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2. Changes of Metabolites and Related Flavors in Mandarin Fish (Siniperca chuatsi) during 24 h of Refrigeration

Author

ZHOU Beibei, WU Minglin, JIANG Yangyang, SUN Yongxu, LI Haiyang, CUI Kai

Subjects
mandarin fish, refrigeration, non-targeted metabolomics, differential metabolites, metabolic pathway, flavor, Food processing and manufacture, TP368-456
Abstract

The study aimed to investigate the changes of metabolites and related flavors in the muscle of fresh mandarin fish (Siniperca chuatsi) during 24 h of postmortem refrigeration at 4 ℃ by ultra performance liquid chromatography coupled with high resolution mass spectrometry (UPLC-HR-MS). Non-targeted metabolomics was applied for multivariate statistical analysis, metabolic pathway analysis and related flavor calculation of the differential metabolites after 0 versus 2, 4, 6, 9, 12 and 24 h. The results showed that 33 differential metabolites were identified, including 3 organic acids and derivatives (amino acids and organic acids); 8 organic heterocyclic compounds (purines, purine derivatives and lactones); 7 nucleosides, nucleotides and analogues (purine nucleosides, purine nucleotides and pyrimidine nucleotides); 9 organic oxygen compounds (carbohydrates and their conjugates, as well as a small amount of alcohols); 4 lipid and lipid molecules (fatty alcohols and fatty acid esters); 1 flavonoid glycoside; and 1 alkaloid or its derivatives. Through metabolomic pathway analysis (MetPA) and Kyoto Encyclopedia of Genes and Genomes analysis, 9 key differential metabolites were identified including succinic acid, L-aspartic acid, 5’-adenosine monophosphate (AMP), inosine, adenyl succinic acid, xanthine, 5’-guanosine monophosphate (GMP), ribose 5-phosphate, and hypoxanthine. The major metabolic pathway involving these key differential metabolites was purine metabolism, followed by L-aspartic acid metabolism, glutamic acid metabolism and the tricarboxylic acid (TCA) cycle. By calculating the relative taste activity value (RTAV) and relative equivalent umami concentration (REUC), it was found that umami amino acids such as L-aspartic acid together with AMP and GMP mainly contributed to the taste of fresh mandarin fish, while the major taste compounds were AMP and GMP after 2–6 h of refrigeration and GMP after 9–24 h. Overall, the taste of fresh mandarin fish was best. During 24 h of storage at 4 ℃, the umami of mandarin fish decreased due to a decrease in L-aspartic acid content, but it increased between 9 and 24 h due to the accumulation of GMP.

Published
2024
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13. Identification and characterization of FpRco1 in regulating vegetative growth and pathogenicity based on T-DNA insertion in Fusarium pseudograminearum

Author

Li, Haiyang, Zhang, Yuan, Qin, Cancan, Wang, Zhifang, Hao, Lingjun, Zhang, Panpan, Yuan, Yongqiang, Ding, Chaopu, Wang, Mengxuan, Zan, Feifei, Meng, Jiaxing, Zhuang, Xunyu, Liu, Zheran, Wang, Limin, Zhou, Haifeng, Chen, Linlin, Wang, Min, Xing, Xiaoping, Yuan, Hongxia, Li, Honglian, and Ding, Shengli

Published
2024
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23. SR-HetGNN:Session-based Recommendation with Heterogeneous Graph Neural Network

Author

Chen, Jinpeng, Li, Haiyang, Zhang, Xudong, Zhang, Fan, Wang, Senzhang, Wei, Kaimin, and Ji, Jiaqi

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract

The Session-Based Recommendation System aims to predict the user's next click based on their previous session sequence. The current studies generally learn user preferences according to the transitions of items in the user's session sequence. However, other effective information in the session sequence, such as user profiles, are largely ignored which may lead to the model unable to learn the user's specific preferences. In this paper, we propose SR-HetGNN, a novel session recommendation method that uses a heterogeneous graph neural network (HetGNN) to learn session embeddings and capture the specific preferences of anonymous users. Specifically, SR-HetGNN first constructs heterogeneous graphs containing various types of nodes according to the session sequence, which can capture the dependencies among items, users, and sessions. Second, HetGNN captures the complex transitions between items and learns the item embeddings containing user information. Finally, local and global session embeddings are combined with the attentional network to obtain the final session embedding, considering the influence of users' long and short-term preferences. SR-HetGNN is shown to be superior to the existing state-of-the-art session-based recommendation methods through extensive experiments over two real large datasets Diginetica and Tmall.

Published
2021

40. Adaptive iterative singular value thresholding algorithm to low-rank matrix recovery

Author

Cui, Angang, Peng, Jigen, and Li, Haiyang

Subjects
Mathematics - Optimization and Control
Abstract

The problem of recovering a low-rank matrix from the linear constraints, known as affine matrix rank minimization problem, has been attracting extensive attention in recent years. In general, affine matrix rank minimization problem is a NP-hard. In our latest work, a non-convex fraction function is studied to approximate the rank function in affine matrix rank minimization problem and translate the NP-hard affine matrix rank minimization problem into a transformed affine matrix rank minimization problem. A scheme of iterative singular value thresholding algorithm is generated to solve the regularized transformed affine matrix rank minimization problem. However, one of the drawbacks for our iterative singular value thresholding algorithm is that the parameter $a$, which influences the behaviour of non-convex fraction function in the regularized transformed affine matrix rank minimization problem, needs to be determined manually in every simulation. In fact, how to determine the optimal parameter $a$ is not an easy problem. Here instead, in this paper, we will generate an adaptive iterative singular value thresholding algorithm to solve the regularized transformed affine matrix rank minimization problem. When doing so, our new algorithm will be intelligent both for the choice of the regularized parameter $\lambda$ and the parameter $a$.

