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

Targeting Tat–TAR RNA Interaction for HIV-1 Inhibition

The HIV-1 Tat protein interacts with TAR RNA and recruits CDK9/cyclin T1 and other host factors to induce HIV-1 transcription. Thus, Tat–TAR RNA interaction, which is unique for HIV-1, represents an attractive target for anti-HIV-1 therapeutics. To target Tat–TAR RNA interaction, we used a crystal structure of acetylpromazine bound to the bulge of TAR RNA, to dock compounds from the Enamine database containing over two million individual compounds. The docking procedure identified 173 compounds that were further analyzed for the inhibition of HIV-1 infection. The top ten inhibitory compounds with IC50 ≤ 6 µM were selected and the three least toxic compounds, T6780107 (IC50 = 2.97 μM), T0516-4834 (IC50 = 0.2 μM) and T5628834 (IC50 = 3.46 μM), were further tested for HIV-1 transcription inhibition. Only the T0516-4834 compound showed selective inhibition of Tat-induced HIV-1 transcription, whereas the T6780107 compound inhibited equally basal and Tat-induced transcription and the T5628834 compound only inhibited basal HIV-1 transcription. The compounds were tested for the inhibition of translation and showed minimal (<25%) effect. The T0516-4834 compound also showed the strongest inhibition of HIV-1 RNA expression and p24 production in CEM T cells and peripheral blood mononuclear cells infected with HIV-1 IIIB. Of the three compounds, only the T0516-4834 compound significantly disrupted Tat–TAR RNA interaction. Additionally, of the three tested compounds, T5628834 and, to a lesser extent, T0516-4834 disrupted Tat–CDK9/cyclin T1 interaction. None of the three compounds showed significant inhibition of the cellular CDK9 and cyclin T1 levels. In silico modelling showed that the T0516-4834 compound interacted with TAR RNA by binding to the bulge formed by U23, U25, C39, G26,C39 and U40 residues. Taken together, our study identified a novel benzoxazole compound that disrupted Tat–TAR RNA interaction and inhibited Tat-induced transcription and HIV-1 infection, suggesting that this compound might serve as a new lead for anti-HIV-1 therapeutics

Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

The theory of modern organizations considers emotional intelligence to be the metric for tools that enable organizations to create a competitive vision. It also helps corporate leaders enthusiastically adhere to the vision and energize organizational stakeholders to accomplish the vision. In this study, the one-dimensional convolutional neural network classification model is initially employed to interpret and evaluate shifts in emotion over a period by categorizing emotional states that occur at particular moments during mutual interaction using physiological signals. The self-organizing map technique is implemented to cluster overall organizational emotions to represent organizational competitiveness. The analysis of variance test results indicates no significant difference in age and body mass index for participants exhibiting different emotions. However, a significant mean difference was observed for the blood volume pulse, galvanic skin response, skin temperature, valence, and arousal values, indicating the effectiveness of the chosen physiological sensors and their measures to analyze emotions for organizational competitiveness. We achieved 99.8% classification accuracy for emotions using the proposed technique. The study precisely identifies the emotions and locates a connection between emotional intelligence and organizational competitiveness (i.e., a positive relationship with employees augments organizational competitiveness)

Selective Stability Indicating Liquid Chromatographic Method Based on Quality by Design Framework and In Silico Toxicity Assessment for Infigratinib and Its Degradation Products

Infigratinib, a protein kinase inhibitor employed in the therapeutic management of cholangiocarcinoma, was subjected to various stress conditions, including hydrolytic (acidic and alkaline), oxidative, photolytic, and thermal stress, in accordance with the rules established by the International Council for Harmonization. A cumulative count of five degradation products was observed. The application of the Quality by Design principle was utilized in the development of a rapid and specific separation method for Infigratinib and its degradation products. The methodology employed in this study was derived from an experimental design approach, which was utilized to examine the critical process parameters associated with chromatographic systems. The reversed-phase high-performance liquid chromatography technique, employing a C18 column and a mobile phase composed of a gradient mixture of 25 mM ammonium acetate buffer at pH 6.0 and acetonitrile, successfully facilitated the chromatographic separation. The methodology was expanded to include the utilization of UPLC-quadrupole tandem mass spectrometry in order to conduct a comprehensive analysis of the structural properties and characterize the degradation products. Overall, five degradation products were found in different stress conditions. The method was verified at certain working points, wherein a linearity range (5.0–200.0 µg/mL) was developed and other parameters such as accuracy, repeatability, selectivity, and system suitability were evaluated. Finally, the toxicity and mutagenicity of Infigratinib and its degradation products were predicted using in silico software, namely DEREK Nexus® (version 6.2.1) and SARAH Nexus® (version 3.2.1). Various toxicity endpoints, including chromosomal damage, were predicted. Additionally, two degradation products were also predicted to be mutagenic

