Application of TiO2 Nanotubes Gas Sensors in Online Monitoring of SF6 Insulated Equipment

Titanium dioxide nanotube arrays (TNTAs) are a typical three-dimensional nanomaterial. TNTA has rich chemical and physical properties and low manufacturing costs. Thus, TNTA has broad application prospects. In recent years, research has shown that because of its large specific surface area and nanosize effect, the TNTAs have an enormous potential for development compared with other nanostructure forms in fields such as light catalysis, sensor, and solar batteries. TNTAs have become the hotspot of international nanometer material research. The tiny gas sensor made from TNTA has several advantages, such as fast response, high sensitivity, and small size. Several scholars in this field have achieved significant progress. As a sensitive material, TNTA is used to test O2, NO2, H2, ethanol, and other gases. In this chapter, three SF6 decomposed gases, namely SO2, SOF2 and SO2F2, are chosen as probe gases because they are the main by-products in the decomposition of SF6 under PD. Then, the adsorption behaviors of these gases on different anatase (101) surfaces including intrinsic, Pt-doped and Au-doped, are studied using the first principles density functional theory (DFT) calculations. The simulation results can be used as supplement for gas-sensing experiments of TNTA gas sensors. This work is expected to add insights into the fundamental understanding of interactions between gases and TNTA surfaces for better sensor design

Multiple Factors Drive Variation of Forest Root Biomass in Southwestern China

The roots linking the above-ground organs and soil are key components for estimating net primary productivity and carbon sequestration of forests. The patterns and drivers of root biomass in forest have not been examined well at the regional scale, especially for the widely distributed forest ecosystems in southwestern China. We attempted to determine the spatial patterns of root biomass (RB, Mg/ha), annual increment root biomass (AIRB, Mg/ha/year), ratio of root and above-ground (RRA), and the relative contributions of abiotic and biotic factors that drive the variation of root biomass. Forest biomass and multiple factors (climate, soil, forest types, and stand characteristics) of 318 plots in this region (790,000 km2) were analyzed in this research. The AB (the mean values for forest aboveground biomass per ha, Mg/ha), RB, AIRB, and RRA were 126 Mg/ha, 28 Mg/ha, 0.69 Mg/ha and 0.22, respectively. AB, RB, AIRB, and RRA varied across all the plots and forest types. Both RB and AIRB showed significant spatial patterns of distribution, while RRA did not show any spatial patterns of distribution. Up to 28.4% of variation in total of RB, AIRB, and RRA can be attributed to the climate, soil, and stand characteristics. The explained or contribution rates of climate, soil, and stand characteristics for variation of whole forest root biomass were 6.7%, 16.9%, and 10.9%, respectively. Path analysis in structural equation model (SEM) indicated the direct influence of stand age on RB. AIRB was greater than that of the other factors. Climate, soil and stand characteristics in different forest types could explain 9.7%–96.1%, 15.4%–96.4%, and 36.7%–99.4% of variations in RB, AIRB, and RRA, respectively, which suggests that the multiple factors may be important in explaining the variations in forest root biomass. The results of the analysis of root biomass per ha, annual increment of root biomass per ha, and ratio of root and above-ground in the seven forest types categorized by climate, soil, and stand characteristics may be used for accurately determining C sequestration by the forest root and estimating forest biomass in this region

MASANet: Multi-Angle Self-Attention Network for Semantic Segmentation of Remote Sensing Images

As an important research direction in the field of pattern recognition, semantic segmentation has become an important method for remote sensing image information extraction. However, due to the loss of global context information, the effect of semantic segmentation is still incomplete or misclassified. In this paper, we propose a multi-angle self-attention network (MASANet) to solve this problem. Specifically, we design a multi-angle self-attention module to enhance global context information, which uses three angles to enhance features and takes the obtained three features as the inputs of self-attention to further extract the global dependencies of features. In addition, atrous spatial pyramid pooling (ASPP) and global average pooling (GAP) further improve the overall performance. Finally, we concatenate the feature maps of different scales obtained in the feature extraction stage with the corresponding feature maps output by ASPP to further extract multi-scale features. The experimental results show that MASANet achieves good segmentation performance on high-resolution remote sensing images. In addition, the comparative experimental results show that MASANet is superior to some state-of-the-art models in terms of some widely used evaluation criteria

Typical Internal Defects of Gas-Insulated Switchgear and Partial Discharge Characteristics

Gas-insulated switchgear (GIS) is a common electrical equipment, which uses sulfur hexafluoride (SF6) as insulating medium instead of traditional air. It has good reliability and flexibility. However, GIS may have internal defects and partial discharge (PD) is then induced. PD will cause great harm to GIS and power system. Therefore, it is of great importance to study the intrinsic characteristics and detection of PD for online monitoring. In this chapter, typical internal defects of GIS and the PD characteristics are discussed. Several detection methods are also presented in this chapter including electromagnetic method, chemical method, and optical method

A Cross-project Defect Prediction Model Using Feature Transfer and Ensemble Learning

Cross-project defect prediction (CPDP) trains the prediction models with existing data from other projects (the source projects) and uses the trained model to predict the target projects. To solve two major problems in CPDP, namely, variability in data distribution and class imbalance, in this paper we raise a CPDP model combining feature transfer and ensemble learning, with two stages of feature transfer and the classification. The feature transfer method is based on Pearson correlation coefficient, which reduces the dimension of feature space and the difference of feature distribution between items. The class imbalance is solved by SMOTE and Voting on both algorithm and data levels. The experimental results on 20 source-target projects show that our method can yield significant improvement on CPDP

