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Arbitrary constraints might be included into the problem domain in many engineering applications, which represent specific features such as multi-domain interfaces, cracks with small yield stresses, stiffeners attached on the plate for reinforcement and so on. To imprint these constraints into the final mesh, additional techniques need to be developed to treat these constraints properly.

This paper proposes an automatic approach to generate quadrilateral meshes for the geometric models with complex feature constraints. Firstly, the region is decomposed into sub-regions by the constraints, and then the quadrilateral mesh is generated in each sub-region that satisfies the constraints. A method that deals with constraint lines and points is presented. A distribution function is proposed to represent the distribution of mesh size over the region by using the Laplace equation. The density lines and points can be specified inside the region and reasonable mesh size distribution can be obtained by solving the Laplace equation.

An automatic method to define sub-regions is presented, and the user interaction can be avoided. An algorithm for constructing loops from constraint lines is proposed, which can deal with the randomly distributed constraint lines in a general way. A method is developed to deal with constraint points and quality elements can be generated around constraint points. A function defining the distribution of mesh size is put forward. The examples of constrained quadrilateral mesh generation in actual engineering analysis are presented to show the performance of the approach.

An automatic approach to constrained quadrilateral mesh generation is presented in this paper. It can generate required quality meshes for special applications with complex internal feature constraints.

Gives a bibliographical review of the finite element meshing and remeshing from the theoretical as well as practical points of view. Topics such as adaptive techniques for meshing and remeshing, parallel processing in the finite element modelling, etc. are also included. The bibliography at the end of this paper contains 1,727 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1990 and 2001.

To introduce an interval arithmetic (IA)‐based approach to tolerance analysis and design and to yield evaluation. The technique distinguishes itself by its reliability in the detection of even non convex and/or non simply connected regions of acceptability.

Range methods, and IA among them, inherently allow the evaluation of an overestimation of the true range spanned by a performance function of a given system because of variation of its parameters values. Such a feature is of great help in performing the robust design of any system, that is, in doing the placement of the design set within the region of acceptability, even in the presence of tolerances and parameters values drifts.

The IA‐based branch and bound numerical procedure proposed in the paper has shown its reliability and robustness during the tolerance analysis of systems exhibiting quite involved regions of acceptability.

Many remarks are reported in the paper to complete the research and extend it to a larger class of problems.

The procedure might be built into simulation environments in order to help the designer in dominating tolerances effects.

This paper shares in suggesting the use of a powerful tool, i.e. range methods, in the area of tolerance design and reliability.

A low cost two dimensional pyroelectric array technology has been developed, together with advanced signal processing algorithms. This enables the availability of intelligent passive infrared detector systems for surveillance, counting, and related applications. The technology has also made possible for the first time a truly affordable low‐resolution thermal imager for condition monitoring, plant maintenance, and process control.

The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.

The research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.

The built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.

The NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.

The authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.

Various technology-enhanced learning software and tools exist where technology becomes the main driver for these developments at the expense of pedagogy. The literature reveals the missing balance between technology and pedagogy in the continuously evolving technology-enhanced learning domain. Consequently, e-learners struggle to realise the pedagogical value of such e-learning artefacts. This paper aims to understand the different pedagogical theories, models and frameworks underpinning current technology-enhanced learning artefacts to pave the way for designing more effective e-learning artefacts.

To achieve this goal, a review is conducted to survey the most influential pedagogical theories, models and frameworks. To carry out this review, five major bibliographic databases have been searched, which has led to identifying a large number of articles. The authors selected 34 of them for further analysis based on their relevance to our research scope. The authors critically analysed the selected sources qualitatively to identify the most dominant learning theories, classify them and map them onto the key characteristics, criticism, approaches, models and e-learning artefacts.

The authors highlighted the significance of pedagogies underpinning e-learning artefacts. Furthermore, the authors presented the common and special aspects of each theory to support our claim, which is developing a hybrid pedagogical approach. Such a hybrid approach remains a necessity to effectively guide learners and allow them to achieve their learning outcomes using e-learning artefacts.

The authors found that different pedagogical approaches complement rather than compete with each other. This affirms our recommended approach to adopt a hybrid approach for learning to meet learners' requirements. The authors also found that a substantive consideration for context is inevitable to test our evolving understanding of pedagogy.

The purpose of this study was to prepare water-based binders, which aimed to avoid printhead blockage and to improve dimensional accuracy of inkjet 3D printing (3DP) technology, and a feasible algorithm of full-color printing was realized.

