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The high correlation between rolling bearing composite faults and single fault samples is prone to misclassification. Therefore, this paper proposes a rolling bearing composite fault diagnosis method based on a deep graph convolutional network. First, the acquired raw vibration signals are pre-processed and divided into sub-samples. Secondly, a number of sub-samples in different health states are constructed as graph-structured data, divided into a training set and a test set. Finally, the training set is used as input to a deep graph convolutional neural network (DGCN) model, which is trained to determine the optimal structure and parameters of the network. A test set verifies the feasibility and effectiveness of the network. The experimental result shows that the DGCN can effectively identify compound faults in rolling bearings, which provides a new approach for the identification of compound faults in bearings. [ABSTRACT FROM AUTHOR]

Accurate status prediction is critical for detecting potential failures of Doubly‐fed induction generator (DFIG). A unique Digital Twin (DT) fault prediction paradigm is put forth in this research, which uses an edge intelligence paradigm to ensure high‐performance fault prediction. Edge‐based DT provides compute and storage capabilities on edge devices to enable effective data processing. The stator current spectrum and instantaneous power spectrum of the DFIG are simulated and assessed, followed by the development of a verification prototype on the DFIG and the qualitative observation of the internal performance of the DFIG. The encoder is constructed using Squeeze‐and‐Excitation (SE) Convolutional Neural Network (SE‐CNN) with the channel attention mechanism, and the decoder is constructed based on a long short‐term memory (LSTM) network to form the self‐encoding model named SE‐CNN‐LSTM. In order to forecast problems and provide a foundation for predictive maintenance, the status indicator is built based on the prediction residuals of the status variables. Based on the outcomes of edge‐cloud co‐simulation and data collected from the Supervisory Control and Data Acquisition system at the Da Bancheng Wind Farm in Xinjiang, China, it is demonstrated that the framework and technology are workable and capable of predicting generator failures. [ABSTRACT FROM AUTHOR]

To model and evaluate the reliability of wind turbine (WT) under imperfect repair, an improved Log-linear Proportional Intensity Model (LPIM)-based method was proposed. Initially, using the three-parameter bounded intensity process (3-BIP) as the benchmark failure intensity function of LPIM, an imperfect repair effect-aware WT reliability description model was developed. Among them, the 3-BIP was used to describe the evolution process of the failure intensity in the stable operation stage with running time, while the LPIM reflected the repair effect. Second, the estimation problem for model parameters was transformed into a minimum solution problem for a nonlinear objective function, which was then solved using the Particle Swarm Optimization algorithm. The confidence interval of model parameters was finally estimated using the inverse Fisher information matrix method. Key reliability indices interval estimation based on the Delta method and point estimation was derived. The proposed method was applied to a wind farm's WT failure truncation time. The proposed method has a higher goodness of fit based on verification and comparison. As a result, it can bring the evaluated reliability closer to engineering practice. [ABSTRACT FROM AUTHOR]

Purpose: With the rapid increase in the number of installed wind turbines (WTs) worldwide, requirements and expenses of maintenance have also increased significantly. The condition monitoring (CM) of WT provides a strong "soft guarantee" for preventive maintenance. The supervisory control and data acquisition (SCADA) system records a huge amount of condition data, which has become an effective means of CM. The main objective of the present study is to summarize the application of SCADA data to fault detection in wind turbines, analyze its advantages and disadvantages and predict the potential of future investigations on the use of SCADA data for fault detection. Design/methodology/approach: The authors first review the means of WT CM and summarize the characteristics of CM based on SCADA data. To ensure the quality of SCADA data, data preprocessing methods are analyzed and compared. Then, the failure modes of the key components are discussed and the SCADA data used for fault detection of each component are compared. Moreover, the fault detection methods for WT are classified and a general framework for fault detection is proposed. Finally, the issues in the WT fault detection method based on SCADA data are reviewed. Findings: Based on the performed analyses, it is found that although the fault detection accuracy based on SCADA data is relatively poor, it has low capital expenses and low computational cost. More specifically, when there is scarce fault data, the normal SCADA data can be used to detect the fault time. However, the specific fault type cannot be identified in this way. When a large amount of fault data are accumulated in the SCADA system, it can not only detect the occurrence time of the fault but also identify the specific fault type. Originality/value: The main contribution of the present study is to summarize the pre-processing methods for SCADA data, the data required for fault detection of key components and the characteristics of the fault detection model. Then we propose a general fault detection framework for wind turbines based on SCADA data, where the maintenance workers can choose the appropriate fault detection method according to different fault detection requirements and data resources. This article is expected to provide guidance for fault detection based on time-series sensor signals and be of interest to researchers, maintenance workers and managers. [ABSTRACT FROM AUTHOR]

