In recent decades, the redevelopment of waterfronts in global cities has become a focal point for large-scale real estate investments, often driven by neoliberal policies. These projects, characterized by the increasing involvement of state agencies, aim to transform obsolete industrial areas into lucrative spaces for tourism, commerce, and luxury living. This article scrutinizes the intricate dynamics of state-led waterfront re-development, through the lens of Istanbul’s Galataport project. It analyzes the multifaceted dimensions of the transformation process, shedding light on the historical backdrop, socio-political underpinnings, and economic imperatives that have shaped the development of Galataport from 2002 to 2022. Through a comprehensive analysis of primary sources, including governmental reports, policy documents, and scholarly literature, the article accentuates the pivotal role of the state and state actors in orchestrating the transformation of Istanbul’s urban landscape. Furthermore, it examines the implications of the Galataport project on urban governance and socio-cultural and spatial dynamics. It concludes that the central government pursued a speculative entrepreneurial approach in the Galataport project, clearing various legal obstacles while neglecting public interest. This case study takes the first step towards a comprehensive critical re-evaluation of the recent urban development/governance model to contribute to a nuanced understanding of contemporary urban/waterfront development paradigms in Türkiye and similar geographies.

The digital era has ushered in significant advancements in Generative Artificial Intelligence (GAI), particularly through Generative Models and Large Language Models (LLMs) like ChatGPT, revolutionizing educational paradigms. This research, set against the backdrop of Society 5.0 and aimed at sustainable educational practices, utilizes qualitative analysis to explore the impact of Generative AI in various learning environments. It highlights the potential of LLMs to offer personalized learning experiences, democratize education, and enhance global educational outcomes. The study finds that Generative AI revitalizes learning methodologies and supports educational systems’ sustainability by catering to diverse learning needs and breaking down access barriers. In conclusion, the paper discusses the future educational strategies influenced by Generative AI, emphasizing the need for alignment with Society 5.0’s principles to foster adaptable and sustainable educational inclusion.

The current manuscript overviews the potential of inimitable zero dimensional carbon nanoentities, i.e., nanodiamonds, in the form of hybrid nanostructures with allied nanocarbons such as graphene and carbon nanotube. Accordingly, two major categories of hybrid nanodiamond nanoadditives have been examined for nanocompositing, including nanodiamond-graphene or nanodiamond/graphene oxide and nanodiamond/carbon nanotubes. These exceptional nanodiamond derived bifunctional nanocarbon nanostructures depicted valuable structural and physical attributes (morphology, electrical, mechanical, thermal, etc.) owing to the combination of intrinsic features of nanodiamonds with other nanocarbons. Consequently, as per literature reported so far, noteworthy multifunctional hybrid nanodiamond-graphene, nanodiamond/graphene oxide, and nanodiamond/carbon nanotube nanoadditives have been argued for characteristics and potential advantages. Particularly, these nanodiamond derived hybrid nanoparticles based nanomaterials seem deployable in the fields of electromagnetic radiation shielding, electronic devices like field effect transistors, energy storing maneuvers namely supercapacitors, and biomedical utilizations for wound healing, tissue engineering, biosensing, etc. Nonetheless, restricted research traced up till now on hybrid nanodiamond-graphene and nanodiamond/carbon nanotube based nanocomposites, therefore, future research appears necessary for further precise design varieties, large scale processing, and advanced technological progresses.

This research explores the advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries, highlighting its pivotal role in mitigating the global incidence of occupational incidents and diseases, which result in approximately 2.3 million fatalities annually. Traditional OHS practices often fall short in completely preventing workplace incidents, primarily due to limitations in human-operated risk assessments and management. The integration of AI technologies has been instrumental in automating hazardous tasks, enhancing real-time monitoring, and improving decision-making through comprehensive data analysis. Specific AI applications discussed include drones and robots for risky operations, computer vision for environmental monitoring, and predictive analytics to pre-empt potential hazards. Additionally, AI-driven simulations are enhancing training protocols, significantly improving both the safety and efficiency of workers. Various studies supporting the effectiveness of these AI applications indicate marked improvements in risk management and incident prevention. By transitioning from reactive to proactive safety measures, the implementation of AI in OHS represents a transformative approach, aiming to substantially reduce the global burden of occupational injuries and fatalities in high-risk sectors.

Health data governance is essential for optimal processing of data collection, sharing, and reuse. Although the World Health Organization (WHO) has proposed practical guidelines for managing health data during the pandemic, the Organization for Economic Cooperation and Development (OECD) found that many countries still lack the use of health data for decision-making. Therefore, this research aimed to identify and assess the challenges faced by health organization in implementing health data governance from various countries based on research articles. The challenges were assessed based on key components of health data governance from practitioner and scientist perspectives. These components include stakeholder, policy, data management, organization, data governance maturity assessment, and goals. The method used followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for collecting and reporting. Data were collected from several databases online with large repositories of academic studies, including IEEE Xplore, ScienceDirect, National Library of Medicine, ProQuest, Taylor and Francis Group, Scopus, and Wiley Online libraries. Based on the 41 papers reviewed, the results showed that policy was found to be the biggest challenge for health data governance. This was followed by data management such as quality, ownership, and access, as well as stakeholders and data governance organization. However, there were no challenges regarding maturity assessment and data governance goals, as the majority of research focused on implementation. Policy and policymaker awareness were identified as major components for the implementation of health data governance. To address challenges in data management and governance organization, creating committees focused on these components proved to be an effective solution. These results provided valuable recommendations for regulators and leaders in a healthcare organization to optimally implement health data governance.

Government performance means the results of government work. Its use is to evaluate government accountability, decision-making, efficiency, effectiveness, transparency, and achievement of goals. Purpose: This paper aims to explore the understanding of performance measurement tools commonly used in government, the reasons for using them, and the implementation of performance measurement in Indonesia. Method: This study uses a meta-synthesis method, an integrative review approach from 2000–2021, in the Scopus database using the keywords measurement system, performance measurement, performance measurement government, measurement system government. Results and Discussion: The final sample consisted of 23 studies, and the results showed that the most commonly used performance measurement was the balanced scorecard. This is because the balanced scorecard is able to explain the vision, mission, strategy, results, and operational actions, so that it can achieve local government goals. Research implications: Insight into government performance measurement can be used to determine the strengths and weaknesses of various performance measurement tools so that the government can implement performance measurement tools that are more appropriate for its government. Originality/Value: This study offers an adaptation of existing methods to measure government performance more effectively. In addition, this study focuses on the context of developing countries, which can provide new contributions to the literature.