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Machine learning is having a major impact on banking, law and other organisations. The speed with which this technology is developing to undertake tasks that are not only complex and technical but also time-consuming and that are subject to constantly changing parameters is astounding. The purpose of this paper is to explore the extent to which machine learning can be used as a solution to lighten the compliance and regulatory burden on charitable organisations in the UK; so that they can comply with their regulatory duties and develop a coherent and streamlined action plan in relation to technological investment.

The subject is approached through the analysis of data, literature and domestic and international regulation. The first part of the study summarises the extent of current regulatory obligations faced by charities, these are then, in the second part, set against the potential technological solutions provided by machine learning as of July 2021.

It is suggested that charities can use machine learning as a smart technological solution to ease the regulatory burden they face in a growing and impactful sector.

The work is original because it is the first to specifically explore how machine learning as a technological advance can assist charities in meeting the regulatory compliance challenge.

The purpose of this paper is to review the criminal enforcement of insider dealing cases in People's Republic of China's (PRC) securities market and to provide feasible suggestions for improvement for a more coherent and streamlined insider dealing regulatory framework in the PRC during the enforcement of China's new Securities Law (SL 2020) in March 2020.

Through analysing the previous literature on public interest theories and economic theories of regulation, this paper examines the necessity to regulate insider dealing in China with criminal law to ensure fairness and avoid monopolies in its securities market. The paper reviews the criminalising of severe insider dealing cases in China from the Nanking National Government in the 1920s to the inception of the securities market of the PRC in the 1990s to the present day. The investigation, prosecution, enforcement and trial of criminal offences of insider dealing in China are thoroughly examined.

The paper finds a tendency for over reliance on the investigation and the administrative judgement of the China Securities Regulatory Commission in criminal investigation, prosecution and trial in the PRC.

To the best of the authors’ knowledge, this paper is one of the first papers to critically and thoroughly analyse the criminal enforcement of insider dealing in China following the recent enforcement of China’s new Securities Law in March 2020.

Artificial intelligence has had a major impact on organisations from Banking through to Law Firms. The rate at which technology has developed in terms of tasks that are complex, technical and time-consuming has been astounding. The purpose of this paper is to explore the solutions that AI, RegTech and CharityTech provide to charities in navigating the vast amount of anti-money laundering and counter-terror finance legislation in the UK; so that they comply with the requirements and mitigate the potential risk they face but also develop a more coherent and streamlined set of actions.

The subject is approached through the analysis of data, literature and, domestic and international regulation. The first part of the paper explores the current obligations and risks charities face, these are then, in the second part, set against the examination of potential technological solutions as of August 2020.

It is suggested that charities underestimate the importance of the nature and size of the threat posed to them, this is significant, as demonstrated, given the growing size and impact of the sector. Technological solutions are suggested to combat the issues charities face.

The study is original because it is the first to create the notion of CharityTech and to specifically explore what technological advances can assist charities in meeting the regulatory compliance challenge.

Robot vision is a fundamental device for human–robot interaction and robot complex tasks. In this paper, the authors aim to use Kinect and propose a feature graph fusion (FGF) for robot recognition.

The feature fusion utilizes red green blue (RGB) and depth information to construct fused feature from Kinect. FGF involves multi-Jaccard similarity to compute a robust graph and word embedding method to enhance the recognition results.

The authors also collect DUT RGB-Depth (RGB-D) face data set and a benchmark data set to evaluate the effectiveness and efficiency of this method. The experimental results illustrate that FGF is robust and effective to face and object data sets in robot applications.

The authors first utilize Jaccard similarity to construct a graph of RGB and depth images, which indicates the similarity of pair-wise images. Then, fusion feature of RGB and depth images can be computed by the Extended Jaccard Graph using word embedding method. The FGF can get better performance and efficiency in RGB-D sensor for robots.

The Chinese population is rapidly ageing before they are rich. The purpose of this paper is to describe healthcare seeking behaviour and the critical factors associated with healthcare seeking behaviour.

Using a purposive sampling method, the authors recruited 44 adults aged 60 years or older from three provinces, representing the developed (Shanghai), undeveloped (Ningxia) regions and the regions in between (Hubei). From July to September 2008, using a semi-structured guide, the authors interviewed participants in focus group discussions.

The healthcare needs for chronic and catastrophic diseases were high; however, the healthcare demands were low and healthcare utilizations were even lower owing to the limited accessibility to healthcare services, particularly, in underdeveloped rural areas. “Too expensive to see a doctor” was a prime complaint, explaining substantial discrepancies between healthcare needs, demands and use. Care seeking behaviour varied depending on insurance availability, perceived performance, particularly hospital services, and prescription medications. Participants consistently rated increasing healthcare accessibility as a high priority, including offering financial aid, and improving service convenience. Improving social security fairness was the first on the elderly’s wish list.

Healthcare demand and use were lower than needs, and were influenced by multiple factors, primarily, service affordability and efficiency, perceived performance and hospital service quality.