New Formula ZD4IgS_Q Applied to Solving Future Nonlinear Systems of Equations with Abundant Numerical Experiment Verification

Yunong Zhang,Jinjin Guo,Binbin Qiu,Yang Shi,Zhi Yang 제어로봇시스템학회 2017 제어로봇시스템학회 국제학술대회 논문집 Vol.2017 No.10

In this paper, a new formula called Zhang discretization 4-instant g-square with subtype Q (i.e., ZD4IgS_Q) is proposed, which is used for discretizing continuous-time zeroing neurodynamics (CTZN) model. Besides, in order to solve future nonlinear systems of equations (FNSoE), also termed discrete time-varying nonlinear systems of equations, a ZD4IgS_Q-type discrete-time zeroing neurodynamics (DTZN) model with O(g³) steady-state residual error pattern is acquired by adopting the formula ZD4IgS_Q. For comparison purposes, an Euler-type DTZN model with O(g²) steadystate residual error pattern is also presented. Abundant numerical experimental results show that, compared with Eulertype DTZN model, the ZD4IgS_Q-type DTZN model has better computational performance in terms of solving FNSoE.

Hybrid Model for Renewable Energy and Load Forecasting Based on Data Mining and EWT

Zhang Jinjin,Zhang Qian,Li Guoli,Wu Junjie,Wang Can,Li Zhi 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.3

Accurate renewable resource (RES) and load prediction play key roles in the power grid planning schemes, eff ective dispatch, and stable operation of power systems. The proportions of wind and solar energy continue to increase, leading to wind and light abandonment. Thus, the absorption of wind and photovoltaic power is particularly important. On the basis of accurately predicting load, wind power and photovoltaic output, the accommodation capacity of wind and photovoltaic power is analyzed. The work contains fi ve parts, as follows: (1) empirical wavelet transform (EWT) is used to decompose wind power and load. At the same time, isolated forest (iForest) and fuzzy C-means clustering (FCM) are used to process photovoltaic data. (2) Low frequency and intermediate frequency components of load are predicted by improved random forest (IRF). High frequency component of load is clustered by improved density-based spatial clustering of applications with noise (IDBSCAN). The processing model is selected on the basis of the characteristics of each class sample. Each component of wind power are predicted by IRF. (3) Photovoltaic power of each category is predicted by IRF. (4) Diff erent components of load and wind power data are added. The photovoltaic power forecast data are synthesized according to the time point. (5) The forecast value of load, wind power, and photovoltaic output of a city are comprehensively evaluated by the summarized prediction level indicators. Three accommodation indicators are used for analyzing the accommodation capacity of wind power and photovoltaic. Results show that the forecasting methods of load, wind power, and photovoltaic power can generate better forecasting results than conventional methods. The analysis results of supplementary prediction level and accommodation indices provide reference for eff ective grid dispatching, sustainable, and healthy energy development.

Antiviral peptide nanocomplexes as a potential therapeutic modality for HIV/HCV co-infection

( Jinjin Zhang ),( Andrea Mulvenon ),( Edward Makarov ),( Jill Wagoner ),( Jaclyn Knibbe ),( Jong Oh Kim ),( Natalia Osna ),( Tatiana K Bronich ),( Larisa Y Poluektova ) 영남대학교 약품개발연구소 2013 영남대학교 약품개발연구소 연구업적집 Vol.23 No.0

It is estimated that 4 to 5 million people are currently co-infected with Human Immunodeficiency Virus (HIV) and Hepatitis C Virus (HCV). HIV/HCV co-infection is associated with unique health risks including increased hepatotoxicity of antiretrovirals, accelerated progression of HCV and liver diseases. The standard interferon-based therapy is effective only in about 50% of patients and often is associated with autoimmune and neuro-psychiatric complications. The treatment of co-infection (HIV/HCV) requires new strategic approaches. To this end, the formulations of an amphiphatic α-helical peptide, a positively charged analog of C5A peptide derived from the HCV NS5A protein, with a reported virocidal activity were prepared by electrostatic coupling with anionic poly(amino acid)-based block copolymers. The self-assembled antiviral peptide nanocomplexes (APN) were ca. 35 nm in size, stable at physiological pH and ionic strength, and retained in vitro antiviral activity against HCV and HIV. Moreover, incorporation of the peptide into APN attenuated its cytotoxicity associated with the positive charge. In vivo APN were able to decrease the viral load in mice transplanted with human lymphocytes and HIV-1-infected. Overall, these findings indicate the potential of these formulations for stabilization and delivery of antiviral peptides while maintaining their functional activity. Published by Elsevier Ltd.

