Constructive adaptation of 3D-printable polymers in response to typically destructive aquatic environments.

In response to environmental stressors, biological systems exhibit extraordinary adaptive capacity by turning destructive environmental stressors into constructive factors; however, the traditional engineering materials weaken and fail. Take the response of polymers to an aquatic environment as an example: Water molecules typically compromise the mechanical properties of the polymer network in the bulk and on the interface through swelling and lubrication, respectively. Here, we report a class of 3D-printable synthetic polymers that constructively strengthen their bulk and interfacial mechanical properties in response to the aquatic environment.

Magnetoactive Acoustic Topological Transistors.

Topological field-effect transistor is a revolutionary concept that physical fields are used to switch on and off quantum topological states of the condensed matter. Although this emerging concept has been explored in electronics, how to realize it in the acoustic realm remains elusive. In this work, a class of magnetoactive acoustic topological transistors capable of on-demand switching on and off topological states and reconfiguring topological edges with external magnetic fields is presented.

Understanding COVID-19 nonlinear multi-scale dynamic spreading in Italy.

The outbreak of COVID-19 in Italy took place in Lombardia, a densely populated and highly industrialized northern region, and spread across the northern and central part of Italy according to quite different temporal and spatial patterns. In this work, a multi-scale territorial analysis of the pandemic is carried out using various models and data-driven approaches. Specifically, a logistic regression is employed to capture the evolution of the total positive cases in each region and throughout Italy, and an enhanced version of a SIR-type model is tuned to fit the different territorial epidemic dynamics via a differential evolution algorithm.

Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study.

Vibrations of complex structures such as bridges mostly present nonlinear and non-stationary behaviors. Recently, one of the most common techniques to analyze the nonlinear and non-stationary structural response is Hilbert-Huang Transform (HHT). This paper aims to evaluate the performance of HHT based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique using an Artificial Neural Network (ANN) as a proposed damage detection methodology.

An alternative measurement tool for the identification of hysteretic responses in biological joints.

In structural engineering, sophisticated multi-dimensional analysis techniques, such as the Restoring Force Method (RFM), have been established for complex, nonlinear hysteretic systems. The purpose of the present study was to apply the RFM to quantify nonlinear spine hysteresis responses under applied moments. First, synthetic hysteretic spine responses (n=50) were generated based on representative results from pure moment flexion-extension loading of a human cadaveric lumbar spine segment.

Application of the restoring force method for identification of lumbar spine flexion-extension motion under flexion-extension moment.

The restoring force method (RFM), a nonparametric identification technique established in applied mechanics, was used to maximize the information obtained from moment-rotation hysteresis curves under pure moment flexion-extension testing of human lumbar spines. Data from a previous study in which functional spine units were tested intact, following simulated disk injury, and following implantation with an interspinous process spacer device were used. The RFM was used to estimate a surface map to characterize the dependence of the flexion-extension rotation on applied moment and the resulting axial displacement.