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.

The Neighborhood Median Pixel Method (NMPM) classifies land cover by summing per-band scores across 10 input features from Landsat-8 Operational Land Imager (OLI) data – bands 1 through 7 alongside three widely used index images: NDVI, NDWI, and NDBI. These features are typically treated equally within the classification framework, assuming uniform informational value across all bands and indices. However, indices such as NDVI, NDWI, and NDBI are specifically designed to highlight spatial properties of their respective land cover classes—particularly in urban settings—and are therefore expected to carry more relevant information for specific classification tasks. In this study, we experimented with different weighting schemes and found that giving greater emphasis to the indices led to a modest increase in overall classification accuracy, achieving an average overall accuracy of 0.9475 compared to 0.94 of the original implementations for the equal-weight baseline and other weighting strategies with a 9x9 neighborhood size. The results demonstrate how a targeted methodological innovation in image processing—assigning greater weight to highly relevant features—can enhance the reliable and efficient classification performance of the NMPM. This contributes to more accurate land cover mapping, particularly in complex urban environments, and supports data-driven development planning and resource management.

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.

Fiber-reinforced rigid pavement offers a viable option for sustainable concrete development due to its superior mechanical properties, notably enhanced durability and crack resistance. Nonetheless, cracks remain inevitable. Recent studies have concentrated on harnessing biological mechanisms to introduce self-healing properties to concrete. A particularly promising method employs microorganisms like Bacillus subtilis, which can induce the precipitation of calcium carbonate (CaCO3) when exposed to moisture and air. This microbial-induced calcite precipitation (MICP) efficiently seals cracks, thereby reducing need for manual repairs and extensive maintenance. This study examined the application of 2.36 × 109 CFU of Bacillus Subtilis ATCC 6633 as a bio-admixture in fiber-reinforced concrete pavement. The study assessed the effectiveness of various concentrations. The research concluded that Bacillus subtilis effectively stimulated self-healing of cracks in concrete width of 1–2 mm and material recovery over the seven (7) day period, as verified by Absorption Test (Sorptivity), Ultrasonic Pulse Velocity (UPV), and X-ray Diffraction Analysis (XRD). However, the efficacy of the self-healing process varied based on the concentration of Bacillus subtilis. Higher concentrations (10%) of Bacillus subtilis improve fracture healing but diminished overall material performance. Furthermore, the direct application of 5% Bacillus subtilis proved to be a highly effective variant among the tested formulations, exhibiting an enhancement of flexural strength by 13.13% at 14 days, surpassing the design specifications.

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 Neighborhood Median Pixel Method has previously been introduced as an image processing technique in remote sensing, developed to classify Landsat-8 OLI satellite image pixels into categories of vegetation, water, and built-up areas. This method relies on a lookup table based on the median pixel values within a pixel’s neighborhood and a scoring system that assigns point values for classification. While a 9x9 neighborhood size was originally proposed, a succeeding study suggested a 13x13 neighborhood for better classification accuracy. This study focuses on refining the scoring system used in the Neighborhood Median Pixel Method, particularly the original set of arbitrary point values 14, 4, and 1. Particle Swarm Optimization was employed to systematically explore and optimize these point values, iteratively seeking an ideal configuration for the scoring system. After optimization, the Neighborhood Median Pixel Method exhibited a slight increase in overall accuracy. Using the 9x9 neighborhood size, the accuracy rose from 94% to 94.75%, while with the 13x13 neighborhood size, the accuracy improved from 95.75% to 96%. Furthermore, results indicate that varying point value configurations yield similar classification outcomes, suggesting that the method’s scoring system is robust across multiple configurations and that the originally proposed point value set remains adequate for effective classification.

Artificial intelligence (AI) is revolutionizing digital marketing by enhancing targeting, personalization, and automation, leading to data-driven advertising strategies. AI-powered analytics optimize customer engagement, enabling businesses to deliver highly tailored ads that improve conversion rates and return on investment. Automated tools streamline content creation and campaign management, while chatbots enhance customer interactions on a scale. In the Philippines, AI adoption in marketing is expanding, with local retailers and online platforms leveraging AI for personalized recommendations and programmatic advertising. However, challenges remain, including data privacy concerns, algorithmic bias, and a widening skills gap, as AI proficiency becomes increasingly essential for marketers. While AI offers efficiency, a word cloud analysis highlights concerns about its impact on creativity and the human touch in branding. According to the Technology Acceptance Model, Filipino marketers and multimedia arts students must develop AI literacy and strategic thinking to remain competitive. Ethical considerations also require greater oversight in AI-powered advertising to ensure responsible consumer engagement. The future of digital marketing in the Philippines depends on balancing AI-driven efficiency with human creativity, storytelling, and cultural relevance. Businesses must invest in upskilling initiatives and ethical frameworks to maximize AI’s potential while mitigating risks. Further research should examine AI’s long-term impact on job roles, industry dynamics, and consumer trust. As AI becomes more integrated into marketing strategies, success will hinge on how well professionals merge automation with authentic, human-centric advertising practices.

The Philippines, known for its extraordinary biodiversity, holds a unique and ecologically vital position in the world of conservation. Despite the exceptional richness of its biodiversity, the gravity of the threats it faces has been notable for the lack of attention given to endangered animals in the Philippines. With that, this study aims to create an innovative learning material to inform children through a 360 interactive video about the lives of critically endangered species in the Philippines. To validate the study, mixed method technique was used to collect data and information. The researchers interviewed subject matter experts, technical experts, and 8 to 10- year-old target audiences. It revealed that innovative learning material like a 360 interactive video is an effective way to engage and help inform children in the Philippines about critically endangered species. This research offers valuable insights into the effectiveness of 360 interactive video promoting the advocacy supporting the SDG’s Quality Education, Life on Land, and Life on Water for the children of Manila City, contributing to broader societal goals of understanding.