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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.

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.

This paper aims to solve the problem that the changing of groove size and assembly gap would affect the precision of the multi-pass path planning and the welding quality and realize the automatic welding of a thick plate.

First, a double-sided double arc welding (DSAW) system with a self-designed passive vision sensor was established, then the image of the groove was captured and the characteristic parameters of groove were extracted by image processing. According to the welding parameters and the extracted geometry size, multi-pass path planning was executed by the DSAW system.

A DSAW system with a self-designed passive vision sensor was established which can realize the welding thick plate by double-sided double arc by two robots. The clear welding image of the groove was acquired, and an available image processing algorithm was proposed to accurately extract the characteristic parameters of the groove. According to the welding parameters and the extracted geometry size, multi-pass path planning can be executed by the DSAW system automatically.

Gas metal arc welding is used for root welding and filler passes in DSAW. Multi-pass path planning for thick plate by Double-sided Double Arc Welding (DSAW) based on vision sensor was proposed.

This paper aims to develop a method to extract the weld seam from the welding image.

The initial step is to set the window for the region of the weld seam. Filter and edge‐operator are then applied to acquire edges of images. Based on the prior knowledge about characteristics of the weld seam, a series of routines is proposed to recognize the seam edges and calculate the seam representation.

The proposed method can be used to extract seams of different deviations from noise‐polluted images efficiently. Besides, the method is low time‐consuming and quick enough for real time processing.

Weld seam extraction is the key problem in passive vision based seam tracking technology. The proposed method can extract the weld seam even when the image is noisy, and it is quick enough to be applied in seam tracking technology. The method is expected to improve seam tracking results.

A useful method is developed for weld seam extraction from the noise‐polluted image based on prior knowledge of weld seam. The method is robust and quick enough for real time processing.

The purpose of this paper is to investigate the optimal operational strategies in a green platform supply chain and provide suggestions on the selection of sales and financing modes for the capital-constrained manufacturer.

This study combines different sales channels with financing modes and investigates three sales-financing modes, i.e. traditional sales-prepayment financing (TSPF), traditional sales-bank financing (TSBF) and online sales e-retailer financing (OSEF). By establishing and comparing Stackelberg game models of these sales-financing modes from the perspectives of economy, environment and social welfare, the optimal strategies of emission reduction, financing, pricing and service improvement are obtained.

The results suggest that as the commission rate increases to a certain level, a threshold of the cost coefficient of emission reduction can be found such that the manufacturer should choose OSEF below this threshold and TSBF above this threshold. OSEF is Pareto optimal when this cost coefficient is low, and this mode can lead to the highest social welfare when the platform loan interest rate is relatively low. The Pareto areas in TSBF and OSEF enlarge as the default coefficient decreases.

These results can provide operational insights on how to select sales channels and financing modes when manufacturer faces financial constraints in emission reduction.

This paper combines different sales and financing modes to study their comprehensive influence on the decision-makings of chain members and the resulting performance of economy, environment and social welfare.

Currently, for the evaluation of enterprise credit, many specific values of indexes are difficult to obtain, so decision makers tend to give a form of uncertain linguistic variable. To solve this kind of problem, the purpose of this paper is to introduce an uncertain pure linguistic approach on evaluation of enterprise integrity based on grey information.

Initial uncertain linguistic variables given by experts are transferred into interval grey numbers, and their greyness of degree is computed. Then, the greyness of degree is applied to adjust the weights of experts. Moreover, the core of each interval grey number is calculated, and through giving the positive ideal point and negative ideal point, which are binary numbers, the comprehensive grey relational grade between the linguistic number and the two points is calculated, respectively, as well to get the ranking result of projects by considering both core and greyness of degree.

The model is applied to a case, and the result verifies the validity and practicability of the model which reveals high effectiveness.

This model provides a new feasible method in a growing number of fuzzy evaluation schemes in the fields of enterprise integrity and contributes to getting better and more accurate results.

In this paper, the greyness of degree is introduced to the model to adjust the experts’ weights, and it reflects the thought of “making full use of the information” in grey system theory and further enriches the system of grey decision-making theory as well as expanding its application scope.

