Developing highly active electrocatalysts for the oxygen evolution reaction is a pivotal challenge in sustainable water electrolysis. Herein, we report a novel in situ oxidative phase-restructuring strategy to fabricate oxygen vacancy-rich NiFe2O4 (NFO) directly on nickel foam. Distinct from conventional hydrothermal methods that typically yield thermodynamically stable crystals with limited intrinsic defects, our unique one-pot process involves the formation of a reduced metallic intermediate. The subsequent drastic phase transformation from this metallic state to a spinel oxide thermodynamically enforces the generation of abundant oxygen vacancies to relieve lattice stress, resulting in unique polycrystalline nanocrystal assemblies (NFO-1). Electrochemical evaluations reveal that NFO-1 significantly outperforms its thermodynamically equilibrated counterpart (NFO-2), exhibiting a low overpotential of 330 mV at 20 mA cm−2 and a remarkable mass activity of 6.78 A g−1. This superior performance is primarily attributed to intrinsic oxygen vacancies generated during the oxidative phase evolution, which optimize the active-site electronic structure and enhance charge–transfer kinetics. Furthermore, the catalyst demonstrates excellent durability over 1200 cycles. This work highlights oxidative phase restructuring as a powerful pathway to engineer intrinsic defects for high-efficiency energy-conversion applications.

The metaverse and artificial intelligence (AI) are increasingly intersecting in educational contexts, yet limited empirical research has examined how generative AI avatars function within socially interactive virtual environments. This study investigates the deployment of generative AI avatars within a socio-educational metaverse environment. Using a developmental evaluation approach, data were collected through interviews with seven institutional stakeholders, teacher-generated reflections, internal documentation, embedded user feedback captured through in-platform reporting tools, and longitudinal field memos across an iterative deployment cycle. Findings indicate that the transition from scripted NPCs to generative AI avatars recalibrated users’ attribution of agency, intensified dialogic unpredictability, and elevated social realism beyond visual fidelity. Voice-mediated interaction emerged as a threshold mechanism for co-presence, while algorithmic improvisation exposed tensions between pedagogical intent and stochastic response generation. The deployment further revealed affective frictions, expectation misalignments, and the mediating role of AI literacy in shaping trust, participation, and interpretive coherence. Overall, the study advances a sociotechnical understanding of AI avatars as co-constructors of meaning and interaction, offering implications for the design, implementation, and governance of future AI-enhanced metaverse learning environments.

As artificial intelligence (AI) continues to permeate the education sector, the question is no longer about what it can do but what drives its successful adoption. Despite a growing body of literature on AI in education, research specifically addressing its adoption in developing countries is still lacking, even though the use of AI is potentially even more widely adopted there than in other places. Thus, we examined the critical factors influencing AI adoption in Ghanaian higher education institutions. Anchored in the diffusion of innovation (DoI) Theory and the unified theory of acceptance and use of technology 2 (UTAUT2), we specifically investigated the role of interoperability, relative advantage, pedagogical alignment, accessibility and affordability, and ethical considerations in shaping its adoption in these institutions. Quantitative data from 230 participants across 34 universities in Greater Accra was analyzed using a structural-equation-modeling approach. With an explanatory power of 77.3%, our model confirms the significant role of all five factors in shaping AI adoption. Our findings highlight the necessity for structured AI implementation strategies, including phased rollouts, professional development initiatives, and continuous system optimization to facilitate sustainable integration in resource-limited contexts. This study provides empirical evidence to guide policymakers and institutional leaders in aligning AI-driven educational innovations with strategic and contextual imperatives.

Artificial intelligence (AI) has reshaped various aspects of human lives, particularly through its capabilities to address complex sustainability challenges. Despite the rapid expansion of AI applications, their contribution to cleaner production and sustainable development remains underexplored, especially in developing nations. In Southeast Asia (SEA), where AI adoption has grown substantially across environmental, economic, and social dimensions, research that examines its role in cleaner production outcomes remains fragmented. In view of this gap, this study conducts a systematic literature review (SLR) of AI applications related to cleaner production and sustainable development by examining relevant themes, application areas, and sustainability dimensions addressed by AI, while evaluating the maturity of AI methodologies, alignment with cleaner production outcomes, and integration with circular economy and resource efficiency goals. Moreover, it investigates the barriers and challenges that constrain AI application and offers future research directions to advance AI deployment for cleaner production and sustainable development across SEA countries.

