Modeling Early Clustering of Impact-induced Ejecta Particles Based on Laboratory and Numerical Experiments

A projectile impact onto a granular target produces an ejecta curtain with heterogeneous material distribution. Understanding how the heterogeneous pattern forms is potentially important for understanding how crater rays form. Previous studies predicted that the pattern formation is induced by inelastic collisions of ejecta particles in early stages of crater formation and terminated by the ejecta’s expanding motion. In this study, we test this prediction based on a hypervelocity impact experiment together with N-body simulations where the trajectories of inelastically colliding granular particles are calculated. Our laboratory experiment suggests that pattern formation is already completed on a timescale comparable to the geometrical expansion of the ejecta curtain, which is ∼10 μs in our experiment. Our simulations confirm the previous prediction that the heterogeneous pattern grows through initial inelastic collisions of particle clusters and subsequent geometric expansion with no further cluster collisions. Furthermore, to better understand the two-stage evolution of the mesh pattern, we construct a simple analytical model that assumes perfect coalescence of particle clusters upon collision. The model shows that the pattern formation is completed on the timescale of the system’s expansion independently of the initial conditions. The model also reproduces the final size of the clusters observed in our simulations as a function of the initial conditions. It is known that particles in the target are ejected at lower speeds with increased distance to the impact point. The difference in the ejection speed of the particles may result in the evolution of the mesh pattern into rays.

Micromechanics-Based Prediction Models and Experimental Validation on Elastic Modulus of Recycled Aggregate Concrete

Elastic modulus is one of the most important mechanical properties of concrete (including recycled aggregate concrete), and it has a notable guiding significance for engineering. There is a lack of micromechanical research on the elastic modulus of recycled aggregate concrete. This paper adopts four models based on micromechanics, including the Voigt model, Reuss model, Eshelby method, and Mori–Tanaka method, to predict the elastic modulus of recycled aggregate concrete. The optimal model is determined by comparing the results of the four models with the experimental data. On this basis, some previous prediction methods for the elastic modulus of concrete are employed to be compared with the most satisfactory models in this paper. Several experimental data from the open literature are also utilized to better illustrate the reliability of the prediction models. It is concluded that the Mori–Tanaka method unfailingly produces more accurate predictions compared to other models. It gives the best overall approximation for various data and has extensive effects in predicting the elastic modulus of RAC. This work may be helpful in promoting the development of micromechanics research in recycled aggregate concrete.

Load noise prediction of a power transformer

In this article, we propose a new regression equation to predict the noise of a power transformer based on the winding vibration under a loading condition. A regression between load noises and tank vibrations for multiple transformers with different rated powers was confirmed through measurements and regression analysis. A regression equation for load noise and winding vibration was derived considering the fact that the winding vibration level is proportional to the tank vibration level. The electromagnetic force, which is the excitation force of the winding, was obtained using the equivalent magnetic circuit network method to obtain the winding vibration required for the regression equation. Subsequently, the obtained force was applied to a finite element model for the winding to achieve the vibration response. The winding vibration obtained through these methods is closely correlated with the load noise, and the amount of winding vibration transferred to the tank could be changed according to the distance between the tank and the winding. Accordingly, an equation for predicting the load noise was established considering the winding vibration and the correlation factors according to the distance of the transmission path. The proposed prediction equation is considerably more accurate than the previous prediction equation.

