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Stimulating the development of soil suppressiveness against certain pathogens represents a sustainable solution toward reducing pesticide use in agriculture. However, understanding the dynamics of suppressiveness and the mechanisms leading to pathogen control remain largely elusive. Here, we investigated the mechanisms used by the rhizosphere microbiome induces bacterial wilt disease suppression in a long-term field experiment where continuous application of bio-organic (BF) fertilizers triggered disease suppressiveness when compared to chemical fertilizer (CF) application. We further demonstrated in a greenhouse experiment that the suppressiveness of the rhizosphere bacterial communities was triggered mainly by changes in community composition rather than only by the abundance of the introduced biocontrol strain. Metagenomics approaches revealed that members of the families Sphingomonadaceae and Xanthomonadaceae with the ability to produce secondary metabolites were enriched in the BF plant rhizosphere but only upon pathogen invasion. We experimentally validated this observation by inoculating bacterial isolates belonging to the families Sphingomonadaceae and Xanthomonadaceae into conducive soil, which led to a significant reduction in pathogen abundance and increase in non-ribosomal peptide synthetase (NRPS) gene abundance. We conclude that priming of the soil microbiome with bio-organic fertilizer amendment fostered reactive bacterial communities in the rhizosphere of tomato plants in response to biotic disturbance.

Vortex-induced vibration of a regulating valve not only wastes the energy of the control system, but also causes serious damage to the valve trims. It is very important to suppress vortex-induced vibration of the flow field in a valve by optimizing the valve trim structure. In this paper, a multistage pressure-reducing valve is proposed, and the flow fields in the valve with the lightweight, flat bottom, circular truncated cone and cylinder valve plug are simulated using the ANSYS Workbench. To verify the reliability of the simulation, a flow test device for the valve is established and the flow rates at different openings are measured. The test result shows that the valve designed by simulation conforms to the specified flow characteristics. By monitoring the lift coefficient of the vortex cross flow, the amplitude and main frequency of vortex-induced vibration are evaluated. The effect of the valve plug shapes on the vortex-induced vibration characteristics is studied. The results show that the shape of the valve plug has an obvious effect on the amplitude of vortex-induced vibration. The conical valve plugs can play the role of flow guiding, the amplitude of vortex-induced vibration of the valve is smallest, it is about 1/4 of that of other valve plugs at medium and high openings. This work is of significance for suppressing the vortex-induced vibration of the multistage pressure-reducing valve.

Soil microbiome manipulation can potentially reduce the use of pesticides by improving the ability of soils to resist or recover from pathogen infestation, thus generating natural suppressiveness. We simulated disturbance through soil fumigation and investigated how the subsequent application of bio-organic and organic amendments reshapes the taxonomic and functional potential of the soil microbiome to suppress the pathogens Ralstonia solanacearum and Fusarium oxysporum in tomato monocultures. The use of organic amendment alone generated smaller shifts in bacterial and fungal community composition and no suppressiveness. Fumigation directly decreased F. oxysporum and induced drastic changes in the soil microbiome. This was further converted from a disease conducive to a suppressive soil microbiome due to the application of organic amendment, which affected the way the bacterial and fungal communities were reassembled. These direct and possibly indirect effects resulted in a highly efficient disease control rate, providing a promising strategy for the control of the diseases caused by multiple pathogens.

Considering the increasing coupling between transmission and distribution networks, an affine arithmetic-based interval power flow algorithm of integrated transmission and distribution (I-T&D) networks is proposed. First, a novel two-step approximation method for sine and cosine functions is proposed to reduce the number of noise variables in the affine form of the injected power, as the basis of the proposed algorithm. Then, this algorithm is divided into four stages, which are implemented alternatively between a transmission system operator (TSO) and multiple distribution system operators (DSOs) by exchanging parameters at TSO-DSO boundaries. Three-phase unbalanced I-T&D networks and boundary tap changer action are also discussed. In addition, an asynchronous power flow method is proposed to mitigate the influences of communication networks on the proposed interval power flow algorithm. Numerical experiments demonstrate the effectiveness of the proposed two-step approximation method, as well as the conservative property, fairly good approximation effect, high efficiency, and low data exchange amount of the proposed algorithm in various circumstances, including single-phase and three-phase networks, varying degrees of unbalance, various smart inverter controls, etc.

