Average Absolute Deviation Percent Research Articles

Energy levels and thermodynamic models of the general molecular oscillator under a 2D electromagnetic potential field

This study employs the parametric Nikiforov-Uvarov approach (PNUA) to resolve the radial Schrödinger equation (RSE) for the general molecular oscillator with a 2D electromagnetic potential coupling. Analytical approximations are developed for the energy levels, molar enthalpy, and constant-pressure molar heat capacity, with a focus on their applicability to diatomic molecules. The generated equations are employed to investigate the physical properties of real substances like BeCl (X 2Σ+), CsF (X 1Σ+), CuCl (X 1Σ+), CO+ (X 2Σ+), 7Li2 (1 3Δg), and P2 (X 1Σg +) molecules. The percentage average absolute deviations (PAAD) deduced with the analytical model equations are found to agree with the findings on diatomic molecules. Analysis of PAAD values also reveals that the predicted molar enthalpy and heat capacity of the diatomic molecules are better if the magnetic and Aharonov-Bohm components of the EM potential fields are finite.

7938 Validation of Inflammatory Cytokine Multiplex Array With Variations in Target Panel

Abstract Disclosure: W.M. Sanders: None. C.A. Karvonen-Gutierrez: None. D.S. McConnell: None. Background: Multiplex arrays are an invaluable tool for conducting large-scale biomarker research as they provide a high-throughput, cost-effective method for quantifying a panel of endocrine markers. However, the shared reaction environment between antibodies of differing reactivities creates the potential for inducing a technical difference in the assay methods due to any variation in panel selection. Methods: We evaluated the R&D Systems Luminex HS Cytokine Panel A (Catalog # FCSTM09) for systematic differences when measuring a “panel” of IL-1β, TNF-α, and IL-10 vs. measuring “IL-10 only.” We selected 72 serum samples and assayed them in singleton 4 times: 2 separate “panel” batches, and 2 separate “IL-10 only” batches. Differences in log-transformed IL-10 results for sample replicates within and between the “panel” and “IL-10 only” batches were assessed for significance with paired t-tests (α=.01). We also assessed the differences graphically with Bland-Altman plots of differences and absolute percent differences.All batches included control materials from R&D Systems and internal lab serum pools (male and female). To retroactively assess the test batches, we graphically compared their quality control results to those of the subsequent, larger assay project we were piloting. Results: The “IL-10 only” assay yielded an average result of .879 pg/mL (SD .57) on batch 1 and .712 pg/mL (SD .58) on batch 2, with an average paired sample difference of .167 pg/mL, and an average absolute percent difference of 28.8%. The “panel” assay produced an average IL-10 result of .645 pg/mL (SD .50) on batch 1 and .720 pg/mL (SD .52) on batch 2, with an average paired sample difference of .075 pg/mL, and an average absolute percent difference of 15.5%. Comparing pooled IL-10 results from the “IL-10 only” batches with the “panel” batches, there was an average paired difference of .113 pg/mL and absolute percent difference of 16.2%. Paired t-testing of log-transformed results for within “panel,” within “IL-10 only,” and between pooled results from the two methods demonstrated that each had significant differences (p<.0001). The Bland-Altman plots further revealed that these differences were ubiquitously large for samples with low concentrations of IL-10.Test batch quality control results were consistent with those in the primary assay project. The serum pool QCs exhibited significant inter-assay variation – the male pool average result was .41 pg/mL (SD .10) vs. .359 in the test batches, and the female pool average was .57 pg/mL (SD .10) vs. .548 in the test batches. Conclusion: The multiplex assay had considerable inter-assay variation at low concentrations of IL-10, and low results were prevalent. Due to this, we determined that any methodological differences between the “panel” and “IL-10 only” methods were undetectable compared to ambient variation, and irrelevant given the sample characteristics. Presentation: 6/3/2024

Diffusion in Polymethylene Chain Solutions with a Polar Component.

