Background/purpose: The diagnosis of peri-implantitis using periapical radiographs is crucial. Recently, artificial intelligence may apply in radiographic image analysis effectively. The aim of this study was to differentiate the degree of marginal bone loss of an implant, and also to classify the severity of peri-implantitis using a deep learning model. Materials and methods: A dataset of 800 periapical radiographic images were divided into training (n = 600), validation (n = 100), and test (n = 100) datasets with implants used for deep learning. An object detection algorithm (YOLOv7) was used to identify peri-implantitis. The classification performance of this model was evaluated using metrics, including the specificity, precision, recall, and F1 score. Results: Considering the classification performance, the specificity was 100%, precision was 100%, recall was 94.44%, and F1 score was 97.10%. Conclusion: Results of this study suggested that implants can be identified from periapical radiographic images using deep learning-based object detection. This identification system could help dentists and patients suffering from implant problems. However, more images of other implant systems are needed to increase the learning performance to apply this system in clinical practice.

We explore whether investors would receive excess profits by round-turn trading (hereafter referred to as trading) Bitcoin futures based on Bollinger Bands trading strategy (BBTS). Since investors are suggested to first buy (then sell) Bitcoin futures as oversold (overbought) signals emitted by the BBTS (i.e. penetrating lower (upper) Bollinger Bands regarded as a buying (selling) signal), we aim to explore whether investors would have better returns by trading such futures according to the BBTS. Results show that the average holding period return (AHPR) is over 20% for trading Bitcoin futures following the BBTS. Furthermore, after we adjust the 60-day moving average (MA) instead of the 20-day MA for the BBTS, the AHPR is above 50%. It is noted that if the margin could be deemed as an investment amount, its rate of return would be much higher than the 50% for trading Bitcoin futures.

Based on technological innovation and climate change, clean energy has been paid increasing attention to by worldwide investors, thereby increasing their interest in investing in firms that specialize in clean energy. However, traditional energy still plays an important role nowadays, because extreme weather has often occurred in the winters of recent years. We thus explore whether investing the strategies adopted by diverse technical trading rules would matter for investing in energy-related ETFs. By employing two representative global ETFs with more than 10 years of data, iShares Global Clean Energy ETF as the proxy of clean energy performance and iShares Global Energy ETF as that of traditional energy performance, we then revealed that momentum strategies would be proper for buying the green energy ETF, but contrarian strategies would be appropriate for buying the energy ETF. Furthermore, based on investment strategies adopted by diverse technical trading rules, we showed that the performance of clean energy outperforms that of energy, indicating that green energy does matter for the economy. Moreover, while observing the price trend of these two ETFs, we found that such two ETFs may have opposite share price performances, implying that, while the green energy ETF reached a relatively high price, investors following the contrarian strategies suggested in this study may reap profits by investing the energy ETF.

Since the yields of trading stocks are affected by numerous elements, this study aims to explore whether the profitability of trading stock with the use of technical trading rules, such as Stochastic Oscillator Indicators (SOI) and Relative Strength Index (RSI), would matter in the fourth quarter of the year. By employing the data of Dow Jones 30 (DJI 30) and NASDAQ 100 (NDX100) from 2011 to 2020 and from 1991 to 2020, this study reports that investors applying SOI and RSI would have higher cumulative abnormal returns (CARs) in Quarter 4 as compared with those in Quarters 1–3, respectively. The revealed findings indicate that the quarterly effect should be taken into account by investors when trading DJI 30 and NDX100, the representative indices for US stock markets. More importantly, this study may contribute to the existing literature due to the rare discussion of this interesting issue in the past research.