Developing a Model Based on the Radial Basis Function to Predict the Compressive Strength of Concrete Containing Fly Ash

A supplemental pozzolanic material such as fly ash may result in a reduction in the concrete’s adverse environmental effect by reducing the discharge of carbon dioxide throughout the cement production procedure. This pozzolanic material also enhances the mechanical characteristics as well as the durability of concrete material. Considering the boundless passion for utilizing fly ash and conducting extensive research studies, the extent to which this supplement can be added to concrete has a limitation equal to almost one-third of cement material’s weight. In the current study, a model based on the Radial Basis Function (RBF) is developed to estimate the compressive strength of concrete containing various amounts of fly ash at any arbitrary age. Having parameters used as inputs in ANN modeling such as concrete additives and characteristics of fly ash, the output was compressive strength. It was concluded that the estimated results agree well with the experimental measurements with an MSE of 0.0012 for the compressive strength. Simple and practical equations are proposed to present a simple means to determine the compressive strength of fly ash-based concrete

MicroRNA-34a: A Key Regulator in the Hallmarks of Renal Cell Carcinoma

Renal cell carcinoma (RCC) incidence has increased over the past two decades. Recent studies reported microRNAs as promising biomarkers for early cancer detection, accurate prognosis, and molecular targets for future treatment. This study aimed to evaluate the expression levels of miR-34a and 11 of its bioinformatically selected target genes and proteins to test their potential dysregulation in RCC. Quantitative real-time PCR for miR-34a and its targets; MET oncogene; gene-regulating apoptosis (TP53INP2 and DFFA); cell proliferation (E2F3); and cell differentiation (SOX2 and TGFB3) as well as immunohistochemical assay for VEGFA, TP53, Bcl2, TGFB1, and Ki67 protein expression have been performed in 85 FFPE RCC tumor specimens. Clinicopathological parameter correlation and in silico network analysis have also implicated. We found RCC tissues displayed significantly higher miR-34a expression level than their corresponding noncancerous tissues, particularly in chromophobic subtype. MET and E2F3 were significantly upregulated, while TP53INP2 and SOX2 were downregulated. ROC analysis showed high diagnostic performance of miR-34a (AUC = 0.854), MET (AUC = 0.765), and E2F3 (AUC = 0.761). The advanced pathological grade was associated with strong TGFB1, VEGFA, and Ki67 protein expression and absent Tp53 staining. These findings indicate miR-34a along with its putative target genes could play a role in RCC tumorigenesis and progression

Preparation and Preliminary Dielectric Characterization of Structured C\u3csub\u3e60\u3c/sub\u3e-Thiol-Ene Polymer Nanocomposites Assembled Using the Thiol-Ene Click Reaction

Fullerene-containing materials have the ability to store and release electrical energy. Therefore, fullerenes may ultimately find use in high-voltage equipment devices or as super capacitors for high electric energy storage due to this ease of manipulating their excellent dielectric properties and their high volume resistivity. A series of structured fullerene (C60) polymer nanocomposites were assembled using the thiol-ene click reaction, between alkyl thiols and allyl functionalized C60 derivatives. The resulting high-density C60-urethane-thiol-ene (C60-Thiol-Ene) networks possessed excellent mechanical properties. These novel networks were characterized using standard techniques, including infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), and thermal gravimetric analysis (TGA). The dielectric spectra for the prepared samples were determined over a broad frequency range at room temperature using a broadband dielectric spectrometer and a semiconductor characterization system. The changes in thermo-mechanical and electrical properties of these novel fullerene-thiol-ene composite films were measured as a function of the C60 content, and samples characterized by high dielectric permittivity and low dielectric loss were produced. In this process, variations in chemical composition of the networks were correlated to performance characteristics

Increasing the Accuracy and Optimizing the Structure of the Scale Thickness Detection System by Extracting the Optimal Characteristics Using Wavelet Transform

