Ettore Barbieri

Understanding trophic positions is essential for analysing complex food webs. While calculating these positions can be straightforward when feeding relationships and their proportions are known, determining diet coefficients traditionally requires substantial time and effort. Modern analytical techniques, such as stable isotope analysis, provide an efficient alternative for estimating trophic positions. Stable isotope analysis provides trophic position estimates, but this information alone does not reveal prey–predator relationships. To help reconstruct food webs, a method is needed to convert trophic positions into diet coefficients. Our method employs a superposition of potential prey pairs, with weighting coefficients given by probabilities computed using Bayesian statistics. We present a mathematical inverse analysis approach to estimate diet coefficients using a minimal dataset consisting only of trophic positions. This approach provides a robust and parsimonious framework for reconstructing potential feeding networks from minimal data, offering a quantitative bridge between, but not limited to, stable isotope measurements and ecological network structure.

A borehole fiber-optic strainmeter and pore-pressure gauges were installed at the C9038B deep-seafloor borehole observatory at the Nankai trough subduction zone for the precise measurement of seafloor crustal deformation. To monitor activities associated with subduction processes, such as slow-slip events, through continuous observations at the borehole observatory, the characteristics of these observations must be clarified. In the first two years of observations after installation, clear tidal and tsunami responses were recorded. To understand the physical origins of these observational characteristics, we performed finite-element method (FEM) simulations. The response characteristics of observations were modeled by considering both the crustal structure around the observatory and the instrument used for observations. The simulation sequentially solved two elastic models comprising the crustal structure around the observatory and sensor structure. The sedimentary layer was modeled as V-shaped, with the major components of the sensor units being included in the simulations. The elastic properties assigned to these models were based on drilling parameters and seismic surveys. Consequently, the simulation reproduced the tidal response of strain and pore pressure observations. The loading response coefficient derived from the FEM-based tidal analysis also successfully explained the signals associated with the tsunami generated by the Kamchatka earthquake on 29 July 2025. This demonstrated that the numerical results accurately represented the response of the observatory to external loading events. Furthermore, to investigate the general tendencies of the borehole observations, similar simulations were performed using models that considered various elastic parameters and sedimentary layer geometries. Regarding changes in the elastic parameters, the strain and pore pressure observations were sensitive to Young’s modulus and Poisson’s ratio, respectively. Changes in the shape of the sedimentary layer caused perturbations in the strain observations; however, these effects were relatively smaller than those associated with variations in the elastic parameters within realistic ranges.

Determining the mechano-structural relations in biological materials with hierarchical structure is crucial to understanding natural optimization strategies and designing functional bioinspired composites. However, measuring the nanoscale mechanics and dynamic response is challenging when the specimen geometry and loading environment are physiologically complex. To overcome this challenge, we develop a combination of synchrotron X-ray diffraction testing and analytical modelling to explore the mechano-structural changes during bending loads on stomatopod cuticle. Stomatopod cuticle is an example of a hierarchical biomaterial optimized for high impact and bending resistance. Using models for large deformations of elastic continua, we measure cuticle strains from macroscopic deformations and combine diffraction-based fibril strains with stresses to quantify the local elastic moduli and nanoscale strain concentration factors, which are found to vary across cuticle sub-regions and under different flexion loading modes. This approach has the advantage of identifying constituent biomaterial properties and mechanisms in situ and is also suitable for studying time-dependent changes, such as concurrent strains of the nanofibrous phase that occur during physiological loading.

The pyroresistive response of conductive polymer composites (CPCs) has attracted much interest because of its potential applications in many electronic devices requiring a significant responsiveness to changes in external physical parameters such as temperature or electric fields. Although extensive research has been conducted to study how the properties of the polymeric matrix and conductive fillers affect the positive temperature coefficient pyroresistive effect, the understanding of the microscopic mechanism governing such a phenomenon is still incomplete. In particular, to date, there is little body of theoretical research devoted to investigating the effect of the polymer thermal expansion on the electrical connectivity of the conductive phase. Here, we present the results of simulations of model CPCs in which rigid conductive fillers are dispersed in an insulating amorphous matrix. By employing a meshless algorithm to analyze the thermoelastic response of the system, we couple the computed strain field to the electrical connectedness of the percolating conductive particles. We show that the electrical conductivity responds to the local strains that are generated by the mismatch between the thermal expansion of the polymeric and conductive phases and that the conductor-insulator transition is caused by a sudden and global disconnection of the electrical contacts forming the percolating network.