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Adaptive bump inlet can adaptively change the shape of inlet bump surface according to the flight speed of aircraft, ensuring that the inlet has good inlet-engine match performance in a wide speed range. This paper aims to use a composite flexible skin reinforced by shape memory alloy (SMA) fiber as the deformable structure at bump surface to realize the adjustable bump surface of adaptive bump inlet.

According to the deformation and load-bearing requirements of adaptive bump, SMA is applied to the design of adaptive bump inlet due to its characteristic of super-elasticity. A kind of SMA fiber is studied. A composite flexible skin reinforced by SMA is proposed, and its mechanical properties are analyzed. On this basis, an adaptive bump inlet is designed in which the composite flexible skin reinforced by SMA is used as bump surface, and the shape of the bump surface is adjusted by way of pressuring. The design scheme and specific parameters of the adaptive bump are given.

An adaptive bump surface that meets the design requirements of the inlet is designed, which can effectively adjust the inlet throat area with a throat area change rate of 20%.

An adaptive bump inlet with composite flexible skin as a deformable structure at bump surface is designed, and SMA is applied as the reinforcing fiber.

The change of production methods, the industrial revolutions, technological developments, and digital transformation have affected almost all functions in the enterprises. Accounting and auditing areas are also quite affected by this transformation. Another important result of technology and digitalization is the rapid increase in errors, frauds, and irregularities. Enterprises are looking for new solutions and investigations against irregularities and frauds. Audits for errors, frauds, or irregularities are among the interests of forensic accounting. Many methods are used to identify errors and frauds in the forensic accounting. However, it is inevitable that digital technologies should be utilized in forensic accounting applications as a result of the rapid spread of automation and computer programs in enterprises within the framework of digitalized business activities. Hence, enterprises will be able to get more effective results through computer programs and artificial intelligence in terms of fraud audit in forensic accounting. Expert system applications use artificial intelligence to enable computer programs to behave just like people. One of the most widely used, most easily applicable, and most understandable types of expert system is rule-based expert system. The aim of this study is to determine the accounting fraud that may occur in enterprises within the framework of forensic accounting through rule-based expert systems. For this purpose, various applications have been implemented in a large-scale production enterprise through the use of rule-based expert systems for the determination of accounting fraud. Benford’s Law, risk levels, and various other criteria were used in the creation of expert systems. According to the results obtained from the study, it has been seen that by means of rule-based expert system applications, enterprises can better detect existing frauds and prevent further irregularities in the future. The study is important and it is expected that the study will contribute to the literature because it is shown in the study that the rule-based expert systems, applied in many fields under the title of social sciences, can also be applied in the field of forensic accounting and auditing.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

The key aim of this study is to highlight current financial technology (FinTech) trends by conducting a bibliometric review of literature derived from the Scopus database.

A bibliometric analysis was conducted on articles gathered from the Scopus database. Microsoft Excel was used to perform the frequency analysis, VOSviewer for visualising the data, and Harzing’s Publish or Perish for the metrics citation.

According to this investigation, research into FinTech has been consistently increasing since 2008. The results indicate that the most active publisher of FinTech literature is Bina Nusantara University in Indonesia. In terms of country of publication, China is identified as the most active. The most cited author is Buckley, R.P., with Rabbani, M.R., having the most publications. It was also identified that FinTech researches come under three primary domains namely business management, computer science and economics.

The primary limitation of this current study is that it only relied on one data source, i.e. Scopus. Implications wise, researchers and practitioners can gain a deeper understanding of FinTech from this study, which also describes the trend in related publications on the concept. Future studies could significantly benefit from the findings of the present paper.

The outcomes of this study can assist researchers in better comprehending and summarising the key drivers of FinTech. In addition, the findings can help new researchers identify the starting point for their research on FinTech.

As far as the authors are aware, this is the first study that reviews FinTech publications derived from Scopus from 2008 to 2022. Hence, it is a pioneering study into FinTech bibliometric analysis, providing an understanding of the structural knowledge by reviewing the timeline of academic progression in FinTech.

The purpose of this study is to systematically review the state-of-art literature on the net-zero economy in the field of supply chain management.

