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Polyvinylpyrrolidone (PVP) is known as a polymer with special adsorption properties for polyphenolic compounds. In this paper, chemical‐bonded stationary phase (PVP@Silica) was prepared and applied to the separation of flavonoids. System constants determined by linear solvation energy relationships with methanol–water mixtures as the mobile show that the column can provide stronger hydrogen bonding and π–π interaction than C18 column. Under reversed‐phase conditions, traditional flavonoids have much longer retention on PVP@Silica than on C18 column with methanol–water mobile phase due to multimodal retention mechanism, including hydrogen bonding and π–π interaction. In addition, a U‐shaped retention curve was observed in acetonitrile–water mobile phase because of the enhanced hydrogen bonding under the high proportion of acetonitrile. The selectivity to polyhydroxy structure gives the stationary phase unique separation ability for flavonoids. The separation orthogonality was further investigated by a sample set containing 33 flavonoids with different substitution structures. The (RP‐PVP@Silica)‐(RP‐C18) system exhibited 60% orthogonality metric (OM) for these flavonoids with methanol–water mobile phase. A high OM of 63.5% was achieved in (RP‐PVP@Silica)‐(HILIC‐PVP@Silica) system. Finally, PVP@Silica was applied to the purification of total flavonoids in Ginkgo biloba extract (GBE). The offline (RP‐PVP@Silica)‐(RP‐C18) two‐dimensional separation system was used for the analysis of flavonoids in GBE. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a wideband millimeter wave (mmWave) channel model for aerial reconfigurable intelligent surface (ARIS) communications, in which the reconfigurable intelligent surface (RIS) is attached to an unmanned aerial vehicle (UAV) to enable intelligent signal reflections in the air. The model is established according to standard 3GPP channel models and considers important propagation factors, for example, large‐scale fading, small‐scale fading, and radiation pattern of reflection units (RUs). In addition, the model assumes three‐dimensional (3D) rotations of RIS and can capture its effects on channel characteristics. Based on the model, we propose a design method for RIS reflection phases. Statistics including channel amplitude, spatial, temporal, and frequency correlation functions (CFs), and space‐Doppler power spectral density (SD‐PSD) are obtained. The impacts of RU number, UAV height and speed, and RIS rotation on channel statistics are investigated. Results suggest that the designed reflection phases can effectively mitigate multipath fading and Doppler effect. Compared to the yaw angle of the UAV platform, channel characteristics are more sensitive to the roll and pitch angles. [ABSTRACT FROM AUTHOR]

Objective. Emotion recognition on the basis of electroencephalography (EEG) signals has received a significant amount of attention in the areas of cognitive science and human–computer interaction (HCI). However, most existing studies either focus on one-dimensional EEG data, ignoring the relationship between channels, or only extract time–frequency features while not involving spatial features. Approach. We develop spatial–temporal features-based EEG emotion recognition using a graph convolution network (GCN) and long short-term memory (LSTM), named ERGL. First, the one-dimensional EEG vector is converted into a two-dimensional mesh matrix, so that the matrix configuration corresponds to the distribution of brain regions at EEG electrode locations, thus to represent the spatial correlation between multiple adjacent channels in a better way. Second, the GCN and LSTM are employed together to extract spatial–temporal features; the GCN is used to extract spatial features, while LSTM units are applied to extract temporal features. Finally, a softmax layer is applied to emotion classification. Main results. Extensive experiments are conducted on the A Dataset for Emotion Analysis using Physiological Signals (DEAP) and the SJTU Emotion EEG Dataset (SEED). The classification results of accuracy, precision, and F-score for valence and arousal dimensions on DEAP achieved 90.67% and 90.33%, 92.38% and 91.72%, and 91.34% and 90.86%, respectively. The accuracy, precision, and F-score of positive, neutral, and negative classifications reached 94.92%, 95.34%, and 94.17%, respectively, on the SEED dataset. Significance. The above results demonstrate that the proposed ERGL method is encouraging in comparison to state-of-the-art recognition research. [ABSTRACT FROM AUTHOR]

Shells of Castanea mollissima (CMS), an agricultural remain and often considered waste from chestnut processing industry, have been proven a resource for traditional Chinese medicine. One new phenol, named castanolB(1), andsix known phenolic compounds (2⁻7) were isolated froma water-soluble extract of CMS. Their chemical structures were determined using preparative HPLC and various spectral analyses, and then were compared to literatures, which indicated the first identification of the seven compounds from C. mollissima. The physicochemical property of compound (2) was also reported for the first time. After antiproliferative screening of compounds (1⁻7) on LPS-induced SMMC-7721 and HepG2 hepatoma cells, castanolB (1) showed the best suppression. CastanolB(1) also significantly induced cell apoptosis. Furthermore, castanolB (1) decreasedsecretion of TNF-α and IL-6. Mechanistically, TLR4⁻NF-κB pathway was inhibited bycastanolB (1) with downregulation of TLR4, IKKβ, and NF-κB p65. This study presents a new phenol and shows its profiles of anticancer and anti-inflammation via inhibiting the TLR4⁻NF-κB pathway.

