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Electrocardiography can provide useful prognostic information in acute pulmonary embolism (APE). Several abnormal QRS changes in lead V1, including notched or fragmented QRS, incomplete or complete right bundle branch block (IRBBB or CRBBB) and the QR sign, which are associated with APE, are of prognostic significance. To illustrate this, we describe lead V1 QRS changes in combination with the clinical state of six APE patients. The dynamic ECG changes suggest that a change from a diminution of the S wave amplitude to notched S wave, next to RBBB and then to the QR sign indicate worsening of the patients' condition, and vice versa. Also, a diminution of the S wave amplitude in lead V1 associated with a final R' wave in the right precordial accessory leads indicates the possibility of hidden RBBB. Understanding dynamic QRS changes in APE may aid in risk stratification.

The appearance of the nitrogen-rich energetic MOFs provides another opportunity for the new-generation of high energetic explosives. In this work, an insensitive energetic nitrogen-rich ligand, 3-amino-1, 2, 4-triazole (Hatz) was imported into the system of transition-metal azides via the coordination polymerization strategy in the hope to prepare mixed-ligand nitrogen-rich high energetic MOF materials with low sensitivity and finally a solvent-free 3D energetic compacted metal-organic framework (MOF), Cd3(atz)4(N3)2 (1) was successfully isolated under hydrothermal conditions. Compound 1 was characterized by single crystal X-ray diffraction, IR spectroscopy, elemental analysis (EA), different scanning calorimetry (DSC), and thermogravimetry analysis (TGA). Topological analysis shows that the 3D framework of 1 can be abstracted into a ten-connected topological network. The volatile azido ion can be viewed as a counter anion to be stabilized in a 3D framework as the connection of Cd(II) ions with two independent atz− ligands still remains a 3D framework. Compound 1 has a high calculated density (ρ) of 2.517 g cm−3. The standard molar enthalpy of formation (ΔfHo) of 1 was calculated to be 1330.10 kJ mol−1, which is higher than those of most of the previously 3D energetic MOFs. Sensitivity tests demonstrate that 1 is insensitive to external mechanical stimuli. TGA demonstrates that 1 has an excellent thermostability which can be stable up to 372 °C. Compound 1 can be served as a high-energy-density material with a favorable level of safety due to its excellent energetic performances, low sensitivities and excellent thermostability.

Hot compression tests of modified 2.25Cr–1Mo steel were conducted on a Gleeble-3500 thermo-mechanical simulator at the temperatures ranging from 1173 to 1473 K with the strain rate of 0.01–10 s −1 and the height reduction of 60%. Based on the experimental results, an artificial neural network (ANN) model and constitutive equations were developed to predict the hot deformation behavior of modified 2.25Cr–1Mo steel. A comparative evaluation of the constitutive equations and the ANN model was carried out. It was found that the relative errors based on the ANN model varied from −4.63% to 2.23% and those were in the range from −20.48% to 12.11% by using the constitutive equations, and the average root mean square errors were 0.62 MPa and 7.66 MPa corresponding to the ANN model and constitutive equations, respectively. These results showed that the well-trained ANN model was more accurate and efficient in predicting the hot deformation behavior of modified 2.25Cr–1Mo steel than the constitutive equations.

Isothermal hot compression of 28CrMnMoV steel was conducted on a Gleeble-3500 thermo-mechanical simulator in the temperature range of 1173–1473 K with the strain rate of 0.01–10 s−1 and the height reduction of 60%. Based on the experimental results, constitutive equations and an artificial neural network (ANN) model with a back-propagation learning algorithm were developed for the description and prediction of the hot compressive behavior of 28CrMnMoV steel. Then a comparative evaluation of the constitutive equations and the trained ANN model was carried out. It was obtained that the relative errors based on the ANN model varied from −3.66% to 3.46% and those were in the range from −13.60% to 10.89% by the constitutive equations, and the average absolute relative errors were 0.99% and 4.09% corresponding to the ANN model and the constitutive equations, respectively. Furthermore, the average root mean square errors of the ANN model and the constitutive equations were obtained as 1.43 MPa and 5.60 MPa respectively. These results indicated that the trained ANN model was more efficient and accurate in predicting the hot compressive behavior of 28CrMnMoV steel than the constitutive equations.

Hot compression tests of T24 ferritic steel were carried out using Gleeble-3500 thermo mechanical simulator in the temperature range of 1323–1473 K with the strain rate of 0.01–10 s−1 and the height reduction of 60%. The flow behavior of T24 ferritic steel was characterized based on analysis of the true stress–strain curves. Constitutive equations incorporating the effects of temperature, strain rate and strain have been developed to model the hot deformation behavior of T24 ferritic steel. Material constants α, n, ln A and activation energy Q in the constitutive equations were calculated as a function of strain. The flow stress values of T24 ferritic steel predicted by the proposed constitutive equations show a good agreement with experimental results, which indicated that the developed constitutive equations could give an accurate and precise prediction for the flow stress of T24 ferritic steel.

The true stress–strain data from isothermal hot compression tests on Gleeble-3500 thermo mechanical simulator, in a wide range of temperatures (1323–1473 K) and strain rates (0.01–10 s−1), were employed to establish the constitutive equations based on modified Zerilli–Armstrong and strain-compensated Arrhenius-type models respectively, and develop the artificial neural network model to predict the high-temperature flow stress of T24 steel. Furthermore, a comparative study has been made on the capability of the three models to represent the elevated temperature flow behavior of this steel. Suitability of the three models were evaluated by comparing the accuracy of prediction of deformation behavior, correlation coefficient and average absolute relative error of prediction, the number of material constants, and the time needed to evaluate these constants. The results showed that the predicted values by the modified Zerilli–Armstrong model could agree well with the experimental values except under the strain rate of 0.01 s−1. The predicted flow stress of the other two models shows good agreement with the experimental data. However, the artificial neural network model could track the deformation behavior more accurately throughout the entire temperature and strain rate range though it is strongly dependent on extensive high quality data and characteristic variables and offers no physical insight.

The true stress–strain data from isothermal hot compression tests, in a wide range of temperatures (1173–1473 K) and strain rates (0.01–10 s −1 ), were employed to study the deformation behavior and develop constitutive equation of V150 grade oil casing steel based on an Arrhenius-type equation. The flow stress increases with the decrease of deformation temperature and the increase of strain rate, which can be represented by Zener–Hollomon parameter in an exponential equation. The influence of strain was incorporated in the developed constitutive equation by considering the effect of strain on material constants. The flow stress predicted by the constitutive equation shows good agreement with the experimental value through the whole experimental temperature and strain rate range except a slight deviation for predicting the stress deformed at 1173 K under 0.01 s −1 , and the average relative error is 4.21%.