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The purpose of this paper is to propose a new approach for organizing texts for researchers based on projection from researcher space (consisting of reasoning ability and research ability) onto text space (consisting of text features).

The projection from researcher space onto text space is performed on the grounds of the differences between the sets of essential text features which are consistent, respectively, for the reasoning ability (as researcher's existing ability) and the research ability (as researcher's desired ability) using the concept of a dependency graph.

Through the projection from researcher space onto text space, one can expect to find an effective text which can help a person with certain reasoning ability acquire a certain research ability in the related domain.

Acquisition of reasoning ability (ies) may call for a comprehensive questionnaire or test protocol whose validation by the expert may not necessarily be convenient. Appropriate questionnaires/test protocols, as well as knowledge‐based models for the text features at different layers (to assure derivation of more reliable features), is suggested as future work.

A salient benefit of the proposed approach is its flexibility in responding to a wide range of users with different models. It thus can be used as an efficient tool for online e‐learning and e‐research purposes in cyber‐learning environment.

The originality of the paper mostly lies in the concept of projection from researcher space onto text space as an approach for provision of appropriate text features; and the application of dependency graph as a potential means for identifying those text features whose prerequisites exist in the research abilities of the researcher.

Corporate social responsibility (CSR) and fintech have emerged as critical megatrends in the banking industry. This study aims to examine the impact of financial technology on the relationship between CSR and banks' financial stability. Specifically, it investigates the moderating effect of fintech on the association between CSR and the financial stability of conventional banks operating in Qatar, UAE, Saudi Arabia, Kuwait, Bahrain, Jordan, Pakistan and Turkey from 2010 to 2021.

To achieve the authors’ objective, the authors apply Baron and Kenny's three-link model, tested with fixed and random effects regression models.

The results reveal that the development of fintech decreases banks' financial stability, whereas it promotes banks' involvement in CSR strategies. Furthermore, the findings indicate that fintech plays a moderating role in the relationship between CSR and financial stability. It positively moderates the impact of CSR on financial stability. The robustness analysis highlights the mutual reinforcement of fintech and CSR dimensions in improving the financial stability of banks. Thus, by fostering community and product responsibility, fintech could enhance the financial stability of banks.

Finally, the authors recommend that banks focus more on developing technological and environmentally friendly financial products.

This study contributes significantly by providing valuable insights for managers and policymakers seeking to improve banks' financial stability through the simultaneous adoption of new financial technology products and the strong commitment to CSR practices.

The purpose of this study is to minimize cost and minimize building condition. Weibull distribution approach was employed to generate deterioration curves over time. The third floor of Concordia University’s Engineering And Visual Arts (EV) Complex in Montreal, Canada, served as a case study to test the maintenance model and determine the optimal maintenance activities to be performed.

This research has demonstrated that there is insufficient fund allocation for the maintenance of non-residential buildings. Therefore, this research focused on designing and developing a maintenance optimization model that provides the type of spaces (architectural system) in a building. Sensitivity analysis was used to calculate weights to validate the model. Particle swarm optimization, based explicitly on multiple objectives, was applied for the optimization problem using MATLAB.

Following 100 iterations, 13 non-dominant solutions were generated. Not only was the overall maintenance cost minimized, but the condition of the building was also maximized. Moreover, the condition prediction model demonstrated that the window system type has the most rapid deterioration in educational buildings.

The model is flexible and can be modified by facility managers to align with the required codes or standards.