A hydrodynamic bead model based on Kirkwood-Riseman theory has been used to calculate the translational diffusion constant, D, for solutes with polymethylene chains in solutions with a polar component. A comparison with the experimental values for nonpolar n-alkanes in polar 2-propanol, tetrahydrofuran, chlorobenzene, chloroform, 1-octanol, quinoline, and chloroform (78 D values) and polar primary alcohols in nonpolar benzene and n-octane (24 D values) gives an average absolute percentage difference of ∼3% between the 102 experimental and calculated D values. Consideration of the solvent's temperature-dependent changes in the degree of aggregation due to hydrogen bonding was necessary for the n-alkanes in 1-octanol. Good agreement was not found for primary alcohols in 1-octanol due to solute-solvent hydrogen bonding. A correlation that depends on the solutes' molar volumes instead of their chain lengths gives worse agreement than the bead model for the n-alkanes in polar solvents and primary alcohols and n-alkanes in nonpolar solvents.

Energy spectra with the Dirac equation of the q-deformed generalized Pöschl-Teller potential via the Feynman approach for .

The diatomic molecules of potassium is widely used in industrial chemicals and alternative energy. Besides that, is very useful for researching molecular interactions and energy states, especially in the context of quantum chemistry and spectroscopy. In the present work, a newly proposed diatomic potential model within relativistic and non-relativistic quantum mechanics has been considered, to obtain corresponding energy eigenvalues and related normalized eigenfunctions. The Dirac equation has been solved for an arbitrary spin-orbit quantum number using the path integral technique with the -deformed generalized Pöschl-Teller potential . By including a Pekeris-type approximation to handle the centrifugal factor, it was possible to obtain the spin and pseudospin-symmetric solution of the relativistic energy eigenvalues and wave equation. To assess the correctness of this work, Maple software was used to present some numerical findings for various values of and . With the constraint , it was shown that in the situation of pseudospin symmetry, only bound states exist with negative energy. In the non-relativistic limits, the non-relativistic ro-vibrational energy expression of the diatomic molecule is derived from the relativistic energy equation under spin symmetry. Under Varshni conditions, both vibrational and ro-vibrational energies of the molecule were computed and compared with the data. The average absolute percentage deviations from the data obtained for the potassium molecule are . This demonstrates that the model is a very consistent model to study and characterize diatomic molecules.

Experimental study and thermodynamic modeling of CO2+CH4 gas mixture hydrate phase equilibria for gas separation efficiency

Gas separation is a critical application of gas hydrates, and accurately predicting separation performance is crucial. In this study, we used thermodynamic calculations to predict the equilibrium phase of gas hydrates for various mole fractions of CO2 + CH4 gas mixtures. We also determined the mole fraction of each gas component trapped within the hydrate clathrate. To predict the equilibrium points, we used the Soave–Redlich–Kwong（(SRK) equation of state for the gas phase, the nonrandom two-liquid (NRTL)model for the liquid phase, and the Chen–Guo model for the hydrate phase. We modified the hydrate fugacity formula and introduced a new function to improve the accuracy of the Chen–Guo model. By incorporating experimental equilibrium results from our study and another study, we developed a correlation based on gas mixture composition and temperature, resulting in highly accurate predictions. The use of this new correlation for hydrate fugacity calculation significantly improved precision, as evidenced by an average absolute deviation percent of calculated pressures (AADP) of 1.34% for pure CO2 and 1.25% for CH4. When considering the 27 data points of different CO2 + CH4 mixtures, the AADP% was 1.98%.To implement the model to predict equilibrium phases, we used the Chen–Guo framework to determine the mole fraction of each gas component in the hydrate mixture. Interestingly, we discovered a linear correlation between the CO2 mole fraction in the hydrate and equilibrium pressure, with a slope of approximately 0.001 and a y-intercept of less than one, for all gas compositions. Therefore, we can conclude that low thermodynamic conditions (temperature and pressure) result in a high CO2 mole fraction in the hydrate phase and great separation efficiency.

Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection

This study introduces an innovative data-driven and machine-learning framework designed to accurately predict site scores in the site screening study for specific offshore CO2 storage sites. The framework seamlessly integrates diverse sub-surface geospatial data sources with human aided expert-weighted criteria, thereby providing a high-resolution screening tool. Tailored to accommodate varying data accessibility and the significance of criteria, this approach considers both technical and non-technical factors. Its purpose is to facilitate the identification of priority locations for projects associated with Carbon Capture, Utilization, and Storage (CCUS). Through aggregating and analyzing geospatial datasets, the study employs machine learning algorithms and an expert-weighted model to identify suitable geologic CCUS regions. This process adheres to stringent safety, risk control, and environmental guidelines, addressing situations where human analysis may fail to recognize patterns and provide detailed insights in suitable site screening techniques. The primary emphasis of this research is to bridge the gap between scientific inquiry and practical application, facilitating informed decision-making in the implementation of CCUS projects. Rigorous assessments encompassing geological, oceanographic, and eco-sensitivity metrics contribute valuable insights for policymakers and industry leaders. To ensure the accuracy, efficiency, and scalability of the established offshore CO2 storage facilities, the proposed machine learning approach undergoes benchmarking. This comprehensive evaluation includes the utilization of machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Extreme Learning Machine (MLELM), and Deep Neural Network (DNN) for predicting more suitable site scores. Among these algorithms, the DNN algorithm emerges as the most effective in site score prediction. The strengths of the DNN algorithm encompass nonlinear modeling, feature learning, scale invariance, handling high-dimensional data, end-to-end learning, transfer learning, representation learning, and parallel processing. The evaluation results of the DNN algorithm demonstrate high accuracy in the testing subset, with values of AAPD (Average Absolute Percentage Difference) = 1.486 %, WAAPD (Weighted Average Absolute Percentage Difference) = 0.0149 %, VAF (Variance Accounted For) = 0.9937, RMSE (Root Mean Square Error) = 0.9279, RSR (Root Sum of Squares Residuals) = 0.0068, and R2 (Coefficient of Determination) = 0.9937.

A study on thermophysical properties of binary mixtures of n-hexane with benzene and some alkyl-substituted benzenes within temperature range (293.15–323.15) K: Experimental and modeling approach

The current work reports densities and sound speeds, and related thermodynamic excess properties, namely excess molar volumes and excess isentropic compressibilities, measured at temperatures from (293.15 to 323.15) K and under atmospheric pressure conditions for binary mixtures of n-hexane with benzene, toluene, ethylbenzene, o-xylene, m-xylene, and p-xylene. Redlich-Kister polynomial correlated the thermodynamic excess properties to test the quality of experimental data. Excess properties contributed to understanding molecular interactions between involved molecules and the peculiarities of their packing in the mixture. The Jouyban-Acree model was used to correlate the mixtures' experimental densities, sound speeds, and their related derived properties, namely isobaric thermal expansivities and isentropic compressibilities. The average absolute percentage deviation of the correlated values from the experimental ones was better than 0.036 %, 0.058 %, 0.040 %, and 0.146 % for density, sound speed, isobaric thermal expansivity, and isentropic compressibility, respectively, attesting to the robustness of the Jouyban-Acree correlations. Additionally, the Perturbed Chain Statistical Associating Fluid Theory Equation of State modeled the densities of the current mixtures. Schaaff's Collision Factor Theory and Nomoto's relation were compared for their ability to model the sound speeds of the mixtures. The efficacy of these models was tested by computing the average absolute percentage deviations between experimental and computed values. The modeled densities are reasonably concordant with experimental data, with an overall deviation of 0.02 %. Notably, Nomoto's relation exhibited superior performance over Schaaff's theory in modeling sound speeds of current mixtures, with an overall deviation of 0.33 %. The current findings underline the efficacy and versatility of the used models for thermodynamic modeling in systems of varying complexity.

