The concept of smart cities has received a significant interest recently among researchers. United Nations reports that more than half of the world population currently lives in urban environment, and is estimated to increase to about 70% by 2050. This trend needs serious attention by governments to create solutions that will make sure that their citizens will continue to have sustainable and improved quality of life. Smart cities use all the available technologies to achieve that goal and to improve the efficiency of their operations. This massive undertaking of making existing cities smart and building the new smart cities, comes with many opportunities as well as some challenges. Digitization of services, smart operations, and connectivity are essential parts of smart cities. The smart cities will certainly promote cohesive, connected, healthier, and happier communities. This paper elaborates the opportunities and the challenges in moving the smart cities concept forward.

This paper focuses on how best to communicate your scholarly work to wider audiences. Topics covered include inter-, multi-, and trans-disciplinarity in academia: what are the differences between them, what are the benefits to your academic work, and how to learn from and leverage cross-disciplinary partnerships to boost your messaging. Using the communication theories of Systems Theory and Symbolic Interactionism as a framework, I discuss how best to approach communicating about your research to other disciplines and non-academic audiences. Examples include writing opinion/editorial pieces for mainstream media, networking with science communicators, and connecting with your university’s strategic communications officers.

It is not possible to understand the whole with only a specialized field. The content is not always correctly conveyed to non-specialists. In this study, based on students’ awareness of preparing for the future under a pandemic, understanding of the utility of masks against infectious diseases, and the transition of students’ daily behavior under a pandemic, student group activity was carried out with the theme of “designing tools and systems to contribute to social life in pandemic”. In addition, to motivate students to improve their transdisciplinary communication skills, cross-cultural online networking was carried out based on biomedical engineering as a multidisciplinary field.

This paper discusses new challenges in learning informatics from an interdisciplinary perspective. As topics of learning informatics must cover very wide ranges, this paper will provide an overview with a combination of several ideas and applications. From a methodological point of view, the challenges include the concepts of 'knowledge networks', 'learner personas', and 'visualization of the experience'. From the viewpoints of educational applications, they contain the concepts of 'class design', 'peer review' and 'case learning'. The introduction of various concepts from fields such as computer sciences, marketing research, and design thinking of soft systems domains, whose areas are far from education applications, would add new insights to learning informatics research.

The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies is transforming industries, societies, and the nature of human-machine interactions. This paper explores the impact of AI and the critical role of transdisciplinary research in understanding, harnessing, and guiding its evolution. We discuss the evolution of AI from rule-based systems to complex neural networks, followed by various use cases and a case study of the MITRE Corporation's xAcademy platform. Challenges and safety recommendations are presented, and future work is proposed to address ethical, social, and economic implications of AI. The aim is to foster a comprehensive understanding of AI's potential and promote interdisciplinary collaboration to ensure an ethically sound and equitable AI-powered future.

For a long time, expressiveness in musical performance was a question of musical performance and research. It was difficult to explain why musicians arouse aesthetic emotions in their listeners with their interpretation. Even so, was more complex to explain why the performance of the same work by various artists provoked different emotional responses in listeners. Thanks to empirical research, our understanding of this phenomenon has improved. However, these theories and discoveries have not permeated teaching practice, so they have not yet taken a new direction in music teaching.

In this paper, we proposed a new approach to expressiveness in musical interpretation in a systemic and complex way. For this, we propose a systemic model that allows us to observe the dynamic behavior of the phenomenon of emotional communication in musical interpretation. Furthermore, we suggest a systemic transdisciplinary didactic proposal to encourage the performer to develop their musical identity, reinforce their sensitivity and creativity, and enrich their expression and participatory intention in interpretation.

This paper presents a theory of good pedagogy that i) unifies several current theories, ii) is cybernetic, that is, content independent, and iii) operationally defined. The paper builds off a recent theoretical unification of pedagogy using the four pedagogic pillars of executive function, goal-setting, attribution theory, and self-efficacy. A novelty of the theory is avoidance of jargon, such as higher cognitive, which, while having intuitive appeal, has meaning which is ambiguous. The theory also avoids secondary terms such as creative or analytic and instead focuses on root psychological processes such as executive function and multiple modalities. The theory is applied in a multi-disciplinary setting addressing both machine and human mastery of tasks. This multi-disciplinarity is synergistic allowing simultaneous considerations of emotional (human) and efficiency (machine) issues. The theory is easily applied to new situations without the need for special training. It is hoped that instructors will begin to use this approach in their instructional design.

In the early 2020s, advances in transformer-based deep neural networks enabled the development and growth of a number of generative artificial intelligence (GenAI) systems notable for accepting natural language prompts as input. These include large language model chatbots such as ChatGPT, Bard, and others. GenAI has applications across a wide range of industries, including art, writing, software development, product design, healthcare, finance, gaming, and more. In this paper, we place these recent advances in a historical, cybernetic context. We analyze ethical issues that arise in the area of software engineering and cyber-physical systems. In addition, we explore AI-based challenges in healthcare and medicine, including a number involving GenAI. This research shows the importance of rigorous ethical analysis and resulting safeguards to address the emerging issues with AI.

