Publications

[Effect of pigmentation on the strength of dental Y-TZP/porcelain bilayered structure].

To investigate the effect of two methods of pigmentation on the flexural strength of dental Y-TZP/porcelain layered structure.KaVo zirconia substructures were pigmented by dipping presintered blocks in the coloring solution VITA LL1 and LL5, and colored TZ-3YS zirconia substructures were fabricated by adding pigments before isostatic pressing. The colors No.1 and No.5 were used for the test. The specimens were made in monolithic or bilayered forms, and the flexural strength was tested. XRD and SEM with EDX were used to analyze the characteristics of the surface structure.In KaVo group, no significant differences were found in the flexural strength between white and LL1 and LL5 colored monoclinic materials, nor in bilayered structures. While in TZ-3YS group, significant differences were noted in the flexural strength between color No.5 white and color No.1 monoclinic materials, but not between the latter two subgroups. The flexural strength was significantly lowered by veneering with porcelain in both zirconia groups, and similar findings were observed with the monoclinic materials. Only the tetragonal phase was detected in both of the zirconia groups.Pigmentation has no apparent effects on the bonding strength between the veneering porcelain and zirconia. Both coloring methods are appropriate when the concentration of the pigments is under deliberate control.

Power Transmission Scheduling for Generators in a Deregulated Environment Based on a Game-Theoretic Approach

In a deregulated environment of the power market, in order to lower their energy price and guarantee the stability of the power network, appropriate transmission lines have to be considered for electricity generators to sell their energy to the end users. This paper proposes a game-theoretic power transmission scheduling for multiple generators to lower their wheeling cost. Based on the embedded cost method, a wheeling cost model consisting of congestion cost, cost of losses and cost of transmission capacity is presented. By assuming each generator behaves in a selfish and rational way, the competition among the multiple generators is formulated as a non-cooperative game, where the players are the generators and the strategies are their daily schedules of power transmission. We will prove that there exists at least one pure-strategy Nash equilibrium of the formulated power transmission game. Moreover, a distributed algorithm will be provided to realize the optimization in terms of minimizing the wheeling cost. Finally, simulations were performed and discussed to verify the feasibility and effectiveness of the proposed non-cooperative game approach for the generators in a deregulated environment.

Daily stress detection by using electrocardiogram in the first hour of sleep at night

Stress refers to a series of physiological reactions to help the human body overcome threats. In this study, the machine learning method was used to analyze the stress in students' daily life environment. The purpose is to explore the neurophysiological model of sleep under stress, so that they can effectively adjust their personal stress and improve their sleep quality. First, we collected ECG and triple-axis acceleration data of 33 college students in their daily life, calculated the inter-beat intervals from every two consecutive R waves in these data. Secondly, we extracted 39 linear and non-linear RR parameters as stress physiological features, and applied Leave-One-Subject-Out cross test and Sequential Backward Selection algorithm for feature selection while training some traditional classifiers. Finally, a binary classification model of stressed and non-stressed states recognition in the first hour of sleep at night was constructed, and the generalization accuracy of the model was 75.00% on the validation data set independent of model training and feature selection. The results show that it is feasible to monitor students' stress state at night by machine learning.

Letting wine polyphenols functional: Estimation of wine polyphenols bioaccessibility under different drinking amount and drinking patterns

Dynamic real-time forecasting technique for reclaimed water volumes in urban river environmental management

Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction

Plasma protein affinity, antioxidant, and anti-lung cancer properties of O-methylated flavonol rhamnazin

Medicinal herbs can be used for the development of antioxidant and anticancer platforms. However, the mechanisms by which the bioactive materials show these properties remain largely unclear. Also, a detailed understanding of the interaction between small compounds and carrier proteins like albumin can provide useful information about the regulation of their stability and circulation time properties. In this paper, the interaction of rhamnazin (C17H14O7), an O-methylated flavonol exists in Rhamnus petiolaris, with human serum albumin (HSA) was investigated. Also, the antioxidant and anti-lung cancer effects of rhamnazin were assessed. The results showed that a static rhamnazin-HSA complex was formed by the contribution of hydrophobic forces, which led to an increase in the content of the α-helix structure of HSA. Cellular assays revealed that rhamnazin (10 µM) recovered cell viability in human proximal renal tubular epithelial cells (HK2) reduced by H2O2 (0.5 mM) through reduction of ROS, restoration of CAT and SOD activities and GSH content, regulation of Bax, Bcl-2, and caspase-3. Moreover, the anti-lung cancer data demonstrated that rhamnazin induced apoptosis on lung adenocarcinoma A549 cells associated with a p53-dependent manner via overexpression of apoptotic Bax and caspase-3 and down-regulation of anti-apoptotic Bcl-2. To summarize, the potential binding of rhamnazin and HSA, as well as its antioxidant/anticancer properties, are critical for understanding the solubility and stability of this bioactive compound in plasma, as well as its therapeutic implications.