The high penetration of uncertain resources challenges the security of power system operation. By taking the impact of rescheduling under contingencies into consideration, reliability‐constrained unit commitment (RCUC) is developed to address this challenge. Although several efforts have been made in modelling reliability constraints, the existing methods can only manage oversimplified low‐order temporal‐independent contingencies without considering wide‐range contingencies or their state‐transition‐process issue. To quantify the impact of rescheduling on the normal‐state scheduling process denoted by UC problem, this paper builds up a Bayesian inference method for encoding reliability constraints in wide‐range temporal‐dependent contingencies. Three predictors, for example, expected‐generator‐rescheduling‐power, expected‐energy‐not‐serviced and lost‐of‐load‐probability, are selected to describe the possible corrective behaviours in rescheduling process and quantified by using Bayesian inference method. Then, these predictors are reformatted as a set of linearized constraints to be incorporated into UC. The proposed RCUC comprehensively considers the effect of rescheduling in wide‐range temporal‐dependent contingencies. Therefore, it can reveal the influence of generator rescheduling in wide‐range contingencies and keep better reliability performance than those methods reported in previous RCUC studies. The modified IEEE 30‐bus test system and IEEE 118‐bus test system are used to show the proposed model's effectiveness. [ABSTRACT FROM AUTHOR]

To more precisely assess the reliability of wind turbine gears, a competing failure model is established with consideration given to the degradation of internal fatigue strength and external random shocks. Firstly, the nonlinear Gamma process is adopted to depict the degradation of fatigue strength, and the strength‐stress interference theory is used to establish the threshold of degradation failure that changes over time. Also, the non‐homogeneous Poisson process is employed to depict the arrival of the shock, and classification of the shocks. Secondly, the correlation between degradation and shock is described, while the effect of shocks on degradation is demonstrated as a sudden increase in the extent of degradation. Besides, the decline rate of residual strength curve is introduced to describe the change in intensity parameters of the Poisson process, and then the effect of degradation on shocks is described. Finally, the sun gear in the wind turbine gearbox is an example. Monte Carlo simulation is used to calculate the reliability of the dependent competing failure model. This is then compared with the conventional stress‐strength interference model. The results verify that when the relationship between degradation and the shock process is disregarded, the reliability of gears may be overestimated. [ABSTRACT FROM AUTHOR]

The effects of papain and high-pressure processing (HPP), applied separately or in combination, on the tenderization of yak meat were studied regarding texture, water-holding capacity (WHC), cooking loss (CL), pH, and color. When papain was used alone, both the papain concentration level and treatment time could affect the tenderization. The maximal shear force (MSF) of yak meat was decreased by 46.85% and the WHC was increased by 9.93% when 80 U/mL papain was injected into yak meat and then incubated at 55 °C for 120 min. However, the color was significantly deteriorated. When the HPP treatment was performed alone, the MSF of yak meat displayed an initial decrease followed by an increase as the processing pressure or treatment time rose. Although an improved tenderizing effect was achieved at 250 MPa/15 min, leading to a MSF decline of 49.27% and a WHC increase of 10.50%, the meat turned white. Applying treatment sequences of an HPP and papain combination can significantly influence the tenderization effect. Optimal tenderization was obtained by the injection of papain (80 U/mL) and incubation at 55 °C for 30 min followed by HPP at 50 MPa/15 min (Pap → HPP). Results from this process indicated that the MSF of yak meat was decreased by 46.30% and 50.27%, respectively, compared to the separate applications of both HPP (50 MPa/15 min) and papain (80 U/mL, 30 min) treatments. Meanwhile, no significant color change was detected. The microstructure of yak meat was analyzed following the application of different treatments. Results suggest that the meat treated with Pap → HPP exhibited larger spacing and more damaged cells than untreated meat or the sample subjected to other treatments. These findings provided a possible explanation for the specific tenderization result when exposed to the combined treatments. [ABSTRACT FROM AUTHOR]

