This study conducts a comprehensive bibliometric analysis to map the landscape and research trends of artificial intelligence (AI) applications within higher education. Utilizing data from the Scopus database, encompassing 4,696 datasets from 1939 to 2024, we employed VOSviewer for visualizing and analyzing co‑authorship networks, citation patterns, and keyword occurrences. The analysis identifies primary research areas, influential authors, and emerging topics, offering valuable insights into the dynamic field of AI in higher education. Key findings include the identification of significant research themes such as AI applications in education, student engagement, and the development of learning systems. Influential contributors were highlighted for their substantial impact on the research landscape. The study also revealed strong collaborative networks, particularly involving key figures, underscoring the importance of co‑authorship in advancing AI research. Strong collaborative networks refer to the co‑authorship and international partnerships that connect these contributors, producing high‑impact research through shared expertise, resources, and cross‑regional knowledge exchange. The findings validate the hypotheses that significant research areas and influential contributors can be identified, and that collaborative networks and emerging technologies play crucial roles in the field’s advancement. Influential contributors are the authors, institutions, or countries whose publications and citation impacts significantly shape the research landscape of AI in higher education, setting key directions for scholarship and practice. This study provides a roadmap for future research, emphasizing the importance of strategic collaborations and innovative technologies in shaping the future of AI in higher education.

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Living systems maintain structural and functional stability while adapting to environmental changes, a capability independent of specific system‑environment states. Existing frameworks, such as self‑organization theory and free energy principles, cannot measure system‑environment interaction at the causal level. In this article, we propose a new causal indicator, Flexibility, to measure a system’s ability to respond to its environment. We construct this indicator based on information theory and interventional operations from causal inference, which implies the indicator depends only on the dynamical causal mechanism. We show this indicator satisfies the axiom system of the partial information decomposition (PID) framework and decomposes into two components, Expansiveness and Introversion, which correspond to different strategic tendencies for environmental adaptation. This decomposition reveals that Flexibility depends on the entanglement between system‑environment variables and noise magnitude. Through experiments on cellular automata (CA), random Boolean networks, and real gene regulatory networks (GRNs), we validate that the indicator identifies the most complex and computationally capable CA (Langton’s parameter at 0.5), while demonstrating that feedback loops carrying important biological functions in GRNs exhibit the highest flexibility. We also find that flexibility peaks at a moderate level of dynamical noise. Furthermore, we combine this framework with machine learning techniques to demonstrate its applicability when the underlying dynamics are unknown.

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Urban areas are increasingly leveraging digital technologies like Geographic Information Systems (GIS), the Internet of Things (IoT), and Artificial Intelligence (AI) to tackle complex challenges stemming from rapid population growth, climate change, and infrastructural strain. These tools are revolutionizing urban planning and management by enabling data-driven decision-making, sophisticated scenario modeling, and real-time monitoring of city systems. This allows for optimized public service delivery, enhanced disaster resilience, and more inclusive citizen engagement through digital participatory platforms. However, the integration of these technologies faces significant barriers, including institutional inertia, data fragmentation, ethical concerns over privacy and bias, and the risk of creating or worsening digital divides. To successfully navigate this transformation, a strategic approach is essential. This paper proposes a unique four-pillar framework for digital urban transformation that moves beyond a purely technological focus. The framework integrates technological innovation and robust data ecosystems with parallel and necessary policy reforms, capacity building within institutions, a firm commitment to equitable access for all citizens, and robust participatory governance. This comprehensive structure ensures that the digital evolution of cities is guided by principles of inclusivity and ethics. Ultimately, the study posits that these digital tools are not merely technical solutions but are powerful catalysts for a fundamental paradigm shift in urban development, steering cities toward a future that is more adaptive, resilient, and equitable for all their inhabitants.

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Vector Space Models (VSM) and neural word embeddings are core components in recent Machine Learning (ML) and Natural Language Processing (NLP) pipelines. By encoding words, sentences and documents as high-dimensional vectors via distributional semantics, they enable Information Retrieval (IR) systems to capture semantic relatedness between queries and answers. This paper compares different semantic representation strategies for query-statement matching, evaluating paraphrase identification within an IR framework using partial and syntactically varied queries of different lengths. Motivated by the Word Mover’s Distance (WMD) model, similarity is evaluated using the distance between individual words of queries and statements, as opposed to the common similarity measure of centroids of neural word embeddings. Results from ranked query and response statements demonstrate significant gains in accuracy using the combined approach of similarity ranking through WMD with the word embedding techniques. Our top-performing WMD + GloVe system consistently outperformed Doc2Vec and an LSA baseline across three return-rate thresholds, achieving 100% correct matches within the top-3 ranked results and 89.83% top-1 accuracy. Beyond the substantial gains from WMD-based similarity ranking, our results indicate that large, pre-trained word embeddings, trained on vast amounts of data, result in portable, domain-agnostic language processing solutions suitable for diverse business use cases. 

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