Dynamic Real-Time Forecasting Technique for Reclaimed Water Volumes in Urban River Environmental Management

In recent years, wastewater recycling has gained popularity as an alternative water source to address urban water shortages. Accurate wastewater quantity prediction is crucial for urban river water resource management. Decomposition ensemble models have shown excellent predictive capabilities but are challenged by boundary effects when decomposing the original data sequence. To address this, a rolling forecast decomposition ensemble scheme was developed. It involves using a machine learning (ML) model for prediction and progressively integrating prediction outcomes into the original sequence using complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Long short-term memory (LSTM) is then applied for sub-signal prediction and ensemble. The ML-CEEMDAN-LSTM model was introduced for wastewater quantity prediction, compared with non-decomposed ML models, CEEMDAN-based LSTM models, and ML-CEEMDAN-based LSTM models. Three ML algorithms—linear regression (LR), gradient boosting regression (GBR), and LSTM—were examined, using real-time prediction data and historical monitoring data, with historical data selected using the decision tree method. The study used daily water volumes data from two reclaimed water plants, CH and WQ, in Beijing. The results indicate that (1) ML models varied in their selection of real-time factors, with LR performing best among ML models during testing; (2) the ML-CEEMDAN-LSTM model consistently outperformed ML models; (3) the ML-CEEMDAN-LSTM hybrid model performed better than the CEEMDAN-LSTM model across different seasons. This study offers a reliable and accurate approach for reclaimed water volumes forecasting, critical for effective water environment management.

Adaptive Attention Module for Image Recognition Systems in Autonomous Driving

Lightweight, high‐performance networks are important in vision perception systems. Recent research on convolutional neural networks has shown that attention mechanisms can significantly improve the network performance. However, existing approaches either ignore the significance of using both types of attention mechanisms (channel and space) simultaneously or increase the model complexity. In this study, we propose the adaptive attention module (AAM), which is a truly lightweight yet effective module that comprises channel and spatial submodules to balance model performance and complexity. The AAM initially utilizes the channel submodule to generate intermediate channel‐refined features. In this module, an adaptive mechanism enables the model to autonomously learn the weights between features extracted by global max pooling and global average pooling to adapt to different stages of the model, thus enhancing performance. The spatial submodule employs a group‐interact‐aggregate strategy to enhance the expression of important features. It groups the intermediate channel‐refined features along the channel dimension into multiple subfeatures for parallel processing and generates spatial attention feature descriptors and channelwise refined subfeatures for each subfeature; subsequently, it aggregates all the refined subfeatures and employs a “channel shuffle” operator to transfer information between different subfeatures, thereby generating the final refined features and adaptively emphasizing important regions. Additionally, AAM is a plug‐and‐play architectural unit that can be directly used to replace standard convolutions in various convolutional neural networks. Extensive tests on CIFAR‐100, ImageNet‐1k, BDD100K, and MS COCO demonstrate that AAM improves the baseline network performance under various models and tasks, thereby validating its versatility.

Contribution of non-Saccharomyces yeasts to aroma-active compound production, phenolic composition and sensory profile in Chinese Vidal icewine

Preparation and characterization of Li4Ti5O12 from ilmenite

Controlled synthesis of β-NaYF 4 :Yb, Er microphosphors and upconversion luminescence property

Thermosonication Combined with Natural Antimicrobial Nisin: A Potential Technique Ensuring Microbiological Safety and Improving the Quality Parameters of Orange Juice

Currently, thermal pasteurisation (TP) remains the most widely applied technique for commercial orange juice preservation; however, a high temperature causes adverse effects on the quality attributes of orange juice. In order to explore a novel non-thermal sterilization method for orange juice, the impacts of thermosonication combined with nisin (TSN) and TP treatments on the quality attributes including microbial and enzyme inactivation and the physicochemical, nutritional, functional, and sensory qualities of orange juice were studied. Both TP and TSN treatments achieved desirable bactericidal and enzyme inactivation effects, and nisin had a significant synergistic lethal effect on aerobic bacteria in orange juice (

Thermosonication combined with ε-polylysine (TSε): A novel technology to control the microbial population and significantly improve the overall quality attributes of orange juice

A Novel Strategy for Liquid Exfoliation of Ultrathin Black Phosphorus Nanosheets

As an emerging two-dimensional layered material, black phosphorus nanosheets show unparalleled optical and electronic properties. Although black phosphorus nanosheets have attracted much attention in the photoelectric field, their applications in biomedical field were still limited due to their poor biocompatibility of current synthesis strategies. Herein, we propose a novel synthetic strategy for black phosphorus nanosheets that rely on Tween 20-assisted liquid exfoliation and post-processing in deoxygenated water. Microscopic and spectroscopic analysis suggested that the produced black phosphorus nanosheets dispersions exhibited good stability and higher yield compared with other currently prepared methods. Because of their ultrahigh exfoliation efficiency, the black phosphorus flakes present few-layer and even monolayer, which are thinner than the most dispersions of black phosphorus. Thus, this method enables mass-production of high-quality few-layer black phosphorus with high biocompatibility, and has the potential to be directly used in the biological field.

Enhancing denitrifying anaerobic methane oxidation for nitrogen removal with low-temperature biochar

Is there plant aromas in your wines: the presentation and stylistic contribution of plant aromas in Cabernet Sauvignon Wines from typical regions in China

Suitable crop loading: An effective method to improve “Shine Muscat” grape quality