An Adaptive Elastic Support Seat-Based Magnetorheological Elastomer for Human Body Vibration Reduction.

This paper introduces an electromagnetic structure utilizing the controllable mechanical properties of magnetorheological elastomer (MRE) materials through magnetic flux. An adaptive elastic foundation composed of these materials is explored for vibration reduction and frequency modulation. This study investigates these effects using both a single-mass model and a coupled human-seat model.

Vibration reduction of human body biodynamic response in sitting posture under vibration environment by seat backrest support.

Four-degree-of-freedom (4-DOF) human-chair coupling models are constructed to characterize the different contact modes between the head, chest back, waist back and backrest. The seat-to-head transfer ratio (STHT) is used as an evaluation metric for vibration reduction effectiveness. The simulated vibration reduction ratio of the model is close to the experimental results, which proves the validity of the model.

Tailoring Interfaces for Enhanced Methanol Production from Photoelectrochemical CO Reduction.

Efficient and stable photoelectrochemical reduction of CO into highly reduced liquid fuels remains a formidable challenge, which requires an innovative semiconductor/catalyst interface to tackle. In this study, we introduce a strategy involving the fabrication of a silicon micropillar array structure coated with a superhydrophobic fluorinated carbon layer for the photoelectrochemical conversion of CO into methanol. The pillars increase the electrode surface area, improve catalyst loading and adhesion without compromising light absorption, and help confine gaseous intermediates near the catalyst surface.

The Magneto-Mechanical Hyperelastic Property of Isotropic Magnetorheological Elastomers with Hybrid-Size Magnetic Particles.

Isotropic magnetorheological elastomers (MREs) with hybrid-size particles are proposed to tailor the zero-field elastic modulus and the relative magnetorheological rate. The hyperelastic magneto-mechanical property of MREs with hybrid-size CIPs (carbonyl iron particles) was experimentally investigated under large strain, which showed differential hyperelastic mechanical behavior with different hybrid-size ratios. Quasi-static magneto-mechanical compression tests corresponding to MREs with different hybrid size ratios and mass fractions were performed to analyze the effects of hybrid size ratio, magnetic flux density, and CIP mass fraction on the magneto-mechanical properties.

The Compound Effect of Spatial and Temporal Resolutions on the Accuracy of Urban Flood Simulation.

Flood disaster is one of the critical threats to cities. With the intellectualization tendency of Industry 4.0, refined urban flood models can effectively reproduce flood inundation scenarios and support the decision-making on the response to the flood.

Interpretable tree-based ensemble model for predicting beach water quality.

Tree-based machine learning models based on environmental features offer low-cost and timely solutions for predicting microbial fecal contamination in beach water to inform the public of the health risk. However, many of these models are black boxes that are difficult for humans to understand, which may cause severe consequences such as unexplained decisions and failure in accountability. To develop interpretable predictive models for beach water quality, we evaluate five tree-based models, namely classification tree, random forest, CatBoost, XGBoost, and LightGBM, and employ a state-of-the-art explanation method SHAP to explain the models.

cm-Scale Synthesis of MoTe Thin Films with Large Grains and Layer Control.

Owing to the small energy differences between its polymorphs, MoTe can access a full spectrum of electronic states from the 2H semiconducting state to the 1T' semimetallic state and from the T Weyl semimetallic state to the superconducting state in the 1T' and T phase at low temperature. Thus, it is a model system for phase transformation studies as well as quantum phenomena such as the quantum spin Hall effect and topological superconductivity. Careful studies of MoTe and its potential applications require large-area MoTe thin films with high crystallinity and thickness control.

Improving the robustness of beach water quality modeling using an ensemble machine learning approach.

Microbial pollution of beach water can expose swimmers to harmful pathogens. Predictive modeling provides an alternative method for beach management that addresses several limitations associated with traditional culture-based methods of assessing water quality. Widely-used machine learning methods often suffer from high variability in performance from one year or beach to another.