Progress of Study on Interception of Soil Mulching with an Insight into Karst Soil Leakage Control: A Review

Soil erosion is a global issue of great concern, especially in karst areas with special environments, where subsurface soil leakage is closely related to soil erosion, which has become a key factor limiting agricultural development. To explore how to improve soil erosion in karst areas to enhance soil quality and maintain the sustainable use of the land in the long term, a total of 176 studies on the interception characteristics of soil mulching and erosion management were reviewed using a systematic review approach, through the WoS and CNKI databases. Firstly, quantitative analysis was conducted in terms of the annual volume, content and countries of the published literature. Secondly, from four aspects (theoretical research, mechanism research, technology research and technical demonstration), the main progress and landmark achievements of soil mulching interception and erosion management were classified. It is shown that the interception characteristics of soil mulching can produce an effective blockage for soil leakage in karst areas. Based on the global classification, compared to synthetic materials, natural materials have received more attention. We propose five key scientific questions that still need to be addressed. This review explores the insightful role of soil mulching for karst soil leakage management and aims to provide theoretical support for future research on sustainable land development in karst areas

A direct bonding copper degradation monitoring method for insulated gate bipolar transistor modules: Boundary‐dependent thermal network combined with feedback control

Abstract The direct bonding copper (DBC) substrates of insulated gate bipolar transistor (IGBT) modules degrade inevitably under cycling thermo‐mechanical stress, causing potential threat to the reliability of IGBT modules. However, little attention has been paid to monitoring their degradation. This paper proposes a DBC degradation monitoring method for IGBT modules, which combines boundary‐dependent thermal network and feedback control. A thermal network is employed to describe the internal material degradation of IGBT modules and can be extracted from a finite‐element method model. The boundary conditions including power losses and DBC degradation are considered, enabling the thermal network suitable for various working conditions and different DBC degradation conditions of IGBT modules. The DBC degradation is characterised by its equivalent thermal conductivities measured in the thermal cycling ageing experiments. On the basis of the boundary‐dependent thermal network, feedback control is applied to monitor DBC degradation by regulating boundary‐dependent thermal impedances. Finally, the proposed model is verified from the effectiveness and accuracy of DBC degradation monitoring and junction temperature calculation. This method casts new light on thermal network modelling and could provide a feasible method for the monitoring of DBC degradation

Soil Moisture and Nutrient Changes of Agroforestry in Karst Plateau Mountain: A Monitoring Example

To explore soil nutrients and moisture changes in different karst mountain agroforestry, in the plateau mountains of Southern China Karst, we used secondary tree and irrigation forest (C) as a reference for our study and selected four mixed agroforestry species (walnut + maize + potato (HYM), walnut + maize (HTY), poplar + ryegrass (YSH), and maize + ryegrass (YMH)) for comparison. First, soil moisture change characteristics were monitored in situ in the field. Second, for soil samples, soil bulk density, porosity, and permeability were analyzed, soil nutrient (K, Na, Ca, and Mg) characteristics were tested and analyzed. Then, we explored the relationship between agroforestry and soil moisture, soil moisture and soil nutrients, soil moisture and precipitation, and agroforestry and soil nutrients. It is shown (1) during the monitored period, variation trends in soil nutrients in four types of agroforestry was small, but it increased/decreased significantly compared with the secondary forest, which the variation range was more than 5%; (2) the changes of soil water content were significantly affected by precipitation, soil porosity and permeability, the moisture content changes of HYM, HTY, YSH, and YMH agroforestry were significantly correlated with precipitation, soil porosity, and permeability; (3) under the same precipitation conditions, different types had different lags on soil water regulation, with the average HYM 0.8 h, HTY 0.6 h, YSH 0.3 h, and YMH 0.4 h, each type soil responded at 2–3 h after rain, and the soil moisture content returned to the normal level; and (4) the variation of soil moisture content fluctuated seasonally, and the most obvious was HYM and HTY agroforestry, their Cv value between winter and summer exceeded 21%. The results provide basic theoretical support for further exploring the relationship among agroforestry, soil, moisture, and nutrients and enrich the content of the development of agroforestry in karst areas. They are of importance to promote ecological restoration and agroforestry development in karst areas

