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The main aim of this study is to investigate the mixed-mode I/II failure and the cracking manner of three-dimensional (3D)-printed components made by the fused deposition modeling technique in an experimental and theoretical manner.

Acrylonitrile butadiene styrene (ABS) material and a modified printing method (that increases the adhesion and integrity between the layers and strands) are used for manufacturing the semicircular bending (SCB) test samples. In addition to precracking, the effect of additional stress concentration on the stress field is studied by introducing three small holes to the SCB fracture samples. The critical mixed-mode I/II failure loads obtained from the experiments are predicted using different stress/strain-based fracture theories, including maximum tangential stress (MTS), maximum tangential strain (MTSN), generalized form of MTS and MTSN and combination of them with equivalent material concept (EMC). The effects of plastic deformation, as well as the structural stress concentration, are considered for a more realistic prediction of mixed-mode fracture load.

The stress-based criteria are more suitable than the strain-based theories. Among the investigated fracture models, the EMC–generalized maximum tangential stress theory provided the best agreement with the experimental results obtained from 3D-printed SCB tests.

The influences of stress risers and applicability of different failure theories in cracked layered 3D-printed parts are studied on the fracture behavior of tested specimens under mixed-mode I/II.

This study explores evolving tourist preferences post-COVID-19, focusing on the growing demand for sustainable tourism. Using the X.0 wave/tomorrow age theory when X.0 = 5.0, it identifies transformative trends influencing the tourism industry's adaptation to new sustainability expectations.

A mixed-methods approach combines extensive surveys and interviews with diverse tourist profiles to examine behaviors and preferences. The seven pillars of sustainability (7PS) model frames the analysis.

Tourism is shifting toward sustainable practices, emphasizing cultural differences, environmental stewardship, social engagement, economic resilience, technological infrastructure, educational methods and political supports. The integration of X.0 wave theory with SME 5.0 concepts highlights the importance of responsible tourism aligned with evolving tourist expectations.

This study pioneers the application of the X.0 wave/tomorrow age theory to tourism, offering a novel framework for sustainable practices. It provides insights for making tourism resilient, ecologically sound and socially responsible, meeting post-pandemic visitor demands.

The purpose of this study is to use structural equation modeling to examine the interaction between the variables of corporate image, credibility, trust and satisfaction in the context of a virtual sporting event. The aim is to determine whether these variables have significant relationships with each other and which of them has the greatest influence on the prediction of participants' satisfaction.

A structured questionnaire was used, based on previously validated scales. The survey was administered using the LimeSurvey platform. The sample consisted of a total of 588 participants of the Medellín virtual marathon.

The results of the study reveal significant findings regarding the relationships between the variables of corporate image, credibility, trust, and satisfaction in virtual sporting events. In particular, it is highlighted that trust emerges as the most influential factor in participants' satisfaction, which offers an insightful understanding of the importance of this variable in the user experience in virtual sporting events.

This study emphasizes the importance of brand analysis in the sports environment, stressing that the actions undertaken by managers should highlight both the corporate image and the connections with users, given their fundamental role in customer satisfaction. Likewise, the study of these variables within the sports context provides new knowledge and fills existing gaps within the academy. Limitations include the sample and the lack of consideration of all brand variables.

The need to cultivate a strong and well-managed image to build trust with participants is emphasized for organizers of virtual sporting events. It is crucial to work on establishing long-term credibility, especially in the relatively new context of virtual racing. Maintaining, and building the virtual career offering is essential to strengthening relationships, demonstrating a robust corporate image. In addition, since trust and credibility have a significant impact on participant satisfaction in this type of event, managers must communicate the assurance that virtual careers offer an experience free of uncertainty and risk, which is particularly attractive to a new customer base interested in this format.

This article presents an original contribution by investigating the relationships between corporate image, credibility, trust, and satisfaction in the context of virtual sporting events. It employs a structural equation model to assess the significance and predictive capacity of these variables. Notably, the study identifies trust as the most influential factor in predicting participant satisfaction. These findings offer valuable insights into the relative importance of brand variables in shaping user satisfaction within the virtual sporting event domain. By shedding light on these dynamics, the research aids event managers in making informed resource allocation decisions, contributing to a nuanced understanding of brand impact in this context.

This study aims to examine the potential of two artificial intelligence (AI)-based algorithms, namely, adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP), for indirect estimation of the ultimate bearing capacity (q_ult) of rock foundations, which is a considerable civil and geotechnical engineering problem.

The input-processing-output procedures taking place in ANFIS and GEP are represented for developing predictive models. The great importance of simultaneously considering both qualitative and quantitative parameters for indirect estimation of q_ult is taken into account and explained. This issue can be considered as a remarkable merit of using AI-based approaches. Furthermore, the evaluation procedure of various models from both engineering and accuracy viewpoints is also demonstrated in this study.

A new and explicit formula generated by GEP is proposed for the estimation of the q_ult of rock foundations, which can be used for further engineering aims. It is also presented that although the ANFIS approach can predict the output with a high degree of accuracy, the obtained model might be a black-box. The results of model performance analyses confirm that ANFIS and GEP can be used as alternative and useful approaches over previous methods for modeling and prediction problems.

The superiorities and weaknesses of GEP and ANFIS techniques for the numerical analysis of engineering problems are expressed and the performance of their obtained models is compared to those provided by other approaches in the literature. The findings of this research provide the researchers with a better insight to using AI techniques for resolving complicated problems.