The genetic property of subclinical eating behaviour (SEB) and the link between SEB and polycystic ovary syndrome (PCOS) has been studied before but the role of leptin within this connection has never been investigated. The objective of this study was 1). to study the genetic property of SEB. 2). To find a link between leptin, SEB and PCOS. One hundred and fifty four (77 pairs) female-female Iranian twins including 96 MZ individuals (48 pairs) and 58 DZ individuals (29 pairs) participated in the study. Clinical, biochemical and ultrasound tools were used to diagnose polycystic ovary syndrome. BITE questionnaire was filled out for subjects. Eight percent of subjects were diagnosed for subclinical eating disorder. No significant difference was found between intraclass correlation of MZ and DZ (z = 0.57, P = 0.569). Serum leptin level correlated significantly with bulimia score (P < 0.007). The mean (+/-SD) value for bulimia score was found to be higher among PCOS(positive) subjects (3.27 +/- 5.51) in comparison with PCOS(negative) subjects (2.06 +/- 4.48) (P < 0.001). The genetic property of subclinical eating disorder was not confirmed as shared environment might have played a major role in likeliness of DZ twins as well as MZ. Leptin is linked with both subclinical eating disorder and PCOS.
Suspensions containing microencapsulated phase change materials (MPCMs) play a crucial role in thermal energy storage (TES) systems and have applications in building materials, textiles, and cooling systems. This study focuses on accurately predicting the dynamic viscosity, a critical thermophysical property, of suspensions containing MPCMs and MXene particles using Gaussian process regression (GPR). Twelve hyperparameters (HPs) of GPR are analyzed separately and classified into three groups based on their importance. Three metaheuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and marine predators algorithm (MPA), are employed to optimize HPs. Optimizing the four most significant hyperparameters (covariance function, basis function, standardization, and sigma) within the first group using any of the three metaheuristic algorithms resulted in excellent outcomes. All algorithms achieved a reasonable R-value (0.9983), demonstrating their effectiveness in this context. The second group explored the impact of including additional, moderate-significant HPs, such as the fit method, predict method and optimizer. While the resulting models showed some improvement over the first group, the PSO-based model within this group exhibited the most noteworthy enhancement, achieving a higher R-value (0.99834). Finally, the third group was analyzed to examine the potential interactions between all twelve HPs. This comprehensive approach, employing the GA, yielded an optimized GPR model with the highest level of target compliance, reflected by an impressive R-value of 0.999224. The developed models are a cost-effective and efficient solution to reduce laboratory costs for various systems, from TES to thermal management.
Optimization of thermophysical properties (TPPs) of MXene-based nanofluids is essential to increase the performance of hybrid solar photovoltaic and thermal (PV/T) systems. This study proposes a hybrid approach to optimize the TPPs of MXene-based Ionanofluids. The input variables are the MXene mass fraction (MF) and temperature. The optimization objectives include three TPPs: specific heat capacity (SHC), dynamic viscosity (DV), and thermal conductivity (TC). In the proposed hybrid approach, the powerful group method of data handling (GMDH)-type ANN technique is used to model TPPs in terms of input variables. The obtained models are integrated into the multi-objective particle swarm optimization (MOPSO) and multi-objective thermal exchange optimization (MOTEO) algorithms, forming a three-objective optimization problem. In the final step, the TOPSIS technique, one of the well-known multi-criteria decision-making (MCDM) approaches, is employed to identify the desirable Pareto points. Modeling results showed that the developed models for TC, DV, and SHC demonstrate a strong performance by R-values of 0.9984, 0.9985, and 0.9987, respectively. The outputs of MOPSO revealed that the Pareto points dispersed a broad range of MXene MFs (0-0.4%). However, the temperature of these optimal points was found to be constrained within a narrow range near the maximum value (75 °C). In scenarios where TC precedes other objectives, the TOPSIS method recommended utilizing an MF of over 0.2%. Alternatively, when DV holds greater importance, decision-makers can opt for an MF ranging from 0.15 to 0.17%. Also, when SHC becomes the primary concern, TOPSIS advised utilizing the base fluid without any MXene additive.