As calls to center lived experience in higher education intensify, so too do concerns about the ethical, emotional, and structural risks involved in integrating real-life narratives into pedagogy. This study introduces Simulated Lived Experience (SLE) as a novel pedagogical modality that leverages the immersive affordances of learning environments like the metaverse to approximate systemic conditions of marginalization without reproducing trauma or requiring emotional labor from marginalized individuals. Drawing on critical pedagogy frameworks and affect theory, the research explores how SLE enables learners to engage with ethical discomfort, narrative complexity, and affective dissonance through the enactment of virtual selves. A qualitative design was employed, with data collected via semi-structured interviews from 12 participants who engaged in metaverse-based simulations portraying exclusionary dynamics related to disability, race, and institutional access. Thematic analysis generated four key findings: (1) virtual simulations evoke affective authenticity but also ethical unease, (2) embodied disorientation fosters structural insight, (3) narrative authorship and representation are ethically contested, and (4) discomfort acts as a catalyst for critical reflection. The study concludes that while SLE cannot replace lived experience, it can function as a powerful epistemic mediator when designed collaboratively, approached reflexively, and grounded in epistemic care.

In an era of accelerating digital transformation, optimizing resource management through effective data governance has become vital for local governments in developing nations such as the Philippines. This study introduces a data-driven governance platform designed to enhance resource allocation and decision-making in healthcare services through integrated forecasting algorithms and data governance principles. Anchored on a comprehensive framework for local data governance, the system centralizes, analyzes, and forecasts health-related data to support evidence-based planning and resource distribution. Employing both descriptive and developmental research designs, the study developed and tested the DALAY system using forecasting techniques such as exponential smoothing to predict medical supply needs and service demand. The findings demonstrate that integrating forecasting algorithms within a structured data governance framework can significantly improve resource efficiency, transparency, and responsiveness in local government operations. The system thus provides a replicable model for strengthening data-driven governance and optimizing community resource management in the Philippines. The findings indicate that robust data governance can lead to improved operational effectiveness, enhanced accountability, and ultimately better outcomes for citizens of the Philippines. This study aligns with SDG 9 that highlights the role of ICT in modernizing governance, fostering innovation, and improving data-driven decision making.

This study extended a previous paper that focuses on the acceptability of selected Bachelor of Science in Computer Science (BSCS) and Information Technology (BSIT) students on the use of Intelligent Tutoring System (ITS) as an educational technology tool for C/C++ Programming. A one-shot case study research design was carried out in 5 programming classes taught by the author. A Slovin's formula computation from the population was 35.54. A stratified sampling method was employed with the 4 intervals between students to mitigate bias. The study involved 39 participants, out of which 74.36% were male and 25.64% were female computer science and IT students. Utilizing the Technology Acceptance Model (TAM) as an evaluation tool online enabled importing the dataset into IBM SPSS for finding the correlations and factor loading calculations. Cronbach alpha was conducted by the author with a value of 0.947, which signifies the measure of internal consistency. The seven (7) factors of TAM were analyzed to reveal coefficient values for comparisons and derive their relative implications. Research indicates that every factor significantly influences the acceptance of ITS among BSCS and BSIT students. Interestingly, PerUse→Att has the highest coefficient value (0.883) next in the rank was SocNor→Att by a factor of 0.822 signifying their impact on ITS (Att), leaving SocNor→PerEas ranking last amongst relations with a 0.630 coefficient value. Finally, the results implied CS and IT students are open to the notion of incorporating intelligent teaching tools into their laboratory sessions to supplement their programming activity and increase their efficiency when building console applications.