Directed acyclic graphs are widely used to describe directional pairwise relations. Such relations are estimated by reconstructing a directed acyclic graph's structure, which is challenging when the ordering of nodes of the graph is unknown. In such a situation, existing methods such as the neighbourhood and search-and-score methods have high estimation errors or computational complexities, especially when a local or sequential approach is used to enumerate edge directions by testing or optimizing a criterion locally, as a local method may break down even for moderately sized graphs. We propose a novel approach to simultaneously identifying all estimable directed edges and model parameters, using constrained maximum likelihood with nonconvex constraints. We develop a constraint reduction method that constructs a set of active constraints from super-exponentially many constraints. This, coupled with an alternating direction method of multipliers and a difference convex method, permits efficient computation for large-graph learning. We show that the proposed method consistently reconstructs identifiable directions of the true graph and achieves the optimal performance in terms of parameter estimation. Numerically, the method compares favourably with competitors. A protein network is analysed to demonstrate that the proposed method can make a difference in identifying the network's structure. [ABSTRACT FROM AUTHOR]

• Frequency containment reserve using wind farm and contingency state analysis. • An enhanced model of Frequency-constrained Contingency-State-Analysis is developed. • Both conventional generators and converter-interfaced generators as well as Under-Frequency-Load-Shedding are considered in the new models. • A set of new frequency dynamic indexes are calculated based on a Cross-Entropy-based Monte Carlo simulation. With the increasing penetration of converter-interfaced generators, the frequency containment reserve (FCR) from conventional generators keeps going down, leading to a potential risk of frequency instability under contingencies. Consequently, Converter-interfaced generators are required to provide FCR and participate in the corrective rescheduling. Nevertheless, how to assess the FCR and quantify the adequacy of FCR under contingencies is a big challenge in modern new power system. To address this challenge, a cross-entropy-based frequency-constrained contingency-state-analysis (FC-CSA) model is proposed in this paper. Notably, both frequency control (FC) of units (i.e., conventional synchronous generators and converter-interfaced generators), and under frequency load shedding (UFLS) are incorporated in the primary frequency response. Then a unified system frequency response (SFR) function representing frequency dynamic is derived. This SFR function is extracted and reformulated as a group of mixed-integer linear constraints and participates in the traditional CSA model. Moreover, a set of frequency dynamic indexes, i.e., Expectation of UFLS risk, Expectation of FCR from conventional and converter-interfaced generators, is extended to depict the FCR that the power system requires. These indexes are calculated by the FC-CSA in a cross-entropy-based monte carlo simulation (CE-MCs). Case studies on a modified IEEE 6-bus test system and IEEE 118-bus test system are carried out to demonstrate the effectiveness of the proposed FC-CSA model. [ABSTRACT FROM AUTHOR]

A novel method is proposed to investigate the pattern of variation in the residual strength and reliability of wind turbine gear. First, the interaction between loads and the effect of the loading sequence is considered based on the fatigue damage accumulation theory, and a residual strength degradation model with few parameters is established. Experimental data from two materials are used to verify the predictive performance of the proposed model. Secondly, the modeling and simulation of the wind turbine gear is conducted to analyze the types of fatigue failures and obtain their fatigue life curves. Due to the randomness of the load on the gear, the rain flow counting method and the Goodman method are employed. Thirdly, considering the seasonal variation of load, the decreasing trend of gear fatigue strength under multistage random load is calculated. Finally, the dynamic failure rate and reliability of gear fatigue failure under multistage random loads are analyzed. The results demonstrate that the randomness of residual strength increases with increasing service time. The seasonality of load causes fluctuations in the reliability of gear, providing a new idea for evaluating the reliability of the wind turbine gear. [ABSTRACT FROM AUTHOR]