To satisfy the robust and real-time requirements of power flow calculation for large-scale power systems, a globally convergent method is proposed with trust-region techniques, which shows satisfying robustness and convergence. Then, this method is combined with Newton's method to achieve a two-stage algorithm, benefiting from their different advantages. In the first stage, the proposed globally convergent method is used for searching power flow solution. When the values of state variables in a certain iteration are close enough to the real operational point, the algorithm enters the second stage to use Newton's method to achieve the solution. This two-stage algorithm can achieve an accurate solution for solvable cases, and can also achieve a least-square solution, which is an approximate solution for unsolvable cases. Numerical experiments show that the proposed globally convergent method and two-stage algorithm have better robustness and efficiency than the existing methods in previous research. They have universality for well- and ill-conditioned systems as well as the cases under ill-conditioned operational modes, heavy loads and inappropriate initial values. They can also handle different limit violations, such as reactive power limit in PV buses and voltage limit with tap changers action, which could significantly benefit real practice.

Acetylene (C2H2) gas concentration is a key parameter in transformer monitoring. In current work, the selective acetylene sensors which based on flower-like ZnO structures with NiO nanoparticles were successfully fabricated. The NiO–ZnO composites were synthesized by two-step hydrothermal method. And various of characterization analyses had been applied to the exploration of crystal structure and the p-n heterojunction. According to the systematic gas sensitivity tests, the response of NiO–ZnO (5%) to 50 ppm C2H2 was 15.23 at 200 °C whereas the response of pure ZnO was 4.1 in the same condition. In addition, the response value of NiO–ZnO (5%) to 50 ppm C2H2 was 3.6 times to 50 ppm H2. Such a good gas-sensing property of NiO–ZnO composites is due to p-n heterojunction and high catalytic activity of NiO.

Placenta-specific 8 (PLAC8) is reported to regulate cellular functions in the progression of various diseases. However, its role in periodontitis is still unclear.Human periodontal ligament cells (hPDLCs) were treated with lipopolysaccharide of Porphyromonas Gingivalis (LPS-PG) to mimic periodontitis in vitro. Real-time quantitative polymerase chain reaction (RT-qPCR) was performed to measure the mRNA expression levels and western blot was for protein levels. Wound healing and transwell migration assays were performed to assess the cell mobility of hPDLCs. Both mRNA and protein levels of inflammatory cytokines including IFN-γ, IL-17, TNF-α, IL-4, IL-10 and IL-13 were accessed to evaluated process of periodontitis in vitro. Furthermore, the protein expressions of mitogen-activated protein kinase kinase (MEK), extracellular regulated protein kinase (ERK) and their phosphorylated products quantified by western blotting assay were determined to confirm the activation of the MEK/ERK signaling pathway.The microarray analysis results showed that PLAC8 was most significantly downregulated in periodontium samples of patients with periodontitis, which participates in blood coagulation and integrin-mediated signaling pathway. PLAC8 was also markedly downregulated in the LPS-PG-treated hPDLCs. Moreover, overexpression of PLAC8 ameliorated inflammation and promoted cell mobility of LPS-PG-treated hPDLCs, while inhibition of PLAC8 exhibited the opposite effects. MEK/ERK was selected based on analyses of the protein-protein interaction (PPI) network as the potential signaling pathway interacted with PLAC8, and PLAC8 showed regulatory function on activation of the MEK/ERK pathway. Additionally, U0126, the inhibitor of MEK, abrogated the effects of PLAC8 on inflammation and cell mobility of LPS-PG-treated hPDLCs.Overexpression of PLAC8 protected hPDLCs from dysfunction of inflammation and cell mobility via activating MEK/ERK pathway, indicating a novel therapeutic target for periodontitis.

Load modeling is critical for power system transient analysis and simulation. In recent studies, ambient signal-based parameter identification is considered as an effective method to obtain time-varying model parameters. However, low fluctuation amplitude of ambient signals may exacerbate the issue of multiple local optimal solutions, which is derived from the nonconvex and nonlinear characteristics of parameter identification problem. In this paper, a multi-start strategy based global optimization method is presented for ambient signal-based load parameter identification. Firstly, the selection of start points is formulated as a maximum diversity problem which can be solved by greedy algorithm. Then, we apply trust region-based local solvers to identify model parameters and obtain global optimum from multiple local solutions. Simulation results in the IEEE-39 bus system have demonstrated the global accuracy and efficiency of the proposed method.

