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Automatic extraction of vertebra regions from a spinal magnetic resonance (MR) image is normally required as the first step to an intelligent spinal MR image diagnosis system. In this work, we develop a fully automatic vertebra detection and segmentation system, which consists of three stages; namely, AdaBoost-based vertebra detection, detection refinement via robust curve fitting, and vertebra segmentation by an iterative normalized cut algorithm. In order to produce an efficient and effective vertebra detector, a statistical learning approach based on an improved AdaBoost algorithm is proposed. A robust estimation procedure is applied on the detected vertebra locations to fit a spine curve, thus refining the above vertebra detection results. This refinement process involves removing the false detections and recovering the miss-detected vertebrae. Finally, an iterative normalized-cut segmentation algorithm is proposed to segment the precise vertebra regions from the detected vertebra locations. In our implementation, the proposed AdaBoost-based detector is trained from 22 spinal MR volume images. The experimental results show that the proposed vertebra detection and segmentation system can achieve nearly 98% vertebra detection rate and 96% segmentation accuracy on a variety of testing spinal MR images. Our experiments also show the vertebra detection and segmentation accuracies by using the proposed algorithm are superior to those of the previous representative methods. The proposed vertebra detection and segmentation system is proved to be robust and accurate so that it can be used for advanced research and application on spinal MR images.

House price prediction is a popular topic, and research teams are increasingly performing related studies by using deep learning or machine learning models. However, because some studies have not considered comprehensive information that affects house prices, prediction results are not always sufficiently precise. Therefore, we propose an end to end joint self-attention model for house prediction. In this model, we import data on public facilities such as parks, schools, and mass rapid transit stations to represent the availability of amenities, and we use satellite maps to analyze the environment surrounding houses. We adopt attention mechanisms, which are widely used in image, speech, and translation tasks, to identify crucial features that are considered by prospective house buyers. The model can automatically assign weights when given transaction data. Our proposed model differs from self-attention models because it considers the interaction between two different features to learn the complicated relationship between features in order to increase prediction precision. We conduct experiments to demonstrate the performance of the model. Experimental data include actual selling prices in real estate transaction data for the period from 2017 to 2018, public facility data acquired from the Taipei and New Taipei governments, and satellite maps crawled using the Google Maps application programming interface. We utilize these datasets to train our proposed and compare its performance with that of other machine learning-based models such as Extreme Gradient Boosting and Light Gradient Boosted Machine, deep learning, and several attention models. The experimental results indicate that the proposed model achieves a low prediction error and outperforms the other models. To the best of our knowledge, we are the first research to incorporate attention mechanism and STN network to conduct house price prediction.

In this study, The Fenton process was applied as a pretreatment method to treat industrial wastewater containing 4-nitrophenol (4-NP). The effect of oxidant dosages on the decomposition of 4-NP and the reaction kinetics were investigated. More than 99% of 4-NP was readily decomposed when the reaction was carried out at oxidant concentrations of 5 mM H2O2 and 5 mg/L Fe2+ for 2 hours. The total nitrogenous compounds and the nitrogen gas evolved, accounted for 88% of the initial nitrogen concentration. However, the maximum DOC removal efficiency was 30.6%; and only 1/3 of 4-NP was mineralized to carbon dioxide by the Fenton process. 4-NP degradation profiles fitted well into a pseudo first-order kinetic equation; degradation rate constant (min-1) of 4-NP increased from 4.3×10-3 to 66.1×10-3 with increasing dosages of H2O2 and Fe2+. In addition, the t value was calculated for studying the significance of simulation by the t-test. It was found that the t value was greater than the value for 99% confidence. This result suggested that the 4-NP decomposition profile could be described by the pseudo first-order kinetic equation. Biodegradability of 4-NP before and after the reaction was 0.018 and 0.594, respectively.