Network meta-analysis (NMA) expands the scope of a conventional pairwise meta-analysis to simultaneously compare multiple treatments, which has an inherent appeal for clinicians, patients, and policy decision makers. Two recent reports have shown that the impact of excluding a treatment on NMAs can be substantial. However, no one has assessed the impact of excluding a trial from NMAs, which is important because many NMAs selectively include trials in the analysis. This article empirically examines the impact of trial exclusion using both the arm-based (AB) and contrast-based (CB) approaches, by reanalyzing 20 published NMAs involving 725 randomized controlled trials and 449,325 patients. For the population-averaged absolute risk estimates using the AB approach, the average fold changes across all networks ranged from 1.004 (with standard deviation 0.004) to 1.072 (with standard deviation 0.184); while the maximal fold changes ranged from 1.032 to 2.349. In 12 out of 20 NMAs, a 1.20-fold or larger change is observed in at least one of the population-averaged absolute risk estimates. In addition, while excluding a trial can substantially change the estimated relative effects (e.g., log odds ratios), there is no systematic difference in terms of changes between the two approaches. Changes in treatment rankings are observed in 7 networks and changes in inconsistency are observed in 3 networks. We do not observe correlations between changes in treatment effects, treatment rankings and inconsistency. Finally, we recommend rigorous inclusion and exclusion criteria, logical study selection process, and reasonable network geometry to ensure robustness and generalizability of the results of NMAs. [ABSTRACT FROM AUTHOR]

Clinical trials are the gold-standard means to evaluate the efficacy and safety of new drugs and other medical technologies. They can provide a scientific basis for treatment decisions for patients and their caregivers as well as sound reimbursement policies for governments and insurers. There has been a spectacular shift from government and academia to industry in the organization of clinical trials in the past decade. Currently, most of the trials are funded by industry and their research does make great contributions to the biomedical innovations, although limitations are inevitable. The finding that industry sponsorship is strongly associated with results that favor the experimental therapy brings concerns of sponsorship bias and quality issues to clinical trial results. However, existing research has its own limitations, and there is no solid evidence of causality. Industry-funded clinical trials are a two-edged sword, and further research on sponsorship bias is encouraged. A stronger regulatory policy and a new model of alliance between academia and industry are also imperative. [ABSTRACT FROM AUTHOR]

A new form of laser beam, called anomalous vortex beam (AVB), has been proposed recently. Here we study the propagation characteristics of the AVB passing through a paraxial ABCD optical system with an annular aperture. The approximate analytical expressions are derived based on the Collins integral formula. Some numerical examples are given to illustrate the intensity distribution of the AVB through a paraxial ABCD optical system with an annular aperture. It is shown that the field distribution depends on the topological charge $$m$$ , the waist $$w_{0}$$ , and the width of annular aperture. [ABSTRACT FROM AUTHOR]

Estimation of multiple directed graphs becomes challenging in the presence of inhomogeneous data, where directed acyclic graphs (DAGs) are used to represent causal relations among random variables. To infer causal relations among variables, we estimate multiple DAGs given a known ordering in Gaussian graphical models. In particular, we propose a constrained maximum likelihood method with nonconvex constraints over elements and element-wise differences of adjacency matrices, for identifying the sparseness structure as well as detecting structural changes over adjacency matrices of the graphs. Computationally, we develop an efficient algorithm based on augmented Lagrange multipliers, the difference convex method, and a novel fast algorithm for solving convex relaxation subproblems. Numerical results suggest that the proposed method performs well against its alternatives for simulated and real data. Statistical Analysis and Data Mining 2011 DOI: 10.1002/sam.11168 [ABSTRACT FROM AUTHOR]

• A conditional PDF model is presented to characterize fatigue life distribution under CAC loading. • A fatigue damage accumulation model is developed to account for the loading sequence effects. • The fatigue damage accumulation rule of material under the multi-level cyclic loading is elaborated. • A general framework is proposed to predict fatigue life under the multi-level cyclic loading. Fatigue life prediction of materials under the cyclic loading is still a particularly challenging task remains to be resolved. This study aims to develop a generic framework for fatigue life prediction under the multi-level cyclic (MLC) loading with consideration of the loading sequence effects. Firstly, a conditional probability density function (PDF) model is presented to quantify the fatigue life distributions of materials under any constant amplitude cyclic (CAC) loading. Subsequently, a fatigue damage accumulation model is proposed from the perspective of cumulative failure probability, which is capable of accounting for the nonlinear characteristics of damage accumulation and the loading sequence effects. Finally, a generic framework for fatigue life prediction of materials under the MLC loading is developed based on the proposed models. The fatigue life data of fiber reinforced composites under the two-level and three-level cyclic loading are utilized to verify the proposed framework. The results show that the predicted fatigue lives agree well with the fatigue test data of fiber reinforced composites. Moreover, the proposed framework can be easily extended to calculate fatigue life of materials under any MLC loading. [ABSTRACT FROM AUTHOR]