Theoretical prediction of molar entropy of modified shifted Morse potential for gaseous molecules

The modified shifted Morse potential is a molecular potential that describes the molecular vibration and atomic interaction. This potential has not received much report as one of the useful potential function. In this study, the molar entropy of a modified shifted Morse is obtained via the vibrational partition function, rotational partition function and translational partition function. Numerical results are obtained for iodine molecule (I2), silicon carbide (SiC), carbon phosphide (CP) and fluorine molecule (F2) for temperatures of 200 K to 6000 K. The predicted results are compared with the experimental results obtained from the National Institute of Standards and Technology (NIST) data base. The predicted values aligned with the experimental results for the four molecules. The average absolute percentage deviation obtained for each molecule is less than one, which shows a good representation of the modified shifted Morse potential for thermodynamic functions.

Density and viscosity measurement of R513A and a modified residual entropy scaling model for predicting the viscosity of HFC/HFO refrigerants

Liquid density and viscosity of alternative refrigerants R513A were measured with a vibrating-wire viscosimeter in the temperature of 253 K to 363 K when pressure changes from 1 MPa to 12 MPa. The combined expended uncertainties with a confidence level of 0.95 (k = 2) of density and viscosity are 0.2 % and 2 %, respectively. In addition, a viscosity model of HFCs (fluorocarbons)/, HFOs (hydro-fluoroolefins) and their mixtures was provided by coupling the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state and a modified residual entropy scaling (RES) method. Obvious quasi-linear relation between the proposed entropy variable and the modified residual viscosity has been confirmed. The overall average absolute percentage deviations between the calculated results with the proposed RES model and the literature experimental data of pure and mixture refrigerants are 2.63 % and 2.05, respectively.

Generalized Analytical Well-Test Solutions for Vertically Fractured Wells in Commingled Reservoirs

Abstract Layered zones with vertical fracture are often found in analyzing well-test data of deep/ultra-deep reservoirs and tight reservoirs. Analyzing and modeling the well-test data in a computation-accurate and easy-program manner have been a challenge for these problems due to the lack of suitable solutions. This work thus presents the generalized analytical well-test solutions for vertically fractured wells in infinite and bounded commingled reservoirs with computation accuracy and functional simplicity. These solutions are derived based on the early-time approximate solution of the infinite/finite-conductivity fracture model, Laplace and Fourier cosine transformation, pressure superposition principle, and Duhamel principle. Subsequently, model validation is carried out by comparing the pressure and derivative results with those of commercial saphir software. The results show that the average absolute percent deviation between the presented analytical solutions and saphir for three kinds of outer boundaries is ∼2% for pressure results and ∼4% for pressure derivative results. Finally, a field case in Xinjiang oilfield is interpreted, indicating that the proposed analytical well-test solutions are feasible to interpret the parameters of commingled reservoirs.

Repeatability and reproducibility of quantitative OCT angiography measurements from table-top and portable Flex Spectralis devices.

The recent development of a portable investigational handheld OCT-angiography (OCTA) device has allowed for expansion of imaging into the operating room (OR) in addition to standard in-clinic imaging. The aim of this study was to assess intravisit repeatability and intervisit reproducibility of retinal microvasculature measures and central retinal thickness for in-clinic table-top and portable OR compatible OCTA devices. Repeated 10 × 10 OCTA images were acquired in 20 healthy adult participants on two separate visit days using Spectralis spectral-domain OCTA table-top and investigational armature suspended Flex systems. Intravisit and intervisit intraclass correlation coefficients and average absolute percent difference were calculated for quantitative microvasculature measures and CRT. 120 OCTA images were acquired from 20 subjects (n = 20, mean age 26.7 ± 1.61years, range 24-30years) with both devices across two separate imaging days. FAZ and CRT measurements had near complete intravisit and intervisit agreement with ICCs between .97 and 1 for both table-top (FAZ ICC .97, .97; CRT ICC .98-1, .98-.99) and Flex (FAZ ICC .97, .95; CRT ICC .99-1, .98-.99) devices. Vessel density measures demonstrated greater variance with only fair to strong agreement (ICC .32-.75) and average absolute percent differences ranging from 2.96 to 6.63%. FAZ and CRT measures for both devices demonstrated high repeatability and reproducibility; retinal vessel density measures demonstrated less. Differences of less than 7% for retinal microvasculature measurements across time and devices are most likely attributable to expectable variance between repeat scans.