Effective teaching forms the bedrock of education, directly influencing program accreditation and institutional performance. A competitive and supportive learning environment, fostered by strong faculty performance, is crucial for an academic institution to achieve its vision and mission. This study incorporates Sustainable Development Goals (SDGs) principles, ensuring that faculty performance evaluation contributes to long-term educational sustainability. Addressing the pressing need for robust faculty performance assessment, data mining algorithms are employed to extract insightful information regarding effective instruction, utilizing both structured and unstructured data. The developed system aims to empower institutions to identify their strengths, address areas for improvement, and cultivate continuous growth in teaching and learning processes by discerning trends within faculty data. Furthermore, sentiment analysis methods are utilized to evaluate qualitative input, with Laravel 8.0 serving as the framework for algorithm implementation. Expert evaluations of the system yielded a grand mean score of 4.38, deemed 'Very Acceptable,' thereby affirming its reliability and efficacy in supporting faculty performance reviews and advancing SDG objectives.

The Digital Academic Information System is an integrated platform designed to support and manage core academic functions, including research, extension, and instruction. The system streamlines research management by tracking proposals, publications, and collaborations; facilitates extension services by organizing community engagement programs and reporting outcomes; and enhances instruction through tools for course management, faculty workload tracking, and student performance monitoring. By centralizing these features, the system promotes efficiency, transparency, and improved decision-making within academic institutions. This study focuses on the evaluation of the developed digital academic system that focuses on research, instruction and extension integration which processes essentials to state universities and colleges. The researcher used the descriptive- developmental type of research. The system provides a real-time overview of the status of the performance and accomplishment of the academic institution in the mentioned areas. The evaluation of a Digital Academic Information System (DAIS) using Agile methodology and software quality model assessment provides a dynamic and structured approach to system development and analysis. Agile enables iterative development with continuous stakeholder feedback, ensuring that evolving academic requirements are met efficiently. By integrating a software quality model—such as ISO/IEC 25010—the evaluation further assesses critical attributes like functionality, usability, reliability, and maintainability The system obtained the overall weighted mean of 3.73 which interpret as Excellent in terms of Product Quality evaluated by the IT Experts. This means that the system is approved by the IT Experts and highly recommended to use.

Air quality monitoring has become increasingly critical in urban environments, particularly in densely populated megacities like Manila, Philippines. This research presents the design and conceptual framework for a comprehensive web-based air quality monitoring and mapping system that leverages fuzzy logic algorithms to provide intelligent, real-time assessment of atmospheric conditions across Metro Manila. The proposed system addresses the inherent uncertainties and complexities associated with environmental data by implementing a sophisticated fuzzy inference system specifically calibrated for Manila's unique atmospheric conditions, pollution sources, and regulatory requirements. The research encompasses a thorough analysis of Manila's current air quality challenges, including the identification of primary pollutants such as particulate matter (PM2.5 and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ground level ozone (O3). The proposed system architecture integrates multiple technological components including a distributed sensor network, centralized data processing infrastructure, fuzzy logic engine, web-based visualization platform, and real-time mapping capabilities. The fuzzy inference system is specifically designed to accommodate Manila's tropical climate conditions, high population density, and diverse pollution sources ranging from vehicular emissions to industrial activities. The methodology incorporates adaptive membership functions that adjust to seasonal variations and local environmental patterns, ensuring accurate and contextually relevant air quality assessments. The system design emphasizes scalability, real-time processing capabilities, and user accessibility through responsive web interfaces optimized for both desktop and mobile platforms. The technical implementation framework encompasses comprehensive hardware specifications for sensor deployment, software architecture for data processing and visualization, database design for efficient time-series data management, and API development for system integration and third-party access. Expected outcomes of this research include improved public awareness of air quality conditions, enhanced decision-making capabilities for environmental authorities, and the establishment of a robust foundation for future environmental monitoring initiatives in Manila and similar urban environments. The fuzzy logic approach provides a more nuanced and human-interpretable assessment of air quality compared to traditional crisp methodologies, enabling better communication of environmental risks to diverse stakeholder groups. This comprehensive study contributes to the growing knowledge in environmental informatics and smart city technologies, demonstrating the practical application of artificial intelligence techniques in addressing real-world environmental challenges. The research provides a detailed roadmap for implementing intelligent air quality monitoring systems in developing urban environments, with particular emphasis on cost-effectiveness, technological accessibility, and community engagement.