was experimentally investigated, and a single-factorial approach was employed to identify the optimal conditions, taking into account various parameters such as current density, pH, COD concentration, electrode surface area, and NaCl concentration. The experimental findings revealed that the most favorable conditions for COD removal were determined to be 24 mA/cm2 for current density, pH 8, a COD concentration of 500 mg/l, an electrode surface area of 25.26 cm2, and a NaCl concentration of 0.5 g/l. Correlation equations were proposed to describe the relationship between COD removal and the aforementioned parameters, and double-factorial models were examined to analyze the impact of COD removal over time. The most favorable outcomes were observed after a reaction time of 20 min. Furthermore, an artificial neural network model was developed based on the experimental data to predict COD removal from wastewater generated by the oil industry. The model exhibited a mean absolute error (MAE) of 1.12% and a coefficient of determination (R2) of 0.99, indicating its high accuracy. These findings suggest that machine learning-based models have the potential to effectively predict COD removal and may even serve as viable alternatives to traditional experimental and numerical techniques.

Solar light driven enhanced photocatalytic treatment of azo dye contaminated water based on Co-doped ZnO/ g-C3N4 nanocomposite.

The current research concentrated on the Co-precipitation synthesis of g-C3N4 (CN), ZnO, ZnO/CN, and Co-doped ZnO/CN nanocomposite, as well as the solar light enhanced photocatalytic treatment of Reactive Red 120 (RR120) from genuine wool textile effluent. The 3D flower-like structure of Co-doped ZnO distributed on the surface of CN thin sheets, according to structural studies employing XRD and SEM examinations Electrochemical experiments exhibited that the Co-doped ZnO/CN nanocomposite has a large electroactive surface area. The optical band-gap values of CN, ZnO, ZnO/CN, and Co-doped ZnO/CN nanocomposites were 2.68, 3.13, 2.38, and 2.23 eV, respectively, according to optical characterizations. The synergistic effects and heterojunction produced by Co-doped ZnO and CN can be linked to the narrow gap in nanocomposites. After 75, 60, 50, and 40 min of exposure to solar light, photocatalytic degradation assays for 250 mL of 20 mg/L RR120 solution in the presence of CN, ZnO, ZnO/CN, and Co-doped ZnO/CN nanocomposites demonstrated 100% dye treatment. The applicability of photocatalysts for decolorization of 250 mL of 10 mg/L RR120 prepared from actual wool textile wastewater was investigated, and the results showed that Co-doped ZnO/CN nanocomposites for treatment of RR120 from actual wool textile wastewater were highly efficient at photocatalytic degradation.

UV and solar-based photocatalytic degradation of organic pollutants from ceramics industrial wastewater by Fe-doped ZnS nanoparticles.

UV and solar-based photocatalytic degradation of 2,4-dichlorophenol (2,4-DCP) as an organic contaminant in ceramics industry wastewater by ZnS and Fe-doped ZnS NPs was the focus of this research. Nanoparticles were prepared using a chemical precipitation process. The cubic, closed-packed structure of undoped ZnS and Fe-doped ZnS NPs was formed in spherical clusters, according to XRD and SEM investigations. According to optical studies, the optical band gaps of pure ZnS and Fe-doped ZnS nanoparticles are 3.35 and 2.51 eV, respectively, and Fe doping increased the number of carriers with high mobility, improved carrier separation and injection efficiency, and increased photocatalytic activity under UV or visible light. Doping of Fe increased the separation of photogenerated electrons and holes and facilitated charge transfer, according to electrochemical impedance spectroscopy investigations. Photocatalytic degradation studies revealed that in the present pure ZnS and Fe-doped ZnS nanoparticles, 100% treatment of 120 mL of 15 mg/L phenolic compound was obtained after 55- and 45-min UV-irradiation, respectively, and complete treatment was attained after 45 and 35-min solar light irradiation, respectively. Because of the synergistic effects of effective surface area, more effective photo-generated electron and hole separation efficiency, and enhanced electron transfer, Fe-doped ZnS demonstrated high photocatalytic degradation performance. The study of Fe-doped ZnS's practical photocatalytic treatment capability for removing 120 mL of 10 mg/L 2,4-DCP solution made from genuine ceramic industrial wastewater revealed Fe-doped ZnS's excellent photocatalytic destruction of 2,4-DCP from real industrial wastewater.

Mechanisms of Action of Noninvasive Brain Stimulation with Weak Non-Constant Current Stimulation Approaches.