Seasonal Changes and Vertical Distribution of Fine Root Biomass During Vegetation Restoration in a Karst Area, Southwest China

In karst ecosystems, plants absorbing smaller amounts of nutrients, owing to shallow soil, show limited growth. In addition, fine roots (diameter < 2 mm) contribute to the regulation of nutrient cycles in terrestrial ecosystems. However, the spatial and temporal variations of fine root biomass in different vegetation types of the karst region remains poorly understood. In this study, we investigated the seasonal and vertical variation in biomass, necromass, and total mass of fine roots using sequential soil coring under different stages of vegetation restoration (grassland, shrubland, secondary forest, and primary forest) in Southwest China. The results showed that the fine root biomass and necromass ranged from 136.99 to 216.18 g m−2 and 47.34 to 86.94 g m−2, respectively. The total mass of fine roots and their production ranged from 187.00 to 303.11 g m−2 and 55.74 to 100.84 g m−2 year−1, respectively. They showed a single peak across the vegetation restoration gradient. The fine root biomass and total fine root mass also showed a single peak with seasonal change. In autumn, the fine root biomass was high, whereas the necromass was low. Most of the fine roots were concentrated in the surface soil layer (0–10 cm), which accounted more than 57% root biomass, and decreased with increasing soil depth. In addition, fine root production showed a similar vertical pattern of variation with biomass. Overall, our results suggested that fine roots show clear seasonal and vertical changes with vegetation succession. Moreover, there was a higher seasonal fluctuation and a greater vertical decreasing trend in late-successional stages than in the early-successional stages. The conversion of degraded land to forest could improve the productivity of underground ecosystems and vegetation restoration projects in the fragile karst region should, therefore, continue

Spatial Patterns and Drivers of Microbial Taxa in a Karst Broadleaf Forest

Spatial patterns and drivers of soil microbial communities have not yet been well documented. Here, we used geostatistical modeling and Illumina sequencing of 16S rRNA genes to explore how the main microbial taxa at the phyla level are spatially distributed in a 25-ha karst broadleaf forest in southwest China. Proteobacteria, dominated by Alpha- and Deltaproteobacteria, was the most abundant phylum (34.51%) in the karst forest soils. Other dominating phyla were Actinobacteria (30.73%), and Acidobacteria (12.24%). Soil microbial taxa showed spatial dependence with an autocorrelation range of 44.4–883.0 m, most of them within the scope of the study plots (500 m). An increasing trend was observed for Alphaproteobacteria, Deltaproteobacteria, and Chloroflexi from north to south in the study area, but an opposite trend for Actinobacteria, Acidobacteira, and Firmicutes was observed. Thaumarchaeota, Bacteroidetes, Gemmatimonadetes, and Verrucomicrobia had patchy patterns, Nitrospirae had a unimodal pattern, and Latescibacteria had an intermittent pattern with low and high value strips. Location, soil total phosphorus, elevation, and plant density were significantly correlated with main soil bacterial taxa in the karst forest. Moreover, the total variation in soil microbial communities better explained by spatial factors than environmental variables. Furthermore, a large part of variation (76.8%) was unexplained in the study. Therefore, our results suggested that dispersal limitation was the primary driver of spatial pattern of soil microbial taxa in broadleaved forest in karst areas, and other environmental variables (i.e., soil porosity and temperature) should be taken into consideration

Superior Facet Joint Violations during Single Level Minimally Invasive Transforaminal Lumbar Interbody Fusion: A Preliminary Retrospective Clinical Study

Background: Facet joint violation (FV) was reported as variable iatrogenic damage that can be a crucial risk factor leading to the adjacent segment degeneration (ASD). “Blind” screw placement technique in minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) contributes to the increasing incidence of FV that can be influenced by several potential factors. Many controversies about these factors and clinical outcomes of different types of FV patients exist, yet they have not been analyzed. Methods 99 cases undergoing single-segment MIS-TLIF from July 2013 to December 2015 were retrospectively analyzed. Computed tomography (CT) was applied to determine the incidence of FV, and then the correlation between FV and relevant factors, including gender, age, body mass index (BMI), top-screw level, and decompression, was analyzed. A total of 53 cases were followed up after one year, 31 cases in noninjury (A group) and 22 patients in FV injury (B group). Results The incidence of FV was 39. 39% (39/99) in the patients and 23.23% (46/198) in the screws. Logistic regression analysis showed that screw at L5 in patients with BMI > 30 kg/m2 was vulnerable to FV (P < 0.05). Moreover, postoperative average intervertebral disc height (AIDH) of fusion segment, visual analog scale (VAS), and Oswestry disability index (ODI) scores improved significantly in group A and B when compared with preoperative data (P < 0.05). Adjacent superior average intervertebral disc height (ASAIDH) presented decrease, but adjacent superior intervertebral disc Cobb angle (ASIDCA) appeared to increase in the two groups at the final follow-up compared with postoperative 3 days (P < 0.05). Low back VAS and ODI scores in group A (31 cases) were lower than those in group B (22 cases) in the final follow-up (P < 0.05). Conclusion MIS-TLIF is an effective treatment for lumbar degenerative disease, but FV occurred at a higher incidence. Facet joints should be protected in MIS-TLIF to avoid FV