Safety assessment of sand casting explosion accidents by testing cavity pressure of the sand mold to protect employee health.

Excessive cavity pressure may result in a sand casting explosion, and corresponding measures should be adopted to prevent these consequences. In this study, the pressure variations in the cavity were first investigated based upon on-site testing by taking the resin contents into consideration, and then the evolution characteristics of sand casting explosion accidents were analyzed in depth by system dynamics, chaos theory, and the bow-tie model. When the resin contents are 1.3 wt%, 1.4 wt%, and 1.5 wt%, the pressures of the gas vent increase by 27.0 Pa, 32.8 Pa, and 35.6 Pa, respectively. To reduce the pressure of the cavity, the resin content should be reduced. The evolutionary process of sand casting explosion accidents has a noticeable butterfly effect and randomness, whose occurrence is comprehensively affected by human, object, environment, management and emergency subsystems. The leading causes of sand casting explosion accidents mainly include the extensive gas evolution characteristics of foundry sand, cavity exhaust blockage, and inadequate safety monitoring. The leading consequences of sand casting explosion accidents mainly include casualties, secondary disasters, and social panic. The implications of these findings concerning sand casting explosion accidents can be regarded as the foundation for accident prevention in practice.

Merazin hydrate produces rapid antidepressant effects by activating CaMKII to promote neuronal activities and proliferation in hippocampus.

In our previous studies, we demonstrated that merazin hydrate (MH) had rapid antidepressant effects, but the deep mechanism needed to be further investigated. In this study, we used depressive-like model, behavioral tests, molecular biology and pharmacological interventions to reveal the underlying mechanisms of MH's rapid antidepressants. We found that a single administration of MH was able to produce rapid antidepressant effects in chronic unpredictable mild stress (CUMS) exposed mice at 1 day later, similar to ketamine. Moreover, MH could not only significantly up-regulated the expressions of cFOS, but also obviously increased the number of Ki67 positive cells in hippocampal dentate gyrus (DG). Furthermore, we also found that the phosphorylated expression of calcium/calmodulin-dependent protein kinase II (CaMKII) was significantly reduced by CUMS in hippocampus, which was also reversed by MH. In addition, pharmacological inhibition of CaMKII by using KN-93 (a CaMKII antagonist) blocked the MH's up-regulation of cFOS and Ki67 in hippocampal DG. To sum up, this study demonstrated that MH produced rapid antidepressant effects by activating CaMKII to promote neuronal activities and proliferation in hippocampus.

Progressive attention module for segmentation of volumetric medical images.

PURPOSE: Medical image segmentation is critical for many medical image analysis applications. 3D convolutional neural networks (CNNs) have been widely adopted in the segmentation of volumetric medical images. The recent development of channelwise and spatialwise attentions achieves the state-of-the-art feature representation performance. However, these attention strategies have not explicitly modeled interdependencies among slices in 3D medical volumes. In this work, we propose a novel attention module called progressive attention module (PAM) to explicitly model the slicewise importance for 3D medical image analysis. METHODS: The proposed method is composed of three parts: Slice Attention (SA) block, Key-Slice-Selection (KSS) block, and Channel Attention (CA) block. First, the SA is a novel attention block to explore the correlation among slices for 3D medical image segmentation. SA is designed to explicitly reweight the importance of each slice in the 3D medical image scan. Second, the KSS block, cooperating with the SA block, is designed to adaptively emphasize the critical slice features while suppressing the irrelevant slice features, which helps the model focus on the slices with rich structural and contextual information. Finally, the CA block receives the output of KSS as input for further feature recalibration. Our proposed PAM organically combines SA, KSS, and CA, progressively highlights the key slices containing rich information for the relevant tasks while suppressing those irrelevant slices. RESULTS: To demonstrate the effectiveness of PAM, we embed it into 3D CNNs architectures and evaluate the segmentation performance on three public challenging data sets: BraTS 2018 data set, MALC data set, and HVSMR data set. We achieve 80.34%, 88.98%, and 84.43% of the Dice similarity coefficient on these three data sets, respectively. Experimental results show that the proposed PAM not only boosts the segmentation accuracy of the standard 3D CNNs methods consistently, but also outperforms the other attention mechanisms with slight extra costs. CONCLUSIONS: We propose a new PAM to identify the most informative slices and recalibrate channelwise feature responses for volumetric medical image segmentation. The proposed method is evaluated on three public data sets, and the results show improvements over other methods. This proposed technique can effectively assist physicians in many medical image analysis. It is also anticipated to be generalizable and transferable to assist physicians in a wider range of medical imaging applications to produce greater value and impact to health.