A systematic literature review of 79 articles published from 2009 to 2021 has been conducted to minimise the researchers' bias and maximise the reliability and replicability of the study.

The thematic analysis reveals that studies in the field of net-zero economy have mostly been done on decarbonisation in the supply chain, emission control and life cycle analysis and environmental and energy management. The findings highlight the strong positive association between digitalisation, circular economy and resources optimization practices with net-zero economy goals. The study also addresses the challenges linked with the net-zero economy at the firm and country levels.

Practitioners in companies and academics might find this review valuable as this study reviews, classifies and analyses the studies, outlines the evolution of literature and offers directions for future studies using the theory, methodology and context (TMC) framework.

This is the first study that uses a structured approach to analyse studies done in the net-zero field by assessing publications from 2009 to 2021.

The main aim of this study is to contribute to the discussion on the factors that can influence the high performance of information systems (IS) project team members, from the individual perspective. This study also allows both IS project team members and their managers to have a thorough picture of high-performing project teams, helping them improve team design, management and performance in today's demanding business environment.

To address the research questions, the authors carried out an exploratory case study of a small-sized holding company and a qualitative analysis of the data.

Results show a set of perceived factors that can influence (facilitate/hinder) the high performance of IS project team members. “Proper reward systems” was the most mentioned facilitating factor. “Negative affectivity” and “Lack of competence” were the two most referred as hindering factors. Most of the perceived factors are classified in the literature as non-technical.

Besides being among the very few empirical studies consolidating knowledge on the high performance of IS project team members, this paper extends the authors' previous research (done at the team level) to the individual team member level (as opposed to the team or organizational levels). In spite of IS being a highly technical industry, this study came across mostly human-centered factors transversal to different professionals (IS and non-IS) involved in project teams.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

Traditionally, urban informality has been discussed in terms of housing and markets, usually along the periphery of urban areas where there is disinvestment and decline. This article looks at urban informality through the lens of informal fresh food retail throughout the city of Mumbai, India. In India, fresh produce has traditionally been sold in informal street markets comprising vendors that operate through carts and make-shift stalls set-up on the streets. This article aims to assess the conditions surrounding fresh produce retail that fuel its informality.

This study uses a mixed methods approach by spatially analyzing the location of informal fresh food vendors in ArcGIS, developing a qualitative analysis of the level of proliferation of this network through interviews conducted with vendors and conducting surveys of residents' access patterns and purchasing habits for fresh produce in the city.

Results from this study indicate that the role of density, transportation systems, domestic/household structure, cultural traditions and a bureaucratic system rife with its own challenges have resulted in a distinct infrastructure of food retail networks that has harvested forms of inequalities and injustices that inherently fuel this informal economy.

There is no published study to date that has been done to spatially assess the informal food network in any dense city in India, let alone Mumbai to date. Urban informality, by its nature, is hard to capture, and yet this study takes a holistic view of the food systems in Mumbai, by addressing the location, supply (through vendor interviews) and demand factors (through resident surveys).

Based on the social interaction theory and trust theory, this study investigates the switching of users on social question and answer (Q&A) platforms from knowledge seekers to knowledge contributors.

We used Python to gather data from Zhihu, performed hypothesis testing on the models using Poisson regression and finally conducted a mediation effect analysis.

The findings reveal that knowledge seeking impacts users' motivation for information interaction, emotional interaction and trust. Notably, information interaction and trust exhibit a chained mediation effect that subsequently influences knowledge contribution.

Current studies on user knowledge behavior typically examine individual actions, rarely connecting knowledge seeking and knowledge contribution. However, the balance of knowledge inflow and outflow is crucial for social Q&A platforms. To cover this gap, this paper empirically investigates the switching between knowledge seeking and knowledge contribution based on the social interaction theory and trust theory.

Ground Penetrating Radar is a multidisciplinary Nondestructive Evaluation technique that requires knowledge of electromagnetic wave propagation, material properties and antenna theory. Under some circumstances this tool may require auxiliary algorithms to improve the interpretation of the collected data. Detection, location and definition of target’s geometrical and physical properties with a low false alarm rate are the objectives of these signal post-processing methods. Basic approaches are focused in the first two objectives while more robust and complex techniques deal with all objectives at once. This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys. We show that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.