A Study on the Fault Location of Secondary Equipment in Smart Substation Based on the Graph Attention Network.

The inability to locate device faults quickly and accurately has become prominent due to the large number of communication devices and the complex structure of secondary circuit networks in smart substations. Traditional methods are less efficient when diagnosing secondary equipment faults in smart substations, and deep learning methods have poor portability, high learning sample costs, and often require retraining a model. Therefore, a secondary equipment fault diagnosis method based on a graph attention network is proposed in this paper. All fault events are automatically represented as graph-structured data based on the K-nearest neighbors (KNNs) algorithm in terms of the feature information exhibited by the corresponding detection nodes when equipment faults occur. Then, a fault diagnosis model is established based on the graph attention network. Finally, partial intervals of a 220 kV intelligent substation are taken as an example to compare the fault localization effect of different methods. The results show that the method proposed in this paper has the advantages of higher localization accuracy, lower learning cost, and better robustness than the traditional machine learning and deep learning methods.

Does national air quality monitoring reduce local air pollution? The case of PM2.5 for China.

Fine particulate matter (PM2.5) has become a major pressing challenge for China and remains a concern of its central government. This paper draws on a natural experiment generated by the National Ambient Air Quality Monitoring Network (NAAQMN) program in China to explore whether national air quality monitoring reduces local air pollution. In this study, we use a city-level dataset for 4200 Chinese cities covering 2001-2015 and a difference-in-differences (DID) assessment design to assess the impact of the NAAQMN program on local PM2.5 emissions in China. The results suggest that the NAAQMN program significantly reduces the local PM2.5 concentrations by 1.325 mg/m3, and each additional NAAQMN program will cause a decrease of 0.154 mg/m3 in the local PM2.5 concentrations. Furthermore, we determine the heterogeneous impacts of the NAAQMN program on local PM2.5 emission levels through the local government leaders' characteristics, PM2.5 emission levels, and economic development levels. In addition, a mediation effect is found between the NAAQMN program and local PM2.5 emissions through the efficiency of environmental governance. The Chinese government should continue to promote the implementation of the NAAQMN program by promoting the NAAQMN program to the county and rural areas as well as adding the sites of the NAAQMN program in the existing cities. Also, during the process of promoting the NAAQMN program, sufficient differentiation in policies should be developed for different cities.

How does technological innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using panel quantile regression.

To verify how does the development of technological innovation effectively mitigate carbon dioxide (CO2) emissions in Organization for Economic Co-operation and Development (OECD) countries, this study first investigates the direct impacts and moderating effects of technological innovation, measured by the development of patents on CO2 emissions by employing a balanced panel dataset for 35 OECD countries covering 1996-2015. Also, to examine the potential heterogeneity and asymmetry, the panel quantile regression approach is utilized. The empirical results indicate that technological innovation directly reduces CO2 emissions; however, this impact is significantly heterogeneous and asymmetric across quantiles. Furthermore, through analyzing the influencing mechanism, the technological innovation affects the impacts of economic growth and renewable energy through its moderating effects. Moreover, the moderating effects of technological innovation is also heterogenous. Accordingly, the main contribution of this study is that the potential heterogeneity and asymmetry of both the direct impact and moderating effect of technological innovation on CO2 emissions in OECD countries are systematically analyzed by employing the panel quantile regression approach.

Decoupling and decomposition analysis of investments and CO2 emissions in information and communication technology sector.

Organization of Economic Cooperation and Development (OECD) economies are facing a substantial increase in the information and communication technology (ICT) investments in the context of rapid spread of the Coronavirus Disease-2019 (COVID-2019) pandemic and constraints of emissions reduction. However, the mechanism of the impact of ICT investments on carbon dioxide is still unclear. Therefore, by employing the decoupling-factor model and Generalized Divisia Index Method, we explore the decoupling states of ICT investments and emission intensity, and the driving factors of ICT investments' scale, intensity, structure, and efficiency effects on carbon emissions in 20 OECD economies between 2000 and 2018. The results indicate that the number of economies with an ideal state of strong decoupling rose to nine between 2009 and 2018 compared to no economies between 2000 and 2009. The emission intensity of ICT investments contributes to a significant increase of carbon emissions, and the structure and efficiency of ICT investments always restrain the growth of carbon emissions. Significant emissions changes caused by the driving factors are shown in many economies before and after the crisis, reflecting the differences in the strategic choices of ICT investments and the impact on emissions due to the crisis such as the COVID-2019 pandemic. And policy implications for energy and carbon dioxide mitigation strategies in the post-COVID-2019 era are also provided.

Emission accounting and drivers in Central Asian countries.

