Solidification microstructure of M2 high speed steel by different casting technologies

The present work investigated the solidification microstructure of AISI M2 high speed steel manufactured by different casting technologies, namely iron mould casting and continuous casting. The results revealed that the as-cast structure of the steel was composed of the iron matrix and the M2C eutectic carbide networks, which were greatly refined in the ingot made by continuous casting process, compared with that by the iron mould casting process. M2C eutectic carbides presented variation in their morphologies and growth characteristics in the ingots by both casting methods. In the ingot by iron mould casting, they have a plate-like morphology and grow anisotropically. However, in the ingot made by continuous casting, the carbides evolved into the fiber-like shape that exhibited little characteristics of anisotropic growth. It was noticed that the fiber-like M2C was much easier to decompose and spheroidize after heated, as a result, the carbides refined remarkably, compared with the case of plate-like carbides in the iron mould casting ingot

FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout

Federated Learning (FL) emerges as a distributed machine learning paradigm without end-user data transmission, effectively avoiding privacy leakage. Participating devices in FL are usually bandwidth-constrained, and the uplink is much slower than the downlink in wireless networks, which causes a severe uplink communication bottleneck. A prominent direction to alleviate this problem is federated dropout, which drops fractional weights of local models. However, existing federated dropout studies focus on random or ordered dropout and lack theoretical support, resulting in unguaranteed performance. In this paper, we propose Federated learning with Bayesian Inference-based Adaptive Dropout (FedBIAD), which regards weight rows of local models as probability distributions and adaptively drops partial weight rows based on importance indicators correlated with the trend of local training loss. By applying FedBIAD, each client adaptively selects a high-quality dropping pattern with accurate approximations and only transmits parameters of non-dropped weight rows to mitigate uplink costs while improving accuracy. Theoretical analysis demonstrates that the convergence rate of the average generalization error of FedBIAD is minimax optimal up to a squared logarithmic factor. Extensive experiments on image classification and next-word prediction show that compared with status quo approaches, FedBIAD provides 2x uplink reduction with an accuracy increase of up to 2.41% even on non-Independent and Identically Distributed (non-IID) data, which brings up to 72% decrease in training time

Driver’s Shy Away Effect in Urban Extra-Long Underwater Tunnel

For urban extra-long underwater tunnels, the obstacle space formed by the tunnel walls on both sides has an impact on the driver\u27s driving. The aim of this study is to investigate the shy away characteristics of drivers in urban extra-long underwater tunnels. Using trajectory offset and speed data obtained from real vehicle tests, the driving behaviour at different lanes of an urban extra-long underwater tunnel was investigated, and a theory of shy away effects and indicators of sidewall shy away deviation for quantitative analysis were proposed. The results show that the left-hand lane has the largest offset and driving speed from the sidewall compared to the other two lanes. In the centre lane there is a large fluctuation in the amount of deflection per 50 seconds of driving, increasing the risk of two-lane collisions. When the lateral clearances are increased from 0.5 m to 2.19 m on the left and 1.29 m on the right, the safety needs of drivers can be better met. The results of this study have implications for improving traffic safety in urban extra-long underwater tunnels and for the improvement of tunnel traffic safety facilities

Fermentative intensity of L-lactic acid production using self-immobilized pelletized Rhizopus oryzae

L-Lactic acid is a promising three-carbon building-block chemical, widely used in the food, pharmaceutical, leather and textile industries and Rhizopus oryzae is an important filamentous fungus for the production of L-lactic acid with high optical purity. This study investigated the medium compositions for the maximum biomass cultivation of R. oryzae L-lactic acid fermentation, and optimized the operation parameters for semi-continuous repeated fermentation in a stirred tank fermentor using response surface method (RSM) analysis. The results indicated that a higher biomass cultivation of 3.750±0.05 g/L was achieved when the medium was composed of 12% (w/v) glucose, 0.4% (w/v) ammonium sulfate and 0.045% (w/v) monopotassium phosphate. The optimal fermentation conditions for the initial batch were as follows: the aeration was 0.75 L/(L·min), inoculation of germs was 11% and agitation speed was 560 rpm. The fermentative intensity of the initial batch and the sequentially repeated batches with self-immobilized pelletized R. oryzae were 2.162 g/(L·h) and 3.704 g/(L·h), respectively. Key words: Self-immobilized, Rhizopus oryzae, pellet, lactic acid, response surface method (RSM)