Science is split into subject-oriented disciplines that are grounded on varying shades of the scientific method. Being a social activity, communication lays at the creative core of the sciencebuilding process of facts and explanations. To communicate means, in its most seminal sense, to bring something in common between distinct parts or to transfer something from one part to another. This ‘commonality,’ or what lays in common to the parts, can occur not just among persons but also among persons and nature or persons and non-human things. This article starts discussing communication at the human scale from the very beginning, widening the scope from its inner kernel in individuals towards social interactions in the scientific community. The perspectives, concepts, and guidelines unveiled by this exercise are applied in the article’s text itself.

This transdisciplinary discussion draws parallels between Aldous Huxley's dystopian vision in his novel 'Brave New World' and the current era dominated by Large Language Models (LLM) and Generative Artificial Intelligence, scrutinizing its significant implications and potential effects. It explores how AI can influence human behavior, reshape societal norms, and potentially lead to the homogenization of linguistic expression. Highlighting the risks of unregulated AI-driven tools, the discussion stresses their potential to standardize and diminish the richness of human language, creativity, and authentic expression. The transformative potential of AI across various domains is recognized, with a strong emphasis on ethical considerations, democratic governance, and preserving core human values. Using 'Brave New World' as a literary framework, it advocates for a transdisciplinary dialogue to critically evaluate AI's impact on social ethos, logos, and pathos. The discussion underscores a global collective responsibility to utilize AI to enrich shared human experiences without compromising the nuances that define our identity and autonomy. It addresses the consequences of uniformity and stability, the role of technology and consumerism, emotional experiences, deep relationships, personal growth, and the threat of totalitarian control, with AI emerging as a nascent regime powered by opaque algorithms implemented by transnational, global companies. Serving as both a reflection on humanity's role in an automated age and a call to action, it advocates for a technological deployment that enhances, rather than diminishes, our inherent humanity.

The proposal of this paper is articulated as follows:

• Recognize Human Ecology as a reference of everyday social practices, from knowledge to the management of living environments.
• Consider ternary ecosystems Man Society Environment as complex disordered systems, relational dynamic structures, which interact with corresponding disordered systems and share the intrinsic prerogatives that unite them in the multiplicity of their becoming.
• Respond to the looming threats of mechanical simplification of spontaneous evolutionary phenomena progressively stifled by the Artificial World in which Ecosystems (Humans and Nature) are now immersed.
• Rediscover conditions of creative autonomy and appropriate stimuli for the Ecosystemic, unceasingly manifold becoming.
• Rediscover in people and human societies a sense of belonging to their Living Environments and the ability to manage, protect and promote their evolution, in an experiential dimension of Action-Research and self-Governance, beyond the current stereotypes of traditional politics and instrumentation and the new constraints posed by the Digital World to the World of Nature.
• Verification through cyclical self-monitoring of the processes activated (Action-Research) and their evolutionary and propagation potential in the different contemporary contexts of the ternary M/S/E Ecosystems affected by the Digital World.

Entrepreneurship plays a central role in driving economic growth and fostering innovation worldwide. As the business landscape becomes increasingly dynamic and competitive, the need for effective entrepreneurship education has never been more critical. This paper presents an integrated framework for entrepreneurship education that addresses the limitations of traditional approaches and aims to equip aspiring entrepreneurs with the necessary mindset, skills, and ecosystem engagement to thrive in the ever-evolving entrepreneurial landscape. The framework focuses on mindset development, skills acquisition, and ecosystem engagement as essential pillars for empowering and nurturing future innovators, contributing to the advancement of entrepreneurship education and the cultivation of a new generation of successful entrepreneurs and innovators.

This paper explores the field of human-machine collaboration in the era of artificial intelligence (AI). Using a strategy paper approach, the study synthesizes qualitative narratives, quantitative trends, and interdisciplinary viewpoints to formulate comprehensive strategies and informed perspectives. The synthesized strategies emphasize the role of trust, transparency, and ethical considerations in fostering harmonious collaboration. The interdisciplinary vision emphasizes tailor-made approaches for different disciplines, combining human expertise with AI capabilities. The synthesis paves the way for future research and provides a strategic compass to navigate the evolving landscape of AI integration. In conclusion, the paper's synthesis resonates as a transformative symphony, harmonizing the potential of human-AI collaboration for progress and innovation.

The Artificial Intelligence (AI) research field is interdisciplinary. In this research, based on students’ understanding of the current situation, AI is considered in relation to biomedical engineering, which is one of the interdisciplinary fields. Students expected the effectiveness of AI for society while having ethical and security concerns. Along with the increase in communication speed with large memory capacity and networking, many results have been obtained in the field of biomedical engineering: decoding DNA base sequences and digitizing medical images. On the other hand, security issues are being discussed in information networks: handling of personal genetic information. AI is expected to be used as a means of interdisciplinary communication: interdisciplinary participation is desirable. In the future, attitudes about using AI may become one of the main principles of human society.

The development of ChatGPT and introduction in November 2022 by OpenAI has generated a turbulence in the conversational Artificial Intelligence (AI) arena with a new technology that generates written answers in response to verbal input as written queries such as that used in Chatbots, AI system conversations, and virtual assistants. All of the references used in this paper are publications in the year 2023. This paper provides a brief introduction to what is ChatGPT and its development, its benefits and limitations, illustration of how ChatGPT works and its future in AI and Trans-Disciplinary Communications such as Trans-AI/DS the transformative transdisciplinary and translational artificial intelligence and data science (DS).