Perceptions of gender equality, work environment, support and social issues for women doctors at a university hospital in Riyadh, Kingdom of Saudi Arabia

<div><p>The Kingdom of Saudi Arabia (KSA) is an Islamic monarchy and was established in 1932. Saudi women first entered the medical field in 1975 and the country has since seen a steady increase in women pursuing medicine. However, there is limited data on gender related issues for women doctors practicing in Saudi Arabia. Therefore, our study objective was to assess the perception amongst peers regarding gender equality and social issues faced by women doctors in Saudi Arabia. An online anonymous cross-sectional survey was administered in English to doctors at King Khalid Hospital, affiliated to King Saud University, in Riyadh, between April and May of 2016. Of 1015 doctors, 304 (30%) participated, of which 129 (42.4%) were females and 231 (76%) were Saudi nationals. The average age was 32.4 years (±SD: 8.7). The majority opined that there was no gender discrimination in salaries (73.7% p-value = 0.4), hospital benefits (62.2% p-value = 0.06) or entry into any field of Medicine/Pediatrics (68.4% p-value = 0.207). However, only a minority believed that there was no gender discrimination for entry into surgery (37.3% p-value = .091). A higher proportion of male doctors agreed that promotion opportunities are equal (66.3% vs 45.7%, p-value = 0.002). However, of 54 consultants, only 18 (33.3%) were women. Over half of the women (52.3%) reported that they never wear the face veil. Only a minority of male and female doctors (12.2%) believed women doctors should wear the veil since they examine male patients. Fewer respondents believed that female doctors face harassment from male doctors (14.5%) whereas 30.7% believed female doctors face harassment from male patients. More females, than males, agreed with the statement that female doctors are as committed to their careers as are males (92.2% vs 67.4%, p-value<0.0001). Of 304 participants, 210 (69.1%) said that they would still choose to become a doctor with approximately equal proportions between males and females (68% vs 70.5%, p-value = 0.79). In conclusion, our survey of male and female doctors at a government university hospital in Saudi Arabia revealed that the majority believed there was gender equality amongst doctors in terms of salaries, benefits, opportunities for promotion and entry into any field of medicine or pediatrics, but not surgery. However, there were significantly fewer women at consultant positions, a deficiency that needs to be addressed.</p></div

Prediction of Emotional Empathy in Intelligent Agents to Facilitate Precise Social Interaction

The research area falls under the umbrella of affective computing and seeks to introduce intelligent agents by simulating emotions artificially and encouraging empathetic behavior in them, to foster emotional empathy in intelligent agents with the overarching objective of improving their autonomy. Raising the emotional empathy of intelligent agents to boost their autonomic behavior can increase their independence and adaptability in a socially dynamic context. As emotional intelligence is a subset of social intelligence, it is essential for successful social interaction and relationships. The purpose of this research is to develop an embedded method for analyzing empathic behavior in a socially dynamic situation. A model is proposed for inducing emotional intelligence through a deep learning technique, employing multimodal emotional cues, and triggering appropriate empathetic responses as output. There are 18 categories of emotional behavior, and each one is strongly influenced by multimodal cues such as voice, facial, and other sensory inputs. Due to the changing social context, it is difficult to classify emotional behavior and make predictions based on modest changes in multimodal cues. Robust approaches must be used to be sensitive to these minor changes. Because a one-dimensional convolutional neural network takes advantage of feature localization to minimize the parameters, it is more efficient in this exploration. The study’s findings indicate that the proposed method outperforms other popular ML approaches with a maximum accuracy level of 98.98 percent when compared to currently used methods

Demographic characteristics of 304 doctors at King Khalid University Hospital, Riyadh.

<p>Demographic characteristics of 304 doctors at King Khalid University Hospital, Riyadh.</p

Hospital attire of women doctors at King Khalid University Hospital.

<p>Hospital attire of women doctors at King Khalid University Hospital.</p