The stock market is one of the most significant aspects of a county’s economy. Stock price prediction is not easy to implement due numerous features influencing it. This study will choose BMW as the target company, and predict its stock price with various state-of-art scenarios. The central claim of this article is to construct three reliable models that could predict the stock price of BMW by extracting and analyzing previous days’ stock prices. The major analysis is done by modeling and exploring data analysis, and is composed of several graphical methods. For the modeling part, three models (i.e., Multiple Linear Regression, LSTM, and Random Forest Regression) are constructed to predict BMW stock price. The database consists of five years of BMW stock prices on Kaggle to explore more analysis. Then, step by step checking and demonstrating are presented to prove the feasibility and precision of these three models. According to the analysis, the most precise model that has the highest accuracy and lowest error by analyzing regression and model results. These model final outcomes indicate that the Multiple Linear Regression model presents a higher accuracy and lower mean squared error than LSTM and Random Forest Regression. These results shed light on guiding further exploration of BMW stock price prediction.
Biosensors always respond to the targets of interest in a specific manner, employing biological or bio-mimic recognition elements such as antibodies and aptamers. Inspired by target recognition in nature, an aptamer-mediated, gold nanoparticle-based sensing approach is developed in this work for effective determination of malathion. The sensing system consists of negatively charged aptamer probes, and polycationic proteins, protamine, as well as exceptional colorimetric nanoprobes, barely gold nanoparticles (AuNPs). Protamine molecules bound to aptamer probes hinder the aggregation of AuNPs, while no such inhibition is maintained when aptamer-specific malathion is introduced into the solution, thus leading to the solution colour change from red to blue observable by the naked eye. The assay is accomplished via a mix-and-measure step within 40 min with a detection limit as low as 1.48 μg/L (3σ/s rule). The assay method also exhibits high selectivity and good applicability for the quantification of malathion in tap water with recovery rates of 98.9%–109.4%. Additionally, the good detection accuracy is also confirmed by the high-performance liquid chromatography method. Therefore, the non-enzymatic, label- and device-free characteristics make it a robust tool for malathion assay in agricultural, environmental, and medical fields.
Nowadays, the popularity of the NBA along with the penetration of gambling ideas is rising all around the world. More and more fans prefer to bet on sports gambling. People want to predict the final score difference for each individual game. In addition, there are many either superficial or underlying factors that will affect the game’s result. The central claim of this article is to establish a reliable model that could predict each game’s result by extracting and analyzing previous games’ outcomes. The major analysis is done by modeling and exploratory data analysis, and is composed of several graphical methods. For the modeling part, ideally the model could predict the games’ score differences by analyzing previous dozens of games’ data sets. The database consists of four seasons 2013-2017 on Goldsheet website to explore more analysis. Then step by step checking and demonstrating to prove the feasibility and precision of the model. The model will collect and analyze the data information, like teams, rebounds, assists, turnovers, three points, free throws, blocks, and injury data. Considering the transfer between teams and other changes, the model will predict the future game result in each season independently by using each team’s previous game result in that season. This article indicates the idea of linear regression model to find the best fit by comparing the correlation strength of each variable, which include the home-field advantage, teams’ technical statistics, and injury data, to the result of a game. The model final outcomes implies that the game’s result has the most correlation with the home-field advantage and player injury; however, team’s basic technical statistics, included the rebounds, blocks, turnovers, free throws, three points, and assists, have low correlation coefficient with the result to demonstrate that they are not significant to a game’s result.
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