Time Series Predictive Analysis of Bitcoin with ARMA-GARCH model in Python and R

Bitcoin arises recent years as a most successful and widely used cryptocurrency. Though it was first invented as a decentralized currency, Bitcoin has become more and more popular as a financial asset with a market capitalization of around $117 billion, as increasing number of investors and portfolio managers start to invest in and trade it. In this paper, we are interested in finding out the future course of Bitcoin prices and returns and examining the predictive power of the ARMA- GARCH model. The paper uses Python and R environment to analyze and model financial time series. The first part covers the preliminary analysis of the daily closing prices and returns of Bitcoin, and also the stationarity of the return series. The second part intends to fit an appropriate ARMA-GARCH model. The last part focuses on using fitted model to predict future returns and prices of Bitcoin and compare it to our validation dataset.Bachelor of Scienc

LSTM Deep Neural Network Based Power Data Credit Tagging Technology

The value of power data credit reporting in the social credit system continues to increase, and the government, users and the whole society have deep expectations and support for power data credit reporting. This paper will combine the data labeling theory as the support, define the power data label and explain its labeling implementation. Based on the construction of knowledge graph, the method of labeling power data is introduced in detail: demand analysis method, index selection method, data cleaning method and data desensitization method. Use the sorted data labels to establish a label system for power data, and through its system, visualize the comprehensive situation of enterprise power data credit information to meet the development of power data credit business. This paper takes shell enterprises as the main representatives of credit risk enterprises, analyzes the power data in the three stages before and after loans, and builds a value mining model for power credit data. In the future, the data labeling technology and value mining model of the power data credit business will be comprehensively applied, and the power data label library and credit model library will be established and continuously improved, so as to facilitate the evaluation of the operation of the enterprise at different stages

Life Cycle Assessment of a Coke Cleaning Agent

The life cycle assessment of the coke cleaning agent developed by a university-enterprise cooperation project was conducted. This cleaning agent has the characteristics of phosphorus-free, environmentally friendly, and broad market prospects. The life cycle assessment of the established model showed that the GWP of producing 1kg of coke cleaning agent is 1.19 kg CO2 eq, PED is 13.17 MJ, WU is 186.74 kg, AP is 3.63E-03 kg SO2 eq, ADP is 7.75E-05 kg antimony eq, EP is 1.30E-03 kg PO43-eq, RI is 1.16E-03 kg PM2.5 eq, ODP is 4.63E-06 kg CFC-11 eq, and POFP is 1.85E-03 kg NMVOC eq .The uncertainty of the results is between 4.20% and 24.05%. The carbon footprint (GWP) analysis showed that the production process of isotridecanol polyoxyethylene ether, isopropanol, fatty alcohol polyoxyethylene ether M and isodecanol polyoxyethylene ether contributed significantly. The average sensitivity analysis showed that the most influential processes were sodium lauryl amphoacetate, isopropanol, and tripropylene glycol methyl ether. Citation: Gong, Y., Yang, C., Qu, Y., Li, J., Yang, B., Ding, Y., and Zhang, B. (2022). Life Cycle Assessment of a Coke Cleaning Agent. Trends in Renewable Energy, 8(1), 67-83. DOI: 10.17737/tre.2022.8.1.0014

Design and exploration of university fluid simulation course

With the development of the times, fluid simulation technology has been used in university courses. As many university courses are too theoretical and difficult to understand, in order to achieve better learning results, the author believes that visualizing abstract concepts with simulation tools can help promote the course and enable students to achieve ideal learning results. This course introduces CFD, a fluid simulation tool. Through learning this course, students can more deeply understand the principles of fluid mechanics and enhance their understanding and application ability of theoretical knowledge through simulation practice

