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Abstract Membrane proteins are considered the major source of drug targets and are indispensable for drug design and disease prevention. However, traditional biomechanical experiments are costly and time-consuming; thus, many computational methods for predicting membrane protein types are gaining popularity. The position-specific scoring matrix (PSSM) method is an excellent method for describing the evolutionary information of protein sequences. In this study, we propose an improved capsule neural network (ICNN) model based on a capsule neural network to acquire sufficient relevant information from the PSSM. Furthermore, accounting for the complementarity between traditional machine learning and deep learning, we propose a hybrid framework that combines both approaches to predict protein types. This framework trains 41 baseline models based on the PSSM. The optimal subset features, selected after traversal, are fused using a two-level decision-level feature fusion approach. Subsequently, comparisons are made using three combined strategies within an ensemble learning framework. The experimental results demonstrate that solely relying on PSSM input, the proposed method not only surpasses the optimal methods by 1.52 $$\%$$ % , 2.26 $$\%$$ % and 2.67 $$\%$$ % on Dataset1, Dataset2, and Datasets3, respectively, but also exhibits superior generalizability. Furthermore, the code and dataset can be free download at https://github.com/ruanxiaoli/membrane-protein-types .

Abstract Keyframe extraction can effectively help users quickly understand video content. Generally, keyframes should be representative of the video content and simultaneously be diverse to reduce redundancy. Aiming to find the features of frames and filter out representative frames of the video, we propose a method of keyframe recommendation based on feature intercross and fusion (KFRFIF). The method is inspired by the implied relations between keyframe-extraction problem and recommendation problem. First, we investigate the application of a recommendation framework to the keyframe extraction problem. Second, the architecture of the proposed KFRFIF is put forward. Then, an algorithm for extracting intra-frame image features based on the combination of multiple image descriptors is proposed. An algorithm for extracting inter-frame distance features based on the combination of multiple distance calculation methods is designed. Moreover, A recommendation model based on feature intercross and fusion is put forward. An ablation study is further performed to verify the effectiveness of the submodule. Ultimately, the experimental results on four datasets with five outstanding approaches indicate the superior performance of our approach.

Abstract Although social robots equipped with visual devices may leak user information, countermeasures for ensuring privacy are not readily available, making visual privacy protection problematic. In this article, a semi‐supervised learning algorithm is proposed for visual privacy behaviour recognition based on an improved generative adversarial network for social robots; it is called PBR‐GAN. A 9‐layer residual generator network enhances the data quality, and a 10‐layer discriminator network strengthens the feature extraction. A tailored objective function, loss function, and strategy are proposed to dynamically adjust the learning rate to guarantee high performance. A social robot platform and architecture for visual privacy recognition and protection are implemented. The recognition accuracy of the proposed PBR‐GAN is compared with Inception_v3, SS‐GAN, and SF‐GAN. The average recognition accuracy of the proposed PBR‐GAN is 85.91%, which is improved by 3.93%, 9.91%, and 1.73% compared with the performance of Inception_v3, SS‐GAN, and SF‐GAN respectively. Through a case study, seven situations are considered related to privacy at home, and develop training and test datasets with 8,720 and 1,280 images, respectively, are developed. The proposed PBR‐GAN recognises the designed visual privacy information with an average accuracy of 89.91%.

As the problem of an aging population becomes more and more serious, social robots have an increasingly significant influence on human life. By employing regular question-and-answer conversations or wearable devices, some social robotics products can establish personal health archives. But those robots are unable to collect diet information automatically through robot vision or audition. A healthy diet can reduce a person's risk of developing cancer, diabetes, heart disease, and other age-related diseases. In order to automatically perceive the dietary composition of the elderly by listening to people's chatting, this paper proposed a chat-based automatic dietary composition perception algorithm (DCPA). DCPA uses social robot audition to understand the semantic information and percept dietary composition for Mandarin Chinese. Firstly, based on the Mel-frequency cepstrum coefficient and convolutional neural network, a speaker recognition method is designed to identify speech data. Based on speech segmentation and speaker recognition algorithm, an audio segment classification method is proposed to distinguish different speakers, store their identity information and the sequence of expression in a speech conversation. Secondly, a dietetic lexicon is established, and two kinds of dietary composition semantic understanding algorithms are proposed to understand the eating semantics and sensor dietary composition information. To evaluate the performance of the proposed DCPA algorithm, we implemented the proposed DCPA in our social robot platform. Then we established two categories of test datasets relating to a one-person and a multi-person chat. The test results show that DCPA is capable of understanding users' dietary compositions, with an F1 score of 0.9505, 0.8940 and 0.8768 for one-person talking, a two-person chat and a three-person chat, respectively. DCPA has good robustness for obtaining dietary information.

