A hidden Markov model to detect regime changes in cryptoasset markets

The objective of this work is to understand the dynamics of cryptocurrency prices. Specifically, how prices switch between different regimes, going from “bull” to “stable” and “bear” times. For this purpose, we propose a hidden Markov model that aims at explaining the evolution of Bitcoin prices through different, unobserved states. The implementation of the proposed model includes a likelihood ratio test that allows to compare models with different states and with different covariance structures. Our empirical findings show that the time movements of Bitcoin prices across different exchange markets are well-described by the proposed model. In particular, a parsimonious model with a diagonal covariance matrix leads to better predictions, compared with a model with a full covariance matrix.     

The control of abnormal grain growth in low-voltage SnO2 varistors by microseed addition

In this research, the addition effects of three different quantities of micron-sized seeds (microseeds) to a SnO₂ varistor prepared from nanomaterials on the microstructure and electrical properties were studied. Moreover, surge-withstanding capability of low-voltage SnO₂ varistors was investigated. The X-ray diffraction pattern disclosed a single phase SnO₂ for microseed grains. The morphological features of samples were characterized using scanning electron microscopy. The abnormal distribution of grain size with elongated grains of SnO₂ in fine grains matrix was observed in sintered samples without microseeds. The low content of microseed addition (0.3 wt%) had not controlled abnormal grain growth, however, it increased mean grain size to 37 µm. Although the high content of microseeds (7.5 wt%) stopped abnormal grain growth, it had a negative effect on relative density and mean grain size. The normal grain size distribution with maximum mean grain size (45 µm) was obtained in samples containing 1.5 wt% microseeds. These samples showed the lowest breakdown field (240 V/cm) and the highest surge-withstanding capability (1.5 kA/cm²). Furthermore, the standard deviation of the electrical parameters of these samples was improved due to normal grain-size distribution.     

Robust Diversity-based Sine-Cosine Algorithm for Optimizing Hydropower Multi-reservoir Systems

Hydropower energy generation depends on the available water resources. Therefore, planning and operation of the water resource systems are paramount tasks for energy management. Since reservoirs are one of the important components of water resources systems, extracting optimal operating policies for proper management of energy generated from these systems is an imperative step. Optimizing reservoir system operation (ORSO) is a non-linear, large-scale, and non-convex problem with a large number of constraints and decision variables. To solve ORSO problem effectively, a robust diversity-based, sine-cosine algorithm (RDB-SCA) is developed in the present study by introducing several strategies to balance the global exploration and local exploitation ability and to achieve accurate and reliable solutions. An efficient linear operation rule is coupled with the RDB-SCA to maximize the energy generation. The proposed method is then applied to a real-world, multi-reservoir system to extract optimal operational policies and, consequently, maximize the energy production. It is shown that the RDB-SCA is able to generate 24, 14, and 6% more energy than the original SCA, respectively for 2-, 3-, and 4-reservoir systems. The present findings are useful to suggest guidelines for efficient operation of hydropower multi-reservoir systems. This paper is supported by https://imanahmadianfar.com/codes.

     

Designing a secure designated server identity-based encryption with keyword search scheme: still unsolved

There exist only a few papers in the literature which target the problem of “proposing a secure designated server identity-based encryption with keyword search scheme.” In this paper, we prove that they all suffer from security issues, and therefore, this challenging problem still remains open.     

Particle Detachment from Rough Surfaces in Turbulent Flows: An Analytical Expression for Resuspension Fraction

The critical shear velocity for resuspension of micrometer size particles from rough surfaces was studied. The random variation of surface roughness was accounted for. The recently developed Monte Carlo simulations accounted for the statistical variations of physical parameters that control the particle resuspension process. A sensitivity analysis showed that the surface roughness and its random variation was the key factor affecting the particle resuspension from rough surfaces. The theory of probabilistic transformation was used and an analytical expression for evaluating the resuspension fraction of particles of different sizes from rough surfaces versus the shear velocity was developed. The resuspension fractions as predicted by the analytical model were evaluated for several particles sizes for a range of turbulent flow shear velocities. The resulting resuspension fractions were compared with those obtained from the Monte Carlo simulations as well as the available experimental data. It was found that the predictions of the new analytical equation were in good agreement with the Monte Carlo simulation results and the experimental data, especially for smaller size particles. This new analytical expression could be used as a simple empirical equation for estimating flow-induced resuspension of particles from rough surfaces.     

Densification improvement of spark plasma sintered TiB2-based composites with micron-, submicron- and nano-sized SiC particulates

Taguchi design of experiments methodology was used to determine the most influential spark plasma sintering (SPS) parameters on densification of TiB₂–SiC ceramic composites. In this case, four processing factors (SPS temperature, soaking time, applied external pressure and SiC particle size) at three levels were examined in order to acquire the optimum conditions. The statistical analysis identified the sintering temperature as the most effective factor influencing the relative density of TiB₂–SiC ceramics. A relative density of 99.5% was achieved at the optimal SPS conditions; i.e. temperature of 1800?°C, soaking time of 15?min and pressure of 30?MPa by adding 200-nm SiC particulates to the TiB₂ matrix. The experimental measurements and predicted values for the relative density of composite fabricated at the optimum SPS conditions and reinforced with the proper SiC particle size were almost similar. The mechanisms of sintering and densification of spark plasma sintered TiB₂–SiC composites were discussed in details.     

Relative expression and regulation by short-term fasting of lysophosphatidic acid receptors and autotaxin in white and brown adipose tissue depots

Lysophosphatidic acid (lysoPtdOH) levels have previously been reported to decrease in rodents with short-term fasting. We investigated whether a 16 h fast would change expression of autotaxin, the predominant phospholipase D responsible for adipose-derived lysoPtdOH synthesis, or any of the lysophosphatidic acid receptors (1–6) in four white adipose tissue (WAT) depots and interscapular brown adipose tissue (BAT) in male C57Bl/6J mice fed ad libitum, or fasted for 16 h. Aside from small inductions of Lpar1 in epididymal WAT and Lpar2 in epididymal and inguinal WAT, no significant changes were observed in expression of the Lpar family members, or autotaxin in perirenal, retroperitoneal, epididymal, or inguinal WAT or BAT with fasting. Comparison of the relative expression of Lpar1-6 in various depots showed that Lpar6 was the predominant Lpar in both WAT and BAT, and suggests that further work on the adipose-specific role of Lpar6 is warranted.