Hydrogenation treatment under several gigapascals assists diffusionless transformation in a face-centered cubic steel

The use of hydrogen in iron and steel has the potential to improve mechanical properties via altering the phase stability and dislocation behavior. When hydrogen is introduced under several gigapascals, a stoichiometric composition of hydrogen can be introduced for steel compositions. In this study, a face-centered cubic (fcc) stainless steel was hydrogenated under several gigapascals. When the steel was not hydrogenated, the microstructure after depressurization was an fcc with a hexagonal close-packed (hcp) structure. In contrast, the hydrogenation treatment resulted in a fine lath body-centered cubic (bcc) structure arising from diffusionless transformation. In particular, the bcc phase formed through the following transformation sequence: fcc → hcp → dhcp (double hexagonal close-packed phase) → bcc. That is, the use of hydrogenation treatment realized fine microstructure evolution through a new type of diffusionless transformation sequence, which is expected to be used in future alloy design strategies for developing high-strength steels.

Time Series Prediction Method Based on E-CRBM

To solve the problems of delayed prediction results and large prediction errors in one-dimensional time series prediction, a time series prediction method based on Error-Continuous Restricted Boltzmann Machines (E-CRBM) is proposed in this paper. This method constructs a deep conversion prediction framework, which is composed of two E-CRBMs and a neural network (NN). Firstly, the E-CRBM models of the original input sequence and the target prediction sequence are trained, respectively, to extract the time features of the two sequences. Then the NN model is used to connect and transform the time features. Secondly, the feature sequence H1 is extracted from the original input sequence of test data through E-CRBM1, which is used as input of NN to obtain feature transformation sequence H2. Finally, the target prediction sequence is obtained by reverse reconstruction of feature transformation sequence H2 through E-CRBM2. The E-CRBM in this paper introduces the residual sequence of NN feature transformation in the hidden layer of CRBM, which increases the robustness of CRBM and improves the overall prediction accuracy. The classical time series data (sunspot time series) and the actual operation data of reciprocating compressor are selected in the experiment. Compared with the traditional time series prediction method, the results verify the effectiveness of the proposed method in single-step prediction and multi-step prediction.

Analysis of permutation equivalence in -adhesive transformation systems with negative application conditions

$\mathcal{M}$-adhesive categories provide an abstract framework for a large variety of specification frameworks for modelling distributed and concurrent systems. They extend the well-known frameworks of adhesive and weak adhesive HLR categories and integrate high-level constructs such as attribution as in the case of typed attributed graphs.In the current paper, we investigate$\mathcal{M}$-adhesive transformation systems including negative application conditions (NACs) for transformation rules, which are often used in applications. For such systems, we propose an original equivalence on transformation sequences, calledpermutation equivalence, that is coarser than the classical switch equivalence. We also present a general construction of deterministic processes for$\mathcal{M}$-adhesive transformation systems based on subobject transformation systems. As a main result, we show that the process obtained from a transformation sequence identifies its equivalence class of permutation-equivalent transformation sequences. Moreover, we show how the analysis of this process can be reduced to the analysis of the reachability graph of a generated Place/Transition Petri net. This net encodes the dependencies between rule applications of the transformation sequence, including the inhibiting effects of the NACs.