In order to realize fast real-time positioning after a mobile robot starts, this paper proposes an improved ORB-SLAM2 algorithm. Firstly, we proposed a binary vocabulary storage method and vocabulary training algorithm based on an improved Oriented FAST and Rotated BRIEF (ORB) operator to reduce the vocabulary size and improve the loading speed of the vocabulary and tracking accuracy. Secondly, we proposed an offline map construction algorithm based on the map element and keyframe database; then, we designed a fast reposition method of the mobile robot based on the offline map. Finally, we presented an offline visualization method for map elements and mapping trajectories. In order to check the performance of the algorithm in this paper, we built a mobile robot platform based on the EAI-B1 mobile chassis, and we implemented the rapid relocation method of the mobile robot based on improved ORB SLAM2 algorithm by using C++ programming language. The experimental results showed that the improved ORB SLAM2 system outperforms the original system regarding start-up speed, tracking and positioning accuracy, and human–computer interaction. The improved system was able to build and load offline maps, as well as perform rapid relocation and global positioning tracking. In addition, our experiment also shows that the improved system is robust against a dynamic environment.

The experiment took the broccoli (Brassica oleracea var. italica) as the research object, 1-methylcyclopropene (1-MCP), mustard essential oil (MEO) and 1-MCP combined MEO (1-MCP+MEO) were used to treat fresh green broccoli. The effects of physiological and biochemical indexes such as sensory quality, relative conductivity, chlorophyll, vitamin C and malondialdehyde (MDA) content on the quality of broccoli were investigated during storage. The result showed that with the increase of storage time, all groups of preservative treatments could effectively maintain the quality of broccoli during storage, among which 1-MCP+MEO treatment had the best storage effect on broccoli, effectively maintained the appearance of broccoli, avoided yellows and mildew, and slowed down the chlorophyll content and vitamin C degradation rate and made the chlorophyll content of broccoli always higher than that of other treatment groups. 1-MCP+MEO inhibited the increase of relative conductivity and MDA content in broccoli. On the 28th day of storage, the relative conductivity and MDA of the combined treatment group were 14.94% and 9.1 μmol/g, respectively, which were 43.1% and 27.6% lower than that of the control group respectively, and there was a significant difference (P

In this work, Pinus masoniana and Saxifraga stolonifera (L.) Meerb. essential oils were employed to develop preservative materials (i.e., preservative paper and preservative tablet), and effects of the preservative material treatments on Shine Muscat grapes under different temperatures (room temperature and low temperature) were also investigated. The results showed that the preservative paper was more effective than the preservative tablet to maintain the quality of the Shine Muscat in storage time. In addition, grapes treated with the preservative paper under low-temperature storage exhibited the optimum quality among all. Pinus masoniana-Saxifraga stolonifera (L.) Meerb. essential oils preservative paper effectively inhibited the weight loss rate, maintained the color and hardness, slowed down the decrease of total soluble solids, acidity and reducing sugar. After storage for 5 weeks under low temperature with the preservative paper, the weight loss rate of the grapes was 0.36%, significantly lower than that of the control group (0.81%, P

A new fermentation method for kiwi wine was explored by developing the well-known medicinal and edible plant Cyclocarya paliurus (C. paliurus) to create more value with undersized kiwifruits. In this study, the changes in bioactive substances during the C. paliurus–kiwi winemaking process were analyzed on the basis of response surface optimization results, and the antioxidant capacity, aromatic compounds, and sensory quality of the C. paliurus–kiwi composite wine with kiwi wine and two commercial kiwi wines were compared. The results showed that DPPH radical, OH− radical, and ABTS+ scavenging rates remained at over 60.0%, 90.0%, and 70.0% in C. paliurus–kiwi wine, respectively. The total flavonoid content (TFC) and total polyphenol content (TPC) of C. paliurus–kiwi wine were significantly higher than those of the other three kiwi wines. C. paliurus–kiwi wine received the highest score and detected 43 volatile compounds. Ethyl hexanoate, which showed stronger fruity and sweet aromas, was one of the main aroma components of C. paliurus–kiwi wine and different from commercial wines. This wine has a good flavor with a natural and quality feeling of C. paliurus–kiwifruit extract, low-cost processing, and great market potential.

A new soluble polymer foaming (PF) grouting material was developed by using hydrophilic amino resin as the base material and adding other cross-linking, foam, foam stabilizing, toughening, and coupling agents. The PF material exhibited low viscosity, excellent adhesion, and strong penetration. The grouting reinforcement effect of the proposed PF material on broken coal masses was investigated and compared with that of traditional superfine cement (SC) through simulation experiments. Results showed that after grouting, fractures, joints, and other failure planes inside raw coal were filled and bonded, and the mechanical strength of the grout-coal concretion improved relative to the residual strength of raw coal. The average uniaxial compressive strength of SC specimens was 6.16 MPa, whereas that of PF specimens was 10.85 MPa. Moreover, the PF specimens presented an obvious characteristic of plastic strengthening. Analyzing the reinforcement effect of the grouting materials on the basis of single structural plane theory revealed that at the critical state, the average σ3 value of SC specimens was 1.43 MPa, whereas that of PF specimens was 3.09 MPa. In addition, coordination between the lateral and axial deformations of the PF specimens during compression promoted the formation of a bearing structure that can adapt to deformation. This structure is crucial for the safe operation of coal mines.