Estimating construction duration of diaphragm wall using firefly-tuned least squares support vector machine

Treatments of heating and ultrasound improve the inhibition of gallocatechin gallate on tyrosinase

BACKGROUND

Gallocatechin gallate (GCG), a catechin of tea polyphenols, possesses inhibitory ability against tyrosinase, but few studies have reported how common processing methods affect it. In this research, the influence of heating and ultrasound treatments on the inhibition of GCG against tyrosinase was explored by ultraviolet-visible absorption, fluorescence spectroscopy, high-performance liquid chromatography and liquid chromatography-tandem mass spectrometry.

RESULTS

Both heating and ultrasound treatments of GCG alone improved GCG's inhibitory ability against tyrosinase compared with the untreated, and a combination of heating and ultrasound treatment (100 °C, 20 min + 630 W, 20 min) further decreased the relative tyrosinase activity to 26.8%. The treated GCG exhibited a stronger fluorescence quenching effect on tyrosinase, but did not have any influence on the static quenching mechanism. Compared to the untreated GCG, the binding constants of treated GCG by heating, ultrasound and their combination with tyrosinase significantly increased, but the number of binding sites was still approximately one and the main driving force of the treated GCG was still hydrophobic interaction. After treatments of heating, ultrasound and their combination, the composition of GCG solutions was changed.

CONCLUSION

The enhanced inhibition of treated GCG on tyrosinase may be due to partial conversion of GCG into epigallocatechin-3-gallate (EGCG) and gallic acid (GA), which may cooperate with GCG to better inhibit the enzyme activity. This study has provided some valuable information for the application of catechins against tyrosinase in food processing and cosmetic industry. © 2022 Society of Chemical Industry.

Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges

Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. Firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain. Moreover, there is an outstanding track record of FA in solving biomedical engineering (BME) and healthcare (HC) problems. Abundant complexities have been worked out with the assist of FA and its variants. By taking these particulars into concern, in this paper, a first ever in-depth analysis has been addressed on the variants, importance, applications as well as enhancements of FA in BME as well as HC. The major intention behind this investigative work is to motivate the researchers to improve and innovate new solutions for multifaceted problems of healthcare and biomedical engineering using FA.

A Novel Hybrid Swarm Optimized Multilayer Neural Network for Spatial Prediction of Flash Floods in Tropical Areas Using Sentinel-1 SAR Imagery and Geospatial Data

Flash floods are widely recognized as one of the most devastating natural hazards in the world, therefore prediction of flash flood-prone areas is crucial for public safety and emergency management. This research proposes a new methodology for spatial prediction of flash floods based on Sentinel-1 SAR imagery and a new hybrid machine learning technique. The SAR imagery is used to detect flash flood inundation areas, whereas the new machine learning technique, which is a hybrid of the firefly algorithm (FA), Levenberg–Marquardt (LM) backpropagation, and an artificial neural network (named as FA-LM-ANN), was used to construct the prediction model. The Bac Ha Bao Yen (BHBY) area in the northwestern region of Vietnam was used as a case study. Accordingly, a Geographical Information System (GIS) database was constructed using 12 input variables (elevation, slope, aspect, curvature, topographic wetness index, stream power index, toposhade, stream density, rainfall, normalized difference vegetation index, soil type, and lithology) and subsequently the output of flood inundation areas was mapped. Using the database and FA-LM-ANN, the flash flood model was trained and verified. The model performance was validated via various performance metrics including the classification accuracy rate, the area under the curve, precision, and recall. Then, the flash flood model that produced the highest performance was compared with benchmarks, indicating that the combination of FA and LM backpropagation is proven to be very effective and the proposed FA-LM-ANN is a new and useful tool for predicting flash flood susceptibility.