A new Frontier in electric load forecasting: The LSV/MOPA model optimized by modified orca predation algorithm

Electric load forecasting is a vital task for energy management and policy-making. However, it is also a challenging problem due to the complex and dynamic nature of electric load data. In this paper, a novel technique, called LSV/MOPA, has been proposed for electric load forecasting. The technique is a hybrid model that combines the advantages of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), two powerful artificial intelligence algorithms. The hybrid model is further optimized by a newly Modified Orca Predation Algorithm (MOPA), which enhances the forecasting accuracy and efficiency. The LSV/MOPA model has been applied to historical electric load data from South Korea, covering four regions and 20 years. The LSV/MOPA model has been compared with other state-of-the-art forecasting techniques, including SVR/FFA, LSTM/BO, LSTM-SVR, and CNN-LSTM. The results show that the LSV/MOPA model with minimum average mean absolute percentage deviation error, including 365 in northern region, 12.8 in southern region, 8.6 in central region, and 30.8 in eastern region, provides the best fitting and outperforms the other techniques in terms of the Mean Absolute Percentage Deviation (MAPD) index, achieving lower values for all regions and years. The LSV/MOPA model also exhibits faster convergence and better generalization than the other techniques. This study demonstrates the effectiveness and superiority of the LSV/MOPA model for electric load forecasting and suggests its potential applications in other sectors where accurate forecasting is crucial.

Analytical optimization of open hole effects on the tensile properties of SS400 sheet specimens using an integrated FFD-CRITIC-DFA method

Structural components are generally composed of material discontinuities, including open holes, which are considered stress concentrators in engineering components. In view of this, assessing the influence of open holes on the tensile properties is crucial to determine the sensitivity and tensile strength of a particular material. Nevertheless, investigation of the impact of open holes on the tensile properties of SS400 steel sheets is very limited and yet to be explored. Therefore, this study was performed to optimize the effects of open holes on the tensile properties of SS400 sheet specimens based on a Full Factorial Design (FFD) experiment. Four input parameters that represent various hole configurations, which include the hole diameter, location of the hole, number of holes, and hole shape, were considered in this study to develop the experimental-based prediction models to optimize the output performance, namely yield strength, ultimate tensile strength, and ultimate elongation, commonly denoted as YS, UTS, and UE respectively. A total of 10 additional experimental trials were then utilized to verify the constructed models. In addition, the weight fractions for YS, UTS, and UE were identified using the Criteria Importance Through Inter-Criteria Correlation (CRITIC) method. Subsequently, the Desirability Function Analysis (DFA) is utilized to pinpoint the optimal parameter conditions for maximizing the tensile properties. Based on the results, all four parameters showed significant effects on the response variables, except the number of holes for UTS and hole location for UE. The diameter also recorded the highest contribution toward UTS and UE, followed by the hole shape. Regarding YS, hole diameter takes precedence, with the number of holes as the second most influential factor. Furthermore, the average absolute percent deviation for the prediction responses of 10 experimental cases were 1.06 %, 0.90 %, and 0.85 % for YS, UTS, and UE, respectively, confirming the validity of the constructed models. Meanwhile, the CRITIC method estimated the weight fractions for YS, UTS and UE from the experimental data, which were 0.3825, 0.2559, and 0.3616, respectively. The DFA-derived composite desirability, rated at 0.9820, suggests optimal conditions: a 1 mm hole diameter, centered hole location, three holes, and a hexagonal shape. The minimal deviations between predicted and experimental values affirm the robustness of the models. Overall, this investigation yields important insights for optimizing open holes and elevating the tensile performance of SS400 sheet specimens.

