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A high strength low alloy (HSLA)‐100 steel with different initial microstructures (ferrite and martensite) is processed by cold rolling at room temperature to the reduction of thickness about 70%. It is found that the recrystallization kinetics of the sample with a martensitic microstructure is higher than the sample with a ferritic microstructure. Moreover, the results show that the partitioning factor of substitution alloying elements is less effective than the strain in the martensitic phase transformation. Also, hardness results show that the samples with a martensitic microstructure have drastic drop compared with the samples with a ferritic microstructure, which is related to continuous recrystallization. Remarkable ultimate tensile strength and elongation to failure (829 MPa and 27.7%, respectively) are achieved for cold‐rolled martensite after tempering at 625 °C for 360 min. Accordingly, it is also found that the reason for remarkable mechanical properties is the simultaneous presence of recrystallized fine‐grained and deformed martensite, which forms an inhomogeneous microstructure.

Grain refinement of AZ91 alloy was achieved via the addition of a modified Al-B inoculant to the melt, where a pronounced heterogeneous nucleation effect by AlB2 particles was observed via addition of Al-8 wt-% B master alloy up to 0.3 wt-%. Moreover, hot extrusion remarkably enhanced both strength and ductility via developing an equiaxed microstructure by dynamic recrystallisation (DRX), fragmentation/dissolution of eutectics (containing β-Mg17Al12 compound) during hot deformation, and amending casting defects. Both as-cast and extruded data followed the same Hall–Petch plot, implying that the grain size refinement is the main strengthening mechanism. Accordingly, grain refining via inoculation and thermomechanical processing was found to be a viable approach for the enhancement of strength-ductility trade-off through the improvement of tensile toughness.

Prediction is one of the most important premises in making investment decisions. Accordingly, investors are keen to be aware of market trends and price returns. For this purpose, several methods have been used in different fields; however, in the present study, the ability to predict the crisis by Cumulative Residual Entropy (CRE) and its generalized type, Fractional Cumulative Residual Entropy (FCRE), has been investigated. The data used in the research include the overall index, volume, trade value, and foreign exchange rate from October 2010 to July 2021. The results showed that both criteria could predict the crisis, but the FCRE is superior in crisis prediction. The identified periods

In this research, we intended to employ the Pearson correlation and a multiscale generalized Shannon-based entropy to trace the transition and type of inherent mutual information as well as correlation structures simultaneously. An optimal value for scale is found to prevent over smoothing, which leads to the removal of useful information. The lowest Singular Value Decomposition Multiscale Generalized Cumulative Residual Entropy (SVDMWGCRE), or SVD Entropy (SVDE), is obtained for periodic–chaotic series, generated by logistic map; hence, the different dynamic, correlation structures, and intrinsic mutual information have been characterized correctly. It is found out that the mutual information between emerging markets entails higher sensitivity, and moreover emerging markets have demonstrated the highest uncertainty among investigated markets. Additionally, the fractional order has synergistic effects on the enhancement of sensitivity with the multiscale feature. According to the logistic map and financial time series results, it can be inferred that the logistic map can be utilized as a financial time series. Further investigations can be performed in other fields through this financial simulation. The temporal evolutions of financial markets are also investigated. Although the results demonstrated higher noisy information for emerging markets, it was illustrated that emerging markets are getting more efficient over time. Additionally, the temporal investigations have demonstrated long-term lag and synchronous phases between developed and emerging markets. We also focused on the COVID-19 pandemic and compared the reactions of developing and emerging markets. It is ascertained that emerging markets have demonstrated higher uncertainty and overreaction to this pandemic.