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ABSTRACTPlant heme oxygenases (HOs) regulate biosynthesis of phytochrome which accounts for photo‐acceptance and ‐morphogenesis. Recent studies have demonstrated that plant HOs also regulate many other physiological processes including response to environmental stimuli. To elucidate the mechanism by which HOs regulate plant adaptation to heavy metal exposure, three novel HOs genes were isolated from rapeseed (Brassica napus) and their expression patterns were analysed. Alignment of deduced protein sequences revealed that the three BnHOs share high identity with their corresponding orthologos (AtHO1‐3) from Arabidopsis. To investigate whether the BnHO regulates plant tolerance to Hg toxicity, we constructed B. napus transgenic plants overexpressing BnHO‐1. Under Hg stress, the transgenic plants had 1.41–1.59 folds higher biomass than the untransformants. However, overexpression of BnHO‐1 resulted in less accumulation of Hg in some lines of transformants than in untransformants. The transgenic plants show lower abundance of reactive oxygen species and attenuated oxidative injury compared with the untransgenic plants. We cloned the promoter sequences of BnHO‐1 from B. napus. Analysis revealed that the 1119 bp fragment contains a conserved Cd responsive element (CdRE) and others responding to multiple environmental stimuli. Transient expression in tobacco leaves showed differential responses to heavy metals (Zn, Cu, Pb, Hg and Cd).

AbstractVentilation door are commonly found in tunnels and other underground engineering ventilation structures, disaster periods using its explosion isolation, explosion relief, wind regulation characteristics for disaster prevention and mitigation is of great significance. This paper numerically simulates the propagation characteristics of the gas explosion shock wave in the nearby tunnel when the ventilation door are opened at different degrees, and analyzes the influence mechanism of the opening degree on the change law of the shock wave overpressure distribution in the nearby tunnel. The results show that the shock wave forms a strong turbulence area (high pressure area) on both sides in front of the ventilation door, and the area range and the overpressure value decrease with the increase of the opening degree; the ventilation door reduce the intensity of the shock wave, so that the overpressure behind the ventilation door decreases, and the smaller the opening degree, the lower the overpressure behind the ventilation door. The secondary explosion formed shock wave and the ventilation door reflected shock wave meet to form a stronger shock wave, which leads to different opening degrees of ventilation door, its before, after the roadway and after the bifurcation of the main roadway in the measured points of the overpressure change curve is different, the main difference is that the peak overpressure for the first wave or the second wave peak. The peak overpressure in the tunnel before and after the ventilation door decreases and increases respectively with the increase of the opening length, and the overall decay of the peak overpressure at 5 m and 10 m before the ventilation door is 49.56% and 4.04% respectively and only has an effect on the peak overpressure in main tunnel within 20 m from the bifurcation.

Given the considerable scale of distribution networks in urban and rural areas, as well as the lack of management records, adjustments of switches during the distribution system operation are poorly documented. Such deficiency results in the inaccuracy of models stored in the distribution network automation system, and thus misleads the state estimation. With the emergence of information and communication technology, a large number of the feeder and residential smart meter data are accumulated. Such data can help recognize the operation modes of distribution networks by analyzing the relationships between the on/off states of switches and the voltage correlations among buses. However, the limited quantity and quality of the sampling data restrict the implementation of data-driven recognition. In this paper, a physical-probabilistic-network (PPN) model applied for inferring overall operation mode of distribution networks is proposed. Based on which, a belief propagation-based algorithm is proposed for the inference even under situations when there are only partial bus voltages data available. Meanwhile, the required variable for inference can be reduced from the active trail analysis. Experiment results are used to compare its performance with classic methods and to prove its effectiveness and advantages.

