An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle

Flash-based geothermal cycles correspond to environmentally friendly and cost-effective processes in a renewable framework and provide an opportunity for combined cycles. However, these cycles are characterized by significant energy losses and their waste stream’s low/medium operational temperature is the principal defect for managing multiple generation arrangements without assisting other energy resources. Hence, the main aim of this study is to propose a novel polygeneration scheme, integrated with a dual-flash geothermal cycle equipped with self-superheaters, able to mitigate the discussed defect. A new coupled series and parallel design of energy recovery is established, allowing to increase the compatibility of combined cycles and enable a larger production. This design encompasses a single-effect refrigeration cycle, a modified transcritical CO2 cycle, a polymer electrolyte membrane electrolyzer, and a thermal desalination cycle. The proposed process is examined from thermodynamic, sustainability, and economic (exergoeconomic and net present value analyses) points of view. Besides, a detailed sensitivity study is conducted by which the trend of performance variables in response to the increasing five main decision parameters is viewed. Afterward, an intelligent approach relying on an artificial neural network is built to learn and validate the behavior of defined objective functions (exergetic efficiency and products’ levelized cost). Moreover, a multi-objective grey wolf optimization (MOGWO) procedure endeavors to optimize the operation of the system. According to the results of this study, flash tank 2′ s inlet pressure is the effective parameter, and its mean sensitivity index equals 0.289. Besides, the aforementioned objectives are gauged at 37.45% and 0.0625 $/kWh, respectively

Comparison of antimicrobial effect of Ziziphora tenuior, Dracocephalum moldavica, Ferula gummosa, and Prangos ferulacea essential oil with chlorhexidine on Enterococcus faecalis: An in vitro study

Background: Different irrigating solutions with high antimicrobial activity have been introduced for cleaning of the root canal system. However, effects of Prangos ferulacea (PF), Ziziphora tenuior (ZT), Dracocephalum moldavica (DM), and Ferula gummosa (FG) on oral and dental pathogens have not been extensively evaluated due to their optimal biocompatibility and insignificant side effects. The aim of this study was to evaluate the antibacterial effects of essential oils of mentioned plants on Enterococcus faecalis. Materials and Methods: In this in vitro study the plants were collected from Zanjan Province, Iran. Analysis of the essential oil was carried out by gas chromatography/mass chromatography. Micro-broth dilution and disc diffusion methods were used for assessment of the antimicrobial activity, and minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were evaluated. Results: All the four essential oils had antibacterial effects on E. faecalis, and ZT had the greatest antibacterial activity. Assessment of the mean diameter of the growth inhibition zone showed higher antibacterial activity of PF and ZT than that of chlorhexidine. The MIC and MBC of ZT showed that the antimicrobial activity of ZT against E. faecalis was greater than that of other plants evaluated in this study. Conclusion: The results of this study indicated significant antibacterial effects of the mentioned plants on E. faecalis. The greatest antimicrobial activity belonged to ZT. The current study suggests extraction of effective compounds in these medicinal plants to use them in the clinical setting

Thyroid Nodule Detection and Region Estimation in Ultrasound Images: A Comparison between Physicians and an Automated Decision Support System Approach

Background: Thyroid nodules are very common. In most cases, they are benign, but they can be malignant in a low percentage of cases. The accurate assessment of these nodules is critical to choosing the next diagnostic steps and potential treatment. Ultrasound (US) imaging, the primary modality for assessing these nodules, can lack objectivity due to varying expertise among physicians. This leads to observer variability, potentially affecting patient outcomes. Purpose: This study aims to assess the potential of a Decision Support System (DSS) in reducing these variabilities for thyroid nodule detection and region estimation using US images, particularly in lesser experienced physicians. Methods: Three physicians with varying levels of experience evaluated thyroid nodules on US images, focusing on nodule detection and estimating cystic and solid regions. The outcomes were compared to those obtained from a DSS for comparison. Metrics such as classification match percentage and variance percentage were used to quantify differences. Results: Notable disparities exist between physician evaluations and the DSS assessments: the overall classification match percentage was just 19.2%. Individually, Physicians 1, 2, and 3 had match percentages of 57.6%, 42.3%, and 46.1% with the DSS, respectively. Variances in assessments highlight the subjectivity and observer variability based on physician experience levels. Conclusions: The evident variability among physician evaluations underscores the need for supplementary decision-making tools. Given its consistency, the CAD offers potential as a reliable “second opinion” tool, minimizing human-induced variabilities in the critical diagnostic process of thyroid nodules using US images. Future integration of such systems could bolster diagnostic precision and improve patient outcomes