Metaheuristic algorithms currently represent the standard approach to engineering optimization. A very challenging field is large-scale structural optimization, entailing hundreds of design variables and thousands of nonlinear constraints on element stresses and nodal displacements. However, very few studies documented the use of metaheuristic algorithms in large-scale structural optimization. In order to fill this gap, an enhanced hybrid harmony search (HS) algorithm for weight minimization of large-scale truss structures is presented in this study. The new algorithm, Large-Scale Structural Optimization–Hybrid Harmony Search JAYA (LSSO-HHSJA), developed here, combines a well-established method like HS with a very recent method like JAYA, which has the simplest and inherently most powerful search engine amongst metaheuristic optimizers. All stages of LSSO-HHSJA are aimed at reducing the number of structural analyses required in large-scale structural optimization. The basic idea is to move along descent directions to generate new trial designs, directly through the use of gradient information in the HS phase, indirectly by correcting trial designs with JA-based operators that push search towards the best design currently stored in the population or the best design included in a local neighborhood of the currently analyzed trial design. The proposed algorithm is tested in three large-scale weight minimization problems of truss structures. Optimization results obtained for the three benchmark examples, with up to 280 sizing variables and 37,374 nonlinear constraints, prove the efficiency of the proposed LSSO-HHSJA algorithm, which is very competitive with other HS and JAYA variants as well as with commercial gradient-based optimizers.

This article presents a very detailed study on the mechanical characterization of a highly nonlinear material, the immature equine zona pellucida (ZP) membrane. The ZP is modeled as a visco-hyperelastic soft matter. The Arruda–Boyce constitutive equation and the two-term Prony series are identified as the most suitable models for describing the hyperelastic and viscous components, respectively, of the ZP’s mechanical response. Material properties are identified via inverse analysis based on nonlinear optimization which fits nanoindentation curves recorded at different rates. The suitability of the proposed approach is fully demonstrated by the very good agreement between AFM data and numerically reconstructed force–indentation curves. A critical comparison of mechanical behavior of two immature ZP membranes (i.e., equine and porcine ZPs) is also carried out considering the information on the structure of these materials available from electron microscopy investigations documented in the literature.

In tissue formation and regeneration processes, cells often move collectively, maintaining connections through intercellular adhesions. However, the specific roles of cell–substrate and cell-to-cell mechanical interactions in the regulation of collective cell migration are not yet fully understood. Finite element modeling (FEM) may be a way to assess more deeply the biological, mechanical, and chemical phenomena behind cell adhesion. FEM is a powerful tool widely used to simulate phenomena described by systems of partial differential equations. For example, FEM provides information on the stress/strain state of a cell adhering to a substrate, as well as on its mechanobiological behavior. This review paper, after briefly describing basic principles of cell adhesion, surveys the most important studies that have utilized FEM to investigate the structural response of a cell adhering to a substrate and how the forces acting on the cell–substrate adhesive structures affect the global cell mechanical behavior.

Mechanical characterization of soft materials is a complicated inverse problem that includes nonlinear constitutive behavior and large deformations. A further complication is introduced by the structural inhomogeneity of tested specimens (for example, caused by thickness variations). Optical methods are very useful in mechanical characterization of soft matter, as they provide accurate full-field information on displacements, strains and stresses regardless of the magnitude and/or gradients of those quantities. In view of this, the present study describes a novel hybrid framework for mechanical characterization of soft membranes, combining (i) inflation tests and preliminary in-plane equi-biaxial tests, (ii) a one-shot projection moiré optical setup with two symmetric projectors that project cross-gratings onto the inflated membrane, (iii) a mathematical model to extract 3D displacement information from moiré measurements, and (iv) metaheuristic optimization hybridizing harmony search and JAYA algorithms. The use of cross-gratings allows us to determine the surface curvature and precisely reconstruct the shape of the deformed object. Enriching metaheuristic optimization with gradient information and elitist strategies significantly reduces the computational cost of the identification process. The feasibility of the proposed approach wassuccessfully tested on a 100 mm diameter natural rubber membrane that had some degree of anisotropy in mechanical response because of its inhomogeneous thickness distribution. Remarkably, up to 324 hyperelastic constants and thickness parameters can be precisely identified by the proposed framework, reducing computational effort from 15% to 70% with respect to other inverse methods.

Several studies suggest that changes in nuclear morphology due to forces and deformations as result of cell adhesion on biological substrates can induce molecular streaming through nuclear pore openings and alter chromatin structure. The condensed state of chromatin hinders transcription and replication, while its decompaction, induced by adhesion, plays a key role in differentiation. However, assessing nuclear stress/strain in vivo remains challenging, and the impact of substrate curvature on nuclear mechanics and chromatin structures is still unclear. In this study, we developed an axisymmetric finite element model of a mesenchymal stem cell adhering to substrates with different curvatures to analyze nuclear stress distribution and identify locations where adhesion-induced gene expression may occur. Results reveal a nuclear stress field with principal stresses in radial and circumferential directions, leading to chromatin decondensation and nuclear pore opening. The predicted forces acting on chromatin fibers, estimated and compared with experimental data, remain slightly below 5 pN-the threshold at which internucleosomal attraction is disrupted, triggering chromatin condensation-decondensation transition-. During early spreading, nuclear forces achieved through adhesion on convex substrates approach this threshold more closely than in concave or flat cases. These findings provide insights for tissue engineering and regenerative medicine, where early control of stem cell fate through substrate design is crucial. Understanding how mesenchymal stem cells respond to substrate curvature could lead to improved biomaterial surface topographies for guiding cell behavior. Tailoring curvature and mechanical properties may enhance early lineage commitment, optimizing regenerative strategies for tissue repair and organ regeneration.