Characterization and Anti-Ultraviolet Radiation Activity of Proanthocyanidin-Rich Extracts from Cinnamomum camphora by Ultrasonic-Assisted Method.

The ultrasonic-assisted extraction (UAE) method was employed to separate Cinnamomum camphora proanthocyanidin-rich extracts (PCEs). This extraction process was optimized by the Box-Behnken design, and the optimal conditions, on a laboratory scale, were as follows: an ethanol concentration of 75%, a liquid-to-solid ratio of 24 mL/g, an ultrasonic time of 39 min, and an ultrasonic power of 540 W. Under the obtained conditions, the PCE yield extracted by UAE was higher than that from heat reflux extraction and soaking extraction. An ultra-performance liquid chromatography-tandem mass spectrometry analysis was employed to characterize the phloroglucinolysis products of the C. camphora PCEs, by which epigallocatechin, catechin, epicatechin, and (-)-epigallocatechin-3-O-gallate were identified as the terminal units; epigallocatechin, epicatechin, and (-)-epigallocatechin-3-O-gallate were recognized as extension units. The C. camphora PCEs possessed higher anti-ultraviolet activity in vitro compared with the commercially available sunscreen additive of benzophenone with respect to their ethanol solutions (sun protection factor of 27.01 ± 0.68 versus 1.96 ± 0.07 at a concentration of 0.09 mg/mL) and sunscreens (sun protection factor of 17.36 ± 0.62 versus 14.55 ± 0.47 at a concentration of 20%). These results demonstrate that C. camphora PCEs possess an excellent ultraviolet-protection ability and are promising green sunscreen additives that can replace commercial additives.

MUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methods.

Gradient-type distributed optimization methods have blossomed into one of the most important tools for solving a minimization learning task over a networked agent system. However, only one gradient update per iteration makes it difficult to achieve a substantive acceleration of convergence. In this article, we propose an accelerated framework named multiupdates single-combination (MUSIC) allowing each agent to perform multiple local updates and a single combination in each iteration. More importantly, we equip inexact and exact distributed optimization methods into this framework, thereby developing two new algorithms that exhibit accelerated linear convergence and high communication efficiency. Our rigorous convergence analysis reveals the sources of steady-state errors arising from inexact policies and offers effective solutions. Numerical results based on synthetic and real datasets demonstrate both our theoretical motivations and analysis, as well as performance advantages.

Air quality prediction by integrating mechanism model and machine learning model.

AQP (Air Quality Prediction) is a very challenging project, and its core issue is how to solve the interaction and influence among meteorological, spatial and temporal factors. To address this central conundrum, we make full use of the characteristics of mechanism model and machine learning and propose a new AQP method based on DM_STGNN (Dynamic Multi-granularity Spatio-temporal Graph Neural Network). This method is the first time to use the air quality model HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory Model) to assist in building a dynamic spatio-temporal graph structure to learn the spatiotemporal relationship of pollutants. DM_STGNN is based on an elaborate encoder-decoder architecture. At the encoder, in order to better mine the spatial dependency, we built a multi-granularity graph structure, used meteorological, time and geographical features to establish node attributes, used well-known HYSPLIT model to dynamically establish the edges among nodes, and used LSTM (Long Short Term Memory) to learn the time-series relationship of pollutant concentrations. At the decoder, in order to better mine the temporal dependency, we built an attention mechanism based LSTM for decoding and AQP. Additionally, in order to efficiently learn the temporal patterns from very long-term historical time series and generate rich contextual information, an unsupervised pre-training model is used to enhance DM_STGNN. The proposed model makes full use of and fully considers the influence of meteorological, spatial and temporal factors, and integrates the advantages of mechanism model and machine learning. On a project-based dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in AQP. We also compare the proposed model with the state-of-the-art AQP methods on the dataset of Yangtze River Delta city group, the experimental results show the appealing performance of our model over competitive baselines.

Application of an aqueous enzymatic-ultrasound cavitation method for the separation of Sapium sebiferum seed kernel oil.

An aqueous enzymatic-ultrasound cavitation extraction (AEUCE) method was developed to separate Sapium sebiferum seed kernel oil. In this process, neutral proteinase was screened as the propriate enzyme. The Plackett-Burman and Box-Behnken designs were employed to optimize AEUCE. We determined the optimal extraction conditions, producing an oil yield of 84.22 ± 3.17 %. Gas chromatography-mass spectrometry (GC-MS) analysis indicated that the S. sebiferum seed kernel oil was abundant in unsaturated fatty acids (>92 %) and that the compositions of the fatty acid profiles extracted by AEUCE were similar to those obtained from Soxhlet extraction, but their contents were slightly different. The physicochemical properties analysis showed that the oil extracted by AEUCE was comparable to that obtained from Soxhlet extraction. The results showed that the developed AEUCE is an efficient technique that can separate high-quality plant oils. The S. sebiferum seed kernel oil obtained from this extraction method is a promising substitute for vegetable oils used in biodiesel production.