With the rapid changes in the actual power system operation mode, power system wide area real time state estimation based on phasor measurement unit (PMU) plays an increasingly important role in energy management system (EMS). However, complex distribution characteristics of PMU measurement error pose challenges to the accuracy of state estimation. Therefore, a state estimation method combining PMU linear measurement model and linear Bayesian estimation is proposed. Considering the prior information of estimated parameters and the complex probability density function of measurement error, linear Bayesian estimation is applied to power system state estimation based on PMU. The correlation between real measurement error and imaginary measurement error is analyzed, and the prior information of estimated parameters is obtained according to the analysis of system state volatility. Compared with complex number least squares (CLS) method, the applicability and accuracy of this method were verified in IEEE 39 bus system.

Distribution networks with high penetration of distributed generation yield complicated and uncertain power flow, which makes most existing faulted line identification methods not adaptable for industrial applications. Driven by this motivation, a novel single-phase-to-ground (SPTG) faulted line identification method is proposed based on hybrid state estimator (HSE). The first step of the method is to present an HSE for power distribution networks using power flow measurements mixed phasor measurement units. Then, an SPTG fault on a power line is treated as an event that suddenly increases one virtual bus in the monitored network, so as to form the extended bus admittance matrix and augmented HSE based on the specific network topology. In this way, the faulted line identification could be obtained by computing parallel estimated results transversally. Robustness and effectiveness of the proposed HSE and the HSE-based SPTG faulted line identification method are validated by means of a cyber-physical system (a cosimulation platform), where two typical three-phase power distribution networks are considered to simulate with its hybrid measurement system.

Plant growth-promoting rhizobacteria (PGPR) substantially improve plant growth and health, but their effects on the succession of rhizosphere microbiota throughout the growth period triggered by pre-inoculation have not yet been considered. Pepper seedlings cultured from a bio-nursery substrate containing Bacillus velezensis NJAU-Z9 and ordinary nursery substrate were used in this study to evaluate the effects of pre-colonization of a PGPR strain at the seedling stage on yield enhancement. To elucidate the underlying mechanisms involved in the rhizosphere microbiota succession during the whole growth period and their association with yield enhancement, high-throughput sequencing combined with qPCR was conducted. The results showed that, compared to the control without inoculation, pre-inoculation led to a steady yield enhancement in two-season field trials, as well as higher rhizosphere bacterial richness (Chao1) and diversity (Shannon-Wiener). The plant growth stage as the first driving factor, followed by pre-colonization drove the variations of the rhizosphere microbial community composition according to multivariate regression tree analysis and principal coordinate analysis. Variance partitioning analysis (VPA) and Mantel test results showed that the previous plant growth period induced variations in the fungal and bacterial communities at the next stage. Compared to the seedling and flowering stages, the mature-stage microbial community showed a higher degree of explanation of yield enhancement. Additionally, pre-inoculation led to distinctive rhizosphere microbiota succession compared to the control, due to alteration of the initial community. The heat map analysis showed that the rhizosphere microbiota was related to crop yield. In addition to Bacillus velezensis NJAU-Z9, which showed stable abundance in the pepper rhizosphere, stable higher relative abundance of the bacterial genera Sphingomonas, Sphingopyxis, Bradyrhizobium, Chitinophaga, Dyadobacter, Streptomyces, Lysobacter, Pseudomonas and Rhizomicrobium, and the fungal genera Cladorrhinum, Cladosporium and Aspergillus throughout the growth period induced by pre-colonization was associated with yield enhancement. Overall, we conclude that pre-colonization with PGPR changed the initial rhizosphere microbiota and that the plant was triggered to further select distinctive microbes to form unique rhizosphere microbial consortia at the later growth stages, which resulted in plant growth promotion.

Future power distribution networks are characterized by changes of uncertain behavior due to the integration of distributed generators (DG) and prosumers, and the design of meter deployment is more critical for the safe and effective network monitoring system. The use of any available and the newly installed metering devices such as micro phasor measurement units (μPMU) and smart meters (SM), could offer much accurate and real-time measuring data. This paper investigates a novel meter deployment method for the distribution network planning, with the objective of ensuring the quality of the state estimation (SE) under consideration of measurement uncertainty, while keeping the total amount of heterogeneous meters at a minimum. In particular, the measurement uncertainty in the SE is depicted via the interval arithmetic (IA) theory, which could offer advantageous over the existing probabilistic and fuzzy approaches. The case studies and comparison results based on an actual distribution network demonstrate the feasibility of the proposed IA-based SE algorithm and the meter deployment method.

