Numerical simulation of hydrothermal features of Cu-H2O nanofluid natural convection within a porous annulus considering diverse configurations of heater

The purpose of the current study is to numerically investigate the effects of shape factors of nanoparticles on natural convection in a fluid-saturated porous annulus developed between the elliptical cylinder and square enclosure. A numerical method called the control volume-based finite element method is implemented for solving the governing equations. The modified flow and thermal structures and corresponding heat transfer features are investigated. Numerical outcomes reveal very good grid independency and excellent agreement with the existing studies. The obtained results convey that at a certain aspect ratio, an increment in Rayleigh and Darcy numbers significantly augments the heat transfer and average Nusselt number. Further, enhancement of Rayleigh number increases the velocity of nanofluid, while that of aspect ratio of the elliptical cylinder shows the opposite trend

Integrated application of bacterial carbonate precipitation and silicon nanoparticles enhances productivity, physiological attributes, and antioxidant defenses of wheat (Triticum aestivum L.) under semi-arid conditions

The use of calcium carbonate-precipitating bacteria (CCPB) has become a well-established ground-improvement technique. However, the effect of the interaction of CCPB with nanoparticles (NPs) on plant performance is still meager. In this study, we aimed at evaluating the role of CCPB and/or silicon NPs (Si-NPs) on the growth, physio-biochemical traits, and antioxidative defense of wheat (Triticum aestivum L.) under semi-arid environmental conditions. A 2-year pot experiment was carried out to determine the improvement of the sandy soil inoculated with CCPB and the foliar application of Si-NPs on wheat plants. We tested the following treatments: spraying plants with 1.0 or 1.5 mM Si-NPs (control = 0 mM Si-NPs), soil inoculated with Bacillus lichenforms (MA16), Bacillus megaterium (MA27), or Bacillus subtilis (MA34), and the interaction of individual Bacillus species with Si-NPs. Our results showed that soil inoculation with any of the three isolated CCPB and/or foliar application of Si-NPs at the rates of 1.0 or 1.5 mM significantly improved (p ≤ 0.05) the physiological and biochemical attributes as well as the enzymatic antioxidant activities of wheat plants. Therefore, the combined treatments of CCPB + Si-NPs were more effective in enhancing physio-biochemical characteristics and enzymatic antioxidant activities than the individual treatments of CCPB or Si-NPs, thus achieving the best performance in the treatment of MA34 + 1.5 mM Si-NPs. Our results demonstrated that the co-application of CCPB and Si-NPs, particularly MA34 + 1.5 mM Si-NPs, considerably activated the antioxidant defense system to mitigate the adverse effects of oxidative stress, thus increasing tolerance and enhancing the production of wheat plants in sandy soils under semi-arid environmental conditions

Solubility enhancement of decitabine as anticancer drug via green chemistry solvent: Novel computational prediction and optimization

Nowadays, supercritical fluid technology (SFT) has been an interesting scientific subject in disparate industrial-based activities such as drug delivery, chromatography, and purification. In this technology, solubility plays an incontrovertible role. Therefore, achieving more knowledge about the development of promising numerical/computational methods of solubility prediction to validate the experimental data may be advantageous for increasing the quality of research and therefore, the efficacy of novel drugs. Decitabine with the chemical formula C₈H₁₂N₄O₄ is a chemotherapeutic agent applied for the treatment of disparate bone-marrow-related malignancies such as acute myeloid leukemia (AML) by preventing DNA methyltransferase and activation of silent genes. This study aims to predict the optimum value of decitabine solubility in CO₂SCF by employing different machine learning-based mathematical models. In this investigation, we used AdaBoost (Adaptive Boosting) to boost three base models including Linear Regression (LR), Decision Tree (DT), and GRNN. We used a dataset that has 32 sample points to make solubility models. One of the two input features is P (bar) and the other is T (k). ADA-DT (Adaboost Algorithm Decision Tree), ADA-LR (Adaboost Algorithm-Linear Regresion), and ADA-GRNN (Generative Regression Neural Network) models showed MAE of 6.54 ˣ 10ˉ⁵, 4.66 10 ˉ⁵, and 8.35 10 ˉ⁵, respectively. Also, in terms of R-squared score, these models have 0.986, 0.983, and 0.911 scores, respectively. ADA-LR was selected as the primary model according to numerical and visual analysis. Finally, the optimal values are (P = 400 bar, T = 3.38 K 102, Y = 1.064 10ˉ³ mol fraction) using this model

Loss-of-function mutations in UDP-Glucose 6-Dehydrogenase cause recessive developmental epileptic encephalopathy

Developmental epileptic encephalopathies are devastating disorders characterized by intractable epileptic seizures and developmental delay. Here, we report an allelic series of germline recessive mutations in UGDH in 36 cases from 25 families presenting with epileptic encephalopathy with developmental delay and hypotonia. UGDH encodes an oxidoreductase that converts UDP-glucose to UDP-glucuronic acid, a key component of specific proteoglycans and glycolipids. Consistent with being loss-of-function alleles, we show using patients’ primary fibroblasts and biochemical assays, that these mutations either impair UGDH stability, oligomerization, or enzymatic activity. In vitro, patient-derived cerebral organoids are smaller with a reduced number of proliferating neuronal progenitors while mutant ugdh zebrafish do not phenocopy the human disease. Our study defines UGDH as a key player for the production of extracellular matrix components that are essential for human brain development. Based on the incidence of variants observed, UGDH mutations are likely to be a frequent cause of recessive epileptic encephalopathy

Recent developments in metamodel based robust black-box simulation optimization: An overview

In the real world of engineering problems, in order to reduce optimization costs in physical processes, running simulation experiments in the format of computer codes have been conducted. It is desired to improve the validity of simulation-optimization results by attending the source of variability in the model’s output(s). Uncertainty can increase complexity and computational costs in Designing and Analyzing of Computer Experiments (DACE). In this state of the art review paper, a systematic qualitative and quantitative review is implemented among Metamodel Based Robust Simulation Optimization (MBRSO) for black-box and expensive simulation models under uncertainty. This context is focused on the management of uncertainty, particularly based on the Taguchi worldview on robust design and robust optimization methods in the class of dual response methodology when simulation optimization can be handled by surrogates. At the end, while both trends and gaps in the research field are highlighted, some suggestions for future research are directed

Further investigation of the validity and reliability of the photographic figure rating scale for body image assessment

Previous studies have not fully investigated the psychometric properties of the Photographic Figure Rating Scale (PFRS). In 2 studies, we report on the test–retest reliability and convergent validity of ratings derived from the PFRS. In Study 1, 322 female university students in Britain provided self-ratings on the PFRS and objectively measured body mass index (BMI); a subsample of 132 women also completed the task after 5 weeks. In Study 2, 243 women from the community in Austria completed the PFRS along with a battery of other body image scales. Results of Study 1 showed that ratings on the PFRS had good test–retest reliability (all rs > .87) and good convergent validity in relation to BMI. Results of Study 2 showed that PFRS-derived body dissatisfaction scores were significantly correlated with a range of body image variables. These results provide evidence for the convergent validity and good test–retest reliability of the PFRS

Anti-inflammatory Properties Of A Heparin-like Glycosaminoglycan With Reduced Anti-coagulant Activity Isolated From A Marine Shrimp

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