Adaptive fuzzy-SIFT rule-based registration for 3D cardiac motion estimation

Traditional Use, Phytochemical Profiles and Pharmacological Properties of Artemisia Genus from Central Asia

The flora of Kazakhstan is characterized by its wide variety of different types of medicinal plants, many of which can be used on an industrial scale. The Traditional Kazakh Medicine (TKM) was developed during centuries based on the six elements of ancient Kazakh theory, associating different fields such as pharmacology, anatomy, pathology, immunology and food nursing as well as disease prevention. The endemic Artemisia L. species are potential sources of unique and new natural products and new chemical structures, displaying diverse bioactivities and leading to the development of safe and effective phytomedicines against prevailing diseases in Kazakhstan and the Central Asia region. This review provides an overview of Artemisia species from Central Asia, particularly traditional uses in folk medicine and the recent numerous phytochemical and pharmacological studies. The review is done by the methods of literature searches in well-known scientific websites (Scifinder and Pubmed) and data collection in university libraries. Furthermore, our aim is to search for promising and potentially active Artemisia species candidates, encouraging us to analyze Protein Tyrosine Phosphatase 1B (PTP1B), α-glucosidase and bacterial neuraminidase (BNA) inhibition as well as the antioxidant potentials of Artemisia plant extracts, in which endemic species have not been explored for their secondary metabolites and biological activities so far. The main result of the study was that, for the first time, the species Artemisia scopiformis Ledeb. Artemisia albicerata Krasch., Artemisia transiliensis Poljakov, Artemisia schrenkiana Ledeb., Artemisia nitrosa Weber and Artemisia albida Willd. ex Ledeb. due to their special metabolites, showed a high potential for α-glucosidase, PTP1B and BNA inhibition, which is associated with diabetes, obesity and bacterial infections. In addition, we revealed that the methanol extracts of Artemisia were a potent source of polyphenolic compounds. The total polyphenolic contents of Artemisia extracts were correlated with antioxidant potential and varied according to plant origin, the solvent of extraction and the analytical method used. Consequently, oxidative stress caused by reactive oxygen species (ROS) may be managed by the dietary intake of current Artemisia species. The antioxidant potentials of the species A. schrenkiana, A. scopaeformis, A. transiliensis and Artemisia scoparia Waldst. & Kitam. were also promising. In conclusion, the examination of details between different Artemisia species in our research has shown that plant materials are good as an antioxidant and eznyme inhibitory functional natural source.

Scale-Invariant Localization of Electric Vehicle Charging Port via Semi-Global Matching of Binocular Images

Automatic charging for electric vehicles has broad development prospects for meeting the personalized service experience of users while overcoming the inherent safety hazards. An identification and positioning approach suitable for engineering applications is the key to promoting automatic charging. In this paper, a low-cost, high-precision method to identify and position charging ports based on SIFT and SGBM is proposed. The feature extraction approach based on SIFT is adopted to produce the difference of Gaussian (DOG) for scale space construction, and the feature matching algorithm with nearest-neighbor search, which is a kind of machine learning, is utilized to yield the map set of matching points. In addition, the disparity calculation is conducted with a semi-global matching algorithm to obtain high-precision positioning results for the charging port. In order to verify the feasibility of the method, a complete identification and positioning experiment of charging port was carried out based on OpenCV and MATLAB.

Using an Adaptive Neuro-Fuzzy Inference System to Predict Dilution Characteristics of Vertical Buoyant Jets Subjected to Lateral Confinement

In order to predict the dilution characteristics of vertical buoyant jets constrained by lateral obstructions, we propose a new method based on a commonly used machine learning algorithm: the adaptive neuro-fuzzy inference system (ANFIS). By using experimental data to train and test the ANFIS model, this study shows that it had better performance than commonly used empirical equations for laterally confined jets and another artificial intelligence technique—genetic programming. The RMSE values of the ANFIS-based model were lower, and the R2 values were higher, compared with those of the empirical equation and genetic programming models. The reduction in RMSE achieved by using ANFIS to replace the empirical equations or genetic programming algorithm exceeded 20%. This research confirms that the ANFIS technique has real potential in the development of effective and accurate models that can be used to estimate the dilution characteristics of a vertical buoyant jet subjected to lateral confinement, providing a new avenue for the prediction of dilution characteristics using artificial intelligence techniques, which can also be utilized for other effluent mixing problems in marine systems.