Non-ribosomal peptides (NRPs) are one of the largest groups of natural microbial secondary metabolites, which include peptides such as the antibiotics vancomycin and gramicidin, as well as lipopeptides (surfactin, iturin A and bacillomycin). In this study, banana Fusarium wilt disease suppressive and conducive soils were chosen to investigate the role of microbes that harbor the NRPS gene in disease suppression based on the 454-pyrosequencing platform and real-time PCR technique. The results showed that higher abundances and diversity of microbes that harbor the NRPS gene were observed in the suppressive soil samples than in the conducive soil. According to the results of the DNA sequences blastx of NRPS, the main microbial taxa harboring the NRPS gene were identified, and Pseudomonas in Proteobacteria and Streptomyces in Actinobacteria might be remarkably related to Fusarium wilt disease suppression. Furthermore, the Mantel test showed that compared with bacteria community and chemical properties, the microbial community harboring the NRPS gene had a more significant impact on the disease incidences of Fusarium wilt. This study provided non-specific relationships between groups of microbes harboring NRPS genes and Fusarium wilt disease suppression suggesting potential interaction based on correlation evidence, and pointed out a potential mechanism of suppressive soil.

A comprehensive evaluation approach of power quality based on AHP and optimal membership is proposed in this paper. This paper selects the appropriate power quality indexes by analyzing the characteristic of power quality. The continuous power quality indexes can be reasonably analyzed by establishing the fuzzy membership function and the transient power quality can be calculated according to IEEE 1564-2014. In addition, the weight of each index can be determined dynamically which includes both subjective part and objective part. Finally, an IEEE-18 standard test system with non-linear loads and compensation devices is applied to prove the validity of the proposed evaluation system.

A state feedback dynamic surface tracking control algorithm is proposed for parametric linearized manipulator model in this paper. Different from ordinary finite-time control schemes, fixed-time convergence can be achieved in the proposed algorithm via both of high and low-degree terms in the controller such that the settling time can be preestablished by the designed parameters independent of the system initial conditions. Moreover, the problem of the “explosion of complexity” and the singularity can be avoided by constructing an auxiliary controller with a first-order filter. The semi-global fixed-time uniform convergence of all the signals in the closed-loop system can be proved by Lyapunov theorem. In order to avoid the employment of acceleration signals and obtain better tracking performance during the procedure, a kind of observer based on uniform exact differentiators was used to compensate the uncertainties online. Finally the superiority of the proposed method has been verified by simulations based on a 2-DOF robot manipulator.

For traditional parameter estimation schemes of uncertain robot, most of them were proposed to identify unknown parameter with desired precision, but few of them focused on the convergence time. Recently finite-time estimation techniques have been proposed by scholars to achieve estimation in finite time. In this paper, we proposed a novel estimation scheme for uncertain robot systems with fixed time instead of finite time. In order to avoid using acceleration signals during the estimation, a kind of auxiliary filtering technique was employed. Besides, a continuous and recursive update law was employed for the parameter estimation such that the computational burdens of real-time inversion of square matrices could be avoided. Finally the effectiveness of the identification algorithm is verified based on a 2-DOF uncertain robot model.

The state information of district heating network (DHN) is significant to the secure operation and control of the integrated energy system. This paper focuses on the dynamic energy flow calculation method of the hot-water heating network under variable flow rate conditions, i.e. under the quantity regulation mode. First of all, known inputs, desired outputs and mathematical formulation of the energy flow calculation model are clarified. For the partial differential equation describing the temperature dynamics in each pipe, we apply the finite difference method to convert it into a group of algebraic equations for standardized solution. After that, a cross iteration method is proposed to solve the high-dimensional nonlinear model, where the global mass flow rate distribution and the temperatures of return water at each load are alternately updated until both converge to their acceptable precision levels. One nice feature of our method is that the temperatures of return water at each load do not need to be known inputs. Instead, the dynamic response of them can be fully captured, which is crucial for identifying the thermal conditions at each load. Test results of a DHN in the Barry Island demonstrate the validity and effectiveness of our method.