Generalized Analytical Solutions of Vertically Fractured Wells in Commingled Reservoirs: Field Case Study

Summary Accurate identification of the individual-layer parameters for vertically fractured wells in commingled reservoirs is essential for development plan design, reservoir numerical simulation, and stimulation measure selection. Different semi-analytical and numerical models are generally applied in multilayer transient testing (MLT) analysis to determine the properties of individual layer. However, these approaches require numerous computations and are complicated to program due to the fracture and reservoir discretization. This work thus presents the generalized analytical solutions of vertically fractured wells in infinite, closed, or constant-pressure commingled reservoirs with both computational and functional simplicity. The fully analytical solutions are derived based on the early-time approximate solutions of infinite-conductivity fracture and trilinear flow models, infinite-conductivity fracture solutions, pressure superposition principle, and Duhamel principle. A systematic verification by employing a standardized well testing software and trilinear flow model is conducted to ensure the general application accuracy of the presented solutions. The results show that the developed analytical solutions are valid when the dimensionless fracture conductivity is more than 2 (FcD > 2) with an average absolute percent deviation (AAD) of ~2% for pressure and that is ~4% for pressure derivative. The developed analytical solutions also exhibit improvements in early-time pressure and derivative calculation. Finally, a field case of a four-layer fractured well is interpreted by the developed solutions and well testing software to illustrate the feasibility. The interpretation results of two methods are nearly identical, with only a minor difference. The developed analytical solutions are computationally accurate while maintaining functional simplicity and can be considered as an alternative to the current semi-analytical and numerical approaches in MLT analysis.

Bound-state energy spectrum and thermochemical functions of the deformed Schiöberg oscillator

In this study, a diatomic molecule interacting potential such as the deformed Schiöberg oscillator (DSO) have been applied to diatomic systems. By solving the Schrödinger equation with the DSO, analytical equations for energy eigenvalues, molar entropy, molar enthalpy, molar Gibbs free energy and constant pressure molar heat capacity are obtained. The obtained equations were used to analyze the physical properties of diatomic molecules. With the aid of the DSO, the percentage average absolute deviation (PAAD) of computed data from the experimental data of the 7Li2 (2 3Πg), NaBr (X 1Σ+), KBr (X 1Σ+) and KRb (B 1Π) molecules are 1.3319%, 0.2108%, 0.2359% and 0.8841%, respectively. The PAAD values obtained by employing the equations of molar entropy, scaled molar enthalpy, scaled molar Gibbs free energy and isobaric molar heat capacity are 1.2919%, 1.5639%, 1.5957% and 2.4041%, respectively, from the experimental data of the KBr (X 1Σ+) molecule. The results for the potential energies, bound-state energy spectra, and thermodynamic functions are in good agreement with the literature on diatomic molecules.

Modeling of multiphase heat and mass transport and drying behavior of potash particles in a packed bed: Experiments and simulation

The drying behavior of potash particles was experimentally investigated in a convective packed bed dryer under different operating conditions, including inlet gas temperature of 40 °C, 50 °C, and 60 °C, moisture content of 2.8 wt%, and superficial gas velocity of 0.3 m/s. When the temperature increased from 40 °C to 50 °C and then to 60 °C, the drying time decreased from 930 s to 870 s and then to 720 s, respectively. The thermal conductivity and specific heat capacity of potash particles as a function of moisture content and porosity of the packed bed was determined at a 95 % confidence interval to have R2 values of 0.94 and 0.91, respectively. During drying of potash particles, solid bridges form, and the whole bed behaves like a porous medium with void spaces that expand. In this work, a conjugate heat and mass transfer model was developed for batch packed bed drying of wet potash particles. Computational fluid dynamics was used to simulate the momentum, mass, and energy transport in a porous medium based on the physical properties of potash particles. The model successfully correlated the experimental particle moisture content versus time with average absolute deviation percentage values of 14.8, 3.2, and 14.7 for the inlet gas temperatures of 40 °C, 50 °C, and 60 °C, respectively.

