Jiabao Yu - Academia.edu (original) (raw)

Papers by Jiabao Yu

Soil Ecology Letters, 2021

Soil-borne plant diseases cause major economic losses globally. This is partly because their epid... more Soil-borne plant diseases cause major economic losses globally. This is partly because their epidemiology is difficult to predict in agricultural fields, where multiple environmental factors could determine disease outcomes. Here we used a combination of field sampling and direct experimentation to identify key abiotic and biotic soil properties that can predict the occurrence of bacterial wilt caused by pathogenic Ralstonia solanacearum. By analyzing 139 tomato rhizosphere soils samples isolated from six provinces in China, we first show a clear link between soil properties, pathogen density and plant health. Specifically, disease outcomes were positively associated with soil moisture, bacterial abundance and bacterial community composition. Based on soil properties alone, random forest machine learning algorithm could predict disease outcomes correctly in 75% of cases with soil moisture being the most significant predictor. The importance of soil moisture was validated causally in a controlled greenhouse experiment, where the highest disease incidence was observed at 60% of maximum water holding capacity. Together, our results show that local soil properties can predict disease occurrence across a wider agricultural landscape, and that management of soil moisture could potentially offer a straightforward method for reducing crop losses to R. solanacearum.

Journal of Materials Science: Materials in Electronics, 2015

Ca 2 B 2 O 5 :RE (RE = Eu 3? , Tb 3? , Dy 3?) nanofibers were synthesized by the hydrothermal rea... more Ca 2 B 2 O 5 :RE (RE = Eu 3? , Tb 3? , Dy 3?) nanofibers were synthesized by the hydrothermal reaction method. The structural refinement was conducted on the base of the X-ray powder diffraction (XRD) measurements. The surface properties of the Ca 2 B 2 O 5 :RE (RE = Eu 3? , Tb 3? , Dy 3?) nanofibers were investigated by the measurements such as the scanning electron microscope (SEM), transmission electron microscope (TEM), and the energy dispersive spectrum (EDS). The nanofiber has a diameter of about 100 nm and a length of several micrometers. The luminescence properties such as photoluminescence excitation (PLE) and emission spectra (PL), decay lifetime, color coordinates, and the absolute internal quantum efficiency (QE) were reported. Ca 2 B 2 O 5 :Eu 3? nanofibers show the red luminescence with CIE coordinates of (x = 0.41, y = 0.51) and the luminescence lifetime of 0.63 ms. The luminescence of Ca 2 B 2 O 5 :Tb 3? nanofibers is green color (x = 0.29, y = 0.53) with the lifetime of 2.13 ms. However, Dy 3?-doped Ca 2 B 2 O 5 nanofibers present a singlephase white-color phosphor with the fluorescence decay of 3.05 ms. Upon near-UV excitation, the absolute quantum efficiency is measured to be 65, 35, and 37 % for Eu 3?-, Tb 3?-, Dy 3?-doped Ca 2 B 2 O 5 nanofibers, respectively. It is suggested that Ca 2 B 2-O 5 :RE (RE = Eu 3? , Tb 3? , Dy 3?) nanofibers could be an efficient phosphor for lighting and display.

Journal of Marine Science and Engineering

Although broad reinforcement learning (BRL) provides a more intelligent autonomous decision-makin... more Although broad reinforcement learning (BRL) provides a more intelligent autonomous decision-making method for the collision avoidance problem of unmanned surface vehicles (USVs), the algorithm still has the problem of over-estimation and has difficulty converging quickly due to the sparse reward problem in a large area of sea. To overcome the dilemma, we propose a double broad reinforcement learning based on hindsight experience replay (DBRL-HER) for the collision avoidance system of USVs to improve the efficiency and accuracy of decision-making. The algorithm decouples the two steps of target action selection and target Q value calculation to form the double broad reinforcement learning method and then adopts hindsight experience replay to allow the agent to learn from the experience of failure in order to greatly improve the sample utilization efficiency. Through training in a grid environment, the collision avoidance success rate of the proposed algorithm was found to be 31.9 per...

2022 IEEE 22nd International Conference on Communication Technology (ICCT)

Journal of Ethnopharmacology

Food & Function

ECG blocked the expression of ox-LDL receptor protein CD36 in HFD-induced aortic root plaques at ... more ECG blocked the expression of ox-LDL receptor protein CD36 in HFD-induced aortic root plaques at different stages, and blocked inflammation, oxidative stress and cell foaming through Nrf2 and NF-κB signaling pathways.

2022 7th International Conference on Communication, Image and Signal Processing (CCISP)

Archives of Biochemistry and Biophysics

2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT), 2021

Access control is a common part of network security measure. However, the existing access control... more Access control is a common part of network security measure. However, the existing access control mechanism is mostly limited to distinguish users based on digital authentication methods such as MAC addresses, which are vulnerable to forgery and counterfeiting attacks. Using distinguishable physical-layer (PHY) based on fingerprints from devices and MAC-based ID verification is a popular method for enhancing network security. Extracting device finger-prints is the crucial step in this enhanced security method. In this paper, different from the existing fingerprint extraction method based on the preamble, we propose an adaptive filter-based method of fingerprint extraction. The method utilizes more signal regions to extract finer fingerprints, which is not limited to the preamble. Our results show that with a filter order of 65 and 20 times averaging, among 13 devices can be classified with 98% accuracy under the Multiple Discriminant Analysis, Maximum Likelihood (MDA/ML) classifier.

