Social interaction has long been a subject of theoretical inquiry in both Computer-Mediated Communication (CMC) and Human-Computer Interaction (HCI), but seldom has it been examined through the lens of digital embodiment. As the metaverse gains traction as a platform for learning and collaboration, understanding how its affordances construct behavioral engagement demands empirical scrutiny. Thus, this study examines the effects of avatar customization and communication modality on behavioral engagement within a metaverse-based simulation. Using a 2×2 factorial design, participants were randomly assigned to avatar (customized vs. generic) and modality (voice vs. text) conditions, with engagement tracked via a stealth assessment approach across multiple sessions. Findings indicate that avatar customization facilitated broader spatial exploration, while voice-based communication elicited higher interpersonal interaction. Critically, the convergence of both factors produced a compounded effect that yielded selective interaction effects on temporal and social dimensions of engagement. This study contributes a framework of affordance convergence that informs both the theoretical modeling of digital embodiment and the practical design of immersive learning platforms. As educational experiences increasingly unfold within socio-technical systems, the challenge for both HCI and CMC is to design environments where social interaction is both mediated and dynamically co-constructed through the alignment of interactional affordances.

Generative AI offers promising potential to promote environmental sustainability through personalized recommendations that influence individual behavior. This study examines the factors influencing the adoption and actual use of generative AI recommendations for environmental sustainability among Gen Z users in the Philippines by integrating the Theory of Planned Behavior (TPB) and the Technology–Environmental, Economic, and Social Sustainability Theory (T-EESST) with key generative AI attributes, together with trust and perceived risk. Survey data were collected from 531 Gen Z users in higher education institutions in the National Capital Region (NCR), Philippines, and analyzed using a hybrid SEM and ANN approach. Results from SEM indicate that key AI attributes, namely perceived anthropomorphism, perceived intelligence, and perceived animacy, significantly influenced users’ attitude towards generative AI recommendations. Attitude, perceived behavioral control, and trust emerged as significant predictors of behavioral intention, which have an eventual positive relation to actual use and environmental sustainability outcomes. In contrast, subjective norms and perceived risk did not significantly affect behavioral intention, which may suggest that Gen Z users’ engagement with generative AI for environmental sustainability is primarily driven by internal evaluations, perceived capability, and trust rather than social pressure or risk concerns. Complementing these findings, the ANN analysis identified perceived behavioral control, attitude, and trust as the most important factors, reinforcing the robustness of the SEM results. Overall, this study integrates existing sustainability and technology-adoption literature by demonstrating how generative AI recommendations can support environmental sustainability among Gen Z users by combining behavioral theory, sustainability theory, and AI attributes through a hybrid SEM–ANN approach in the context of a developing country.

Introduction: As digital platforms increasingly mediate language learning, the challenge is no longer simply how to deliver content online but how to design environments that cultivate authentic multilingual practice. While multilingualism has long been linked to enhanced metalinguistic awareness and domain-general cognitive flexibility, the role of multimodal digital environments in fostering these outcomes remains underexplored.

Purpose: Grounded in sociocognitive and multimodal interactionist perspectives, this study examines how a cross-device metaverse platform can support multilingual development through spatially organized, task-based, and avatar-mediated interaction. Specifically, it investigates whether multilingual engagement in language-zoned virtual spaces improves learners' communicative performance, affective engagement, and cognitive control compared to conventional instruction.

Methodology: Using a quasi-experimental cluster-assigned pretest-posttest control group design, learners engaged in communicative scenarios across English, Filipino, and Mandarin within language-zoned virtual spaces that cued role-appropriate language use. Data were collected using performance-based role-play assessments (code-switching accuracy, communicative competence), oral fluency measures (WPM), motivation and anxiety questionnaires, and a Stroop interference task to assess cognitive flexibility.

Findings: Compared to peers in a control condition, learners in the metaverse environment demonstrated significantly greater gains in code-switching accuracy, spoken fluency, motivational engagement, and cognitive control. Specifically, experimental participants showed improved context-appropriate language selection and reduced cross-language interference when shifting between English, Filipino, and Mandarin during task-based role-play scenarios. They also produced more fluent spoken output and demonstrated stronger communicative competence ratings in completing real-world interaction tasks. In addition, learners reported higher motivational engagement and cognitive results, further revealing improvements in inhibitory control and attentional regulation. Collectively, these outcomes suggest that spatially cued multilingual interaction in the metaverse supports integrated gains in linguistic performance and executive functioning.