Emerging countries are at the frontier of climate change actions, and carbon emissions accounting provides a quantifiable measure of the environmental impact of economic activities, which allows for comparisons of emissions across different entities. However, currently there is no study covering detailed emissions inventories for emerging countries in Central Asian. This paper compiles detailed and accurate carbon emissions inventories in several Central Asian countries (i.e., Kazakhstan, Kyrgyzstan, Pakistan, Palestine, Tajikistan, and Uzbekistan) during the period 2010-2020. Using the IPCC administrative territorial approach, we for the first time compile their emissions inventories in 47 economic sectors and five energy categories. Moreover, we also investigate decoupling status based on Tapio decoupling model and examine emissions driving factors based on the index decomposition analysis method. The primary results illustrate that carbon emissions in Central Asian countries are increasing with huge differences. Decoupling results highlight that most of the sample countries still need more effort to decouple the economy and emissions except that Pakistan achieves an ideal strong decoupling state. The results of the decomposition indicate that the economy and population both raise emissions, while energy intensity and carbon intensity are negative drivers in some countries. We propose practical policy implications for decarbonization and energy transition roadmap in Central Asian countries.

How natural disasters affect carbon emissions: the global case.

The outbreak of the COVID-19 pandemic has once again made the impacts of natural disasters a hot topic in academia. The environmental impacts of natural disasters, however, remain unsettled in the existing literature. This study aims to investigate the impact of natural disasters on CO2 emissions. For this purpose, we employ a panel dataset covering 138 countries over the period 1990-2018 and two dynamic panel estimation methods. Then, considering the differences in CO2 emissions across various countries, we run a panel quantile regression to examine the asymmetry in the nexus between natural disasters and CO2 emissions. We also discuss the mediating effects of energy consumption between natural disasters and CO2 emissions. After conducting a series of robustness checks, we confirm that our results are stable and convincing. The empirical results indicate that natural disasters significantly reduce CO2 emissions. Nevertheless, the impact of natural disasters on CO2 emissions is asymmetric across different quantiles of CO2 emissions. Furthermore, the technology level serves as an important moderating factor between natural disasters and CO2 emissions. The mediating effect results reveal that natural disasters not only directly reduce CO2 emissions but also indirectly promote carbon reduction by restraining energy consumption. Finally, several policy implications are provided to reduce CO2 emissions and the damage caused by natural disasters.

Improved recurrent generative model for reconstructing large-size porous media from two-dimensional images.

Modeling the three-dimensional (3D) structure from a given 2D image is of great importance for analyzing and studying the physical properties of porous media. As an intractable inverse problem, many methods have been developed to address this fundamental problems over the past decades. Among many methods, the deep learning-(DL) based methods show great advantages in terms of accuracy, diversity, and efficiency. Usually, the 3D reconstruction from the 2D slice with a larger field-of-view is more conducive to simulate and analyze the physical properties of porous media accurately. However, due to the limitation of reconstruction ability, the reconstruction size of most widely used generative adversarial network-based model is constrained to 64^{3} or 128^{3}. Recently, a 3D porous media recurrent neural network based method (namely, 3D-PMRNN) (namely 3D-PMRNN) has been proposed to improve the reconstruction ability, and thus the reconstruction size is expanded to 256^{3}. Nevertheless, in order to train these models, the existed DL-based methods need to down-sample the original computed tomography (CT) image first so that the convolutional kernel can capture the morphological features of training images. Thus, the detailed information of the original CT image will be lost. Besides, the 3D reconstruction from a optical thin section is not available because of the large size of the cutting slice. In this paper, we proposed an improved recurrent generative model to further enhance the reconstruction ability (512^{3}). Benefiting from the RNN-based architecture, the proposed model requires only one 3D training sample at least and generates the 3D structures layer by layer. There are three more improvements: First, a hybrid receptive field for the kernel of convolutional neural network is adopted. Second, an attention-based module is merged into the proposed model. Finally, a useful section loss is proposed to enhance the continuity along the Z direction. Three experiments are carried out to verify the effectiveness of the proposed model. Experimental results indicate the good reconstruction ability of proposed model in terms of accuracy, diversity, and generalization. And the effectiveness of section loss is also proved from the perspective of visual inspection and statistical comparison.

[Heart sound measurement and analysis system with a stethoscope].

This paper is composed with the cardiac sound measurement and analysis system for in-home use of heart abnormality monitoring. The heart sound acquiring system is composed of a traditional chest-piece, earphone, microphone, and IC recorder. The recorded data is transmitted to a computer by USB interface for analysis based on the cardiac sound characteristic waveform (CSCW) method, which is extracted from an analytical model of single degree-of-freedom (SDOF). Furthermore, the characteristic parameters [T1, T2, T11, T12] are defined by the time intervals between the crossed points of the CSCW and a threshold value (THV), which are related to the first sound and the second sound and are used for discriminating normal and abnormal heart sounds. Also, an easy-understanding graphical representation for these parameters is considered, so that, even for an inexperienced user, he or she is able to monitor his or her cardiac status easily. Finally, a case study on the abnormal/normal cardiac sounds is demonstrated to validate the usefulness and efficiency of this proposed system and the cardiac sound characteristic waveform method.

Driving forces and mitigation potential of global CO2 emissions from 1980 through 2030: Evidence from countries with different income levels.