Journal of Food Biochemistry, 2022

Acute lung injury (ALI) is characterized by an excessive inflammatory response, closely related t... more Acute lung injury (ALI) is characterized by an excessive inflammatory response, closely related to sepsis occurrence and development. It has been reported that Schisandrin (Sch) exhibits anti-inflammatory activity. However, whether the beneficial effects of Sch exists during ALI remains to be studied. In this study, the impact of Sch was evaluated by studying lung tissue damage, measuring the concentrations of pro-inflammatory factors, and the expression of apoptotic proteins in the LPS-induced ALI mice model. Protein expression of inflammation-related signaling pathway within the lung tissue and A549 cells were also measured. In addition, the effect of Sch on A549 cell apoptosis and inflammatory markers was also detected. Animal experiments demonstrated that pre-feeding Sch alleviated the production of inflammation mediators, abnormal pathological injuries, and blocked the progression of apoptotic events in the lung tissue. The in vitro experiments showed that Sch pretreatment reduced LPS upregulated interleukin-1β (IL-1β), IL-18, and IL-6 levels, and improved LPS-induced abnormal apoptosis. Sch and the pathway inhibitor AG490 also inhibited the expression levels of p-JAK2 and p-STAT3 in A549 cells. Moreover, pretreatment with Sch significantly inhibited the activation of NLRP3 inflammasomes, reduced inducible nitric oxide synthase, and cyclooxygenase 2 proteins expression during ALI in vitro and in vivo. Overall, Sch effectively alleviated ALI and provided a new mechanism to support the protective effect of Sch for sepsis-induced ALI. PRACTICAL IMPLICATIONS: ALI is characterized by inflammatory injury of the lungs, which is an important cause of high morbidity and mortality in severe patients. Sch is considered as a botanical active ingredient with various pharmacological activities, such as neuroprotective and vascular protective effects. However, the effect of Sch on ALI and its mechanism remains largely unknown. Research data indicate that Sch exerts an anti-inflammatory effect by reducing the production of inflammatory factors and abnormal apoptosis of cells, further alleviating lung damage. The protective effect of Sch was associated with inhibition of the activation of NLRP3 and the JAK2/STAT3 inflammatory pathways. The study, therefore, confirmed that Sch has a potential as an effective drug to prevent ALI diseases.

2019 IEEE Conference on Communications and Network Security (CNS), 2019

Radio frequency (RF) fingerprint has gained wide attention as it takes advantages of inherent cha... more Radio frequency (RF) fingerprint has gained wide attention as it takes advantages of inherent characteristics in hardware for identification and verification. However, performance unreliability with long-ago training data and channel fading interference are two open problems that restrict the development of RF fingerprint identification. To address the former issue, we propose a long-term stacking of repetitive symbols algorithm to turn the measurement noise toward standard Gaussian distribution, which contributes to both classification accuracy and long-term stability. For the latter issue, we propose an artificial noise adding algorithm in training stage to enhance the identification robustness. At last, we implement a robust RF fingerprint generation and classification scheme for practical device identification. In the experiment, we used 50 CC2530 ZigBee devices to verify the performance of proposed RF identification scheme. Our scheme achieved an accuracy of 100% with both training and testing data measured in the same day, and enabled reliable node recognition with the accuracy over 99% for testing data collected 18 months later. Further exploration shows that the scheme is both robust in additive white Gaussian noise channel and slight non-line-of-sight channel scenarios. The performance in multipath scenarios has also demonstrated the reliability and feasibility of our scheme.

2018 IEEE International Conference on Electronics and Communication Engineering (ICECE), 2018

The radio frequency (RF) based device identification is a physical layer solution for wireless de... more The radio frequency (RF) based device identification is a physical layer solution for wireless device authentication. However, the RF fingerprint emitted from wireless device could be seriously distorted due to the channel propagation. Therefore, the characteristics of the wireless channel will affect the identification of radio frequency fingerprint. In this paper, we investigate the impact of channel propagation on RF fingerprint via both simulation and practical experiment. Four RF fingerprint features, including frequency offset, I/Q offset, constellation trace figure offset and transient on/off signal point, are evaluated under different channel conditions. We set up experimental environment with 12 ZigBee devices and one USRP receiver. Both simulation and experiment results show that the influence of channel propagation on frequency offset is the slightest, followed by which is I/Q offset and CTF offset, and the transient features are most easily affected by the wireless channel.