Originality/Value: This study provides empirical evidence that multilingual development is shaped not only by linguistic input but by how digital learning ecologies choreograph spatial, social, and multimodal cues into context-responsive language use. By operationalizing multilingual interaction through spatial language zoning, avatar-mediated tasks, and AI-supported multilingual dialogue, the study positions the metaverse as a semiotically rich pedagogical ecology that can simultaneously foster code-switching competence, oral fluency, motivational engagement, and domain-general executive control. The findings advance multimodal multilingual education theory by demonstrating how context-sensitive interaction design can generate co-emergent communicative, affective, and cognitive benefits in multilingual learners.

Multiple sequence alignment (MSA) is a fundamental technique in computational biology that compares protein, DNA, or RNA sequences to identify regions of similarity reflecting functional, structural, or evolutionary relationships. This systematic literature review examines the diverse land-scape of multiple sequence alignment algorithms, categorizing them based on their underlying approaches and analyzing their strengths, limitations, and applications. We explore seven major categories of alignment methods: dynamic programming, progressive alignment, iterative refinement, Hidden Markov Model-based, consistency-based, structure-based, and machine learning-based approaches. Through comprehensive analysis of recent benchmarks and literature, we identify key innovations, performance characteristics, and emerging trends in the field. This review provides a detailed overview of the evolution of multiple sequence alignment algorithms and their applications in modern bioinformatics.

This study sought to understand the experiences of All-but-Dissertation (ABD) students that led them to withdraw from a Doctor of Information Technology (DIT) programme. A total of 27 students from three Philippine universities were interviewed using a semi-structured format. Results show that most participants were driven by extrinsic motivations and viewed graduate education as a pathway to a better life. The challenges they faced were both internal and external in nature (e.g. study-work conflicts and personal problems), which are comparable to those in other disciplines. Most reasons (e.g. limited research experience and dissertation anxiety) for dropping out from this professional doctorate align with findings from studies on Doctor of Philosophy (PhD) programmes. However, two reasons unique to ABD students in the DIT programme were the inclusion of software development and the selection of computing research topics. Overall, these findings provide empirical evidence for addressing issues related to dissertation delays, prolonged doctoral completion times, and attrition in graduate education. Practical and managerial implications derived from this study could inform graduate school policies and practices, with potential applications across other doctoral disciplines.

The metaverse is reshaping interaction, learning, and community-building in immersive virtual environments. While interest in metaverse adoption is growing, most research has focused on technological predictors and has overlooked the experiential dimensions that are central to sustained engagement in these spaces. This gap limits understanding of how users develop and maintain meaningful virtual existence in the metaverse. Therefore, this study develops the Metaverse Experience and Technology Acceptance (META) model by integrating the principles of the Technology Acceptance Model (TAM) and Embodied Social Presence Theory (ESPT). Structural equation modeling (SEM) was used to analyze data collected from 924 students with metaverse experience. The META model demonstrates strong explanatory power in accounting for both technology acceptance and user experience in virtual worlds. Moreover, the findings indicate that adoption of the metaverse as a digital university extends beyond the functional focus of TAM to include the immersive, social, and embodied elements emphasized in ESPT. By bridging technological and experiential determinants, the META model advances theoretical understanding and offers actionable insights for creating metaverse environments that promote conducive digital existence.

This review examines the integration of climate-smart aquaculture (CSAq) as a strategy to enhance the resilience and sustainability of global aquaculture and coastal agriculture in the face of climate change. CSAq encompasses innovations such as integrated multi-trophic aquaculture (IMTA), genetic advancements, renewable energy integration, and optimized water management, all aimed at minimizing environmental impacts while maintaining productivity. As climate change introduces threats like ocean acidification, temperature fluctuations, and extreme weather events, CSAq offers adaptive solutions critical for preserving marine ecosystems, reducing greenhouse gas emissions, and sustaining food security. The review emphasizes that the successful adoption of CSAq is contingent upon supportive policies, cross-sectoral collaboration, and socio-economic considerations, including gender inclusivity and community involvement. As aquaculture's role in food security continues to grow, CSAq provides a pathway for mitigating climate impacts while promoting sustainable development. This review underscores the necessity of climate-smart approaches for building resilient food systems that can adapt to a changing climate and sustain livelihoods in vulnerable coastal regions.

