Volume 4 Issue 3: December 2025 (In Progress)

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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Random number generation is crucial in areas such as cryptography, simulations, and gaming. True random number generators (TRNGs) rely on unpredictable physical phenomena (e.g., thermal noise or quantum effects), whereas pseudo-random number generators (PRNGs) use deterministic algorithms seeded with an initial value. The choice of seed can significantly affect the statistical quality and security of PRNG outputs. This paper investigates the use of the irrational number  (approximately 2.2360679…) as a source of randomness. We describe how ’s non-repeating, non-terminating decimal expansion might serve as a high-entropy seed or number stream to enhance unpredictability. The methodology includes theoretical analysis of ’s properties (infinite sequence, normality conjectures) and statistical testing of sequences derived from ’s digits. We present a practical case study—a real-time Monte Carlo simulation using -based random sequences—to demonstrate the feasibility and performance of this approach. Results show that -generated sequences exhibit uniform distribution and pass standard randomness tests similar to conventional PRNGs. These findings imply that certain irrational numbers could be leveraged in hybrid random generation schemes. The paper concludes with implications for using mathematical constants in secure and reproducible simulations and outlines future research directions in irrational number-based PRNG design.

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In the digital era, access to financial services has become a cornerstone for economic empowerment and inclusive growth. However, millions in developing regions such as Tanzania remain unbanked, lacking access to formal financial systems. With the growth of fintech innovations, platforms like PalmPay have emerged as crucial tools for bridging this financial gap. PalmPay, a mobile money and financial services platform, has gained widespread popularity across African markets for offering secure, convenient, and user-friendly digital transactions. When integrated with blockchain technology, PalmPay becomes even more effective by enhancing transparency, data security, and trust—elements that are often limited in developing economies. This study explored how PalmPay, as a blockchain-enabled digital financial platform, has been used to reach and serve the unbanked population in Iringa Municipal through strategic digital marketing approaches. Using a purposive sample of 58 respondents, data were collected through qualitative interviews and structured questionnaires focusing on awareness, trust, and adoption influenced by blockchain, as well as the effectiveness of digital marketing strategies. Findings revealed that while most respondents were aware of PalmPay, many had only a partial understanding of blockchain, mainly associating it with improved security and transparency. Trust and adoption were driven by PalmPay’s ability to provide real-time, traceable, and secure transactions, although a digital trust gap persisted among less digitally literate users. Mobile-based marketing campaigns, particularly SMS and in-app promotions, were most effective when localized and supported by personal engagement from community agents or peers. Word-of-mouth influence remained a strong driver of adoption, underscoring the need for community-based and culturally sensitive outreach strategies.

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The optimization of hydrocarbon production is vital in the petroleum industry. Slug flow, however, can lead to production stoppages due to damage to surface equipment. As reservoir pressure declines during oil production, slug flow may occur in surface pipelines. Therefore, developing intelligent separators and implementing effective flow regime control methods are crucial for achieving this goal. This study constructs a smart laboratory pilot to collect experimental data, including liquid level, separator pressure, input mass flow rates, and control signals, under Model Predictive Control (MPC). We employ machine learning techniques, specifically Long Short-Term Memory (LSTM), to develop proxy models for a 3D reservoir simulation, significantly reducing computational time. The LSTM proxies are then integrated into a comprehensive production model that includes a horizontal gas-liquid separator equipped with an MPC controller. The controller efficiently regulates the separator's liquid level and operating pressure in real-time. Experimental results demonstrate that the proposed system effectively mitigates slug flow by adjusting separator pressure, maintaining stable operation across various flow regimes. In a 20-year field-scale simulation, the integrated LSTM-MPC system increased cumulative oil production by approximately 40% compared to a non-optimized system. This study presents a novel approach that combines data-driven reservoir modeling with advanced control strategies, offering a significant improvement in production optimization and flow assurance for the petroleum industry.

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