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Halophilic Halomonas bluephagenesis , a natural producer of poly-3-hydroxybutyrate (PHB), was metabolically engineered to synthesize ethylene glycol and glycolate from xylose. Xylose utilization was achieved by overexpressing either the xylonate pathway or the ribulose-1-phosphate pathway. The key genes encoding for xylonate dehydratase and 2-keto-3-deoxy-xylonate aldolase in the xylonate pathway were screened. With further overexpressing aldehyde reductase gene yjgB , ethylene glycol accumulation was improved to 0.91 g/L, accompanied with 1.48 g/L of PHB accumulation. The disruption of native glycolate oxidase was found to be essential for glycolate production, and the defective recombinant strain produced 0.80 g/L glycolate with 1.14 g/L PHB in shake flask cultures. These results indicated that H. bluephagenesis has the potential to produce diverse metabolic chemicals from xylose. • H. bluephagenesis was firstly engineered to produce ethylene glycol and glycolate. • Two xylose assimilation pathways were established in H. bluephagenesis. • Ethylene glycol and glycolate titers reached 0.91 g/L and 0.80 g/L in shake flasks, respectively. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of the filter design for linear switched systems with interval time-varying delays under asynchronous switching. A Lyapunov function is designed by using the Lyapunov stability principle. When there exists asynchronous switching between the filter and the system, we prove the stability with a prescribed weighted L2 - L∞ performance of the designed linear time-delay augmented filtering error switched systems by using the average dwell time approach. In addition, we use the linear matrix inequality (LMI) method to solve L2 - L∞ filter design problem and obtain the filter parameters by solving a set of matrix inequalities. Finally, some numerical simulations are given to illustrate the validity and reliability of the conclusions.

Although overpressure burst of pipeline with pressure-protection systems occurs infrequently in case of blockage, it endangers personnel safety and causes serious economic loss when it happens. Therefore, assessing the dynamic probabilistic risk of overpressure burst of pipeline with pressure-protection systems in case of blockage is necessary to prevent accidents. This study proposes a method based on a dynamic Bayesian network (DBN) for assessing the dynamic probabilistic risk of overpressure burst of pipeline with pressure-protection systems in case of blockage. First, a fault tree (FT) model of pipeline overpressure burst is constructed. The constructed FT model is then mapped into the DBN model to solve the uncertainty of the model. Second, the leaky Noisy-OR gate is used to define the uncertain logical relationships of relevant nodes in the DBN model. The effect of common cause failures on a pipeline's redundant pressure-protection system is analyzed using the multiple error shock model. Finally, a natural gas export pipeline is presented as an example to demonstrate the proposed method. Results show that the method can effectively assess the dynamic probabilistic risk of overpressure burst of pipeline with pressure-protection systems in case of blockage. [ABSTRACT FROM AUTHOR]

Although geopolymer concrete (GC) is recognized as a green and low‐carbon material, GC structural members, particularly beam‐column joints, have been rarely studied. In the present study, eight reinforced GC joints and three reinforced concrete (RC) interior beam‐column joints were tested under cyclic loading. The test parameters were the concrete type, joint shear demand‐to‐capacity ratio, and axial‐compression ratio. The structural performance, including the failure mode, crack development, cyclic behavior of reinforced GC beam‐column joints, was evaluated. The test results showed that the cyclic behavior and crack development of the reinforced GC beam‐column joints differed from those of RC beam‐column joints. The increase of axial compression ratio improved the seismic performance of reinforced GC beam‐column joints with the joint shear demand‐to‐capacity ratio of 0.96–1.01. When the joint shear demand‐to‐capacity ratio was 1.54–1.61, the increase of axial compression ratio (0.1–0.3) improved the seismic performance, while the axial compression ratio of 0.5 was unfavorable because of significant joint shear damage. The applicability of the control conditions in the current design codes to the reinforced GC joints was evaluated. The control conditions of ACI 318–19 were applicable to the seismic design of reinforced GC beam‐column joints. On the other hand, the control conditions of GB50010‐2010 were not applicable because of the overestimated upper limit value of the joint shear demand‐to‐capacity ratio and axial compression ratio. [ABSTRACT FROM AUTHOR]