Published
2020

41. Condition monitoring and fault diagnosis of induction machine rotor systems based on instantaneous electrical signals analysis

Author

Li, Haiyang and Gu, Fengshou

Subjects
T Technology (General), TA Engineering (General). Civil engineering (General), TK Electrical engineering. Electronics Nuclear engineering
Abstract

Induction machines (IMs) play vital roles in a wide range of industry applications. But the frequently happening faults--rotor system faults will reduce the performance and efficiency of IMs. The rotor system faults generally include broken rotor bar (BRB) fault and rotor eccentricity fault (REF). Condition monitoring (CM) and early fault detection based on instantaneous electrical signals (IES) are the essential to ensure the safety and performance of IMs. For IMs, both BRB faults and REF will induce the sideband components at their related fault frequencies in the current spectrum. The sidebands around supply frequency in the spectrum analysis are commonly used as the indicators for rotor system fault detection. For REF related sidebands are relatively far away to the supply frequency. However, for BRB, the fault related sidebands are near around the supply frequency together with the sideband amplitude increase as the load increases, which makes the BRB fault indicators are easily buried in the supply frequency leakage especially under light loads. Moreover, when IMSs are controlled by sensorless field orientated control (FOC) drives, it becomes more challenging to diagnose rotor system faults than by openloop drives because of the noisy power supply of FOC. Therefore, this research subject investigates the effective methods to monitor the condition of the rotor system. It firstly investigates the current models of healthy motor, motors with partial or fully breakage bars at different positions, and motor with different degree eccentricity and the abnormal load. It proposes the breakage position, the breakage level and the external load affect the amplitude of rotor system fault indicator. Furthermore, in view of the fact that the leakage of the frequency analysis methods can bury the rotor system fault indicators under light loads. This research also proposes two kinds of methods to accurately demodulate and extract the fault features for the CM and fault diagnosis of IM, which are 1) An energy operator, implemented from time domain and frequency domain respectively, and 2) A novel morphological gradient (NMG) of mathematical morphology (MM), implemented by changing signal shape. The performance and effectiveness of the proposed signal processing methods are first verified through simulation study and then verified by the experimental study with different fault severity under different loads. The research also proposes that not only the current components in the low frequency band, but also the current components in the higher frequency bands can be used for CM and fault diagnosis of rotor system. And this proposal is verified by experimental study.

Published
2021

47. Efficient and fully simulated oblivious transfer protocol on elliptic curve

Author

Jiashuo SONG, Zhenzhen LI, Haiyang DING, Zichen LI

Subjects
oblivious transfer, fully simulation, bilinear pairing, zero-knowledge proof, secure multi-party computation, Electronic computers. Computer science, QA75.5-76.95
Abstract

Oblivious transfer protocol, an important technology in secure multi-party computation, is the research hotspot on network and information security.Based on the bilinear pairs and the difficult problems on elliptic curves, an efficient 1-out-of-N oblivious transfer protocol in the semi-honest model and in the standard malicious model were proposed respectively.The protocol in semi-honest model was designed.It only needed two rounds of interaction.The receiver needed two times of bilinear pair arithmetic and one time of multi point arithmetic, and the sender needed n times of multi point arithmetic and n times of modular exponentiation.The security of the protocol was based on the discrete logarithm problem on elliptic curves.A zero-knowledge proof protocol and the oblivious transfer protocol in the standard malicious model were proposed respectively.The oblivious transfer protocol only needed four rounds of interaction.The receiver needed three times of bilinear pair arithmetic and three times of multi point arithmetic, and the sender needed n+1 times of multi point arithmetic and n+1 times of modular exponentiation.Besides, it can resist malicious behaviors of the party.The results show that the average running time of the protocol in the semi-honest model and in the standard malicious model were 0.787 9 s and 1.205 6 s respectively, which can further demonstrate the efficiency of the protocol.

Published
2023
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50. Autonomous Time-Optimal Many-Revolution Orbit Raising for Electric Propulsion GEO Satellites via Neural Networks

Author

Li, Haiyang, Topputo, Francesco, and Baoyin, Hexi

Subjects
Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Signal Processing
Abstract

Geostationary Earth orbit (GEO) satellites are of great significance in the space market. Low-thrust propulsion has been highly developed in the last decades because it is fuel-saving. Therefore, the design of GEO satellites is rapidly changing from classic high-thrust propulsion more and more toward low-thrust propulsion. However, the transfer time will be quite long using low-thrust propulsion and it will be very expensive if the ground supports the whole orbit raising. Therefore, autonomous orbit raising is necessary. Deep neural networks are trained to learn the optimal control. Results show that DNNs can be applied in this long-duration optimal control problem and have excellent performance.

Published
2019

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