Permanent magnet (PM) electrical machines are becoming one of the most popular type of the machines used in electrical vehicle drive applications. The main drawback of permanent magnet machines, despite obvious advantages, is associated with the flux control capability, which is limited at high rotor speeds of the machine. This paper aims to present a new arrangement of permanent magnets and flux barriers in the rotor structure to improve the field weakening control of hybrid excited machines. The field weakening characteristics, back-emf waveforms and efficiency maps of this novel machine have been reported.

In the study, finite element analysis was used to perform simulation research. Then, based on the simulation studies, an experimental model was built. The paper also presents selected experimental results.

Obtained results show that the proposed machine topology and novel control strategy can offer an effective flux control method allowing to extend the maximal rotational speed of the machine at constant power range.

The proposed solution can be used in electric vehicles drive to extend its torque and speed range.

The paper presents original design and results of research on a new solution of a hybrid excited machine with magnetic barriers in a rotor.

The purpose of this paper is to describe the trends in residential solid waste collection (RSWC) services in rural China over the past decade and analyze the determinants of these services using nationally representative data.

The authors draw on panel data from three rounds of village-level surveys of 101 villages. The three surveys were conducted in 2005, 2008, and 2012 in five provinces. The authors used fixed-effected regression approach to analyze the determinants of these services.

The results show that in the aftermath of increased investment and policy attention at the national level, the proportion of villages providing RSWC services in rural China increased significantly from 1998 to 2011. However, half of all villages in rural China still did not provide RSWC services as of 2011. Based on econometrics analysis, the authors show that villages that are richer, more populous, and villages with more small hamlets are more likely to provide RSWC services.

The analyses are based on primary survey data and the first to quantify trends in waste management services in the beginning of the twentieth century. The authors believe that the results will have significant policy implications for China in its continuing quest for better waste management policy.

Heavy fog results in low visibility, which increases the probability and severity of traffic crashes, and fog warning system is conducive to the reduction of crashes by conveying warning messages to drivers. This paper aims at exploring the effects of dynamic message sign (DMS) of fog warning system on driver performance.

First, a testing platform was established based on driving simulator and driver performance data under DMS were collected. The experiment route was consisted of three different zones (i.e. warning zone, transition zone and heavy fog zone), and mean speed, mean acceleration, mean jerk in the whole zone, ending speed in the warning zone and transition zone, maximum deceleration rate and mean speed reduction proportion in the transition zone and heavy fog zone were selected. Next, the one-way analysis of variance was applied to test the significant difference between the metrics. Besides, drivers’ subjective perception was also considered.

The results indicated that DMS is beneficial to reduce speed before drivers enter the heavy fog zone. Besides, when drivers enter a heavy fog zone, DMS can reduce the tension of drivers and make drivers operate more smoothly.

This paper provides a comprehensive approach for evaluating the effectiveness of the warning system in adverse conditions based on the driving simulation test platform. The method can be extended to the evaluation of vehicle-to-infrastructure technology in other special scenarios.

The USA is one of the largest oil producers in the world. For this purpose, the authors model and predict the US conventional and unconventional oil production during the period 2000–2030.

In this research, the system dynamics (SD) model has been used. In this model, economic, technical, geopolitical, learning-by-doing and environmental (social costs of carbon) issues are considered.

The results of the simulation, after successfully passing the validation test, show that the US unconventional oil production rate under the optimistic scenario (high oil prices) in 2030 is about 12.62 million barrels/day (mb/day), under the medium oil price scenario is about 11.4 mb/day and under the pessimistic scenario (low oil price) is about 10.18 mb/day. The results of US conventional oil production forecasting under these three scenarios (high, medium and low oil prices) show oil production of 4.62, 4.26 and 3.91 mb/day, respectively.

The contribution of this study is important in several respects: First, by modeling SD that technical, economic, proven reserves and technology factors are considered, this paper models US conventional and unconventional oil production separately. In this modeling, nonlinear relationships and feedback loops are presented to better understand the relationships between variables. Second, given the importance of environmental issues, the modeling of social costs of CO₂ emissions per barrel of oil is also presented and considered as a part of oil production costs. Third, conventional and unconventional US oil production by 2030 is forecast separately, the results of this study could help policymakers to develop unconventional oil and plan for energy self-sufficiency.