Loss of energy, decrement of efficiency, and decrement of the effective diameter of the oil pipe are among the consequences of scale inside oil condensate transfer pipes. To prevent these incidents and their consequences and take timely action, it is important to detect the amount of scale. One of the accurate diagnosis methods is the use of non-invasive systems based on gamma-ray attenuation. The detection method proposed in this research consists of a detector that receives the radiation sent by the gamma source with dual energy (radioisotopes 241 Am and 133 Ba) after passing through the test pipe with inner scale (in different thicknesses). This structure was simulated by Monte Carlo N Particle code. The simulation performed in the test pipe included a three-phase flow consisting of water, gas, and oil in a stratified flow regime in different volume percentages. The signals received by the detector were processed by wavelet transform, which provided sufficient inputs to design the radial basis function (RBF) neural network. The scale thickness value deposited in the pipe can be predicted with an MSE of 0.02. The use of a detector optimizes the structure, and its high accuracy guarantees the usefulness of its use in practical situations

Application of Artificial Intelligence for Determining the Volume Percentages of a Stratified Regime’s Three-Phase Flow, Independent of the Oil Pipeline’s Scale Thickness

As time passes, scale builds up inside the pipelines that deliver the oil or gas product from the source to processing plants or storage tanks, reducing the inside diameter and ultimately wasting energy and reducing efficiency. A non-invasive system based on gamma-ray attenuation is one of the most accurate diagnostic methods to detect volumetric percentages in different conditions. A system including two NaI detectors and dual-energy gamma sources ( 241 Am and 133 Ba radioisotopes) is the recommended requirement for modeling a volume-percentage detection system using Monte Carlo N particle (MCNP) simulations. Oil, water, and gas form a three-phase flow in a stratified-flow regime in different volume percentages, which flows inside a scaled pipe with different thicknesses. Gamma rays are emitted from one side, and photons are absorbed from the other side of the pipe by two scintillator detectors, and finally, three features with the names of the count under Photopeaks 241 Am and 133 Ba of the first detector and the total count of the second detector were obtained. By designing two MLP neural networks with said inputs, the volumetric percentages can be predicted with an RMSE of less than 1.48 independent of scale thickness. This low error value guarantees the effectiveness of the intended method and the usefulness of using this approach in the petroleum and petrochemical industries

Introducing a precise system for determining volume percentages independent of scale thickness and type of flow regime

When fluids flow into the pipes, the materials in them cause deposits to form inside the pipes over time, which is a threat to the efficiency of the equipment and their depreciation. In the present study, a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe is presented using artificial intelligence networks. The method is non-invasive and works in such a way that the detector located on one side of the pipe absorbs the photons that have passed through the other side of the pipe. These photons are emitted to the pipe by a dual source of the isotopes barium-133 and cesium-137. The Monte Carlo N Particle Code (MCNP) simulates the structure, and wavelet features are extracted from the data recorded by the detector. These features are considered Group methods of data handling (GMDH) inputs. A neural network is trained to determine the volume percentage with high accuracy independent of the thickness of the scale in the pipe. In this research, to implement a precise system for working in operating conditions, different conditions, including different flow regimes and different scale thickness values as well as different volume percentages, are simulated. The proposed system is able to determine the volume percentages with high accuracy, regardless of the type of flow regime and the amount of scale inside the pipe. The use of feature extraction techniques in the implementation of the proposed detection system not only reduces the number of detectors, reduces costs, and simplifies the system but also increases the accuracy to a good extent

Optimizing the Gamma Ray-Based Detection System to Measure the Scale Thickness in Three-Phase Flow through Oil and Petrochemical Pipelines in View of Stratified Regime

As the oil and petrochemical products pass through the oil pipeline, the sediment scale settles, which can cause many problems in the oil fields. Timely detection of the scale inside the pipes and taking action to solve it prevents problems such as a decrease in the efficiency of oil equipment, the wastage of energy, and the increase in repair costs. In this research, an accurate detection system of the scale thickness has been introduced, which its performance is based on the attenuation of gamma rays. The detection system consists of a dual-energy gamma source ( 241 Am and 133 Ba radioisotopes) and a sodium iodide detector. This detection system is placed on both sides of a test pipe, which is used to simulate a three-phase flow in the stratified regime. The three-phase flow includes water, gas, and oil, which have been investigated in different volume percentages. An asymmetrical scale inside the pipe, made of barium sulfate, is simulated in different thicknesses. After irradiating the gamma-ray to the test pipe and receiving the intensity of the photons by the detector, time characteristics with the names of sample SSR, sample mean, sample skewness, and sample kurtosis were extracted from the received signal, and they were introduced as the inputs of a GMDH neural network. The neural network was able to predict the scale thickness value with an RMSE of less than 0.2, which is a very low error compared to previous research. In addition, the feature extraction technique made it possible to predict the scale value with high accuracy using only one detector

