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The purpose of this paper is to meet the large demand for the new-generation intelligence monitoring systems that are used to detect targets within a dynamic background.

A dynamic target detection method based on the fusion of optical flow and neural network is proposed.

Simulation results verify the accuracy of the moving object detection based on optical flow and neural network fusion. The method eliminates the influence caused by the movement of the camera to detect the target and has the ability to extract a complete moving target.

It provides a powerful safeguard for target detection and targets the tracking application.

The proposed method represents the fusion of optical flow and neural network to detect the moving object, and it can be used in new-generation intelligent monitoring systems.

The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.

The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.

The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.

The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.

Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.

G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.

G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.

An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.

This paper aims to study a bibliography acquisition approach to verify the bibliography by author name from the integrated system and the metadata from the digital object identifier (DOI) content negotiation proxy. As managed scientific research papers increase at a higher rate, an accurate and automated scheme for bibliography acquisition is desirable.

This study develops a framework using DOI content negotiation proxy as context for the entering of the bibliography. The bibliography acquisition architecture is composed of three point of views to reduce the complexity: terminal UI, service deployed in the cloud and DOI content negotiation proxy. To simplify the service interface and support many kinds of bibliographic formats, this paper presents the independent BibModel and its template-based model transformation engine to support rich bibliographic records.

An important finding of this article is that we do some significant development work to combine the open CrossCite DOI content service and DOI resolvers of registry agencies. As more than 95 per cent of DOIs are owned or managed by CrossRef, DataCite, ISTIC and mEDTA DOI registry agencies, it is a common universal approach for the scientific research paper results with DOIs.

Through a simple method built quickly from freely available parts, it is partially successful, suggesting the scheme can be integrated with third-party systems, such as the management system of scientific research results and the electronic journal management system. The analysis of the application’s effect shows the ability to verify the authenticity of the paper by author name from the system and the metadata from our DOI content negotiation proxy.

This paper proposes an original and simple framework to acquire the metadata of bibliographies automatically. No detailed evaluative study has been carried out supporting both DOI content negotiation and resolver. This work shows how to implement the acquisition approach and how to integrate this framework with the current system.

This paper aims to collect and compile the historical data of Guanfu Salt Farm, officially built by the Song Dynasty (960–1279) within modern Hong Kong territories, to reconstruct its history for the reflection of Hong Kong society of the time.

This paper is largely based on identification and analysis of historical documents, including keyword search on electronic databases and verification with the original sources, with reference to archaeological findings when necessary.

This paper reconstructs the history of Guanfu Salt Farm based on documentary sources with reference to archaeological findings. English translation of Chinese sources is also provided when necessary.

There has been an absence of systematic compilation of historical data of Hong Kong during the Song Dynasty, which are limited in quantity and scattered across different sources. This paper seeks to fill the vacuum of knowledge about pre-colonial Hong Kong, with a more comprehensive reconstruction of the history of Guanfu Salt Farm.

Capital Trading presentation to GEC. Coated‐steel stockholder Capital Trading Ltd has, in conjunction with British Steel Corporation, recently made a presentation to companies within the GEC group on the benefits and applications of coated steels.

By introducing the basic concepts and theories of the philosophy of information created by Kun Wu, and making some comparisons of the philosophy of information and related information theories between Wu and other scholars, this paper aims to have Chinese philosophy of information widely known and understood by more people in the world, thus promoting the international exchanges between Chinese and Western scholars on the topic of philosophy of information.

The main research methods used in this paper are the literature review and the comparative study. On the one hand, it reviews some related concepts and theories in Kun Wu's academic works of philosophy of information. On the other hand, it compares the thoughts and viewpoints of Kun Wu with those of other scholars.

First, Kun Wu is the first person who has established a complete and comprehensive theoretical system of philosophy of information in China; second, Kun Wu's philosophy of information is significant in originality and value, which could be thought as the intellectual quintessence of information age, thus worth learning. Third, with more international exchanges, Chinese philosophy of information created by Wu will surely be more and more influential in philosophical circles at home and abroad.

