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The study deploys Anthias' intersectional framework of social spaces and her concept of translocational positionality to explore the barriers to entrepreneurship for refugee entrepreneurs in the United Kingdom (UK). In particular, the study aims to assess how migrant identities require a specific form of business support.

A total of 32 semi-structured interviews with 14 refugee entrepreneurs and 18 business support agents were conducted between April and October 2022 and, together with field notes, were combined for thematic analysis in NVivo 12.

Organisational, representational, intersubjective and experiential barriers combined to create practical and psychological deterrents to entrepreneurship for refugees. However, an explicitly humanistic and de-centred approach to business support was (partially) able to counter such barriers.

Policymakers and business support agencies should consider intersectional characteristics and the importance of a compassionate and individual approach when designing business support programmes for refugee entrepreneurs.

Two intersectional concepts of social spaces and translocational positionality are brought into conversation with each other, creating a novel approach to framing the barriers to entrepreneurship for refugees.

This study describes the structure and function of a new financial modeling technique, namely, the Artificial Neutral Network (ANN) in predicting financial markets’ behavior. With the advancement of the computer technology to date, ANN allows us to imitate human reasoning and thought processes in identifying the optimal trading strategies in the financial markets. The paper identifies the theory and steps involved in performing ANN and Generic Alogorithm in financial markets, the accuracy of the computer learning process, and the appropriate ways to use this process in developing trading strategies. It further discusses the superiority of ANN over traditional methodologies. The study concludes with the description of successful use of ANN by various financial institutions.

The Missouri State Library was transferred from the Department of Higher Education to the Secretary of State's office in 1992. The State Library has been involved at some level in all of the technology projects and programs described in this article.

This paper explores how the Government of the Hong Kong Special Administrative Region (HKSARG) securitizes internal security, cultural identity and welfare system through refugee policy instruments. It also aims to explore the roles of members of the Legislative Council (Legco) and Chinese newspapers in the securitization process.

The author analyzed 6 landmark verdicts, 342 related documents of the Legco, 2,386 news coverages and 408 editorials/ column articles from 6 selected Chinese newspapers from 2005 to mid-2019. While documents of the Legco were collected from the Legco archives, news reports, editorials and column articles were gathered on Wisenews with the keywords, namely, refugees, asylum seekers, torture claims and non-refoulement claims.

The author argues that the advanced comprehensive security approach helps to comprehend the securitization process in Hong Kong. The HKSARG, Legco members of the pro-government camp and pro-government Chinese newspapers perform as securitizing actors who regard refugees as an existential threat to the referent objects, i.e. internal security, cultural identity and welfare system.

There are two significant limitations, namely, the coverage of newspapers and the absence of poll data. This paper merely selected six Chinese newspapers, which do not cover English newspapers and some other Chinese newspapers in Hong Kong. It may neglect some important data. Additionally, owing to the absence of longitudinal poll data, the author chose not to examine the related materials.

This paper intends to be the first study to provide a longitudinal examination of the transformations of current refugee policies in Hong Kong.

This paper aims to propose a new nonlinear function estimation fuzzy system as a novel statistical approach to estimate nanofluids’ thermal conductivity.

A fuzzy system having a product inference engine, a singleton fuzzifier, a center average defuzzifier and Gaussian membership functions is proposed for this purpose.

Results indicate that the proposed fuzzy system can predict the thermal conductivity of Al₂O₃/paraffin nanofluid with appropriate precision and generalization and it also outperforms the classic interpolation methods.

A new nonlinear function estimation fuzzy system was introduced as a novel statistical approach to estimate nanofluids’ thermal conductivity for the first time.

The purpose of the paper is to find a simple structure of speed controller robust against drive parameter variations. Application of neuro‐fuzzy technique in the controller of PI type creates proper nonlinear characteristics, which ensures controller robustness.

The robustness of the controller is based on its nonlinear characteristic introduced by neuro‐fuzzy technique. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the neuro‐fuzzy system to form the proper shape of the control surface, which represents the nonlinear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behavior of a laboratory speed control system is validated in the experimental setup. The control algorithms of the system are performed by a microprocessor floating point DSP control system.

The proposed controller structure with proper control surface created by the neuro‐fuzzy technique guarantees expected robustness.

The proposed controller was tested on a single machine under well defined conditions. Further investigations are required before any industrial applications can be made.

The proposed controller synthesis and its results may be very helpful in the robotic system where changing of system parameters is characteristic for many industrial robots and manipulators.

The original method of robust controller synthesis was proposed and validated by simulation and experimental investigations.

Trust is one of the main pillars of many communication and interaction domains. Computing is no exception. Fog computing (FC) has emerged as mitigation of several cloud computing limitations. However, selecting a trustworthy node from the fog network still presents serious challenges. This paper aims to propose an algorithm intended to mitigate the trust and the security issues related to selecting a node of a fog network.

The proposed model/algorithm is based on two main concepts, namely, machine learning using fuzzy neural networks (FNNs) and the weighted weakest link (WWL) algorithm. The crux of the proposed model is to be trained, validated and used to classify the fog nodes according to their trust scores. A total of 2,482 certified computing products, in addition to a set of nodes composed of multiple items, are used to train, validate and test the proposed model. A scenario including nodes composed of multiple computing items is designed for applying and evaluating the performance of the proposed model/algorithm.

The results show a well-performing trust model with an accuracy of 0.9996. Thus, the end-users of FC services adopting the proposed approach could be more confident when selecting elected fog nodes. The trained, validated and tested model was able to classify the nodes according to their trust level. The proposed model is a novel approach to fog nodes selection in a fog network.

Certainly, all data could be collected, however, some features are very difficult to have their scores. Available techniques such as regression analysis and the use of the experts have their own limitations. Experts might be subjective, even though the author used the fuzzy group decision-making model to mitigate the subjectivity effect. A methodical evaluation by specialized bodies such as the security certification process is paramount to mitigate these issues. The author recommends the repetition of the same study when data form such bodies is available.

The novel combination of FNN and WWL in a trust model mitigates uncertainty, subjectivity and enables the trust classification of complex FC nodes. Furthermore, the combination also allowed the classification of fog nodes composed of diverse computing items, which is not possible without the WWL. The proposed algorithm will provide the required intelligence for end-users (devices) to make sound decisions when requesting fog services.

This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric estimation and testing of diffusion processes, nonparametric testing of parametric diffusion models, nonparametric pricing of derivatives, nonparametric estimation and hypothesis testing for nonlinear pricing kernel, and nonparametric predictability of asset returns. For each financial context, the paper discusses the suitable statistical concepts, models, and modeling procedures, as well as some of their applications to financial data. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and more importantly, the paper points to several aspects that deserve further investigation.

The purpose of this paper is to build a neural network system to predict corporate credit rating in Jordanian non-financial firms, using 19 different financial characteristics such as profitability, leverage ratios, liquidity, bankruptcy, and sales performance.

The study adopts two neural network techniques namely, Kohonen network and Back Propagation Neural Network (BPNN). Our sample includes the manufacturing firms that have provided the required financial information for the period from 2000 to 2007.

BPNN has successfully predicted firms with high performance gaining A rating and the bankrupted firms with D rating for the period from 2005 to 2007.

This study is the first study to investigate credit rating in Jordan using Neural Network technique.