Symmetry

Problematic soils pose a risk to structures due to excessive or uneven settlement, swelling, and limited bearing capacity. To address some of these issues, mechanical reinforcement using geosynthetic products such as geocells can be employed, given their ease and speed of implementation. The purpose of this study was to assess the impact of geocells on the behavior of soft clay soil in Port Said, Egypt, using a three-dimensional finite element model, namely, the PLAXIS 3D software. The results show that replacing the top 1.5 m of the soft clay with sand significantly reduced settlement. This improvement was further enhanced by the addition of geocells available in the local market, where settlement was additionally reduced by approximately 51% to 63%, along with an appreciable increase in the bearing capacity. The effect of geocell material parameters was also investigated, including cell height, pocket dimensions, embedment depth, and reinforcement width. The results indicate that the best performance among the tested cases was achieved with a cell height of 300 mm, pocket dimensions of 210 × 245 mm, an embedment depth of 0.1 B, and a reinforcement width of 2 B, where B represents the foundation width. Furthermore, using gravel as both the replacement material and the geocell infill further improved performance, particularly in the presence of groundwater, by reducing settlement and increasing bearing capacity. The results indicate that combining gravel replacement with geocell reinforcement represents the most effective solution for improving the stability of soft clay soils in the study area.

Computer models and protocols commonly used for superficial thermotherapy assume anatomically symmetric tissue geometries. However, anatomical variability, particularly the variability of the adipose tissue thickness, may have a significant impact on heat transfer and cause non-uniform thermal doses in the patient population. This paper studies the influence of anatomical asymmetry due to adipose tissue thickness on heat propagation during superficial thermotherapy based on combined computational and experimental analyses. A Finite Element Model of the lower limb was designed with anatomically representative layers and tissue thermal properties. Parametric simulations were conducted for medium (M) and extra-large (XL) models at hot-pack temperatures (38–44 °C) for 15 min. The simulation results were compared with experimental measurements on ex vivo porcine tissue. The results showed that the thickness of adipose tissue significantly affected deep-tissue heating. When the same heating condition was applied, the boundary temperature of the muscle was increased by ~1.38 °C for the 44 °C hot-pack in the M-size model and was restricted to 0.21 °C for the XL-size model. The experimental muscle temperature increased from 22.26 °C to approximately 23.3 °C after 15 min. Under the same initial temperature and nominal 38 °C heating condition, the perfused and zero-perfusion M-size simulations reached approximately 26.8 °C and 30.5 °C, respectively; these endpoint differences support a qualitative comparison rather than quantitative validation. These results show that anatomical asymmetry has a strong effect on heat transfer during superficial thermotherapy and challenge the precept that standard heating protocols provide similar thermal doses in different body morphologies.

The Least Square Projection Twin Support Vector Machine (LSPTSVM) is an effective machine learning tool for solving classification problems. However, LSPTSVM does not account for the contribution of each sample during training, making it susceptible to outliers and noise. This susceptibility diminishes its generalization capability. To remedy this shortcoming, this paper introduces an Intuitionistic Fuzzy LSPTSVM (IFLSPTSVM). This model combines the LSPTSVM with the concept of Intuitionistic Fuzzy Numbers (IFN). In the training process of IFLSPTSVM, the importance of each training sample is gauged using an IFN-based score function that considers its geometric position and surrounding environment. Moreover, the weighted class mean, as opposed to the standard mean used in LSPTSVM, is employed in the calculation of intra-class scatter based on the intuitionistic fuzzy score of the sample. This approach effectively mitigates the impact of noise and outliers and more accurately captures the global information of the class samples. Experimental results on several real-world UCI benchmark datasets and the Case Western Reserve University rolling bearing datasets exhibit the efficacy of the proposed method.