Dynamic response of bridge abutment to sand-rubber mixtures backfill under seismic loading conditions

China is located between the Pacific Ocean seismic belt and the Eurasian seismic belt where the seismic activity is frequent. Bridge plays a key role in transportation lifeline, but its construction and maintenance face potential seismic hazard in China, which may lead to a huge economic loss. This study investigates the dynamic response of bridge abutment to sand-rubber mixtures backfill under seismic loading conditions by finite element method. Sand-rubber mixtures is composed of waste fibers, rubber particles, sand and water with a certain mixing ratio and dry density It has been used as an anti-seismic backfill material for slopes, retaining walls, bridge structures and other geo-structures. A series of numerical simulations was carried out to evaluate the effectiveness of this new material as backfill material for bridge abutment. The seismic performance of bridge abutment with different backfill materials was analyzed with respect to settlements and accelerations of ground, foundation pile and bridge abutment. The results show that the sand-rubber mixture can be used as backfill material, and has a great potential in reducing the settlements and accelerations of ground and foundation pile

Strengthen the application research of big data in food safety

Food safety is essential to national economy and people's livelihood. A good food safety system is extremely important for maintaining social stability and providing scientific data for government decision. Big data provides a new approach to food safety. Through the integration of the data in the whole industrial chain, it will greatly improve the data analysis ability and help in discovering the potential valuable information. It could provide scientific decision-making suggestions for regulators, producers and consumers. It will provide a solution for timely warning of food safety incidents, the accurate traceability and accountability of food products. This paper will briefly introduce some existing big data platforms in food safety area. Then, it will discuss the challenges such as multi-sources data analysis, visualization and market mechanism. At the end of this paper, the future development trend of big data in food safety area is discussed

Random field characterization of uniaxial compressive strength and elastic modulus for intact rocks

Rock properties exhibit spatial variabilities due to complex geological processes such as sedimentation, metamorphism, weathering, and tectogenesis. Although recognized as an important factor controlling the safety of geotechnical structures in rock engineering, the spatial variability of rock properties is rarely quantified. Hence, this study characterizes the autocorrelation structures and scales of fluctuation of two important parameters of intact rocks, i.e. uniaxial compressive strength (UCS) and elastic modulus (EM). UCS and EM data for sedimentary and igneous rocks are collected. The autocorrelation structures are selected using a Bayesian model class selection approach and the scales of fluctuation for these two parameters are estimated using a Bayesian updating method. The results show that the autocorrelation structures for UCS and EM could be best described by a single exponential autocorrelation function. The scales of fluctuation for UCS and EM respectively range from 0.3 m to 8.0 m and from 0.3 m to 8.4 m. These results serve as guidelines for selecting proper autocorrelation functions and autocorrelation distances for rock properties in reliability analyses and could also be used as prior information for quantifying the spatial variability of rock properties in a Bayesian framework

Natural Algae-Inspired Microrobots for Emerging Biomedical Applications and Beyond

Algae-inspired microrobots (AIMs) have attracted intense research over the past decade owing to the abundant desired properties of natural microalgae, such as biocompatibility, autofluorescence, and pharmaceutical activity, which make them ideal candidates for biomedical and related applications. With the deepening and widening of applied research, the functions of AIMs have been greatly enriched and enhanced to meet the needs of demanding application scenarios including targeted drug delivery, anticancer/antibacterial therapy, cell stimulation, wound healing, and biomolecule sensing. Notwithstanding, multiple challenges remain to be tackled for transformative advances and clinical translation. In this review, we aim to provide a comprehensive survey of representative advances in AIMs accompanied by the underlying biological/technological backgrounds. We also highlight existing issues that need to be overcome in AIM developments and suggest future research directions in this field.</p

Experimental study on dynamic properties of sand-rubber mixtures in a small range of shearing strain amplitudes

Sand-rubber mixtures has characteristics of light weight, cheap and environmental-friendly, thereby it has a great potential to be used in geotechnical engineering for sustainable development. Dynamic properties (i.e. shear modulus and damping ratio) of sand-rubber mixtures in a small range of shearing strain amplitudes (i.e. 10-6-10-4) were investigated in this study through a series of resonant column tests. The effects of shearing strain amplitude, confining pressure and rubber content on dynamic shear modulus (G), maximum dynamic shear modulus (Gmax), damping ratio (D) and dynamic shear modulus ratio G/Gmax of the mixtures were also discussed. Based on the analyses of the relationship among confining pressure, rubber content and Gmax, an empirical formula for predicting Gmax considering the effects of confining pressure and rubber content was also proposed. The model prediction agreed with the experimental results very well

FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model

Significant advancements in the development of machine learning (ML) models for weather forecasting have produced remarkable results. State-of-the-art ML-based weather forecast models, such as FuXi, have demonstrated superior statistical forecast performance in comparison to the high-resolution forecasts (HRES) of the European Centre for Medium-Range Weather Forecasts (ECMWF). However, ML models face a common challenge: as forecast lead times increase, they tend to generate increasingly smooth predictions, leading to an underestimation of the intensity of extreme weather events. To address this challenge, we developed the FuXi-Extreme model, which employs a denoising diffusion probabilistic model (DDPM) to restore finer-scale details in the surface forecast data generated by the FuXi model in 5-day forecasts. An evaluation of extreme total precipitation ($\textrm{TP}$), 10-meter wind speed ($\textrm{WS10}$), and 2-meter temperature ($\textrm{T2M}$) illustrates the superior performance of FuXi-Extreme over both FuXi and HRES. Moreover, when evaluating tropical cyclone (TC) forecasts based on International Best Track Archive for Climate Stewardship (IBTrACS) dataset, both FuXi and FuXi-Extreme shows superior performance in TC track forecasts compared to HRES, but they show inferior performance in TC intensity forecasts in comparison to HRES

A cascade dead-zone extended state observer for a class of systems with measurement noise

For high frequency noise, a new $2n$-th order cascade extended state observer with dynamic dead-zone structure is proposed in this paper. Dead zone dynamic consists of two parts. One is to "trim" the effect of noise by cutting off the part that falls in the dead zone. The other part pushes the dead zone amplitude to converge to 0 as soon as possible to ensure the convergence of the estimation error. Moreover, in the cascade structure, the high-gain parameter grows only to a second power, thus avoiding excessive amplification of the measurement noise and solving numerical implementation problems. The design procedure ensures that the extended state observer is input-to-state stable. Numerical simulations show the improvement in terms of total disturbance estimation and noise attenuation. The frequency-domain analysis of the proposed ESO using the describing function method investigates the effect of the dead zone nonlinear parameter on the performance of a closed-loop system

FuXi: A cascade machine learning forecasting system for 15-day global weather forecast

Over the past few years, due to the rapid development of machine learning (ML) models for weather forecasting, state-of-the-art ML models have shown superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution forecast (HRES) in 10-day forecasts at a spatial resolution of 0.25 degree. However, the challenge remains to perform comparably to the ECMWF ensemble mean (EM) in 15-day forecasts. Previous studies have demonstrated the importance of mitigating the accumulation of forecast errors for effective long-term forecasts. Despite numerous efforts to reduce accumulation errors, including autoregressive multi-time step loss, using a single model is found to be insufficient to achieve optimal performance in both short and long lead times. Therefore, we present FuXi, a cascaded ML weather forecasting system that provides 15-day global forecasts with a temporal resolution of 6 hours and a spatial resolution of 0.25 degree. FuXi is developed using 39 years of the ECMWF ERA5 reanalysis dataset. The performance evaluation, based on latitude-weighted root mean square error (RMSE) and anomaly correlation coefficient (ACC), demonstrates that FuXi has comparable forecast performance to ECMWF EM in 15-day forecasts, making FuXi the first ML-based weather forecasting system to accomplish this achievement

Effect of autocorrelation function model on spatial prediction of geological interfaces

This study evaluated the performances of various autocorrelation function (ACF) models in predicting the geological interface using a well-known conditional random field method. Prediction accuracies and uncertainties were compared between a flexible Matérn model and two classical ACF models: the Gaussian model and the single exponential model. The rockhead data of Bukit Timah granite from boreholes at two sites in Singapore as well as simulated data were used for the comparisons. The results showed that the classical models produce a reasonable prediction uncertainty only when its smoothness coefficient is consistent with that of the geological data. Otherwise, the classical models may produce prediction errors much larger than that of the Matérn model. On the other hand, the prediction accuracy of the Matérn model is affected by the spacing of the boreholes. When the borehole spacing is relatively small (< 0.4 × scale of fluctuation), the Matérn model can reasonably quantify the prediction uncertainty. However, when the borehole spacing is large, the prediction by the Matérn model becomes less accurate as compared with the prediction using the classical models with the right value of smoothness coefficient due to the large estimation error of the smoothness coefficient