Metabolomics reveals potential biomarkers in the rumen fluid of dairy cows with different levels of milk production

Zhang, Hua,Tong, Jinjin,Zhang, Yonghong,Xiong, Benhai,Jiang, Linshu Asian Australasian Association of Animal Productio 2020 Animal Bioscience Vol.33 No.1

Objective: In the present study, an liquid chromatography/mass spectrometry (LC/MS) metabolomics approach was performed to investigate potential biomarkers of milk production in high- and low-milk-yield dairy cows and to establish correlations among rumen fluid metabolites. Methods: Sixteen lactating dairy cows with similar parity and days in milk were divided into high-yield (HY) and low-yield (LY) groups based on milk yield. On day 21, rumen fluid metabolites were quantified applying LC/MS. Results: The principal component analysis and orthogonal correction partial least squares discriminant analysis showed significantly separated clusters of the ruminal metabolite profiles of HY and LY groups. Compared with HY group, a total of 24 ruminal metabolites were significantly greater in LY group, such as 3-hydroxyanthranilic acid, carboxylic acids, carboxylic acid derivatives (L-isoleucine, L-valine, L-tyrosine, etc.), diazines (uracil, thymine, cytosine), and palmitic acid, while the concentrations of 30 metabolites were dramatically decreased in LY group compared to HY group, included gentisic acid, caprylic acid, and myristic acid. The metabolite enrichment analysis indicated that protein digestion and absorption, ABC transporters and unsaturated fatty acid biosynthesis were significantly different between the two groups. Correlation analysis between the ruminal microbiome and metabolites revealed that certain typical metabolites were exceedingly associated with definite ruminal bacteria; Firmicutes, Actinobacteria, and Synergistetes phyla were highly correlated with most metabolites. Conclusion: These findings revealed that the ruminal metabolite profiles were significantly different between HY and LY groups, and these results may provide novel insights to evaluate biomarkers for a better feed digestion and may reveal the potential mechanism underlying the difference in milk yield in dairy cows.

Short‑Term Load Forecasting Method Based on EWT and IDBSCAN

Qian Zhang,Jinjin Zhang 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.2

Accurate load forecasting is of great signifcance to the safety and stability of power grid operation. This paper proposes a combined prediction method based on empirical wavelet transform (EWT) and improved density-based spatial clustering of applications with noise (IDBSCAN). The EWT is used to decompose the load to obtain various intrinsic mode functions (IMFs). Then, each IMF is predicted with a rational method. The low frequency and intermediate frequency components are predicted by long short-term memory (LSTM). High frequency components have uncertain characteristics. Therefore, they are clustered by IDBSCAN, according to meteorological factors. The processing model is selected based on the characteristics of each class sample. Finally, the prediction results of each component are superimposed to obtain the total prediction result. Experiments are conducted based on the measured load data of a Chinese city. A comparison between the proposed method, EWT–LSTM, EWT-Elman, and empirical mode decomposition (EMD)-LSTM is made. The results indicate that the proposed method has higher prediction accuracy and refects the randomness of the actual load.

Illumina MiSeq sequencing reveals the effects of grape seed procyanidin on rumen archaeal communities in vitro

Zhang, Hua,Tong, Jinjin,Wang, Zun,Xiong, Benhai,Jiang, Linshu Asian Australasian Association of Animal Productio 2020 Animal Bioscience Vol.33 No.1

Objective: The present study explored the effects of grape seed procyanidin extract (GSPE) on rumen fermentation, methane production and archaeal communities in vitro. Methods: A completely randomized experiment was conducted with in vitro incubation in a control group (CON, no GSPE addition; n = 9) and the treatment group (GSPE, 1 mg/bottle GSPE, 2 g/kg dry matter; n = 9). The methane and volatile fatty acid concentrations were determined using gas chromatography. To explore methane inhibition after fermentation and the response of the ruminal microbiota to GSPE, archaeal 16S rRNA genes were sequenced by MiSeq high-throughput sequencing. Results: The results showed that supplementation with GSPE could significantly inhibit gas production and methane production. In addition, GSPE treatment significantly increased the proportion of propionate, while the acetate/propionate ratio was significantly decreased. At the genus level, the relative abundance of Methanomassiliicoccus was significantly increased, while the relative abundance of Methanobrevibacter decreased significantly in the GSPE group. Conclusion: In conclusion, GSPE is a plant extract that can reduce methane production by affecting the structures of archaeal communities, which was achieved by a substitution of Methanobrevibacter with Methanomassiliicoccus.

Over-expression of PTEN on proliferation and apoptosis in canine mammary tumors cells

Jinjin Tong,Hua Zhang,Dongdong Sun,Yingxue Wang,Chao Yang,Yun Liu 한국통합생물학회 2016 Animal cells and systems Vol.20 No.6

Phosphatase and tensin homolog (PTEN) is an important tumor-suppressor gene which constitutes an important PI3K/Akt pathway by regulating the signaling of multiple biological processes, including apoptosis, metabolism, cell proliferation, and cell growth has been gaining increasing attention. However, the role of PTEN in regulating apoptosis of canine mammary tumors cells still needs further investigation. In this experiment, the effect of PTEN on proliferation and apoptosis in canine mammary tumors (CMT) cells was analyzed. As a result, gene and protein expression levels of apoptosis-related genes were detected. Eukaryotic expression vector pcDNA3.1+-PTEN were successfully constructed and stably transferred into canine CMT cells after geneticin (G418) selection. After pcDNA3.1+-PTEN transfection, compared with control group, the cells proliferation was inhibited and the cell apoptosis was increased in CMT cells. The expression of p-Akt was decreased and the apoptosis-related genes, such as caspase-3, caspase- 9, and Bax, were increased. These data serve to demonstrate the function of PTEN on apoptosis and gene regulatory in PI3K/Akt pathway in CMT cells. Collectively, our data link the tumorsuppressor activities of PTEN to the machinery controlling cell cycle through the modulation of signaling molecules whose signal target is the functional inactivation of the apoptosis gene product.