• Non-gaussian wind speed model can accurately describe the wind characteristics. • Vibration acceleration sensor contributes a lot to the real-time monitoring of wind turbine. • Reliability analysis can provide general maintenance strategy. After decades of rapid development, the wind power industry may be entering a period of technological maturity and maintenance. The components of the wind turbines mostly fail in a high-failure-risk period. Research on the reliability components can offer a basis for decisions on maintenance operations. In this paper, a wind turbine blade is subjected to wind load impacts year-round. The Lévy index is used to describe the instantaneous wind law. The data-driven method is used to describe the variation of blade failure related parameters. Based on this, the degradation failure and Lévy index of Wiener process are constructed. Based on the example analysis of blade failure of wind turbine in a wind farm in Daban city, Xinjiang province, a competitive model of failure probability of wind turbine under non-gaussian wind load is established. The statistical predictive model for competitive failures is closer to the actual characteristics of the wind farm. This offers data to help support site selection for individual turbines, and subsequent blade maintenance. [ABSTRACT FROM AUTHOR]

• The inherent correlation between stiffness degradation and strength degradation of FRP is explored. • A probability model of stiffness degradation is proposed based on the same damage state principle. • The proposed model is flexible enough to fit the stiffness degradation data of FRP laminates with good agreement. The stiffness degradation of fiber reinforced polymer (FRP) has significant effects on its fatigue life and reliability. In this study, the inherent correlation between stiffness degradation and strength degradation is explored, and a probability model that describing stiffness degradation of FRP is presented. Firstly, the evolution of fatigue damage in FRP is analyzed, and then it is characterized by stiffness degradation and strength degradation, respectively. Secondly, the randomness of initial stiffness and critical stiffness of FRP is considered, and a probability model of stiffness degradation is proposed based on the same damage state principle. Finally, the test data of FRP laminates are used to verify the proposed model, and the fitting accuracy is compared with several existing models. The results show that the stiffness degradation of FRP presents a fast-slow-fast trend during fatigue failure process, and the proposed model is capable of fitting stiffness degradation data of FRP with good agreement. [ABSTRACT FROM AUTHOR]

• A novel machine learning method is used to predict the multiaxial fatigue life of various materials. • Non-proportionality and phase differences are used to describe the loading paths in non-data form. • Adam algorithm is used to optimize the original model to avoid getting trapped in a local optimum. • Six metallic materials are used to validate the predictive performance of the proposed model. • The generalization and extrapolation capability of different machine learning models are compared. In this study, a neuro-fuzzy-based machine learning method is developed to predict the multiaxial fatigue life of various metallic materials. First, the fuzzy inference system and neural network are combined to identify and capture the nonlinear mapping relationship between multiaxial fatigue damage parameters and fatigue life. Non-proportionality and phase differences are introduced to characterize different loading paths. Next, the Adam algorithm is employed to update the premise parameters of the original model to achieve fast and accurate convergence. Then, subtractive clustering is applied to extract fuzzy rules between input variables and output for more efficient prediction. Moreover, the hyperparameters in the proposed model are automatically optimized by the adaptive opposition slime mould algorithm to obtain the optimal model. The predictive performance of the proposed model is verified by fatigue experimental data for six materials in published literature, which indicates that the proposed model can effectively predict the fatigue life of various materials under different loading paths. Meanwhile, compared with six classical machine learning models, it is found that the proposed model has better predictive performance and extrapolation capability. [ABSTRACT FROM AUTHOR]

