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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.

This study aims to pilot-test the acceptability, feasibility, preliminary efficacy and user experience of an animated virtual reality (VR) game (“Goliath: Playing with Reality”) aimed at reducing stigma related to psychosis and schizophrenia among health-care students and trainees.

A mixed-methods design was used. Sixteen trainee clinical psychologists with experience working with service users with psychosis/schizophrenia engaged with the VR game using a headset and controllers, and completed measures before and after. Quantitative measures assessing intervention acceptability, appropriateness and feasibility were collected and analysed descriptively. These were complemented by qualitative feedback on user experience, analysed through content analysis. Pre- and post-intervention mental health stigma-related measures were also collected and analysed inferentially.

Most participants found the VR game acceptable, appropriate and feasible for reducing mental health stigma. Although there were no significant changes in stigma-related measures, participants were impressed by the immersive storytelling and the symbolic portrayal of psychosis. Participants also valued the educational aspect, empathised with people living with psychosis/schizophrenia and experienced a range of emotional reactions. However, participants noted usability concerns, and suggestions for improvement were made.

This is one of the first studies to show the potential of “Goliath: Playing with Reality” as a mental health educational intervention for health-care students and trainees. Drawing on narrative theory and immersive-interactive storytelling techniques, the VR game provides experiences that can foster narrative engagement and emotional connection, potentially enhancing its effectiveness in mental health stigma reduction efforts.

The goal of this study is to better understand the driving force behind the use of artificial intelligence (AI) in pharmaceutical manufacturing firms (PMFs) that are recognized as developing countries in the Middle East and North Africa (MENA) region that are listed by the Chambers of the Industries of Jordan, the Kingdom of Saudi Arabia, Morocco, and Algeria. Furthermore, the effect of adopting and using AI in managing raw materials (RMs), products, parts, and components for PMFs through supply chains (SCs).

A self-administrated questionnaire survey was used to gather data from 95 out of 511 participating managers (e.g. manufacturing, supplying, IT, operational, and logistical managers) utilizing a quantitative technique with a random sample size. In fact, 18.8% of the 89 different manufacturing firms (MFs) in the MENA area responded, with five to six managers from each company. The raw data was analyzed using partial least squares structural equation modeling (PLS-SEM).

The study’s findings show that the readiness to embrace artificial intelligence (AI) in the production management supply chain performance (PMSCP) of pharmaceutical manufacturing firms in the Middle East and North Africa (MENA) is positively and significantly influenced directly and indirectly by sustainable strategic supplier reliability (SSSR), shipping process dependability (SPD), technological factors (TFs), and infrastructure transformational development capability (ITDC).

As the studied countries are growing economies, such study findings might offer insightful consequences for stakeholders and policymakers regarding the significance of using artificial intelligence system adoptions in pharmaceutical manufacturing enterprises in the MENA region. The managers may also concentrate on the strong positive direct and indirect links between SSSR, SPD, TFs, and ITDC preparedness to accept AI adoption and its applications and systems in supply chain and production management departments and the consequences of informational and product delivery.

This study tries to examine the effect of artificial intelligence (AI) drivers on the willingness to adopt the human capital supply chain (HCSC) in manufacturing firms (MFs) in developing countries (DCs) including Jordan, Saudi Arabia, Bahrain, Qatar and the United Arab Emirates, which are listed in the Chambers of Industry of these countries.

The quantitative methodology with a simple random sampling method was adopted using a questionnaire survey-based approach to collect data from 233 out of 1,055 participants (human resource (HR) managers and information technology (IT) senior managers) from various MFs (private and commercial), representing a 22% response rate. Covariance-based structural equation modeling (CB-SEM) was used to analyze the raw data using Amos V.25.

The results of this study showed that there are positive and statistically significant direct association effects between the reliability of use (RoU), competitive pressures (CPs) and user confidence (UC) factors on the willingness to adopt AI in HCSC in the MFs in DCs. At the same time, there is no significant effect on a firm’s infrastructure readiness (FIRs), in addition to the indirect effect of UC in the relationship between CPs and FIRs on the willingness to adopt AI in HCSC.

Such findings of this study can provide insightful implications for stakeholders and policymakers regarding the importance of using predictive AI drivers' effect on willingness to adopt the HCSC in the MFs in DCs as emerging economies. Additionally, the managers might focus on the existence of a significant positive indirect effect of UC as a mediating factor in the relationship between FIRs and willingness to adopt AI and its applications in HCSC systems and departments.