Objective: Non-constant current stimulation (NCCS) is a neuromodulatory method in which weak alternating, pulsed or random currents are delivered to the human head via scalp or earlobe electrodes. This approach is widely used in basic and translational studies. However, the underlying mechanisms of NCCS, which lead to biological and behavioral effects in the brain, remain largely unknown. In this review, we characterize NCCS techniques currently being utilized in neuroscience investigations, including transcranial alternating current stimulation (tACS), transcranial pulsed current stimulation (tPCS), transcranial random noise stimulation (tRNS), and cranial electrotherapy stimulation (CES). Method: We unsystematically searched all relevant conference papers, journal articles, chapters, and textbooks on the biological mechanisms of NCCS techniques. Results: The fundamental idea of NCCS is that these low-level currents can interact with neuronal activity, modulate neuroplasticity and entrain cortical networks, thus, modifying cognition and behavior. We elucidate the mechanisms of action for each NCCS technique. These techniques may cause microscopic effects (such as affecting ion channels and neurotransmission systems) and macroscopic effects (such as affecting brain oscillations and functional connectivity) on the brain through different mechanisms of action (such as neural entrainment and stochastic resonance). Conclusion: The appeal of NCCS is its potential to modulate neuroplasticity noninvasively, along with the ease of use and good tolerability. Promising and interesting evidence has been reported for the capacity of NCCS to affect neural circuits and the behaviors under their control. Today, the challenge is to utilize this advancement optimally. Continuing methodological advancements with NCCS approaches will enable researchers to better understand how NCCS can be utilized for the modulation of nervous system activity and subsequent behaviors, with possible applications to non-clinical and clinical practices.

Thermodynamics Investigation and Artificial Neural Network Prediction of Energy, Exergy, and Hydrogen Production from a Solar Thermochemical Plant Using a Polymer Membrane Electrolyzer.

Hydrogen production using polymer membrane electrolyzers is an effective and valuable way of generating an environmentally friendly energy source. Hydrogen and oxygen generated by electrolyzers can power drone fuel cells. The thermodynamic analysis of polymer membrane electrolyzers to identify key losses and optimize their performance is fundamental and necessary. In this article, the process of the electrolysis of water by a polymer membrane electrolyzer in combination with a concentrated solar system in order to generate power and hydrogen was studied, and the effect of radiation intensity, current density, and other functional variables on the hydrogen production was investigated. It was shown that with an increasing current density, the voltage generation of the electrolyzer increased, and the energy efficiency and exergy of the electrolyzer decreased. Additionally, as the temperature rose, the pressure dropped, the thickness of the Nafion membrane increased, the voltage decreased, and the electrolyzer performed better. By increasing the intensity of the incoming radiation from 125 W/m2 to 320 W/m2, the hydrogen production increased by 111%, and the energy efficiency and exergy of the electrolyzer both decreased by 14% due to the higher ratio of input electric current to output hydrogen. Finally, machine-learning-based predictions were conducted to forecast the energy efficiency, exergy efficiency, voltage, and hydrogen production rate in different scenarios. The results proved to be very accurate compared to the analytical results. Hyperparameter tuning was utilized to adjust the model parameters, and the models' results showed an MAE lower than 1.98% and an R2 higher than 0.98.

Designing Effective Multi-Target Drugs and Identifying Biomarkers in Recurrent Pregnancy Loss (RPL) Using In Vivo, In Vitro, and In Silico Approaches.

Recurrent pregnancy loss (RPL) occurs in approximately 5% of women. Despite an abundance of evidence, the molecular mechanism of RPL's pathology remains unclear. Here, we report the protective role of polo-like kinase 1 (PLK1) during RPL. We aimed to construct an RPL network utilizing GEO datasets and identified hub high-traffic genes. We also investigated whether the expressions of PLK1 were altered in the chorionic villi collected from women with RPL compared to those from healthy early pregnant women. Gene expression differences were evaluated using both pathway and gene ontology (GO) analyses. The identified genes were validated using in vivo and in vitro models. Mice with PLK1-overexpression and PLK1-knockdown in vitro models were produced by transfecting certain plasmids and si-RNA, respectively. The apoptosis in the chorionic villi, mitochondrial function, and NF-κB signaling activity was evaluated. To suppress the activation of PLK1, the PLK1 inhibitor BI2536 was administered. The HTR-8/SVneo and JEG-3 cell lines were chosen to establish an RPL model in vitro. The NF-κB signaling, Foxo signaling, PI3K/AKT, and endometrial cancer signaling pathways were identified via the RPL regulatory network. The following genes were identified: PLK1 as hub high-traffic gene and MMP2, MMP9, BAX, MFN1, MFN2, FOXO1, OPA1, COX15, BCL2, DRP1, FIS1, TRAF2, and TOP2A. Clinical samples were examined, and the results demonstrated that RPL patients had tissues with decreased PLK1 expression in comparison to women with normal pregnancies (p < 0.01). In vitro, PLK1 knockdown induced the NF-κB signaling pathway and apoptosis activation while decreasing cell invasion, migration, and proliferation (p < 0.05). Furthermore, the in vivo model proved that cell mitochondrial function and chorionic villi development are both hampered by PLK1 suppression. Our findings revealed that the PLK1/TRAF2/NF-κB axis plays a crucial role in RPL-induced chorionic villi dysfunction by regulating mitochondrial dynamics and apoptosis and might be a potential therapeutic target in the clinic.

Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing.

Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network's central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.