It is a very valuable first-hand material for Western scholars to know and understand Chinese philosophy of information.

This paper aims to the relationship between liquidity fluctuation trends and stocks’ expected returns.

The authors use the multi-fractal detrended fluctuation analysis to study the liquidity non-linear multi-fractal characteristics of stock market and predict the future volatility trend of market liquidity with the help of potential entropy dimensional model and also consider the non-linear volatility trend of liquidity, construct a fractal momentum strategy under different sorting periods and holding periods, to test the liquidity momentum effects of Chinese stock market and, finally, compare with the traditional price momentum strategy.

The result suggests that: Chinese stock market liquidity has obvious non-linear multi-fractal characteristics; the trend entropy dimensional model can accurately predict the future volatility trend of market liquidity; significant liquidity trend momentum effects persist in the Chinese stock market, while price momentum effects exist only in the short term; a significant liquidity premium exists in the Chinese equity market; the fractal momentum strategy constructed in this paper achieves higher returns and less risk than the price momentum strategy.

Based on the above analysis, this paper will further construct a momentum strategy incorporating liquidity trends, i.e. fractal momentum strategy (LMP strategy), with the realistic background that Chinese stock market liquidity has non-linear multi-fractal characteristics – the main contribution of this paper is to overcome the shortcomings of existing momentum strategies, which seldom combine the trend of future liquidity fluctuations with momentum strategies, and to incorporate the real characteristics of the market.

This paper aims to investigate the effects of different ultrasonic-assisted loading degrees on the microstructure, mechanical properties and the fracture morphology of Cu/Zn+15%SAC0307+15%Cu/Al solder joints.

A new method in which 45 μm Zn particles were mixed with 15% 500 nm Cu particles and 15% 500 nm SAC0307 particles as solders (SACZ) and five different ultrasonic loading degrees were applied for realizing the soldering between Cu and Al at 240 °C and 8 MPa. Then, SEM was used to observe and analyze the soldering seam, interface microstructure and fracture morphology; the structural composition was determined by EDS; the phase of the soldering seam was characterized by XRD; and a PTR-1102 bonding tester was adopted to test the average shear strength.

The results manifest that Al–Zn solid solution is formed on the Al side of the Cu/SACZ/Al joints, while the interface IMC (Cu₅Zn₈) is formed on the Cu side of the Cu/SACZ/Al joints. When single ultrasonic was used in soldering, the interface IMC (Cu₅Zn₈) gradually thickens with the increase of ultrasonic degree. It is observed that the proportion of Zn or ZnO areas in solders decreases, and the proportion of Cu–Zn compound areas increases with the variation of ultrasonic degree. The maximum shear strength of joint reaches 46.01 MPa when the dual ultrasonic degree is 60°. The fracture position of the joint gradually shifts from the Al side interface to the solders and then to the Cu side interface.

The mechanism of ultrasonic action on micro-nanoparticles is further studied. By using different ultrasonic loading degrees to realize Cu/Al soldering, it is believed that the understandings gained in this study may offer some new insights for the development of low-temperature soldering methodology for heterogeneous materials.

The purpose of this paper is to propose a new tool for stock investment risk management through studying stocks with what kind of characteristics can be predicted by individual investor behavior.

Based on comment data of individual stock from the Snowball, a thermal optimal path method is employed to analyze the lead–lag relationship between investor attention (IA) and the stock price. And machine learning algorithms, including SVM and BP neural network, are used to predict the prices of certain kind of stock.

It turns out that the lead–lag relationships between IA and the stock price change dynamically. Forecasting based on investor behavior is more accurate only when the IA of the stock is stably leading its price change most of the time.

One limitation of this paper is that it studies China’s stock market only; however, different conclusions could be drawn for other financial markets or mature stock markets.

As for the implications, the new tool could improve the prediction accuracy of the model, thus have practical significance for stock selection and dynamic portfolio management.

This paper is one of the first few research works that introduce individual investor data into portfolio risk management. The new tool put forward in this study can capture the dynamic interplay between IA and stock price change, which help investors identify and control the risk of their portfolios.