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[Purpose/Significance] With the rapid advancement of global science and technology, emerging technologies are constantly evolving, placing higher demands on national strategic planning and resource allocation. Artificial intelligence (AI), as a core driver of the current technological revolution, requires close attention to its internal technical topic evolution to anticipate disruptive changes and guide the direction innovation. Although existing research primarily focuses on identifying technical topics through bibliometric or patent analysis, there is still insufficient quantitative forecasting of their future development. To address this gap, this study proposes an integrated analytical framework that combines BERTopic-based topic modeling with an IWOA-optimized BiLSTM neural network, achieving a unified approach to both topic identification and trend forecasting. Unlike traditional LDA models or expert-based subjective judgment, this method demonstrates significant advancements in semantic representation, model optimization, and prediction accuracy. It expands the theoretical boundaries of emerging technology forecasting and offers valuable quantitative support for science and technology policy-making. [Method/Process] This study utilized 22,243 AI-related patent records collected from 2015 to 2024. BERTopic was applied to extract representative technology topics from patent abstracts. A multi-dimensional evaluation system was constructed using three indicators: novelty, impact, and growth rate, capturing different aspects of emerging technologies. The CRITIC method was employed to objectively assign weights to each dimension, enhancing the robustness and balance of the composite index. BERTopic, which integrates BERT-based semantic embeddings with HDBSCAN density-based clustering, improves both the coherence and granularity of topic extraction. For trend prediction, an Improved Whale Optimization Algorithm (IWOA) was introduced to fine-tune BiLSTM's learning rate, epoch count, and hidden layer size. IWOA enhances global optimization through Gaussian chaos initialization, Levy flight strategy, nonlinear control factors, and elite reverse learning. [Results/Conclusions] Experimental results show that BERTopic achieves superior topic coherence compared to baseline models and successfully identifies five emerging technical areas, including Intelligent Models and Algorithms, Information Processing, Deep Neural Networks, Smart Robotics, and Numerical Control Systems. The IWOA-BiLSTM model outperforms conventional LSTM and BiLSTM models in error metrics (MAPE, RMSE, and MAE), confirming its predictive advantage. Forecast results indicate that these emerging topics will experience sustained growth over the next five years, reflecting strong application potential and industrial value. This study confirms the feasibility and effectiveness of the integrated \"identification–prediction\" framework, providing a data-driven tool for strategic decision-making in science and technology development. Limitations include dependence on data quality and a current focus on the field of AI. Future research should expand the framework to other strategic areas, such as renewable energy, biomedicine, and intelligent manufacturing, to further validate its generalizability.

This paper addresses open data, open governance, and disruptive/emerging technologies from the perspectives of disaster risk reduction (DRR). With an in-depth literature review of open governance, the paper identifies five principles for open data adopted in the disaster risk reduction field: (1) open by default, (2) accessible, licensed and documented, (3) co-created, (4) locally owned, and (5) communicated in ways that meet the needs of diverse users. The paper also analyzes the evolution of emerging technologies and their application in Japan. The four-phased evolution in the disaster risk reduction is mentioned as DRR 1.0 (Isewan typhoon, 1959), DRR 2.0 (the Great Hanshin Awaji Earthquake, 1995), DRR 3.0 (the Great East Japan Earthquake and Tsunami: GEJE, 2011) and DRR 4.0 (post GEJE). After the GEJE of 2011, different initiatives have emerged in open data, as well as collaboration/partnership with tech firms for emerging technologies in DRR. This paper analyzes the lessons from the July 2021 landslide in Atami, and draws some lessons based on the above-mentioned five principles. Some of the key lessons for open data movement include characterizing open and usable data, local governance systems, co-creating to co-delivering solutions, data democratization, and interpreting de-segregated data with community engagement. These lessons are useful for outside Japan in terms of data licensing, adaptive governance, stakeholder usage, and community engagement. However, as governance systems are rooted in local decision-making and cultural contexts, some of these lessons need to be customized based on the local conditions. Open governance is still an evolving culture in many countries, and open data is considered as an important tool for that. While there is a trend to develop open data for geo-spatial information, it emerged from the discussion in the paper that it is important to have customized open data for people, wellbeing, health care, and for keeping the balance of data privacy. The evolution of emerging technologies and their usage is proceeding at a higher speed than ever, while the governance system employed to support and use emerging technologies needs time to change and adapt. Therefore, it is very important to properly synchronize and customize open data, open governance and emerging/disruptive technologies for their effective use in disaster risk reduction.