A self-developed color 3D printer was made by using a piezoelectric printhead of Epson Dx-5. Several water-based binders and corresponding gypsum composite powders were prepared, and the optimum binder-powder assembly was then determined through elementary adhesive testing and roller paving testing. Full-color printing was implemented based on halftoning algorithms that used different threshold matrices for different ink channels, and the performances of various algorithms were evaluated in terms of both subjective and objective indices.

The optimum binder-powder assembly can solve the jamming problem of printhead and realize agreeable dimensional accuracy with the relative error less than 2.5% owing to the satisfying boundary diffusion control ability. And the determined halftone algorithm was verified to be agreeable for 3D color printing.

The prepared approach of water-based binders and gypsum composite powders can be applied to similar 3DP systems even if different materials are introduced. And the used halftone algorithms provide feasible guidelines to the implementation of 3D full-color printing.

This paper aims to analyze the role and performance of different artificial intelligence (AI) algorithms in forecasting future movements in the main indices of the world’s largest stock exchanges.

Drawing on finance-based theory, an empirical and experimental study was carried out using four AI-based models. The investigation comprised training, testing and analysis of model performance using accuracy metrics and F1-Score on data from 34 indices, using 9 technical indicators, descriptive statistics, Shapiro–Wilk, Student’s t and Mann–Whitney and Spearman correlation coefficient tests.

All AI-based models performed better than the markets' return expectations, thereby supporting financial, strategic and organizational decisions. The number of days used to calculate the technical indicators enabled the development of models with better performance. Those based on the random forest algorithm present better results than other AI algorithms, regardless of the performance metric adopted.

The study expands knowledge on the topic and provides robust evidence on the role of AI in financial analysis and decision-making, as well as in predicting the movements of the largest stock exchanges in the world. This brings theoretical, strategic and managerial contributions, enabling the discussion of efficient market hypothesis (EMH) in a complex economic reality – in which the use of automation and application of AI has been expanded, opening new avenues of future investigation and the extensive use of technical analysis as support for decisions and machine learning.

The AI algorithms' flexibility to determine their parameters and the window for measuring and estimating technical indicators provide contextually adjusted models that can entail the best possible performance. This expands the informational and decision-making capacity of investors, managers, controllers, market analysts and other economic agents while emphasizing the role of AI algorithms in improving resource allocation in the financial and capital markets.

The originality and value of the research come from the methodology and systematic testing of the EMH through the main indices of the world’s largest stock exchanges – something still unprecedented despite being widely expected by scholars and the market.

The purpose of this paper extends the authors’ previous contributions on aircraft system identification, such as open loop identification or closed loop identification, to cascade system identification. Because the cascade system is one special network system, existing in lots of practical engineers, more unknown systems are needed to identify simultaneously within the statistical environment with the probabilistic noises. Consider this problem of cascade system identification, prediction error method is proposed to identify three unknown systems, which are parameterized by three unknown parameter vectors. Then the cascade system identification is transferred as one parameter identification problem, being solved by the online subgradient descent algorithm. Furthermore, the nonparametric estimation is proposed to consider the general case without any parameterized process. To make up the identification mission, model validation process is given to show the asymptotic interval of the identified parameter. Finally, simulation example confirms the proposed theoretical results.

Firstly, aircraft system identification is reviewed through the understanding about system identification and advances in control theory, then cascade system identification is introduced to be one special network system. Secondly, for the problem of cascade system identification, prediction error method and online subgradient decent algorithm are combined together to identify the cascade system with the parameterized systems. Thirdly from the point of more general completeness, another way is proposed to identify the nonparametric estimation, then model validation process is added to complete the whole identification mission.

This cascade system corresponds to one network system, existing in lots of practice, such as aircraft, ship and robot, so it is necessary to identify this cascade system, paving a way for latter network system identification. Parametric and nonparametric estimations are all studied within the statistical environment. Then research on bounded noise is an ongoing work.

To the best of the authors’ knowledge, research on aircraft system identification only concern on open loop and closed loop system identification, no any identification results about network system identification. This paper considers cascade system identification, being one special case on network system identification, so this paper paves a basic way for latter more advanced system identification and control theory.

To enhance the detection efficiency of printed circuit board (PCB) soldered dot, this study aims to detect PCB soldered dot with image processing method.

Through a series of image processing algorithms, threshold segmentation and feature extraction of the solder joint images were completed; then, sphericity was confirmed based on the area and perimeter, as well as the shape parameter and eccentricity ratio of the calculated region, paving the way for defect pattern recognition.

It proves that the method with high efficiency and precision can satisfy the requirements of PCB soldered dot detection.

This paper is original in presenting a method for PCB welding spot detection.