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The directional solidification Fe-B alloy was prepared. The microstructures and three-body abrasive wear behaviors of directional solidification alloy were investigated.

Fe-B alloy was melted in medium frequency induction furnace. The hardness was measured on HRS-150 Rockwell-hardness tester and HXD-1000TMC tester. The wear characteristic of the alloy was examined with a block-on-ring geometry. The worn surface of the alloy was investigated by scanning electron microscopy and laser scanning microscopy.

The wear weight loss and worn surface roughness increase with the increasing contact load in wear tests. When the worn surface is perpendicular to the boride growth direction, the highest hardness plane of the boride can resist abrasive effectively under the surrounding and supporting of the martensite matrix.

The relation between boride growth direction and wear direction will cause different boride breaking tendency and wear weight loss.

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.

We empirically investigate the impact of fintech innovation on dividend payout (DP) decisions. In addition, we also examine the mediated and moderated role of intellectual capital (IC) and board characteristics (BC) respectively in the fintech innovation-DP relationship.

Using a sample of 9,441 firm-year observations over the period 2014–2022, we develop a structural model that encompasses fintech innovation, IC, BC and DP decisions. We utilize fixed effects regression to empirically test the model. A battery of tests such as the two-step Generalized Method of Moment, Heckman’s two-stage selection correction and Difference-in-Difference regression are used to check the robustness and sensitivity of the estimates.

Our results suggest that fintech innovation significantly and positively impacts DP decisions and IC partially mediates the fintech innovation–DP relationship. In addition, BC such as independence, age and gender diversity are found to moderate this relationship.

This study’s originality lies in its micro-level analysis of the impact of fintech innovation on DP decisions, considering a novel firm-level innovation metric derived from patent applications. To our knowledge, no previous work has empirically examined the mediating role of IC and the moderating influence of BC in the fintech innovation–DP relationship, offering a unique perspective on the complex interactions shaping dividend policies in the digital era.

The purpose of this paper is to examine (a)symmetric features of task-technology-performance characteristics that are most relevant to fit, satisfaction and continuance intention of using apps in mobile banking transactions.

Exploratory factor analysis was used with maximum likelihood extraction and Varimax rotation on a separate sample of 183 mobile banking apps users prior to the main data collection. The theoretical model was tested applying a factor-based structural equation modelling approach to a sample of 250 experienced mobile banking apps users.

The study unveiled that the task and performance characteristics are more relevant compared to technology characteristics when doing transactions via apps. In addition, the findings uncovered that user satisfaction and continuous intention to use apps stem from the degree of fit in online transactions. The findings of moderation analysis highlighted that users in the lower income group are more concerned about the performance characteristics of banking apps, and there are no differences across age and gender groups. Surprisingly, technology characteristic has a nonlinear nature and this study shows potential boundary conditions of technology characteristics in degree of fit, user satisfaction and continuance intention to use apps.

Findings from the conditional probabilistic queries reveal that with 83.3 per cent of probability, user satisfaction is high when using apps for banking transactions, if the levels of fit, task, performance and technology characteristics are high. Furthermore, with 72 per cent of probability, continuance intention to use apps is high, if the levels of performance and task characteristics are high.

Contributing to task-technology fit theory, this study shows that performance characteristics need to be aligned with task and technology characteristics in order to have better fit when using apps for online banking transactions.

The purpose of this investigation is to identify, evaluate, integrate and summarize relevant and qualified papers through conducting a systematic literature review (SLR) on the application of recommender systems (RSs) to suggest a scholarly publication venue for researcher's paper.

To identify the relevant papers published up to August 11, 2018, an SLR study on four databases (Scopus, Web of Science, IEEE Xplore and ScienceDirect) was conducted. We pursued the guidelines presented by Kitchenham and Charters (2007) for performing SLRs in software engineering. The papers were analyzed based on data sources, RSs classes, techniques/methods/algorithms, datasets, evaluation methodologies and metrics, as well as future directions.

A total of 32 papers were identified. The most data sources exploited in these papers were textual (title/abstract/keywords) and co-authorship data. The RS classes in the selected papers were almost equally used. DBLP was the main dataset utilized. Cosine similarity, social network analysis (SNA) and term frequency–inverse document frequency (TF–IDF) algorithm were frequently used. In terms of evaluation methodologies, 24 papers applied only offline evaluations. Furthermore, precision, accuracy and recall metrics were the popular performance metrics. In the reviewed papers, “use more datasets” and “new algorithms” were frequently mentioned in the future work part as well as conclusions.

Given that a review study has not been conducted in this area, this paper can provide an insight into the current status in this area and may also contribute to future research in this field.

Today, business model innovations leverage digital technologies to gain a competitive advantage and transform business processes. Blockchain is still gaining attention in specific fields and bringing value to business models. There is a dearth of research on how blockchain decentralized autonomous organizations impact organization business model innovations. This study attempts to contribute the body of knowledge based on a review of decentralized autonomous organizations and the business model innovation literature using the integrative and generative approach.

The paper offers an analysis of decentralized autonomous organizations based on digital business models built on the well-established work by Osterwalder and Pigneur (2010). The practical multilayered decentralized autonomous organizations architectural implementation model design is achieved using practical archetypes depicted in the proposed decentralized autonomous organizations business model. The paper evaluates a marketplace comprising 13 decentralized autonomous organizations led platforms with core functionalities.

The paper delivers decentralized autonomous organizations led digital business model canvas elements to explain decentralized autonomous organization business model innovations. It presents the underlying multilayered decentralized autonomous organizations architectural implementation model required to conceptualize a practical business model with an enterprise-ready target operating model.

The paper contributes directly to the practical decentralized autonomous organizations business model canvas, exemplifying the nine elements of decentralized autonomous organizations’ characteristics for any organizational transformation. The tools and accelerators (business model, layered architecture, target operating model and product mapping) developed in the paper address the managerial challenges of redesigning the decentralized business models.

The proposed decentralized autonomous organizations smart contract powered business model provide a digital platform to adhere to rules, follow policies, preserve principles and develop consensus without human interventions. The paper shapes the first of its kind decentralized autonomous organizations marketplace evaluation while mapping it to decentralized autonomous organizations layered architecture product requirement considering business model dimension to adopt actionable target operating model.