Green-to-Grey China: Determinants and Forecasts of its Green Growth

This paper investigates the determinants of China’s green growth and its pathways in the future. We use the OECD conceptual framework for green growth to measure green growth rates for 30 provinces over the period 1998-2011. By estimating a spatial dynamic panel model at provincial level, we find that China has experienced green growth, but with slower speed in the sample period. The average green growth rate is forecast to decline first and then fluctuate around zero over the next two decades. There appears to be a conditional convergence in provincial green growth and positive spatial influence across neighboring areas, yielding a cap of the country’s level of green development in the future. Mass innovation financed by the government and green structural reforms achieved at firm level are likely to stimulate green growth, while political shocks in terms of reappointment of provincial officials could retard China’s progress to a green economy

Green-to-Grey China: Determinants and Forecasts of its Green Growth

This paper investigates the determinants of China’s green growth and its pathways in the future. We use the OECD conceptual framework for green growth to measure green growth rates for 30 provinces over the period 1998-2011. By estimating a spatial dynamic panel model at provincial level, we find that China has experienced green growth, but with slower speed in the sample period. The average green growth rate is forecast to decline first and then fluctuate around zero over the next two decades. There appears to be a conditional convergence in provincial green growth and positive spatial influence across neighboring areas, yielding a cap of the country’s level of green development in the future. Mass innovation financed by the government and green structural reforms achieved at firm level are likely to stimulate green growth, while political shocks in terms of reappointment of provincial officials could retard China’s progress to a green economy

Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence

Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, AI algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency.Comment: 4pages, 5figures, letter

XCloud-VIP: Virtual Peak Enables Highly Accelerated NMR Spectroscopy and Faithful Quantitative Measures

Background: Nuclear Magnetic Resonance (NMR) spectroscopy is an important bio-engineering tool to determine the metabolic concentrations, molecule structures and so on. The data acquisition time, however, is very long in multi-dimensional NMR. To accelerate data acquisition, non-uniformly sampling is an effective way but may encounter severe spectral distortions and unfaithful quantitative measures when the acceleration factor is high. Objective: To reconstruct high fidelity spectra from highly accelerated NMR and achieve much better quantitative measures. Methods: A virtual peak (VIP) approach is proposed to self-learn the prior spectral information, such as the central frequency and peak lineshape, and then feed these information into the reconstruction. The proposed method is further implemented with cloud computing to facilitate online, open, and easy access. Results: Results on synthetic and experimental data demonstrate that, compared with the state-of-the-art method, the new approach provides much better reconstruction of low-intensity peaks and significantly improves the quantitative measures, including the regression of peak intensity, the distances between nuclear pairs, and concentrations of metabolics in mixtures. Conclusion: Self-learning prior peak information can improve the reconstruction and quantitative measures of spectra. Significance: This approach enables highly accelerated NMR and may promote time-consuming applications such as quantitative and time-resolved NMR experiments

Prediction and Validation of Hub Genes Associated with Colorectal Cancer by Integrating PPI Network and Gene Expression Data

Although hundreds of colorectal cancer- (CRC-) related genes have been screened, the significant hub genes still need to be further identified. The aim of this study was to identify the hub genes based on protein-protein interaction network and uncover their clinical value. Firstly, 645 CRC patients’ data from the Tumor Cancer Genome Atlas were downloaded and analyzed to screen the differential expression genes (DEGs). And then, the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed, and PPI network of the DEGs was constructed by Cytoscape software. Finally, four hub genes (CXCL3, ELF5, TIMP1, and PHLPP2) were obtained from four subnets and further validated in our clinical setting and TCGA dataset. The results showed that mRNA expression of CXCL3, ELF5, and TIMP1 was increased in CRC tissues, whereas PHLPP2 mRNA expression was decreased. More importantly, high expression of CXCL3, ELF5, and TIMP1 was significantly associated with lymphatic invasion, distance metastasis, and advanced tumor stage. In addition, a shorter overall survival was observed in patients with increased CXCL3, TIMP1, and ELF5 expression and decreased PHLPP2 expression. In conclusion, the four hub genes screened by our strategy could serve as novel biomarkers for prognosis prediction of CRC patients

Object-oriented polarimetric SAR image classification via the combination of a pixel-based classifier and a region growing technique

ABSTRACTLand-cover type interpretation by the use of remote sensing image classification techniques is always a hot topic. In this paper, an object-oriented method is presented for fully polarimetric synthetic aperture radar (SAR) image classification. Differing from most of the traditional object-oriented classification algorithms, the proposed method employs an innovative classification strategy that combines a pixel-based classifier and a region growing technique. Firstly, taking each individual pixel as a seed pixel, the homogeneous areas are extracted by a region growing technique. Then, using the information of the pixel-based classification result, the pixels located in each homogeneous area are all assigned to a certain class. Finally, the majority voting strategy is deployed to determine the final class label of each pixel. The experiments conducted on two fully polarimetric SAR images reveal that the proposed classification scheme can obtain pleasing classification accuracy and can provide the classification maps with more homogeneous regions than pixel-based classification

Supporting collaborative play via an affordable touching + singing plant for children with autism in China

The prevalence of Autism Spectrum Disorder (ASD) in mainland China is still largely unknown despite a recent increase in public awareness of the disorder itself. Technology-based intervention strategies are scarce in China, which is why we chose to explore one such possibility in this paper. We present a collaborative play environment enabled by an affordable touching and singing plant for children with autism; the plant allows two users (such as the child and a family member) to collaboratively play a variety of music through the touch. The plant is also simple to assemble, making it portable and accessible. To the best of our knowledge, the plant is China\u27s first affordable assistive device for children with autism. A pilot study with 5 children with ASD revealed mixed results

Ordonnance du roi, concernant les bas officiers, soldats, cavaliers & dragons qui ont déserté pour passer dans d'autres régimens . Du 18 juin 1768

[Acte royal. 1768-06-18. Versailles]Avec mode text