Target detection of helicopter electric power inspection based on the feature embedding convolution model.

This study aims to improve the helicopter electric power inspection process by using the feature embedding convolution (FEC) model to solve the problems of small scope and poor real-time inspection. First, simulation experiments and model analysis determine the keyframe and flight trajectory. Second, an improved FEC model is proposed, extracting features from aerial images in large ranges in real time and accurately identifying and classifying electric power inspection targets. In the simulation experiment, the accuracy of the model in electric power circuit and equipment detection is improved by 30% compared with the traditional algorithm, and the inspection range is expanded by 26%. In addition, this study further optimizes the model with reinforcement learning technology, conducts a comparative analysis of different flight environments and facilities, and reveals the diversity and complexity of inspection objectives. The performance of the optimized model in fault detection is increased by more than 36%. In conclusion, the proposed model improves the accuracy and scope of inspection, provides a more scientific strategy for electric power inspection, and ensures inspection efficiency.

Efficient bio-remediation of multiple aromatic hydrocarbons using different types of thermotolerant, ring-cleaving dioxygenases derived from Aeribacillus pallidus HB-1.

As toxic contaminants, aromatic compounds are widespread in most environmental matrices, and bioenzymatic catalysis plays a critical role in the degradation of xenobiotics. Here, a thermophillic aromatic hydrocarbon degrader Aeribacillus pallidus HB-1 was found. Bioinformatic analysis of the HB-1 genome revealed two ring-cleaving extradiol dioxygenases (EDOs), among which, EDO-0418 was assigned to a new subfamily of type I.1 EDOs and exhibited a broad substrate specificity, particularly towards biarylic substrate. Both EDOs exhibited optimal activities at elevated temperatures (55 and 65 °C, respectively) and showed remarkable thermostability, pH stability, metal ion resistance and tolerance to chemical reagents. Most importantly, simulated wastewater bioreactor experiments demonstrated efficient and uniform degradation performance of mixed aromatic substrates under harsh environments by the two enzymes combined for potential industrial applications. The unveiling of two thermostable dioxygenases with broad substrate specificities and stress tolerance provides a novel approach for highly efficient environmental bioremediation using composite enzyme systems.

Effects of nutrient injection on the Xinjiang oil field microbial community studied in a long core flooding simulation device.

Microbial Enhanced Oil Recovery (MEOR) is an option for recovering oil from depleted reservoirs. Numerous field trials of MEOR have confirmed distinct microbial community structure in diverse production wells within the same block. The variance in the reservoir microbial communities, however, remains ambiguously documented. In this study, an 8 m long core microbial flooding simulation device was built on a laboratory scale to study the dynamic changes of the indigenous microbial community structure in the Qizhong Block, Xinjiang oil field. During the MEOR, there was an approximate 34% upswing in oil extraction. Based on the 16S rRNA gene high-throughput sequencing, our results indicated that nutrition was one of the factors affecting the microbial communities in oil reservoirs. After the introduction of nutrients, hydrocarbon oxidizing bacteria became active, followed by the sequential activation of facultative anaerobes and anaerobic fermenting bacteria. This was consistent with the hypothesized succession of a microbial ecological "food chain" in the reservoir, which preliminarily supported the two-step activation theory for reservoir microbes transitioning from aerobic to anaerobic states. Furthermore, metagenomic results indicated that reservoir microorganisms had potential functions of hydrocarbon degradation, gas production and surfactant production. Understanding reservoir microbial communities and improving oil recovery are both aided by this work.

Characterization of a polysaccharide hydrogel with high elasticity produced by a mutant strain Sphingomonas sanxanigenens NX03.

Microbial polysaccharides as renewable bioproducts have attracted lots of attention in various industries. Hesan (Highly elastic Sanxan), an exopolysaccharide produced by a plasma mutagenic strain Sphingomonas sanxanigenens NX03, was characterized. It possessed the same monosaccharide composition as the original polysaccharide Sanxan produced from wild-type strain NX02, but significantly reduced acetyl and glyceryl contents. Textural analysis showed the springiness and cohesiveness of Hesan gel was much higher than Sanxan gel, and rheological behaviors indicated it possessed a lower loss factor, and its conformational transition temperatures at different concentrations were obviously lower than Sanxan gel and high-acyl gellan gel, which suggested that Hesan gel was highly elastic and temperature-sensitive. Additionally, Hesan gel could be efficiently produced through micro-aerobic static culture in shallow (10.46 ± 0.30 g/L) and deep liquids (3.21 ± 0.32 g/L), which was significantly different from the fermentation of other water-soluble polysaccharides. In short, this study characterizes a new mutant strain and its polysaccharide products.

Preparation and laser sintering of a thermoplastic polyurethane carbon nanotube composite-based pressure sensor.

Selective laser sintering (SLS) is a desirable method for fabricating human motion detecting sensors as it can produce a complex shape with different materials that are machinable to specific applications. The bottleneck in the SLS processing of sensors is the preparation of a material that is both flexible and conductive. In this study, carbon nanotubes (CNTs) were selected as a conductive nanofiller and dispersed into a flexible thermoplastic polyurethane (TPU) polymer matrix to prepare TPU/CNT composites for SLS processing pressure sensors. CNTs were first oxidized to prevent them from aggregating in the TPU matrix. TPU/CNT composites were prepared via solution blending and ball milling methods, and the dispersion of the CNTs in the composites was observed by scanning electron microscopy. The thermal properties of TPU/CNT composites with different CNT content were measured, and processing parameters used in the SLS were determined based on differential scanning calorimetry measurements. SLS-processed TPU/CNT composites were prepared with different conductivity and piezoresistive properties. Percolation theory and piezoresistive performance results proved that a 0.25 wt% CNT-containing TPU/CNT composite showed the best pressure sensing ability, and it was successfully used as a sensor to detect plantar pressure distribution in a human foot.