Rail, rivers, road or air : which infrastructure promotes growth in China?

We examine the relationship between growth in transportation and economic output across Chinese provinces from 2005-2014. Panel GMM methods evaluate the impact of changes in air, conventional rail, HSR, roads, and waterways turnover volume on provincial output growth. GMM estimates demonstrate that rail and roads significantly affect economic growth; rail’s impact is particularly significant and its estimates are economically large for agriculture and manufacturing output. In contrast, air, HSR and water usage do not contribute to economic growth. Impulse responses indicate that rail and roads considerably affect GDP growth across China, and there is bi-causality between transportation and economic growth. Cost-benefit analysis highlights that the benefit of roads, and particularly rail, outweigh the costs of infrastructure spending.peer-reviewe

An OLS and GMM Combined Algorithm for Text Analysis for Heterogeneous Impact in Online Health Communities

The increase of doctors\u27 activity in online health communities (OHCs) plays a decisive role in their development. Although the literature on the determinants of doctors\u27 online activities has received considerable attention, the impact of illness severity on these factors remains rare. A network externality analytical framework is constructed to explain the factors (that is, responsiveness, involvement, word-of-mouth, incentives, price, titles and gender) affecting online doctors\u27 behavior, and assess whether factors differ by. By developing text analysis of 4916 doctors\u27 data from a Chinese OHC, this paper applies ordinary least squares (OLS) and General Method of Moments (GMM) to analyze whether the determinants are equal across serious, moderate, and mild illnesses. Our experiment results find that the determinants affecting doctors\u27 online service activity substantially differ across illness severity. Experiments prove the effectiveness of the proposed OLS and GMM methods and demonstrate that they are applicable in online medical field

Distributed Power System Virtual Inertia Implemented by Grid-Connected Power Converters

Renewable energy sources (RESs), e.g., wind and solar photovoltaics, have been increasingly used to meet worldwide growing energy demands and reduce greenhouse gas emissions. However, RESs are normally coupled to the power grid through fast-response power converters without any inertia, leading to decreased power system inertia. As a result, the grid frequency may easily go beyond the acceptable range under severe frequency events, resulting in undesirable load-shedding, cascading failures, or even large-scale blackouts. To address the ever-decreasing inertia issue, this paper proposes the concept of distributed power system virtual inertia, which can be implemented by grid-connected power converters. Without modifications of system hardware, power system inertia can be emulated by the energy stored in the dc-link capacitors of grid-connected power converters. By regulating the dc-link voltages in proportional to the grid frequency, the dc-link capacitors are aggregated into an extremely large equivalent capacitor serving as an energy buffer for frequency support. Furthermore, the limitation of virtual inertia, together with its design parameters, is identified. Finally, the feasibility of the proposed concept is validated through simulation and experimental results, which indicate that 12.5% and 50% improvements of the frequency nadir and rate of change of frequency can be achieved.NRF (Natl Research Foundation, S’pore)Accepted versio

Effect of Surface Microstructures on Hydrophobicity and Barrier Property of Anticorrosive Coatings Prepared by Soft Lithography

Enhancing the hydrophobicity of organic coatings retards their interaction with water and often leads to better protectiveness over metal corrosion. In this study, a soft lithography method was used to prepare epoxy coatings which showed surface microstructures in high replication to sandpapers. The effect of microstructures on coating’s hydrophobicity and barrier property was investigated. Compared to flat coatings, the microstructured coatings showed much higher water contact angles, which further increased with finer sandpapers. Determined by electrochemical impedance spectroscopy (EIS), the flat coating exhibited a higher anticorrosive performance than the microstructured coatings. With the use of finer sandpaper, the groove size of the corresponding microstructured coating was reduced. And a lower anticorrosive performance was observed since more defects might be formed in a given area of coating during the imprinting process. As the groove size of the coatings was further decreased to 5.7 µm, the microstructures became too small for water to easily penetrate through. Therefore, trapped air acted as an additional barrier and contributed to an increased anticorrosive performance compared to other microstructured coatings

Collaborative multiple change detection methods for monitoring the spatio-temporal dynamics of mangroves in Beibu Gulf, China

Mangrove ecosystems are one of the most diverse and productive marine ecosystems around the world, although losses of global mangrove area have been occurring over the past decades. Therefore, tracking spatio-temporal changes and assessing the current state are essential for mangroves conservation. To solve the issues of inaccurate detection results of single algorithms and those limited to historical change detection, this study proposes the detect–monitor–predict (DMP) framework of mangroves for detecting time-series historical changes, monitoring abrupt near-real-time events, and predicting future trends in Beibu Gulf, China, through the synergetic use of multiple detection change algorithms. This study further developed a method for extracting mangroves using multi-source inter-annual time-series spectral indices images, and evaluated the performance of twenty-one spectral indices for capturing expansion events of mangroves. Finally, this study reveals the spatio-temporal dynamics of mangroves in Beibu Gulf from 1986 to 2021. In this study, we found that our method could extract mangrove growth regions from 1986 to 2021, and achieved 0.887 overall accuracy, which proved that this method is able to rapidly extract large-scale mangroves without field-based samples. We confirmed that the normalized difference vegetation index and tasseled cap angle outperform other spectral indexes in capturing mangrove expansion changes, while enhanced vegetation index and soil-adjusted vegetation index capture the change events with a time delay. This study revealed that mangrove changes displayed historical changes in the hierarchical gradient from land to sea with an average annual expansion of 239.822 ha in the Beibu Gulf during 1986–2021, detected slight improvements and deteriorations of some contemporary mangroves, and predicted 72.778% of mangroves with good growth conditions in the future

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images

Mangrove-forest classification by using deep learning algorithms has attracted increasing attention but remains challenging. The current studies on the transfer classification of mangrove communities between different regions and different sensors are especially still unclear. To fill the research gap, this study developed a new deep-learning algorithm (encoder–decoder with mixed depth-wise convolution and cascade upsampling, MCCUNet) by modifying the encoder and decoder sections of the DeepLabV3+ algorithm and presented three transfer-learning strategies, namely frozen transfer learning (F-TL), fine-tuned transfer learning (Ft-TL), and sensor-and-phase transfer learning (SaP-TL), to classify mangrove communities by using the MCCUNet algorithm and high-resolution UAV multispectral images. This study combined the deep-learning algorithms with recursive feature elimination and principal component analysis (RFE–PCA), using a high-dimensional dataset to map and classify mangrove communities, and evaluated their classification performance. The results of this study showed the following: (1) The MCCUNet algorithm outperformed the original DeepLabV3+ algorithm for classifying mangrove communities, achieving the highest overall classification accuracy (OA), i.e., 97.24%, in all scenarios. (2) The RFE–PCA dimension reduction improved the classification performance of deep-learning algorithms. The OA of mangrove species from using the MCCUNet algorithm was improved by 7.27% after adding dimension-reduced texture features and vegetation indices. (3) The Ft-TL strategy enabled the algorithm to achieve better classification accuracy and stability than the F-TL strategy. The highest improvement in the F1–score of Spartina alterniflora was 19.56%, using the MCCUNet algorithm with the Ft-TL strategy. (4) The SaP-TL strategy produced better transfer-learning classifications of mangrove communities between images of different phases and sensors. The highest improvement in the F1–score of Aegiceras corniculatum was 19.85%, using the MCCUNet algorithm with the SaP-TL strategy. (5) All three transfer-learning strategies achieved high accuracy in classifying mangrove communities, with the mean F1–score of 84.37~95.25%