Controlling the production of phytotoxin pyriculol in Pyricularia oryzae by aldehyde reductase

Pyricularia oryzae is one of the most devastating plant pathogens in the world. This fungus produces several secondary metabolites including the phytotoxin pyriculols, which are classified into 2 types: aldehyde form (pyriculol and pyriculariol) and alcohol form (dihydropyriculol and dihydropyriculariol). Although interconversion between the aldehyde form and alcohol form has been predicted, and the PYC10 gene for the oxidation of alcohol form to aldehyde is known, the gene responsible for the reduction of aldehyde to alcohol form is unknown. Furthermore, previous studies have predicted that alcohol analogs are biosynthesized via aldehyde analogs. Herein, we demonstrated that an aldo/keto reductase PYC7 is responsible for the reduction of aldehyde to alcohol congeners. The results indicate that aldehyde analogs are biosynthesized via alcohol analogs, contradicting the previous prediction. The results suggest that P. oryzae controls the amount of pyriculol analogs using two oxidoreductases, PYC7 and PYC10, thereby controlling the bioactivity of the phytotoxin.

Sequence-to-Sequence Video Prediction by Learning Hierarchical Representations

Video prediction which maps a sequence of past video frames into realistic future video frames is a challenging task because it is difficult to generate realistic frames and model the coherent relationship between consecutive video frames. In this paper, we propose a hierarchical sequence-to-sequence prediction approach to address this challenge. We present an end-to-end trainable architecture in which the frame generator automatically encodes input frames into different levels of latent Convolutional Neural Network (CNN) features, and then recursively generates future frames conditioned on the estimated hierarchical CNN features and previous prediction. Our design is intended to automatically learn hierarchical representations of video and their temporal dynamics. Convolutional Long Short-Term Memory (ConvLSTM) is used in combination with skip connections so as to separately capture the sequential structures of multiple levels of hierarchy of features. We adopt Scheduled Sampling for training our recurrent network in order to facilitate convergence and to produce high-quality sequence predictions. We evaluate our method on the Bouncing Balls, Moving MNIST, and KTH human action dataset, and report favorable results as compared to existing methods.

Predicting Futures Price And Contract Portfolios Using The ARIMA Model: A Case of Nigeria’s Bonny Light and Forcados

Market prediction has been the goal of many study as investors sought traded assets since the inception of the capital market. With each asset exchanged for money, investors seek to stay ahead the market trend in the hope of amassing profits. Businesses’ growth (rise/fall) is evident upon their response to market behaviour. Thus, accurate prediction of the market often offers as its reward, enlarged financial portfolio. Market participants thus, seek to manage the risks associated with asset prices and its volatility, which can be rippled with chaos and complex tasks arising from a demand-supply curve. We seek to model the Oil market and forecast its price direction supported with empirical evidence using ARIMA model to analyze inputs in search of an optimal solution. We adopt the OPEC model to: (a) predict spot/futures-prices, (b) investigate why previous prediction was poor and price plummeted, and (c) compares value(s) from Ojugo and Yoro (2020) and Ojugo and Allenotor (2017). Results shows demand-supply curve rise (and a price rise) even though the policies and trend in real life scenario is currently experiencing a price plummet.

Phase Transformation Prediction Considering Crystallographic Orientation in Microgrinding Multiphase Material

This investigation proposes a physics-based model to predict the solid-state phase transformation of maraging steel subjected to microgrinding. In microgrinding, the effect of crystallography is significant on the grinding phase transformation in light of the fact that the depth of cut is on the same order of magnitude as the grain size. This paper proposes a predictive model of phase transformation considering crystallographic orientation (CO) with respect to the grinding direction based on the Taylor factor model. In addition, the flow stress model is modified by adding a CO sensitive term and incorporating the mechanical-thermal loadings. Furthermore, the temperature, temperature rate, strain rate, and Taylor factor are also combined in the model of phase transition. The kinetics parameters of the models are obtained by a regression analysis against experimental data. Finally, the modified models are validated with experiments data and compared with the previous prediction.