Emotion recognition has become a research focus in the brain–computer interface and cognitive neuroscience. Electroencephalogram (EEG) is employed for its advantages as accurate, objective, and noninvasive nature. However, many existing research only focus on extracting the time and frequency domain features of the EEG signals while failing to utilize the dynamic temporal changes and the positional relationships between different electrode channels. To fill this gap, we develop the dynamic differential entropy and brain connectivity features based EEG emotion recognition using linear graph convolutional network named DDELGCN. First, the dynamic differential entropy feature which represents the frequency domain feature as well as time domain feature is extracted based on the traditional differential entropy feature. Second, brain connectivity matrices are constructed by calculating the Pearson correlation coefficient, phase‐locked value and transfer entropy, and then are used to denote the connectivity features of all electrode combinations. Finally, a linear graph convolutional network is customized and applied to aggregate the features from total electrode combinations and then classifies the emotional states, which consists of five layers, namely, an input layer, two linear graph convolutional layers, a fully connected layer, and a softmax layer. Extensive experiments show that the accuracies in the valence and arousal dimensions reach 90.88% and 91.13%, and the precision reaches 96.66% and 97.02% on the DEAP dataset, respectively. On the SEED dataset, the accuracy and precision reach 91.56% and 97.38%, respectively. [ABSTRACT FROM AUTHOR]

Hashing techniques have attracted considerable attention owing to their advantages of efficient computation and economical storage. However, it is still a challenging problem to generate more compact binary codes for promising performance. In this paper, we propose a novel contrastive vision transformer hashing method, which seamlessly integrates contrastive learning and vision transformers (ViTs) with hash technology into a well‐designed model to learn informative features and compact binary codes simultaneously. First, we modify the basic contrastive learning framework by designing several hash layers to meet the specific requirement of hash learning. In our hash network, ViTs are applied as backbones for feature learning, which is rarely performed in existing hash learning methods. Then, we design a multiobjective loss function, in which contrastive loss explores discriminative features by maximizing agreement between different augmented views from the same image, similarity preservation loss performs pairwise semantic preservation to enhance the representative capabilities of hash codes, and quantization loss controls the quantitative error. Hence, we can facilitate end‐to‐end joint training to improve the retrieval performance. The encouraging experimental results on three widely used benchmark databases demonstrate the superiority of our algorithm compared with several state‐of‐the‐art hashing algorithms. [ABSTRACT FROM AUTHOR]

Due to the progressive complexity of traffic networks, the traffic pressure increases sharply and traffic accidents occur frequently, which is largely posed by imprecise traffic information service provided in existing Intelligent traffic service systems (ITSSs). To relieve traffic information trek encountered in complex traffic networks, the personalized traffic information recommendation based on click through rate (CTR) prediction has attracted extensive attention. However, the data sparsity and cold start problems in traditional recommendations hinder their real‐time applications in ITSSs. In this paper, the multisource data with different types of context information are utilized to construct a personalized fine‐grained recommendation method for intelligent traffic services (PF‐ITS), which includes three components: a road condition optimization strategy (RCOS) to capture users' behavior preferences and traffic patterns, an encoder–decoder long short‐term memory (LSTM) model to address the data sparsity and cold start problems with rich context information, and an improved DeepFM model based on attention mechanism (DeepAFM) to exert personalized fine‐grained traffic information recommendation with embeddings of multisource data. More specifically, the RCOS is proposed to perform coarse‐grained route recommendation based on path planning theory, in which the traffic data and drivers' preferences are fully utilized for comprehensive modeling. The encoder–decoder LSTM model is employed for representation learning, in which the traffic sequences and driving behaviors are mapped into dense distributed representations with rich semantic information. The DeepAFM is utilized to achieve effective driving safety guarantee according to an individual's requirements, in which the weighted low‐order feature combinations and high‐order feature interactions are incorporated for personalized fine‐grained recommendation. We also conduct extensive experiments on public data sets and in real‐world scenarios. The experimental results demonstrate that the PF‐ITS method based on RCOS and DeepAFM outperforms the state‐of‐the‐art baseline models in effectiveness and efficiency. [ABSTRACT FROM AUTHOR]