5-Aminovaleric acid (5-AVA), 5-hydroxyvalerate (5HV), copolymer P(3HB- co -5HV) of 3 -hydroxybutyrate (3HB) and 5HV were produced from L-lysine as a substrate by recombinant Halomonas bluephagenesis constructed based on codon optimization, deletions of competitive pathway and L-lysine export protein, and three copies of davBA genes encoding L-lysine monooxygenase (DavB) and 5-aminovaleramide amidohydrolase (DavA) inserted into its genome to form H. bluephagenesis YF117Δ gabT 1+2 , which produced 16.4 g L−1 and 67.4 g L−1 5-AVA in flask cultures and in 7 L bioreactor, respectively. It was able to de novo synthesize 5-AVA from glucose by L-lysine-overproducing H. bluephagenesis TD226. Corn steep liquor was used instead of yeast extract for cost reduction during the 5-AVA production. Using promoter engineering based on P porin mutant library for downstream genes, H. bluephagenesis YF117 harboring pSEVA341-P porin 42 - yqhD EC produced 6 g L−1 5HV in shake flask growth , while H. bluephagenesis YF117 harboring pSEVA341-P porin 42 - yqhD EC -P porin 278 - phaC RE -abfT synthesized 42 wt% P(3HB- co -4.8 mol% 5HV) under the same condition. Thus, H. bluephagenesis was successfully engineered to produce 5-AVA and 5HV in supernatant and intracellular P(3HB- co -5HV) utilizing L-lysine as the substrate. [Display omitted] • 5-Aminovaleric acid (5-AVA), 5-hydroxyvalerate (5HV), and P(3HB- co -5HV) were produced by Halomonas bluephagenesis. • The engineered strain YF117Δ gabT 1+2 , produced 16.4and 67.4 g L−1 5-AVA in flask cultures and in 7 L bioreactor, respectively. • It was able to de novo synthesize around 600 mg L−1 5-AVA from glucose by L-lysine- overproducing H. bluephagenesis TD226. • H. bluephagenesis YF117 harboring pSEVA341-P porin 42 - yqhD EC produced 6 g L−1 5HV in shake flasks. • YF117 harboring pSEVA341-P porin 42 - yqhD EC -P porin 278 - phaC RE - abfT synthesized 42 wt% P(3HB- co -4.8 mol% 5HV). [ABSTRACT FROM AUTHOR]

• • A unified fatigue damage model is proposed based on the performance degradation of composites. • • A pair of residual strength and stiffness models is presented to characterize the degradation rules. • • Correlation between strength degradation and stiffness degradation of composites is elaborated. • • Fatigue life prediction methods are developed from the viewpoint of performance degradation. The performance degradation of composite materials under fatigue loading is so complex that the present knowledge is far from complete. This study aims to present a pair of residual strength and residual stiffness models based on a unified fatigue damage formula, and predict fatigue life under variable amplitude cyclic loading. Firstly, a unified fatigue damage model for composite materials is proposed, which is capable of characterizing fatigue damage development caused by strength degradation and stiffness degradation, respectively. Subsequently, a pair of residual strength and residual stiffness models is presented to estimate the performance degradation of composite materials. Finally, the corresponding life prediction methods are developed from the viewpoints of strength degradation and stiffness degradation, respectively. A set of experimental data for composite laminates are utilized to validate the proposed models and methods. The results show that the proposed residual strength and stiffness models are flexible enough to fit strength and stiffness degradation data with good agreement. Moreover, the developed life prediction methods are capable of calculating fatigue life of composite laminates with high accuracy. [ABSTRACT FROM AUTHOR]

Ligand-induced system plays an important role for microbial engineering due to its tunable gene expression control over timings and levels. An oleic acid (OA)-induced system was recently constructed based on protein FadR, a transcriptional regulator involved in fatty acids metabolism, for metabolic control in Escherichia coli. In this study, we constructed a synthetic FadR-based OA-induced systems in Halomonas bluephagenesis by hybridizing the porin promoter core region and FadR-binding operator (fadO). The dynamic control range was optimized over 150-fold, and expression leakage was significantly reduced by tuning FadR expression and positioning fadO , forming a series of OA-induced systems with various expression strengths, respectively. Additionally, ligand orthogonality and cross-species portability were also studied and showed highly linear correlation among Halomonas spp. , Escherichia coli and Pseudomonas spp. Finally, OA-induced systems with medium- and small-dynamic control ranges were employed to dynamically control the expression levels of morphology associated gene minCD , and monomer precursor 4-hydroxybutyrate-CoA (4HB-CoA) synthesis pathway for polyhydroxyalkanoates (PHA), respectively, in the presence of oleic acid as an inducer. As a result, over 10 g/L of poly-3-hydroxybutyrate (PHB) accumulated by elongated cell sizes, and 6 g/L of P(3HB- co -9.57 mol% 4HB) were obtained by controlling the dose and induction time of oleic acid only. This study provides a systematic approach for ligand-induced system engineering, and demonstrates an alternative genetic tool for dynamic control of industrial biotechnology. • Transcriptional factor FadR-based oleic acid-induced system was firstly constructed in Halomonas bluephagenesis. • Series of oleic acid-induced systems with various dynamic control ranges and species portability were achieved. • The oleic acid-induced systems were employed in cell morphology engineering and P34HB synthesis dynamic control. [ABSTRACT FROM AUTHOR]