Prediction of Electron Beam Welding Penetration Depth Using Machine Learning-Enhanced Computational Fluid Dynamics Modelling

The necessity for precise prediction of penetration depth in the context of electron beam welding (EBW) cannot be overstated. Traditional statistical methodologies, including regression analysis and neural networks, often necessitate a considerable investment of both time and financial resources to produce results that meet acceptable standards. To address these challenges, this study introduces a novel approach for predicting EBW penetration depth that synergistically combines computational fluid dynamics (CFD) modelling with artificial neural networks (ANN). The CFD modelling technique was proven to be highly effective, yielding predictions with an average absolute percentage deviation of around 8%. This level of accuracy is consistent across a linear electron beam (EB) power range spanning from 86 J/mm to 324 J/mm. One of the most compelling advantages of this integrated approach is its efficiency. By leveraging the capabilities of CFD and ANN, the need for extensive and costly preliminary testing is effectively eliminated, thereby reducing both the time and financial outlay typically associated with such predictive modelling. Furthermore, the versatility of this approach is demonstrated by its adaptability to other types of EB machines, made possible through the application of the beam characterisation method outlined in the research. With the implementation of the models introduced in this study, practitioners can exert effective control over the quality of EBW welds. This is achieved by fine-tuning key variables, including but not limited to the beam power, beam radius, and the speed of travel during the welding process.

Using the method of self-similar functions for estimating the normal boiling temperatures of homologous series

An equation for estimating the normal boiling points of homologous series with the general formula R1(CH2)nR2 has been developed. The equation is based on the hypothesis of functional self-similarity. The parameters of the equation have been determined using a current experimental database for 10 homologous series. The equation allows predicting the normal boiling temperature of any member of any homologous series of the type R1(CH2)nR2 if the normal boiling temperature is known for one compound which belongs to this series. The proposed method gives an absolute average percentage deviation from the experimental values of the normal boiling point of 0.72 % for all the homologous series studied. A thermodynamically consistent model for estimating the critical temperature, critical pressure, and normal boiling point of n-alkanes has been developed.

3D Brain Tumour Segmentation Using UNet with Quantitative Analysis of the Tumour Features

Brain tumours are abnormal cells that grow within the brain and may cause severe harm to an individual. The diagnosis of tumours often requires a manual review of Magnetic Resonance (MR) images by a skilled radiologist, which is a laborious task. The study aims to use a deep learning-based approach to segment the brain tumour and quantitatively evaluate the performance of the model by comparing the statistical distribution of five textural and four non-textural feature differences between the ground truth and the prediction. Textural features were extracted from a Grey Level Co-occurrence Matrix (GLCM) and averaged over four different directions. A UNet model was constructed with MONAI using BraTS 2020 dataset. Due to hardware constraints, the model was trained over a short epoch range of 100 but managed to achieve a mean Dice similarity coefficient (DSC) of 0.86. The quantitative analysis showed that the ground truth and the prediction had low differences in most features, except for percentage variation in signal intensity, energy, correlation and Hausdorff distance, which could be attributed to false positive voxels that are far from the ground truth. The textural properties of homogeneity, entropy, and correlation had an average absolute percentage difference of less than 5% from the ground truth.

Performance prediction of a supercritical CO2 Brayton cycle integrated with wind farm-based molten salt energy storage: Artificial intelligence (AI) approach

Using energy storage technologies in combination with renewable energy sources can improve power generation efficiency and reliability. Here, we predict the performance of a wind farm-based molten salt energy storage system combined with a supercritical CO2 Brayton cycle (s-CO2BC) with the aid of artificial intelligence (AI). For this purpose, the Grasshopper Optimization Algorithm (GOA) and Particle Swarm Optimization (PSO) are applied to train a Multilayer Perceptron Artificial Neural Network (MLP-ANN). The proposed AI approaches model the complex interactions between the wind farm, the molten salt energy storage, and the s-CO2BC components. Integrated system simulations under various operating conditions are used for training the performance prediction model. Power generation, energy storage, and system efficiency can all be accurately predicted by a trained AI model. In addition to providing valuable insight into the optimal operation and control strategies of the integrated system, the proposed approach enables the efficient utilization of renewable energy resources and energy storage to generate sustainable power. This study contributes to the development of AI-based optimization approaches and predicts the performance of integrated renewable energy systems, resulting in improved grid integration and stability. According to the results, the PSO method yields average absolute percentage deviations (AAPDs) between 0.47% and 0.51% across different parameters, while GOA yields AAPDs between 0.05% and 0.27%.