Novel evidence on the asymmetric causality between the Chinese stock and real estate markets: evidence from city-level data

Our study re-examines the asymmetric causality between the Chinese stock and real estate markets in 70 cities. Prior research using symmetry hypotheses, has not yet linked these two markets or paid attention to their heterogeneity. We uniquely employed the nonlinear autoregressive distributed lag model, which permits the exploration of bidirectional asymmetric causality. Decreases and increases in stock prices caused short-run changes to real estate prices in 18 of the cities studied; this short-run effect was ultimately carried on in Guangzhou and in three cities. Even after switching the study variables, similar results were obtained. Our findings show that real estate policymakers in specific cities need to take into consideration the asymmetric performance of real estate prices as caused by the asymmetry within stock prices. If government stabilises the real estate market, it can in turn facilitate stock-market stability

Recent advances in hydrothermal carbonisation:from tailored carbon materials and biochemicals to applications and bioenergy

Introduced in the literature in 1913 by Bergius, who at the time was studying biomass coalification, hydrothermal carbonisation, as many other technologies based on renewables, was forgotten during the "industrial revolution". It was rediscovered back in 2005, on the one hand, to follow the trend set by Bergius of biomass to coal conversion for decentralised energy generation, and on the other hand as a novel green method to prepare advanced carbon materials and chemicals from biomass in water, at mild temperature, for energy storage and conversion and environmental protection. In this review, we will present an overview on the latest trends in hydrothermal carbonisation including biomass to bioenergy conversion, upgrading of hydrothermal carbons to fuels over heterogeneous catalysts, advanced carbon materials and their applications in batteries, electrocatalysis and heterogeneous catalysis and finally an analysis of the chemicals in the liquid phase as well as a new family of fluorescent nanomaterials formed at the interface between the liquid and solid phases, known as hydrothermal carbon nanodots

Primary pulmonary meningioma presenting as a pulmonary ground glass nodule: a case report and review of the literature

Abstract Background A primary pulmonary meningioma is an extremely rare entity. Primary pulmonary meningiomas manifested with a ground glass nodule are a very rare occurrence in clinical practice. Case presentation In this study, we report a case of a primary pulmonary meningioma with atypical computed tomography features. A 59-year-old Han Chinese female came to our hospital for treatment and reported that her physical examination revealed a ground glass nodule in the right lung for over 3 months. The histologic result revealed a primary pulmonary meningioma. The patient underwent a thoracoscopic lung wedge resection of the right upper lobe for a ground glass nodule. After 1 year of follow-up, the patient is still alive without evidence of metastasis or recurrence. Conclusions Primary pulmonary meningiomas could have a variety of radiological findings. As there are no specific radiologic features for the diagnosis of primary pulmonary meningiomas, complete resection of the lesion is required for both diagnosis and treatment. It is necessary to note the imaging features of primary pulmonary meningiomas, presenting as a ground glass nodule; this rare tumor should be considered in differential diagnoses

Tackling Few-Shot Challenges in Automatic Modulation Recognition: A Multi-Level Comparative Relation Network Combining Class Reconstruction Strategy

Automatic Modulation Recognition (AMR) is a key technology in the field of cognitive communication, playing a core role in many applications, especially in wireless security issues. Currently, deep learning (DL)-based AMR technology has achieved many research results, greatly promoting the development of AMR technology. However, the few-shot dilemma faced by DL-based AMR methods greatly limits their application in practical scenarios. Therefore, this paper endeavored to address the challenge of AMR with limited data and proposed a novel meta-learning method, the Multi-Level Comparison Relation Network with Class Reconstruction (MCRN-CR). Firstly, the method designs a structure of a multi-level comparison relation network, which involves embedding functions to output their feature maps hierarchically, comprehensively calculating the relation scores between query samples and support samples to determine the modulation category. Secondly, the embedding function integrates a reconstruction module, leveraging an autoencoder for support sample reconstruction, wherein the encoder serves dual purposes as the embedding mechanism. The training regimen incorporates a meta-learning paradigm, harmoniously combining classification and reconstruction losses to refine the model’s performance. The experimental results on the RadioML2018 dataset show that our designed method can greatly alleviate the small sample problem in AMR and is superior to existing methods

Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump

As the pump-and-dump schemes (P&Ds) proliferate in the cryptocurrency market, it becomes imperative to detect such fraudulent activities in advance, to inform potentially susceptible investors before they become victims. In this paper, we focus on the target coin prediction task, i.e., to predict the pump probability of all coins listed in the target exchange before a pump. We conduct a comprehensive study of the latest P&Ds, investigate 709 events organized in Telegram channels from Jan. 2019 to Jan. 2022, and unearth some abnormal yet interesting patterns of P&Ds. Empirical analysis demonstrates that pumped coins exhibit intra-channel homogeneity and inter-channel heterogeneity, which inspires us to develop a novel sequence-based neural network named SNN. Specifically, SNN encodes each channel's pump history as a sequence representation via a positional attention mechanism, which filters useful information and alleviates the noise introduced when the sequence length is long. We also identify and address the coin-side cold-start problem in a practical setting. Extensive experiments show a lift of 1.6% AUC and 41.0% Hit Ratio@3 brought by our method, making it well-suited for real-world application. As a side contribution, we release the source code of our entire data science pipeline on GitHub, along with the dataset tailored for studying the latest P&Ds.Comment: 9 page

A Method of Noise Reduction for Radio Communication Signal Based on RaGAN

Radio signals are polluted by noise in the process of channel transmission, which will lead to signal distortion. Noise reduction of radio signals is an effective means to eliminate the impact of noise. Using deep learning (DL) to denoise signals can reduce the dependence on artificial domain knowledge, while traditional signal-processing-based denoising methods often require knowledge of the artificial domain. Aiming at the problem of noise reduction of radio communication signals, a radio communication signal denoising method based on the relativistic average generative adversarial networks (RaGAN) is proposed in this paper. This method combines the bidirectional long short-term memory (Bi-LSTM) model, which is good at processing time-series data with RaGAN, and uses the weighted loss function to construct a noise reduction model suitable for radio communication signals, which realizes the end-to-end denoising of radio signals. The experimental results show that, compared with the existing methods, the proposed algorithm has significantly improved the noise reduction effect. In the case of a low signal-to-noise ratio (SNR), the signal modulation recognition accuracy is improved by about 10% after noise reduction

Real-Time Estimation of GPS-BDS Inter-System Biases: An Improved Particle Swarm Optimization Algorithm

The restart of the receiver will lead to the change in the non-overlapping frequency inter-system biases (ISB), which will make it difficult to apply the tightly combined RTK method of pre-calibrating ISB to the actual scene. Particle swarm optimization (PSO) algorithm can be used to estimate the fractional part of the inter-system phase bias (F-ISPB) in real time, which is not affected by the receiver restart. However, the standard PSO can easily fall into local optimum and cannot accurately estimate the value of F-ISPB. In this contribution, based on the characteristics of F-ISPB, we propose an improved PSO with adaptive search space and elite reservation strategy to estimate the F-ISPB in real time. When the value of F-ISPB is close to the boundary of the search space, the improved PSO will transform the search space so that F-ISPB will be located near the central region of the new search space, which will greatly reduce the situation of the standard PSO easily falling into local optimum. Since F-ISPB is very stable, an elite retention strategy will help us to estimate F-ISPB faster and more accurately. Three sets of short baseline static data were selected for testing. The results show that the inter-system differenced model based on the improved PSO has a higher ambiguity fixed rate and positioning accuracy than the inter-system differenced model based on the standard PSO and the classical intra-system differenced model, and the fewer the number of satellites, the more obvious the effect