Life Cycle Assessment of A Hydrocarbon-based Electrified Cleaning Agent

The electrified cleaning agent requires a moderate volatilization rate, low ozone-depleting substances value, non-flammable, non-explosive and other characteristics. This study performed a whole life cycle assessment on a hydrocarbon-based electrified cleaning agent. The life cycle model is cradle-to-grave, and the background data sets include power grid, transportation, high-density polyethylene, chemicals, etc. The analysis shows that the global warming potential (GWP) of the life cycle of 1 kg of electrified cleaning agent is 2.08 kg CO2 eq, acidification potential (AP) is 9.49E-03 kg SO2 eq, eutrophication potential (EP) is 1.18E-03 kg PO43-eq, respirable inorganic matter (RI) is 2.13E- 03 kg PM2.5 eq, ozone depletion potential (ODP) is 4.91E-05 kg CFC-11 eq, photochemical ozone formation potential (POFP) is 2.89E-02 kg NMVOC eq, ionizing radiation-human health potential (IRP) is 3.16E-02 kg U235 eq, ecotoxicity (ET) is 2.69E-01 CTUe, human toxicity-carcinogenic (HT-cancer) is 4.32E-08 CTUh, and human toxicity-non-carcinogenic (HT-non cancer) is 2.31E-07 CTUh. The uncertainty of the results is between 3.46-9.95%.The four processes of tetrachloroethylene production, D40 solvent oil production, tetrachloroethylene environmental discharge during product use, and electricity usage during product disposal have substantial effects on each LCA indicator, so they are the focus of process improvement. Changes in power consumption during production and transportation distance of raw materials have little effect on total carbon emissions. Compared with the production process of single-solvent electrified cleaning agent tetrachloroethylene and n-bromopropane, the production of the electrified cleaning agent developed in this study has its own advantages in terms of carbon footprint and other environmental impact indicators. Carbon emissions mainly come from the power consumption of each process, natural gas production and combustion, and other energy materials for heating. It is recommended to use renewable raw materials instead of crude oil to obtain carbon credits based on geographical advantages, and try to use production processes with lower carbon emissions, while the exhaust gas from the traditional production process is strictly absorbed and purified before being discharged

Process Design of Microalgae Slurry Pump

Microalgae are a renewable source of dietary supplements, bioactive compounds, and potential energy. Once harvested, the microalgal medium is dewatered to form a slurry for downstream processing. This article outlines a process design for pumping the microalgae slurry. The pump requirements for delivering the Chlorella slurry with 5, 10 or 20 wt% solids at one tonne per hour (1,000 kg/h) and 10 bar were calculated. The 5 wt% microalgae slurry is a Newtonian fluid with a viscosity of 1.95 mPa×s. The 10 wt% and 20 wt% microalgae slurries are non-Newtonian fluids, whose viscosity depends on the shear rate (g). The viscosity of 10 wt% and 20 wt% microalgae slurries is 1.504 (g = 50 s-1)/1.155 (g = 100 s-1) and 1.844 (g = 50 s-1)/1.219 (g = 100 s-1) mPa×s, respectively. The pump power requirements are mainly governed by the delivery pressure. The effect of the pipe length and the number of elbows is negligible. The effective power of the pump is calculated as 0.267-0.275 kW. To fulfill this duty, a ZGB type single-stage single-suction centrifugal slurry pump can be selected, which would provide enough shear rate to reduce the viscosity of the microalgae slurry and give required shaft power. Citation: Li, J., Qu, Y., Gong, Y., Yang, C., Yang, B., Liu, P., Zhang, B., and Ding, Y. (2020). Process Design of Microalgae Slurry Pump. Trends in Renewable Energy, 6(3), 234-244. DOI: 10.17737/tre.2020.6.3.0012

Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint

Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants. To tackle these challenges, deep learning frameworks have been created to match patients to trials. These frameworks calculate the similarity between patients and clinical trial eligibility criteria, considering the discrepancy between inclusion and exclusion criteria. Recent studies have shown that these frameworks outperform earlier approaches. However, deep learning models may raise fairness issues in patient-trial matching when certain sensitive groups of individuals are underrepresented in clinical trials, leading to incomplete or inaccurate data and potential harm. To tackle the issue of fairness, this work proposes a fair patient-trial matching framework by generating a patient-criterion level fairness constraint. The proposed framework considers the inconsistency between the embedding of inclusion and exclusion criteria among patients of different sensitive groups. The experimental results on real-world patient-trial and patient-criterion matching tasks demonstrate that the proposed framework can successfully alleviate the predictions that tend to be biased

Characteristics of Eddy Current Attenuation and Thickness Measurement of Metallic Plate

Abstract(#br)In eddy current testing, the law of attenuation of eddy current (EC) is of great concern. In conductive half space under the excitation of uniform magnetic field, the EC density decreases exponentially in the depth direction. However, in conductor with finite thickness tested by coil, the distribution of EC in the depth direction is more complicated. This paper studies the characteristics of EC attenuation in metallic plate of finite thickness. Simulation results show that there is an EC reflection at the bottom of plate, which changes the law of EC attenuation. A new concept, namely the equivalent attenuation coefficient, is proposed to quantify the speed of EC attenuation. The characteristics of EC attenuation are utilized to explain the nonmonotonic relation between coil..