Beijing University of Technology

About: Beijing University of Technology is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Microstructure & Computer science. The organization has 31929 authors who have published 31987 publications receiving 352112 citations. The organization is also known as: Běijīng Gōngyè Dàxué & Beijing Polytechnic University.

A Comparison between ARIMA, LSTM, and GRU for Time Series Forecasting

Abstract: A critical area of machine learning is Time Series forecasting, as various forecasting problems contain a time component. A series of observations taken chronologically in time is known as a Time Series. In this research, however, we aim to compare three different machine learning models in making a time series forecast. We are going to use the Bitcoin's price dataset as our time series data set and make predictions accordingly. The results show that the ARIMA model gave better results than the deep learning-based regression models. ARIMA gives the best results at 2.76% and 302.53 for MAPE and RMSE respectively. The Gated Recurrent Unit (GRU) however performed better than the Long Short-term Memory (LSTM), with 3.97% and 381.34 of MAPE and RMSE respectively.

A Monochromatic Electrochemiluminescence Sensing Strategy for Dopamine with Dual-Stabilizers-Capped CdSe Quantum Dots as Emitters

Abstract: A promising electrochemiluminescence (ECL) sensing strategy was proposed with dual-stabilizers-capped CdSe quantum dots (QDs) as ECL emitters. The dual-stabilizers-capped CdSe QDs were covalently immobilized onto p-aminobenzoic acid modified glass carbon electrode with ethylenediamine as a link molecule. This strategy can preserve the completely passivated surface states of dual-stabilizers-capped CdSe QDs, so that the sensor demonstrated eye-visible greenish, band gap engineering and monochromatic ECL emission at 546 nm with a fwhm of 35 nm. Moreover, the proposed sensor could accurately quantify dopamine from 10.0 nM to 3.0 μM with a detection limit of 3.0 nM in practical drug, human urine, and cerebrospinal fluid samples without any signal amplification techniques. This strategy is promising for developing ECL sensors with high sensitivity and spectral selectivity.