Achieving a just and accelerated renewable energy (RE) transition in the Philippines requires not only technological innovation but also coherent and cross-sectoral policy alignment. Non-energy policies can facilitate or hinder the RE development. Non-energy policies, particularly those governing land use, permitting, and environmental regulation, and other significantly shape the feasibility of RE deployment. However, the analyses and evidences on implications of the non-energy policies on RE development are scarce, especially in the context of developing countries. This study provides a comprehensive, stakeholder-informed assessment of 43 national-level policy instruments across five domains in the Philippines: Energy Policy and Regulation, Climate Change and Sustainability, Environmental and Natural Resource Conservation, Agriculture and Rural Development, and Land Use and Property Rights. In this study, using a modified Sustainable Development Goals (SDG) interaction framework, stakeholders from academia, government, industry, and non-governmental organizations evaluated each policy's influence on RE development using a seven-point scale. Weighted average (WA) scores were computed to determine whether policies act as enablers or constraints. Results show that energy and climate policies are strongly supportive due to clear mandates and institutional coordination, whereas land governance and agrarian reform policies are viewed as restrictive because of procedural uncertainty and tenure risks. Environmental policies are generally enabling but raise permitting concerns. Divergent stakeholder perceptions underscore the need for inclusive and transparent governance. The study concludes that accelerating the RE transition will depend on harmonizing institutional mandates, reforming land-use frameworks, enabling decentralized systems, and strengthening technical and governance capacity across all sectors.

The growing demand for sustainable electricity in emerging economies necessitates hybrid systems that leverage local renewable resources while remaining economically viable. This study optimizes and evaluates photovoltaic–biogas (PV–BG) hybrid systems for Rosso, Mauritania, through a techno-economic and environmental framework. HOMER Pro was used for baseline modeling, while Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA) refined both on-grid and off-grid designs. The optimal on-grid configuration—801 kW PV, 100 kW BG generator, and 408 kW converter—achieved a Levelized Cost of Energy (LCOE) of $0.041/kWh, Net Present Cost (NPC) of $1.89 M, Payback Period (PP) of 6.6 years, Internal Rate of Return (IRR) of 14%, and Return on Investment (ROI) of 11%. GWO and WOA further reduced LCOE to $0.038/kWh and $0.036/kWh and NPC to $1.81 M and $1.77 M, shortening PP to 6.4 and 6.1 years. Environmental analysis showed an annual offset of 1,220 tCO2 and a 100% renewable fraction. The results provide a scalable framework for hybrid energy planning, supporting policy development and investment strategies toward low-carbon power systems.

Floods are among the most devastating natural hazards, causing widespread damage to lives, livelihoods, and infrastructure, particularly in vulnerable river basins. The Cagayan River Basin (CRB), the largest and most flood-prone basin in the Philippines, remains a significant challenge for disaster risk management. This study developed an event-based hydrologic–hydraulic modeling framework by coupling HEC-HMS rainfall–runoff simulations with HEC-RAS 2D unsteady flow routing to produce validated flood hazard maps. Inputs included rainfall from 41 gauge stations and observed inflows from the Magat Dam, processed in HEC-DSS. Validation utilized 137 surveyed flood marks collected from post-flood surveys, community reports, government archives, and household RTK measurements, with a concentration in Tuguegarao City. The coupled model reproduced key hydrograph peaks with moderate accuracy (R2 = 0.56, Bias = +0.32 m, RMSE = 1.61 m, MAE = 1.43 m), although NSE (−2.30) reflected the limits of daily rainfall inputs. Simulated hazard maps identified 767.97 km2 of inundated area (approximately 2.77% of CRB), concentrated along the floodplain and at the Magat confluence. Unlike previous scenario-based or localized efforts, this study delivers the first basin-wide, event-validated flood hazard maps for the CRB using integrated depth and depth–velocity criteria. The resulting hazard layers provide a scientific basis for strengthening evacuation planning, guiding land-use and infrastructure decisions, and supporting long-term resilience strategies in one of the Philippines’ most flood-prone rivers.