As an important and widely used electrical equipment, GIS (gas insulated switchgear) greatly affects the stability and safety of the entire power grid. By detecting the concentration of SF 6 decomposed component gases in GIS, we can judge the faults existing in the GIS, and prevent the corrosive decomposed gases from damaging the insulation. However, existing SF 6 decomposition component detection methods can are difficult to achieve relatively accurate simultaneous detection of multi-component gases. In this paper, a method using cavity enhanced Raman spectroscopy was developed to simultaneously detect SO 2 and CF 4 under high concentration SF 6 . Based on frequency-locking cavity enhancement technology, we coupled laser light with a wavelength of 642 nm and a power of about 50 mW into a V-shaped resonant cavity, and realized three orders of power enhancement. Due to the special V-shaped structure of the resonant cavity, the direct reflected light from the cavity mirror will not feedback to the laser, thereby avoiding false locking. Under 1 atmosphere pressure, Raman spectroscopy of CF 4 and SO 2 with a concentration of 1% was obtained. The characteristic Raman spectral lines of CF 4 and SO 2 were determined as 909 cm−1 and 1151 cm−1, respectively. responding to their v1 transition. And the Raman spectrum of CF 4 and SO 2 under the pressure of gradient distribution was obtained, it was found that the Raman characteristic peak intensity of CF 4 and SO 2 was highly linear between pressure relationship. Besides, the mixed gas of CF 4 and SO 2 under high concentration SF 6 was detected simultaneously without any interference. This study laid the foundation for Raman spectroscopy for the detection and quantitative analysis of SF 6 decomposed gas components, indicating that Raman spectroscopy has great potential in the field of SF 6 decomposed gas component detection.

A continuous power flow (CPF) calculation method for the islanded microgrids with droop control is proposed in this paper. The starting point of CPF is solved by the modified factored load flow algorithm. The algorithm ensures the robustness of the starting point calculation with a two-step solution. The tangent prediction method and the Newton-Raphson method are used to determine the prediction and correction steps. Further, this paper proposes an adaptive step size control method based on frequency variation, which avoids the comparison among different nodal voltage magnitudes. In addition, node type switching is considered for handling the limit violations of PV nodes and droop nodes. The case analysis shows that the proposed CPF method has high efficiency and strong robustness.

In this paper, neural network, semi-supervised training, integrated learning, and other techniques are applied to the prediction and analysis of static voltage stability margin of island microgrid power systems and an online prediction method based on the Tri-Training-Lasso-BP network is proposed. The network consists of Tri-Training, the least absolute shrinkage and select operator (Lasso) algorithm and the backpropagation (BP) neural network. The analysis results on an 115-node example show that the proposed method can reduce the requirement of the data volume of the training set, take advantage of the massive data collected during the daily operation of the power system, improve the prediction accuracy of the network and reduce manual intervention. Finally, this paper uses statistical methods to make a comprehensive and objective description of the performance of the method.

To reduce the waste of storage and computing resources in traditional data integration methods, a multidimensional information integration method for power transmission and transformation equipment (PTTE) is proposed. Firstly, a common information model (CIM) defined in IEC 61970/61968 is chosen as the global schema for multi-dimensional information of PTTE, including online and offline data. Secondly, a representational state transfer (REST)-based data integration mechanism is proposed to improve maintenance efficiency and expansibility. Finally, based on CIM and integration mechanism, a multi-dimensional information integration platform is made up for PTTE, and the practical application is introduced in detail, which provides a novel idea about equipment information integration.

Compared with the synchronous generator controllable rectification system, the synchronous generator uncontrolled rectification system has a simpler structure and control method which makes it more widely used. In the synchronous generator rectification system, the establishment of the mathematical model of the rectifier is the focus of analysis. Due to the existence of the rectifier, the system has strong nonlinearity and is difficult to model precisely. Meanwhile, the proportion of constant power load is getting larger and larger, and its negative impedance characteristic has adversely affected the stability of the power systems. Considering that the design of the system controller and the dynamic simulation process both rely on real-time numerical calculations, the amount of calculations will become larger and larger as the scale of the system increases. This makes real-time calculations even impossible. Based on the idea of model reduction, the authors firstly proposed a reduction model of three-phase uncontrolled rectifier generator through strictly mathematical theory. The reduction model retains main dynamic performance of the system. Then the authors combined the proposed model with engineering practice, and reduced the order of power systems with light load and heavy load respectively. The simulation results demonstrated the correctness and effectiveness of the raised model.

The integrated power and natural gas system (IEGS) provides a new way to increase wind power consumption. This paper proposes a data-driven robust unit commitment (UC) of IEGS considering wind power uncertainty. Firstly, wind power forecast errors are clustered via infinite Gaussian mixture model (IGMM), and a data-driven uncertainty set is derived afterward. Then a two-stage IEGS unit commitment model is established, in which dynamic natural gas flow is considered. Based on the second-order cone (SOC) method, the nonconvex constraints of the dynamic natural gas flow are transformed into convex ones. In order to solve this min-max-max-min UC optimization problem, a Column Constraint Generation (C&CG) based algorithm is further developed. Finally, the effectiveness of the proposed approach is demonstrated with 6-bus-6-node IEGS and 118-bus-10-node IEGS.