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This paper aims to discuss research ethics in mixed-methods research (MMR) and MMR development with a focus on ethical challenges that stem from working with technical instruments such as mobile eye-trackers.

The case of an interdisciplinary mixed-methods development study that aimed at researching the impacts of emerging mobile augmented-reality technologies on the perception of public places serves as an example to discuss research-ethical challenges regarding (1) the practical implementation of the study, (2) data processing and management and (3) societal implications of developing instruments to track and understand human practices.

This study reports challenges and experiences in ethical decision-making in the practical implementation of the study regarding the relationship to research subjects, the use of mobile research instruments in public places and the interdisciplinary cooperation among research team members. Further, this paper expounds on ethical challenges and recommendations in data processing and management and with a view to societal implications of method development and the aspirations of transdisciplinarity. This study concludes that institutionalized ethics need to become more flexible, while applied ethics and reflection must make their entry into university curricula across disciplines.

Complex interdisciplinary mobile and mixed-methods projects that involve sensors and instruments such as mobile eye-trackers are on the rise. However, there is a significant lack of engagement with practical research ethical challenges, practices and requirements in both mixed-methods and method-development literature. By taking a context- and process-oriented perspective focusing on doing ethics, the paper contributes a concrete empirical case to these underdeveloped fields.

Surface curvature is needed to analyze the range data of real objects and is widely applied in object recognition and segmentation, robotics, and computer vision. Therefore, it is not easy to estimate the curvature of the scanned data. In recent years, machine learning classification methods have gained importance in various fields such as finance, health, engineering, etc. The purpose of this study is to classify surface points based on principal curvatures to find the best method for determining surface point types.

A feature selection method is presented to find the best feature vector that achieves the highest accuracy. For this reason, ten different feature selections are used and six sample datasets of different sizes are classified using these feature vectors.

The author examined the surface examples based on the feature vector using the machine learning classification methods. Also, the author compared the results for each experiment.

To the best of the author's knowledge, this is the first study to examine surface points according to principal curvatures using machine learning classification methods.

The purpose of this paper is to review cases of artificial reefs built through additive manufacturing (AM) technologies and analyse their ecological goals, fabrication process, materials, structural design features and implementation location to determine predominant parameters, environmental impacts, advantages, and limitations.

The review analysed 16 cases of artificial reefs from both temperate and tropical regions. These were categorised based on the AM process used, the mortar material used (crucial for biological applications), the structural design features and the location of implementation. These parameters are assessed to determine how effectively the designs meet the stipulated ecological goals, how AM technologies demonstrate their potential in comparison to conventional methods and the preference locations of these implementations.

The overview revealed that the dominant artificial reef implementation occurs in the Mediterranean and Atlantic Seas, both accounting for 24%. The remaining cases were in the Australian Sea (20%), the South Asia Sea (12%), the Persian Gulf and the Pacific Ocean, both with 8%, and the Indian Sea with 4% of all the cases studied. It was concluded that fused filament fabrication, binder jetting and material extrusion represent the main AM processes used to build artificial reefs. Cementitious materials, ceramics, polymers and geopolymer formulations were used, incorporating aggregates from mineral residues, biological wastes and pozzolan materials, to reduce environmental impacts, promote the circular economy and be more beneficial for marine ecosystems. The evaluation ranking assessed how well their design and materials align with their ecological goals, demonstrating that five cases were ranked with high effectiveness, ten projects with moderate effectiveness and one case with low effectiveness.

AM represents an innovative method for marine restoration and management. It offers a rapid prototyping technique for design validation and enables the creation of highly complex shapes for habitat diversification while incorporating a diverse range of materials to benefit environmental and marine species’ habitats.

Ongoing environmental threats have intensified the need for firms to take big leaps forward to operate in a manner that is both ecologically sustainable and socially responsible. This paper aims to assess the degree to which firms are adopting citizen science as a tool to achieve sustainability and social responsibility targets.

This study applies a qualitative content analysis approach to assess the current presence of citizen science in sustainability and social responsibility reports issued by Globescan sustainability leaders and by firms ranked by the Fortune 500 and Fortune Global 500.

While the term itself is mostly absent from reports, firms are reporting on a range of activities that could be classified as a form of “citizen science.”

Citizen science can help firms achieve their corporate sustainability and corporate social responsibility goals and targets. Linking sustainability and social responsibility efforts to this existing framework can help triangulate corporate efforts to engage with stakeholders, collect data about the state of the environment and promote better stewardship of natural resources.

Supporting citizen science can help firms work toward meeting UN Sustainable Development Goals, which have highlighted the importance of collaborative efforts that can engage a broad range of stakeholders in the transition to more sustainable business models.

This paper is the first to examine citizen science in a corporate sustainability and social responsibility context. The findings present information to support improvements to the development of locally relevant science-based indicators; real-time monitoring of natural resources and supply chain sustainability; and participatory forums for stakeholders including suppliers, end users and the broader community.