A modified Partially-Averaged Navier-Stokes (PANS) turbulence model has been proposed and coupled with the actuator line method (ALM) to investigate the wind turbine wakes. The hybrid model has not yet been applied in wind turbine related simulations, and our work would be the first attempt to evaluate its capabilities in wind turbine wake study. Comprehensive studies have been carried out regarding mesh resolution effect and different levels of inflow turbulence intensity using ALM-PANS method. Numerical simulations of two wind turbines in tandem have been carried out and validated against the popular Large-eddy Simulation (LES) results and experimental data. Relative errors of the rotor power and thrust predictions from ALM-PANS computations against the experimental data are within 5%, which is the same level as LES computations. Profiles of normalized mean velocity and Reynolds stress in the wake have been presented to describe the wake propagation, and the simulation results from PANS and LES are in good agreement with measured data. Due to model simplicity and its low mesh resolution requirement, ALM-PANS has excellent potential in wind farm investigations. • A modified SST k − ω partially-averaged Navier-Stokes model has been proposed. • The new ALM-PANS model gives good predictions on wind turbine wakes. • Compared to ALM-LES, ALM-PANS method provides reliable results with lower costs. [ABSTRACT FROM AUTHOR]

• The method for tuning gene expression levels were characterized in Halomonas sp. • The highest 3HHx ratio in PHBHHx was obtained from Halomonas sp. with fadB 1 deletion. • Recombinant Halomonas sp. G34 produced PHBHHx containing 0–37 mol% 3HHx. Microbial synthesized poly(3-hydroxybutyrate- co -3-hydroxyhexanoate), namely PHBHHx, with different 3HHx monomer ratios have shown biocompatibility, biodegradability and flexibility for commercial uses. This study aims at the low-cost production of PHBHHx with customized and/or rich 3HHx by engineered Halomonas bluephagenesis (H. bluephagenesis) , which is a rising-star chassis for Next Generation Industrial Biotechnology (NGIB). Recombinant H. bluephagenesis harboring heterologous PHA synthase (PhaC ac) and enoyl coenzyme-A hydratase (PhaJ ac) could produce PHBHHx from glucose and sodium hexanoate. After rationally fine-tuning the expression levels of phaC ac and phaJ ac employing two inducible promoters, P lac and P lux induced by IPTG and AHL, respectively, the resulting H. bluephagenesis TDC-CJ produced 55 wt% P(3HB- co -14.21 mol% 3HHx). Furthermore, two inducible promoters were replaced by constitutive ones, P porin68 and P porin58 , respectively, with similar expression strength to remove the additions of inducers. Notably, PHBHHx of high 3HHx ratios, reaching up to 49 mol%, was further obtained by weakening the native β-oxidation pathway. Finally, chromosomally engineered H. bluephagenesis G34 with two expression cassettes of phaCJ on loci G3 and G4 was constructed to achieve the synthesis of P(3HB- co -36 mol% 3HHx) without supplementing antibiotic. For lab-scale fermentation in a 7-L bioreactor, H. bluephagenesis G34 produced PHBHHx with controllable molar fraction of 3HHx from 0-to-37 mol% by designing glucose/hexanoate ratio. These efforts demonstrate that engineering H. bluephagenesis is of great potential for PHBHHx production of low cost for industrial-scale biomanufacturing purpose. [ABSTRACT FROM AUTHOR]