To mitigate global carbon dioxide (CO2) emissions in an effective manner, it is essential to identify the driving forces and estimate the reduction potential of changes to CO2 emissions. Using an extended logarithmic mean Divisia index (LMDI) method, this study decomposes the changes in global emissions between 1980 and 2015 with consideration of different income levels; it also reports on scenario analysis of the global emissions reduction potential up to the year 2030 to explore feasible mitigation pathways. The results suggest that the key driving force responsible for promoting global emissions from 1980 through 2015 is income, while energy intensity is the most significant factor in inhibiting global emissions. Furthermore, the countries with the largest reductions in global emissions are mainly upper-middle-income (UMI) countries. The key driving forces of emissions changes in countries with different income levels offer mixed results. In addition, the forecast results indicate that the future emissions reduction potential across the globe is significant and that UMI countries offer the greatest emissions mitigation potential. Finally, this study provides several targeted policy suggestions for reducing emissions across the globe.

A facile method to graphene oxide/polyaniline nanocomposite with sandwich-like structure for enhanced electrical properties of humidity detection.

In this paper, a novel graphene oxide (GO)/polyaniline (PANI) sandwich-like nanocomposite has been synthesized by in-situ chemical oxidative polymerization. The GO/PANI is then fabricated onto the interdigitated transducers as sensor for humidity detection. The electrical properties of the thin films are investigated in various relative humidity (RH), including conduction mechanism, sensitivity, reproducibility and humidity hysteresis. The conduction mechanism of the GO/PANI for humidity response is discussed in detail, and the total resistance of GO/PANI is mainly depending on the bulk resistance of PANI. At the lower (60%) RH, the proton hopping transfer plays a very important role for the proton exchange mechanism of GO/PANI thin film. At the higher RH, ionic conduction is not only main conduction process, but also with the proton hopping partially exists for the proton exchange mechanism. Besides, the humidity sensitivity of the thin films enhances with increasing the mass ratio of GO (0, 5, and 50 mg) to PANI due to its larger surface area, hydrophilic functional groups and synergistic effect of π-π* conjugation system, which is also supported by adsorption of QCM humidity response. Meanwhile, the morphology and structure of the thin films are analyzed by fourier transform infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy (UV-vis), scanning electron microscopy (SEM) and transmission electron microscope (TEM), respectively.

CO2 emissions, natural gas and renewables, economic growth: Assessing the evidence from China.

This study aims to test the environmental Kuznets curve (EKC) for carbon dioxide (CO2) emissions in China by developing a new framework based on the suggestion of Narayan and Narayan (2010). The dynamic effect of natural gas and renewable energy consumption on CO2 emissions is also analyzed. Considering the structural break observed in the sample, a series of econometric techniques allowing for structural breaks is utilized for the period 1965-2016. The empirical results confirm the existence of the EKC for CO2 emissions in China. Furthermore, in both the long-run and the short-run, the beneficial effects of natural gas and renewables on CO2 emission reduction are observable. In addition, the mitigation effect of natural gas on CO2 emissions will be weakened over time, while renewables will become progressively more important. Finally, policy suggestions are highlighted not only for mitigating CO2 emissions, but also for promoting growth in the natural gas and renewable energy industries.

What is the probability of achieving the carbon dioxide emission targets of the Paris Agreement? Evidence from the top ten emitters.

This study predicts the probabilities of achieving the carbon dioxide (CO2) emission targets set by the Paris Agreement and the Intended Nationally Determined Contribution (INDC) of the top ten CO2 emitters (TTCE). The TTCE are China, USA, India, Russia, Japan, Germany, South Korea, Iran, Saudi Arabia and Indonesia based on their emission trends over 1991-2015 period. The methods of trend extrapolation and back propagation (BP) neural networks are used in this paper to overcome the weakness of multiple linear regression (MLR) and the assumptions of the environmental Kuznets curve (EKC). The results show that the model performs well and has high predictive accuracy. The volume of the CO2 emissions by the TTCE in 2030 is predicted to increase by 26.5-36.5%, compared with 2005. According to different trends of economic growth, energy consumption, and changes in share of renewable energy, the results show that China, India and Russia will achieve their INDC targets in some scenarios, whereas there will be a shortfall in achieving targets by USA, Japan, Germany, and South Korea. In particular, the carbon reduction situations of Saudi Arabia, Iran and Indonesia are quite severe. Moreover, the results show that there is no common trend that can be used as a suitable benchmark for every country for the implementation of carbon reductions targets of the Paris Agreement and their INDC goals. Finally, there are signs of improvement of the equality of carbon emissions based on the analysis of the Gini coefficient.

Finite-time H∞ control for a class of Markovian jump systems with mode-dependent time-varying delays via new Lyapunov functionals.

This paper is concerned with the problem of finite-time H∞ control for a class of Markovian jump systems with mode-dependent time-varying delays via new Lyapunov functionals. In order to reduce conservatism, a new Lyapunov-Krasovskii functional is constructed. Based on the derived condition, the reliable H∞ control problem is solved, and the system trajectory stays within a prescribed bound during a specified time interval. Finally, numerical examples are given to demonstrate the proposed approach is more effective than some existing ones.