IEEE Wireless Communications Letters, 2021

Radio frequency fingerprint (RFF) has been utilized to mitigate spoofing attacks in open wireless... more Radio frequency fingerprint (RFF) has been utilized to mitigate spoofing attacks in open wireless environments, making use of the inherent characteristics of hardware. However, most existing RFF technologies are data-dependent, e.g., based on preambles or synchronization sequences. In this letter, we propose a novel data-independent RFF extraction scheme, called Least mean square-based Adaptive Filter and Stacking, abbreviated as LAFS, that is implemented on random data segments, like communication data. Intuitively, we extract converged tap coefficients as RFF by minimizing the divergence between the desired signal and the demodulated reference signal. To further improve the effect, we introduce a tap coefficient stacking (TSC) technique to stabilize the RFF. Our experiment on ZigBee devices shows that the proposed LAFS method successfully identifies transmitters with 98.9% accuracy at 10 dB by stacking 25 times.

IEEE Internet of Things Journal, 2021

Radio-frequency fingerprinting (RFF) exploiting hardware characteristics has been employed for de... more Radio-frequency fingerprinting (RFF) exploiting hardware characteristics has been employed for device recognition to enhance the overall security. However, the performance unreliability in long-term experiments, channel fading interference, and unauthorized devices verification are three open problems that restrict the development of RFF recognition. To address these issues, a robust RFF extraction scheme based on three corresponding algorithms is studied. For the first problem, a long-term stacking of repetitive symbols (LSRSs) algorithm is proposed to reduce the acquired signal variance, which contributes to the identification accuracy and long-term stability. For the second issue, we propose an artificial noise adding (ANA) algorithm to enhance the recognition robustness through regularization and channel adaptation. For the third issue, a verification algorithm based on the generative Gaussian probabilistic linear discriminant analysis (GPLDA) model is developed to handle unauthorized devices. Our robust RFF extraction scheme is verified in the experiments with 54 CC2530 ZigBee devices. It enables reliable node identification with the accuracy of 99.50% in the short rang line-of-sight (SLOS) scenarios for signals collected over 18 months, and 95.52% in the extensive multipath fading experiments. The equal error rate (EER) of the verification experiments with six authorized devices versus six unseen unauthorized devices is as low as 0.63%.

Journal of Ethnopharmacology, 2021

ETHNOPHARMACOLOGICAL RELEVANCE Quyu Shengxin capsule (QSC) is an herbal compound commonly used to... more ETHNOPHARMACOLOGICAL RELEVANCE Quyu Shengxin capsule (QSC) is an herbal compound commonly used to treat blood stasis syndrome in China, and blood stasis syndrome is considered to be the root of cardiovascular diseases (CVD) in traditional Chinese medicine. However, the potential molecular mechanism of QSC is still unknown. AIM OF STUDY To study the therapeutic effect of QSC on the abnormal proliferation of VSMCs induced by Ang-II, and to explore its possible mechanism of action. MATERIALS AND METHODS Qualitative analysis and quality control of QSC through UPLC-MS/MS and UPLC. The rat thoracic aorta vascular smooth muscle cells (VSMCs) were cultured in vitro, and then stimulated with Angiotensin Ⅱ (Ang-II) (10-7 mol/L) for 24 h to establish a cardiovascular cell model. The cells were then treated with different concentrations of QSC drug-containing serum or normal goat serum. MTT assay was used to detect the viability of VSMCs and abnormal cell proliferation. In order to analyze the possible signal transduction pathways, the content of various factors in the supernatant of VSMCs was screened and determined by means of the Luminex liquid suspension chip detection platform, and the phosphoprotein profile in VSMCs was screened by Phospho Explorer antibody array. RESULTS Compared with the model group, serum cell viability and inflammatory factor levels with QSC were significantly decreased (P < 0.001). In addition, the expression levels of TGF-β, VEGF, mTOR and JAK-STAT in the QSC-containing serum treatment group were significantly lower than those in the model group. QSC may regulate the pathological process of CVD by reducing the levels of inflammatory mediators and cytokines, and protecting VSMCs from the abnormal proliferation induced by Ang-II. CONCLUSION QSC inhibits Ang-II-induced abnormal proliferation of VSMCs, which is related to the down-regulation of TGF-β, VEGF, mTOR and JAK-STAT pathways.

Frontiers in Neurorobotics, 2021

Water surface object detection is one of the most significant tasks in autonomous driving and wat... more Water surface object detection is one of the most significant tasks in autonomous driving and water surface vision applications. To date, existing public large-scale datasets collected from websites do not focus on specific scenarios. As a characteristic of these datasets, the quantity of the images and instances is also still at a low level. To accelerate the development of water surface autonomous driving, this paper proposes a large-scale, high-quality annotated benchmark dataset, named Water Surface Object Detection Dataset (WSODD), to benchmark different water surface object detection algorithms. The proposed dataset consists of 7,467 water surface images in different water environments, climate conditions, and shooting times. In addition, the dataset comprises a total of 14 common object categories and 21,911 instances. Simultaneously, more specific scenarios are focused on in WSODD. In order to find a straightforward architecture to provide good performance on WSODD, a new ob...

Food & Function, 2021

ECG inhibits the development of atherosclerosis by inhibiting NF-κB and activating the Nrf2 signa... more ECG inhibits the development of atherosclerosis by inhibiting NF-κB and activating the Nrf2 signaling pathway to inhibit oxidative stress in vivo and in vitro.