Multiple Smooth Support Vector Machine with FCM Clustering in Hidden Space

Xian-wei Zhang,Jinjin Liang 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.9

A multiple smooth model is proposed by smoothing technique and piecewise technique for large scale data. Mapping the training data to the hidden space with a hidden function, the proposed model divides the original data into several subclasses by Fuzzy C Means (FCM), whose initial cluster centers are selected by samples with large density indexes; derives the smooth differentiable model by utilizing the entropy function to replace the plus function of the slack vector, and introduces linking rules to combine results of subclasses. Simulations demonstrate that the obtained algorithm maintains good classification accuracies, reduces the training time and hardly varies with kernel parameters.

Cross-architecture Binary Function Similarity Detection based on Composite Feature Model

( Xiaonan Li ),( Guimin Zhang ),( Qingbao Li ),( Ping Zhang ),( Zhifeng Chen ),( Jinjin Liu ),( Shudan Yue ) 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.8

Recent studies have shown that the neural network-based binary code similarity detection technology performs well in vulnerability mining, plagiarism detection, and malicious code analysis. However, existing cross-architecture methods still suffer from insufficient feature characterization and low discrimination accuracy. To address these issues, this paper proposes a cross-architecture binary function similarity detection method based on composite feature model (SDCFM). Firstly, the binary function is converted into vector representation according to the proposed composite feature model, which is composed of instruction statistical features, control flow graph structural features, and application program interface calling behavioral features. Then, the composite features are embedded by the proposed hierarchical embedding network based on a graph neural network. In which, the block-level features and the function-level features are processed separately and finally fused into the embedding. In addition, to make the trained model more accurate and stable, our method utilizes the embeddings of predecessor nodes to modify the node embedding in the iterative updating process of the graph neural network. To assess the effectiveness of composite feature model, we contrast SDCFM with the state of art method on benchmark datasets. The experimental results show that SDCFM has good performance both on the area under the curve in the binary function similarity detection task and the vulnerable candidate function ranking in vulnerability search task.

Melatonin Attenuates Mitochondrial Damage in Aristolochic Acid-Induced Acute Kidney Injury

Sun Jian,Pan Jinjin,Liu Qinlong,Cheng Jizhong,Tang Qing,Ji Yuke,Cheng Ke,wang Rui,Liu Liang,Wang Dingyou,Wu Na,Zheng Xu,Li Junxia,Zhang Xueyan,Zhu Zhilong,Ding Yanchun,Zheng Feng,Li Jia,Zhang Ying,Yua 한국응용약물학회 2023 Biomolecules & Therapeutics(구 응용약물학회지) Vol.31 No.1

Aristolochic acid (AA), extracted from Aristolochiaceae plants, plays an essential role in traditional herbal medicines and is used for different diseases. However, AA has been found to be nephrotoxic and is known to cause aristolochic acid nephropathy (AAN). AA-induced acute kidney injury (AKI) is a syndrome in AAN with a high morbidity that manifests mitochondrial damage as a key part of its pathological progression. Melatonin primarily serves as a mitochondria-targeted antioxidant. However, its mitochondrial protective role in AA-induced AKI is barely reported. In this study, mice were administrated 2.5 mg/kg AA to induce AKI. Melatonin reduced the increase in Upro and Scr and attenuated the necrosis and atrophy of renal proximal tubules in mice exposed to AA. Melatonin suppressed ROS generation, MDA levels and iNOS expression and increased SOD activities in vivo and in vitro. Intriguingly, the in vivo study revealed that melatonin decreased mitochondrial fragmentation in renal proximal tubular cells and increased ATP levels in kidney tissues in response to AA. In vitro, melatonin restored the mitochondrial membrane potential (MMP) in NRK-52E and HK-2 cells and led to an elevation in ATP levels. Confocal immunofluorescence data showed that puncta containing Mito-tracker and GFP-LC3A/B were reduced, thereby impeding the mitophagy of tubular epithelial cells. Furthermore, melatonin decreased LC3A/B-II expression and increased p62 expression. The apoptosis of tubular epithelial cells induced by AA was decreased. Therefore, our findings revealed that melatonin could prevent AA-induced AKI by attenuating mitochondrial damage, which may provide a potential therapeutic method for renal AA toxicity.