Surface Tracking of MgO/Epoxy Nanocomposites: Effect of Surface Hydrophobicity

Surface tracking has been one of the challenges for outdoor organic insulations, in electronic and electrical devices. In this paper, surface tracking behavior of nano-MgO/epoxy composite samples were measured according to the standard IEC 60112. Improved tracking resistance was obtained in nanocomposites with an 18.75% uplift in the comparative tracking index, and a decrease of 58.20% in the surface ablation area at a fixed 425 V. It was observed that the tracking resistance and surface hydrophobicity shared the same tendency—both, the comparative tracking index and surface contact angle increased with an increase of the nanofiller content. Samples with better hydrophobicity exhibited a higher tracking resistance. It could be the case that the conductive pathway of contamination was harder to form, as a result there were fewer discharging processes. With the development of surface tracking, the surface contact angle abruptly decreased, at first, and tended to be constant, which was also accomplished with the failure of samples. In addition, reduced surface resistivity was also found in the nanocomposites, which was beneficial for releasing surface charges and inhibiting distortions in the electric fields

A Novel Hybrid Attention-Driven Multistream Hierarchical Graph Embedding Network for Remote Sensing Object Detection

Multiclass geospatial object detection in high-spatial-resolution remote-sensing images (HSRIs) has recently attracted considerable attention in many remote-sensing applications as a fundamental task. However, the complexity and uncertainty of spatial distribution among multiclass geospatial objects are still huge challenges for object detection in HSRIs. Most current remote-sensing object-detection approaches fall back on deep convolutional neural networks (CNNs). Nevertheless, most existing methods only focus on mining visual characteristics and lose sight of spatial or semantic relation discriminations, eventually degrading object-detection performance in HSRIs. To tackle these challenges, we propose a novel hybrid attention-driven multistream hierarchical graph embedding network (HA-MHGEN) to explore complementary spatial and semantic patterns for improving remote-sensing object-detection performance. Specifically, we first constructed hierarchical spatial graphs for multiscale spatial relation representation. Then, semantic graphs were also constructed by integrating them with the word embedding of object category labels on graph nodes. Afterwards, we developed a self-attention-aware multiscale graph convolutional network (GCN) to derive stronger for intra- and interobject hierarchical spatial relations and contextual semantic relations, respectively. These two relation networks were followed by a novel cross-attention-driven spatial- and semantic-feature fusion module that utilizes a multihead attention mechanism to learn associations between diverse spatial and semantic correlations, and guide them to endowing a more powerful discrimination ability. With the collaborative learning of the three relation networks, the proposed HA-MHGEN enables grasping explicit and implicit relations from spatial and semantic patterns, and boosts multiclass object-detection performance in HRSIs. Comprehensive and extensive experimental evaluation results on three benchmarks, namely, DOTA, DIOR, and NWPU VHR-10, demonstrate the effectiveness and superiority of our proposed method compared with that of other advanced remote-sensing object-detection methods

A Novel Hybrid Attention-Driven Multistream Hierarchical Graph Embedding Network for Remote Sensing Object Detection

Multiclass geospatial object detection in high-spatial-resolution remote-sensing images (HSRIs) has recently attracted considerable attention in many remote-sensing applications as a fundamental task. However, the complexity and uncertainty of spatial distribution among multiclass geospatial objects are still huge challenges for object detection in HSRIs. Most current remote-sensing object-detection approaches fall back on deep convolutional neural networks (CNNs). Nevertheless, most existing methods only focus on mining visual characteristics and lose sight of spatial or semantic relation discriminations, eventually degrading object-detection performance in HSRIs. To tackle these challenges, we propose a novel hybrid attention-driven multistream hierarchical graph embedding network (HA-MHGEN) to explore complementary spatial and semantic patterns for improving remote-sensing object-detection performance. Specifically, we first constructed hierarchical spatial graphs for multiscale spatial relation representation. Then, semantic graphs were also constructed by integrating them with the word embedding of object category labels on graph nodes. Afterwards, we developed a self-attention-aware multiscale graph convolutional network (GCN) to derive stronger for intra- and interobject hierarchical spatial relations and contextual semantic relations, respectively. These two relation networks were followed by a novel cross-attention-driven spatial- and semantic-feature fusion module that utilizes a multihead attention mechanism to learn associations between diverse spatial and semantic correlations, and guide them to endowing a more powerful discrimination ability. With the collaborative learning of the three relation networks, the proposed HA-MHGEN enables grasping explicit and implicit relations from spatial and semantic patterns, and boosts multiclass object-detection performance in HRSIs. Comprehensive and extensive experimental evaluation results on three benchmarks, namely, DOTA, DIOR, and NWPU VHR-10, demonstrate the effectiveness and superiority of our proposed method compared with that of other advanced remote-sensing object-detection methods