Effect of quercetin on steroidogenesis and folliculogenesis in ovary of mice with experimentally-induced polycystic ovarian syndrome

IntroductionPolycystic Ovary syndrome (PCOS) affects the health of many women around the world. Apart from fundamental metabolic problems connected to PCOS, focus of our study is on the role of quercetin on genes relevant to steroidogenesis and folliculogenesis.MethodsEighteen mature parkes strain mice (4-5 weeks old) weighing 18–21 g were randomly divided into three groups of six each as follows: Group I serves as the control and was given water and a regular chow diet ad lib for 66 days; group II was given oral gavage administration of letrozole (LETZ) (6 mg/kg bw) for 21 days to induce PCOS and was left untreated for 45 days; For three weeks, Group III received oral gavage dose of LETZ (6 mg/kg), after which it received Quercetin (QUER) (125 mg/kg bw orally daily) for 45 days.ResultsIn our study we observed that mice with PCOS had irregular estrous cycle with increased LH/FSH ratio, decreased estrogen level and decline in expression of Kitl, Bmp1, Cyp11a1, Cyp19a1, Ar, lhr, Fshr and Esr1 in ovary. Moreover, we observed increase in the expression of CYP17a1, as well as increase in cholesterol, triglycerides, testosterone, vascular endothelial growth factor VEGF and insulin levels. All these changes were reversed after the administration of quercetin in PCOS mice.DiscussionQuercetin treatment reversed the molecular, functional and morphological abnormalities brought on due to letrozole in pathological and physiological setting, particularly the issues of reproduction connected to PCOS. Quercetin doesn’t act locally only but it acts systematically as it works on Pituitary (LH/FSH)- Ovary (gonad hormones) axis. the Side effects of Quercetin have to be targeted in future researches. Quercetin may act as a promising candidate for medical management of human PCOS

SARS-CoV-2 Infection Is at Herd Immunity in the Majority Segment of the Population of Qatar.

BACKGROUND: Qatar experienced a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic that disproportionately affected the craft and manual worker (CMW) population, who comprise 60% of the total population. This study aimed to assess ever and/or current infection prevalence in this population. METHODS: A cross-sectional population-based survey was conducted during July 26 to September 09, 2020, to assess both anti-SARS-CoV-2 positivity through serological testing and current infection positivity through polymerase chain reaction (PCR) testing. Associations with antibody and PCR positivity were identified through regression analyses. RESULTS: The study included 2641 participants, 69.3% of whom were <40 years of age. Anti-SARS-CoV-2 positivity was 55.3% (95% CI, 53.3%-57.3%) and was significantly associated with nationality, geographic location, educational attainment, occupation, and previous infection diagnosis. PCR positivity was 11.3% (95% CI, 9.9%-12.8%) and was significantly associated with nationality, geographic location, occupation, contact with an infected person, and reporting 2 or more symptoms. Infection positivity (antibody and/or PCR positive) was 60.6% (95% CI, 58.6%-62.5%). The proportion of antibody-positive CMWs who had a prior SARS-CoV-2 diagnosis was 9.3% (95% CI, 7.9%-11.0%). Only seven infections were ever severe, and only 1 was ever critical-an infection severity rate of 0.5% (95% CI, 0.2%-1.0%). CONCLUSIONS: Six in every 10 CMWs in Qatar have been infected, suggestive of reaching the herd immunity threshold. Infection severity was low, with only 1 in every 200 infections progressing to be severe or critical. Only 1 in every 10 infections had been previously diagnosed, which is suggestive of mostly asymptomatic or mild infections