Air is a desirable alternative to the less environmental friendly SF6 in compact medium voltage switchgear. However, the poor thermal properties of air requires better thermal designs to keep the temperature rise within the allowable industry limits to avoid premature switch degradation. This publication focuses on the temperature rise of load break switches (LBS) of different designs. A LBS with flexible braid was found to have considerably higher temperature rise than a design with the sliding contact. The study has shown that a stripped switch (with only the current path) can be used together with empirical determined heat transfer coefficients in a first approach for predicting the temperature rise of the critical parts of the LBS. Aluminum construction elements functioning as heat sinks had a huge impact on the temperature rise of the LBS, and design elements that can utilize this might be important when replacing SF6 with air.

Innovation ability has become one of the core elements in the pursuit of China’s green growth, and high-tech industries are playing a leading role in technological innovation in China. With the rapid development of China’s high-tech industries, their innovation efficiency has attracted widespread attention. This article aims to illustrate a shared inputs two-stage network Data Envelopment Analysis (DEA), to measure the innovation efficiency of high-tech industries in China’s 29 provinces from 1999 to 2018. The results indicate that there are obvious differences in the innovation efficiency of the provinces. The technology development efficiency, the technical transformation efficiency, and the overall innovation efficiency of the developed east coast provinces are generally higher than those of the backward central and western provinces. This article further applies the spatial econometrics model to analyze the factors influencing the innovation efficiency of high-tech industries. We have found that government support, R&D input intensity, industries aggregation, economic extroversion, and the level of development of the modern service industries cause varying degrees of impact on innovation efficiency.

Abstract Partial least square regression (PLS-R) methodology have been applied on dataset containing FTIR spectra and densities for a large set of MEA solvent samples. The prediction capabilities for the major compounds were tested on different set of realistic degraded solvent samples ranging from bench scale experiments to pilot plant campaigns. Generally, the methods showed good results with exception of samples with high amount of heat stabile salts (HSS). For the real samples, sample residuals (i.e. part of data not fitted by the model) were also studied, and a clear correlation between the residuals and HSS level in samples were observed. Online techniques based on PLS-R and FTIR should be an attractive alternative for monitoring the major solvent compounds in CO2 capture plants using amine solvents, as this will reduce cost of chemical analysis and could be an important tool for implementation of control strategies for energy saving in the process.

A range of optical fibre-based sensors for the measurement of ethanol, primarily in aqueous solution, have been developed and are reviewed here. The sensing approaches can be classified into four groups according to the measurement techniques used, namely absorption (or absorbance), external interferometric, internal fibre grating and plasmonic sensing. The sensors within these groupings can be compared in terms of their characteristic performance indicators, which include sensitivity, resolution and measurement range. Here, particular attention is paid to the potential application areas of these sensors as ethanol production is globally viewed as an important industrial activity. Potential industrial applications are highlighted in the context of the emergence of the internet of things (IoT), which is driving widespread utilization of these sensors in the commercially significant industrial and medical sectors. The review concludes with a summary of the current status and future prospects of optical fibre ethanol sensors for industrial use.

AbstractPreparation of uniform, spherical Li4Ti5O12 with high tap density is significant to achieve a high volumetric energy density in lithium‐ion batteries. Herein, Li4Ti5O12 microspheres with variable tap density and tunable size distribution were synthesized by a newly designed industrial spray‐drying approach. The slurry concentration, sintering time, sintering conditions after spraying, and the effect of lithium/titanium molar ratio on the lithium‐ion (Li+) storage capability were investigated. A narrow particle size distribution of around 10 μm and a high tap density close to 1.4 g cm−3 of the Li4Ti5O12 spheres can be obtained under the optimized conditions. The Li4Ti5O12 spheres can deliver a much higher capacity of 168 mAh g−1 at a rate of 1 C and show a high capacity retention of 97.7 % over 400 cycles. The synthetic conditions are confirmed to be critical for improving the electron conductivity and Li+ diffusivity by adjusting the crystal and spatial structures. As‐prepared high‐performance Li4Ti5O12 is an ideal electrode for lithium‐ion batteries or capacitors; meanwhile, the presented approach is also applicable for preparing other kind of spherical materials.