The continuous improvement of basketball tactics has high requirements for athletes' position selection. This article proposes an intelligent method for basketball position selection. Massive basketball game data will provide people with richer content. Analyzing massive basketball game data can provide a new method for position efficiency calculation. To solve this problem, we can combine edge computing and data mining technology classification technology to build a basketball game position efficiency calculation model. First of all, we build a basketball game position efficiency calculation architecture through edge computing technology. Secondly, we use random forest algorithm and fuzzy neural network algorithm to analyze relevant basketball game information. The experimental simulation test results verify the superior performance of the basketball game position efficiency calculation model established in this paper. This model can provide help to improve the information level of basketball games. [ABSTRACT FROM AUTHOR]

The oxidation of alkanes is important in the chemical industry. However, green chemical processes in the oxidation reaction of alkanes have developed slowly. Most oxidation reactions of alkanes still rely on sacrificial oxidants, solvents and additives, as well as high temperature and pressure. Hence, the key to make alkane oxidation occur under the mild condition is to find a catalyst with high activity. Here, we prepared a new heterogeneous catalyst used for n-tetradecane oxidation by loading tetraruthenium (IV)-substituted tungstogermanate polyanion [{Ru4O4(OH)2(H2O)4} (γ-GeW10O36)2]10- (GeW10Ru2) on 3-aminopropyltriethoxysilane (Apts)-modified SBA-15. The characterization of this heterogeneous catalyst consists of XRD, N2-adsorption measurements, Raman and FT-IR spectroscopy. In the process of catalytic oxidation of n-tetradecane, air was used as the oxidant to replace sacrificial oxidant without adding any solvents and additives under normal atmospheric pressure and low temperature. As a green catalyst, this heterogeneous catalyst has high-efficiency, good-stability and re-usability. Ultimately, this system proved to be an efficient and green catalytic process through a series of experiments. The turnover frequency (TOF) for n-tetradecane is over 29,000 h−1 under the optimum experimental conditions. [ABSTRACT FROM AUTHOR]

The main purpose of the various methods of evaluating athlete feature recognition is to monitor the current health of the athletes, thereby providing some feedback on the quality of individual training. Based on deep learning and convolutional neural networks, this paper studies athlete target recognition and proposes a feature vector extraction method based on curvature zero point. Moreover, based on the ideas of deep learning and convolutional neural networks, this paper builds an athlete feature recognition model and optimizes the algorithm. In order to verify the feasibility and efficiency of feature extraction algorithm of the sport athletes proposed by this paper and to facilitate comparison with other algorithms, this paper conducts an algorithm performance test on the sport athlete database. The research results show that the method proposed in this paper has certain advantages in the feature extraction of athletes and can be used in subsequent sports training systems. [ABSTRACT FROM AUTHOR]

Non-ribosomal peptides (NRPs) constitute a diverse class of valuable secondary metabolites, with potential industrial applications including use as pharmaceuticals, polymers and dyes. Current industrial production of dyes is predominantly achieved via chemical synthesis, which can involve toxic precursors and generate hazardous byproducts. Thus, alternative routes of dye production are highly desirable to enhance both workplace safety and environmental sustainability. Correspondingly, biological synthesis of dyes from renewable carbon would serve an ideal green chemistry paradigm. Therefore, we engineered the fungal host Rhodosporidium toruloides to produce the blue pigment indigoidine, an NRP with potential applications in the dye industry, using various low-cost carbon and nitrogen sources. To demonstrate production from renewable carbon sources and assess process scalability we produced indigoidine in 2 L bioreactors, reaching titers of 2.9 ± 0.8 g L−1 using a sorghum lignocellulosic hydrolysate in a batch process and 86.3 ± 7.4 g L−1 using glucose in a high-gravity fed-batch process. This study represents the first heterologous production of an NRP in R. toruloides, thus extending the range of microbial hosts that can be used for sustainable, heterologous production of NRPs. In addition, this is the first demonstration of producing an NRP using lignocellulose. These results highlight the potential of R. toruloides for the sustainable, and scalable production of NRPs, with the highest reported titer of indigoidine or any heterologously produced NRP to date. [ABSTRACT FROM AUTHOR]