Bitcoin is a decentralized cryptocurrency, which is a type of digital asset that provides the basis for peer-to-peer financial transactions based on blockchain technology. One of the main problems with decentralized cryptocurrencies is price volatility, which indicates the need for studying the underlying price model. Moreover, Bitcoin prices exhibit nonstationary behavior, where the statistical distribution of data changes over time. This paper demonstrates high-performance machine learning-based classification and regression models for predicting Bitcoin price movements and prices in short and medium terms. In previous works, machine learning-based classification has been studied for an only one-day time frame, while this work goes beyond that by using machine learning-based models for one, seven, thirty and ninety days. The developed models are feasible and have high performance, with the classification models scoring up to 65% accuracy for next-day forecast and scoring from 62 to 64% accuracy for seventh-ninetieth-day forecast. For daily price forecast, the error percentage is as low as 1.44%, while it varies from 2.88 to 4.10% for horizons of seven to ninety days. These results indicate that the presented models outperform the existing models in the literature.
The bumper beam is a crucial component of the automobile bumper system, responsible for absorbing impact energy and enhancing the safety of passengers during collisions. This paper presents the design and experimental analysis of a 3D-printed composite–plastic hybrid light structure, designed as a collapsible energy absorber. Exploratory testing was conducted using low-impact tests to investigate the failure mechanism and energy absorption capacity of a spiral structure. The design process involved optimizing the spiral diameter by testing specimens with varying diameters between 0.5 cm and 2.5 cm, while keeping other geometric parameters constant. The study employed three types of 3D composite structures, including printed thermoplastic, printed thermoplastic reinforced with Kevlar fiber composite, and printed thermoplastic filled with foam. The thermoplastic–foam composite with nine spirals (diameter = 0.97 cm) yielded the best results. The new design demonstrated high energy absorption capacity and a controlled and progressive failure mechanism, making it a suitable candidate for energy absorption applications.
Signing avatars can make a huge impact on the lives of deaf people by making information accessible anytime and anywhere. With technological development, sign language avatars have the potential to be the cost-effective communication solution that will remove the barriers between deaf people and the world. However, most researchers who work in this area are not part of the deaf community and to this day, the deaf community has little to no knowledge about the signing avatar technology. Thus, researchers have created and used evaluation methods that enable them to involve deaf people and take their feedback to develop and improve sign language avatars based on their needs and requirements. In the article, evaluation methods and tools used to assess signing avatars’ functionality, acceptability, and shortcomings were presented and discussed.
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