Artificial intelligence is the ecosphere’s prevalent and most comprehensive general acquaintance common-sense cognitive engine. The artificial intelligence (AI) business platform model is virtually at affluence with cloud SaaS model. It concerns AI solutions that can work together on the top layer of the other digital systems, like a Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) business system. AI admittances in the digital data fluid through the coordination, fueling business enhancements over phases. In this business model, the business will safekeep a recurrent subscription. This paper endeavors to emphasize on the preventative side of the use of AI and machine learning (ML) technology to enterprise digital platform business model innovation and business dynamics. We acme the strategic implications and innovations with analytics. We explore the derivations of data-driven insights, models, and visualizations.

The urgency of improving science, technology, engineering, and mathematics (STEM) learning has beeninternationally recognized. However, the views on the nature and development of proficiencies in STEM education are diverse, and increased focus on integration raises new concerns and needs for further research. The complexity of these factors reaches beyond just helping students achieve high scores in STEM topics assessments. In practice, teachers struggle and lack cohesive understanding of STEM education. Also, students are most of the time disinterested in some STEM subjects and do not understand how STEM knowledge is applied to real-world problems. Connecting ideas across disciplines is challenging when students have little or no understanding of the relevant ideas in the individualdisciplines. Therefore, a STEM education conceptual framework is needed to build a research agenda that will in turn inform stakeholders to realize the full potential of integrated STEM education. In this paper, we present key concepts to build an integrated STEM education framework through mobile serious games which reflect design principles created based on the theoretical understanding of teaching andlearning.

In this paper, a possibility to reduce the environmental burdens by employing thermoelectric generators (TEGs) was analyzed with a cradle-to-grave LCA approach. An upscaling technique was newly introduced to assess the environmental impacts of TEGs over its life cycle. In addition to CO2 emissions, other environmental impacts as well as social impacts were assessed using the Life Cycle Impact Assessment Method based on Endpoint Modeling (LIME2). The analysis was conducted under two scenarios, a baseline scenario with a 7.2% conversion efficiency and a technology innovation scenario with that of 17.7% at different production scales. The results showed that while GHG emissions were positive over the life cycle under the baseline scenario, it became negative (−1.56 × 102 kg-CO2 eq/kg) under the technology innovation scenario due to GHG credits in the use phase. An increase in the conversion efficiency of the TEG and a decrease in the amount of stainless steel used in TEG construction are both necessary in order to reduce the environmental impacts associated with TEG manufacture and use. In addition, to accurately assess the benefit of TEG deployment, the lifetime driving distance needs to be analyzed together with the conversion efficiency.

Virtual reality (VR) technology is an emerging tool that is supporting the connection between conservation research and public engagement with environmental issues. The use of VR in ecology consists of interviewing diverse groups of people while they are immersed within a virtual ecosystem to produce better information than more traditional surveys. However, at present, the relatively high level of expertise in specific programming languages and disjoint pathways required to run VR experiments hinder their wider application in ecology and other sciences. We present R2VR, a package for implementing and performing VR experiments in R with the aim of easing the learning curve for applied scientists including ecologists. The package provides functions for rendering VR scenes on web browsers with A-Frame that can be viewed by multiple users on smartphones, laptops, and VR headsets. It also provides instructions on how to retrieve answers from an online database in R. Three published ecological case studies are used to illustrate the R2VR workflow, and show how to run a VR experiments and collect the resulting datasets. By tapping into the popularity of R among ecologists, the R2VR package creates new opportunities to address the complex challenges associated with conservation, improve scientific knowledge, and promote new ways to share better understanding of environmental issues. The package could also be used in other fields outside of ecology.

