A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin.

The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proactively take essential measures to reduce exposure to detrimental pollutants. Previous research has primarily focused on predicting air quality using only time-series data. However, the importance of remote-sensing image data has received limited attention. This paper proposes a new multi-modal deep-learning model, Res-GCN, which integrates high spatial resolution remote-sensing images and time-series air quality data from multiple stations to forecast future air quality. Res-GCN employs two deep-learning networks, one utilizing the residual network to extract hidden visual information from remote-sensing images, and another using a dynamic spatio-temporal graph convolution network to capture spatio-temporal information from time-series data. By extracting features from two different modalities, improved predictive performance can be achieved. To demonstrate the effectiveness of the proposed model, experiments were conducted on two real-world datasets. The results show that the Res-GCN model effectively extracts multi-modal features, significantly enhancing the accuracy of multi-step predictions. Compared to the best-performing baseline model, the multi-step prediction's mean absolute error, root mean square error, and mean absolute percentage error increased by approximately 6%, 7%, and 7%, respectively.

Research on the method of identifying upper and lower limb coordinated movement intentions based on surface EMG signals.

Rehabilitation robots have gained considerable focus in recent years, aiming to assist immobilized patients in regaining motor capabilities in their limbs. However, most current rehabilitation robots are designed specifically for either upper or lower limbs. This limits their ability to facilitate coordinated movement between upper and lower limbs and poses challenges in accurately identifying patients' intentions for multi-limbs coordinated movement. This research presents a multi-postures upper and lower limb cooperative rehabilitation robot (U-LLCRR) to address this gap. Additionally, the study proposes a method that can be adjusted to accommodate multi-channel surface electromyographic (sEMG) signals. This method aims to accurately identify upper and lower limb coordinated movement intentions during rehabilitation training. By using genetic algorithms and dissimilarity evaluation, various features are optimized. The Sine-BWOA-LSSVM (SBL) classification model is developed using the improved Black Widow Optimization Algorithm (BWOA) to enhance the performance of the Least Squares Support Vector Machine (LSSVM) classifier. Discrete movement recognition studies are conducted to validate the exceptional precision of the SBL classification model in limb movement recognition, achieving an average accuracy of 92.87%. Ultimately, the U-LLCRR undergoes online testing to evaluate continuous motion, specifically the movements of "Marching in place with arm swinging". The results show that the SBL classification model maintains high accuracy in recognizing continuous motion intentions, with an average identification rate of 89.25%. This indicates its potential usefulness in future rehabilitation robot-active training methods, which will be a promising tool for a wide range of applications in the fields of healthcare, sports, and beyond.

Research on Monocular-Vision-Based Finger-Joint-Angle-Measurement System.

The quantitative measurement of finger-joint range of motion plays an important role in assessing the level of hand disability and intervening in the treatment of patients. An industrial monocular-vision-based knuckle-joint-activity-measurement system is proposed with short measurement time and the simultaneous measurement of multiple joints. In terms of hardware, the system can adjust the light-irradiation angle and the light-irradiation intensity of the marker by actively adjusting the height of the light source to enhance the difference between the marker and the background and reduce the difficulty of segmenting the target marker and the background. In terms of algorithms, a combination of multiple-vision algorithms is used to compare the image-threshold segmentation and Hough outer- and inner linear detection as the knuckle-activity-range detection method of the system. To verify the accuracy of the visual-detection method, nine healthy volunteers were recruited for experimental validation, and the experimental results showed that the average angular deviation in the flexion/extension of the knuckle was 0.43° at the minimum and 0.59° at the maximum, and the average angular deviation in the adduction/abduction of the knuckle was 0.30° at the minimum and 0.81° at the maximum, which were all less than 1°. In the multi-angle velocimetry experiment, the time taken by the system was much less than that taken by the conventional method.

Smilasides M and N, two new phenylpropanoid glycosides from Smilax riparia.

Two new phenylpropanoid glycosides, smilasides M and N, together with the known compound 2',6'-diacetyl-3,6-diferuloylsucrose, were isolated and characterized from the roots and rhizomes of Smilax riparia A. DC. The structures of the new compounds were elucidated as 2',6'-diacetyl-3-Z-feruloyl-6-feruloylsucrose (1) and 2',6'-diacetyl-3-feruloyl-6-Z-feruloylsucrose (2) on the basis of extensive analysis of HR-ESI-MS, UV, IR, and 1D and 2D NMR spectroscopic data.

Clematomandshurica saponin E, a new triterpenoid saponin from Clematis mandshurica.

A new triterpenoid saponin, clematomandshurica saponin E, together with four known saponins were isolated and characterized from the roots and rhizomes of Clematis mandshurica (Ranunculaceae), a commonly used traditional Chinese medicine with anti-inflammatory and antirheumatoid activities. On the basis of spectroscopic analysis, including HR-ESI-MS, IR, 1D, and 2D NMR spectral data and hydrolysis followed by chromatographic analysis, the structure of the new triterpenoid saponin was elucidated as 3-O-α-L-rhamnopyranosyl-(1 → 6)-ß-D-glucopyranosyl-(1 → 4)-ß-D-glucopyranosyl-(1 → 4)-ß-D-ribopyranosyl-(1 → 3)-α-L-rhamnopyranosyl-(1 → 2)-α-L-arabinopyranosyl oleanolic acid 28-O-ß-D-glucopyranosyl-(1 → 6)-ß-D-glucopyranoside.

Acylated flavone C-glycosides from Hemistepta lyrata.

Two new acylated flavone C-glycosides, 6''-O-(2'''-methylbutyryl)isoswertisin (1) and 6''-O-(2'''-methylbutyryl)isoswertiajaponin (2), together with four known acylated flavone C-glycosides, were isolated for the first time from the whole plants of Hemistepta lyrata (Compositae). Their structures were elucidated on the basis of chemical and spectroscopic methods including HR-ESI-MS, ESI-MS, UV, IR, and 1D and 2D NMR spectral techniques.