Due to the difficult metallurgical conditions during production, advanced processing techniques are required to produce functionally graded metal matrix composites. In this study, we explored a novel approach by using a combination of two different methods to produce Functionally Graded 7075 Al / SiCp composites. First process was direct semi-solid stirring that was used to prevent particle agglomeration, brittle reaction products, floating or settling of the reinforcements and poor wettability for the production of SiCp (wt. 5-20%) reinforced aluminum matrix composites. In the direct semi-solid mixing process, the matrix material remains in a semi-solid state until the reinforcement addition process is completed. The second novel process was sequential squeeze casting to provide liquid diffusion between two composite layers that were used to produce functionally graded aluminum matrix composite. The microstructure and interlayer zones were characterized by using optical microscopy and scanning electron microscopy. The resulting functionally graded material was subjected to spectrometer analyses, density measurements, and metallographic examinations in order to determine the characteristics of its layers and interfacial zones, as well as to assess the formation of the graded structure. The results obtained from this research provide the potential to use this new combined manufacturing method, which is relatively inexpensive, efficient and easily applicable, in order to produce functionally graded SiCp reinforced aluminum composites.

A simple bounded stochastic motion is employed for modelling equity price dynamics under shocks, where the prices are constrained to lie between two positive bounds. While the stochastic process has an inaccessible boundary, the other boundary is quasi-bounded, implying that the boundary can be breached if the probability leakage condition is met. The quasi-boundedness of the process at the boundary can thus provide an indicator of possible risk of equities under price shocks. Empirical calibration of model parameters of the proposed process for equities can be performed easily due to the availability of an analytically tractable probability density function which generates fat-tailed distributions consistent with empirical observations. The volatility and mean-reversion of the S&P500 dynamics calibrated by the process is positively and negatively co-integrated respectively with the VIX index representing the level of market distress. The process captures the high likelihood of Hertz’s default about two months earlier, using only information until that point, and before the firm filed for Chapter 11 bankruptcy in May 2020 as a result of the Covid-19 pandemic. Empirical calibration of the process for GameStop’s stock price shows that the short squeeze of the stock occurred when the condition for breaching an upper boundary was met on 14 January 2021, i.e., about two weeks before major short sellers closed out their positions with significant losses. The trading volume of the stock was positively co-integrated with the probability leakage ratio.

Human activities and natural events alter the earth’s surface and pose a constant threat to the environment. Thus, accurately monitoring and predicting these changes in a timely manner is crucial to provide solutions and mitigate the environmental consequences beforehand. This research introduces a novel framework, called HCD-Net, for change detection using bi-temporal hyperspectral images. The framework is based on double-stream deep feature extraction and an attention mechanism. The first stream focuses on deep feature extraction through 3D convolution layers and 3D Squeeze-and-Excitation (SE) blocks. The second stream focuses on deep feature extraction through 2D convolution and 2D SE blocks. The extracted deep features are then concatenated and fed to dense layers for decision-making. The efficiency of HCD-Net is compared to the state-of-the-art change detection methods. Additionally, the bi-temporal Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) hyperspectral dataset was used for further evaluation of the change detection results. The results show that HCD-Net has a higher accuracy and the lowest false alarm rate, where the overall classification accuracy is above 96%, and the kappa coefficient is over 0.9.