Artificial Intelligence (AI) and Big Data Analytics (BDA) have the potential to significantly improve resilience of supply chains and to facilitate more effective management of supply chain resources. Despite such potential benefits and the increase in popularity of AI and BDA in the context of supply chains, research to date is dispersed into research streams that is largely based on the publication outlet. We curate and synthesise this dispersed knowledge by conducting a systematic literature review of AI and BDA research in supply chain resilience that have been published in the Chartered Association of Business School (CABS) ranked journals between 2011 and 2021. The search strategy resulted in 522 studies, of which 23 were identified as primary papers relevant to this research. The findings advance knowledge by (i) assessing the current state of AI and BDA in supply chain literature, (ii) identifying the phases of supply chain resilience (readiness, response, recovery, adaptability) that AI and BDA have been reported to improve, and (iii) synthesising the reported benefits of AI and BDA in the context of supply chain resilience.

Industry 5.0 is projected to be an exemplary improvement in digital transformation allowing for mass customization and production efficiencies using emerging technologies such as universal machines, autonomous and self-driving robots, self-healing networks, cloud data analytics, etc., to supersede the limitations of Industry 4.0. To successfully pave the way for acceptance of these technologies, we must be bound and adhere to ethical and regulatory standards. Presently, with ethical standards still under development, and each region following a different set of standards and policies, the complexity of being compliant increases. Having vague and inconsistent ethical guidelines leaves potential gray areas leading to privacy, ethical, and data breaches that must be resolved. This paper examines the ethical dimensions and dilemmas associated with emerging technologies and provides potential methods to mitigate their legal/regulatory issues.

This book offers a multidisciplinary analysis of emerging technologies and their impact on the new international security environment across three levels of analysis. While recent technological developments, such as artificial intelligence (AI), robotics, and automation, have the potential to transform international relations in positive ways, they also pose challenges to peace and security and raise new ethical, legal, and political questions about the use of power and the role of humans in war and conflict. This book makes a contribution to these debates by considering emerging technologies across three levels of analysis: (1) The international system (systemic level) including the balance of power; (2) the state and its role in international affairs and how these technologies are redefining and challenging the state’s traditional roles; and (3) the relationship between the state and society, including how these technologies affect individuals and non-state actors. This provides specific insights at each of these levels and generates a better understanding of the connections between the international and the local when it comes to technological advance across time and space. The chapters examine the implications of these technologies for the balance of power, examining the strategies of the US, Russia, and China to harness AI, robotics, and automation (and how their militaries and private corporations are responding); how smaller and less powerful states and non-state actors are adjusting; the political, ethical, and legal implications of AI and automation; what these technologies mean for how war and power is understood and utilized in the 21st century; and how these technologies diffuse power away from the state to society, individuals, and non-state actors. This volume will be of much interest to students of international security, science and technology studies, law, philosophy, and international relations.

This paper presents an overview of various types of virtual museums (ViM) as native artifacts or as digital twins (DT) of physical museums (PM). Depending on their mission and features, we discuss various enabling technologies and sensor equipment with their specific requirements and complexities, advantages and drawbacks in relation to each other at all stages of a DT’s life cycle. A DT is a virtual construct and embodies innovative concepts based on emerging technologies (ET) using adequate sensor configurations for (meta-)data import and exchange. Our keyword-based search for articles, conference papers, (chapters from) books and reviews yielded 43 contributions and 43 further important references from Industry 4.0, Tourism and Heritage 4.0. After closer examination, a reference corpus of 40 contributions was evaluated in detail and classified along with their variants of DT—content-, communication-, and collaboration-centric and risk-informed ViMs. Their system features correlate with different application areas (AA), new or improved technologies—mostly still under development—and sensors used. Our proposal suggests a template-based, generative approach to DTs using standardized metadata formats, expert/curator software and customers’/visitors’ engagement. It advocates for stakeholders’ collaboration as part of a comprehensive validation and verification assessment (V&VA) throughout the DT’s entire life cycle.