Prediction Model for Timing of Death in Potential Donors After Circulatory Death (DCD III): Protocol for a Multicenter Prospective Observational Cohort Study

Background Controlled donation after circulatory death (cDCD) is a major source of organs for transplantation. A potential cDCD donor poses considerable challenges in terms of identification of those dying within the predefined time frame of warm ischemia after withdrawal of life-sustaining treatment (WLST) to circulatory arrest. Several attempts have been made to develop models predicting the time between treatment withdrawal and circulatory arrest. This time window determines whether organ donation can occur and influences the quality of the donated organs. However, the selected patients used for these models were not always restricted to potential cDCD donors (eg, patients with cancer or severe infections were also included). This severely limits the generalizability of those data. Objective The objectives of this study are the following: (1) to develop a model predicting time to death within 60 minutes in potential cDCD patients; (2) to validate and update previous prediction models on time to death after WLST; (3) to determine timing and patient characteristics that are associated with prognostication and the decision-making process that leads to initiating end-of-life care; (4) to evaluate the impact of timing of family approach on organ donation approval; and (5) to assess the influence of variation in WLST processes on postmortem organ donor potential and actual postmortem organ donors. Methods In this multicenter observational prospective cohort study, all patients admitted to the intensive care unit of 3 university hospitals and 3 teaching hospitals who met the criteria of the cDCD protocol as defined by the Dutch Transplant Foundation were included. The target of enrolment was set to 400 patients. Previously developed models will be refitted in our data set. To further update previous prediction models, we will apply least absolute shrinkage and selection operator (LASSO) as a tool for efficient variable selection to develop the multivariable logistic regression model. Results This protocol was funded in August 2014 by the Dutch Transplant Foundation. We expect to have the results of this study in July 2020. Patient enrolment was completed in July 2018 and data collection was completed in April 2020. Conclusions This study will provide a robust multimodal prediction model, based on clinical and physiological parameters, that can predict time to circulatory arrest in cDCD donors. In addition, it will add valuable insight in the process of WLST in cDCD donors and will fill an important knowledge gap in this essential field of health care. Trial Registration ClinicalTrials.gov NCT04123275; https://clinicaltrials.gov/ct2/show/NCT04123275 International Registered Report Identifier (IRRID) DERR1-10.2196/16733

A Balaton régió népességdinamikája 2017-2062 között

The present study examines the population projection of the settlements of the Balaton Region (BKÜ) by 2062, taking into account 180 settlements. The results are presented for the main age groups of the region, at five-year intervals, and broken down as follows: 18 cities, coastal villages (group of 39 settlements), and non-coastal villages (group of 123 settlements). The most recent research report on which this study is based was produced in 2017. For the two years since then, we can also identify deviations from previously forecasted processes. Using another method, we interrogate the reliability of the previous prediction. The population projection shows that the population of the resort area will fall to 184,000 by 2062, and the proportion of the population over the age of 65 will reach or even exceed 35%. In addition to the aging of the villages, the depopulation of dwarf villages further away from the coast poses a serious problem.

The Ongoing COVID-19 Epidemic Curves Indicate Initial Point Spread in China with Log-Normal Distribution of New Cases Per Day with a Predictable Last Date of the Outbreak Version 2: Evaluation of Previous Prediction and Testing the Method for S Korea and the Use of the Method by an Unexperienced Person

During an epidemic outbreak it is useful for planners and responsible authorities to be able to plan ahead to estimate when an outbreak of an epidemic is likely to ease and when the last case can be predicted in their area of responsibility. Theoretically this could be done for a point source epidemic using epidemic curve forecasting. The extensive data now coming out of China makes it possible to test if this can be done using MS Excel a standard spreadsheet program available to most offices. The available data is divided up for whole China and the different provinces. This and the high number of cases makes the analysis possible. Data for new confirmed infections for Hubei, Hubei outside Wuhan, China excluding Hubei as well as Zhejiang and Fujian provinces all follow a log-normal distribution that can be used to make a rough estimate for the date of the last new confirmed cases in respective areas. In this continuation work 9 additional days were added for the Chinese data to evaluate the previous predictions. We also tested the feasibility for a non-specialist to make similar predictions using additional data from S Korea now available. The extra data now available from China follows the previous predicted trend supporting the usefulness of this simple technique.