General Background: Student learning interest is a crucial factor that supports active participation, attention, and enthusiasm during the learning process in elementary education. Specific Background: Teachers hold a central role in shaping classroom learning conditions and guiding students through various pedagogical responsibilities. Knowledge Gap: Although many studies discuss teacher roles in learning, contextual analysis focusing on the homeroom teacher’s roles in fostering learning interest among fourth-grade students remains limited. Aims: This study aims to analyze the roles of teachers in relation to the learning interest of fourth-grade students at Al-Khairaat Elementary School Tondo. Results: Using a descriptive qualitative approach involving one homeroom teacher and twenty-eight students, data collected through observation, interviews, and documentation reveal that teachers perform eight key roles: educator, learning resource, guide, classroom manager, demonstrator, motivator, facilitator, and evaluator. Empirical findings indicate that these roles are reflected through exemplary attitudes in teacher–student interactions, the use of instructional media such as videos and educational games, flexible classroom seating arrangements, and simple reward systems to encourage participation. Students showed varied levels of learning interest; however, most demonstrated increased activeness, attention, and enthusiasm when interactive and contextual learning strategies were implemented. Novelty: This study provides a contextual description of how the homeroom teacher’s multiple roles operate simultaneously in classroom practice to support student engagement. Implications: The findings suggest that adaptive and sustained implementation of teacher roles can serve as a practical strategy to cultivate elementary students’ learning interest according to classroom characteristics. Highlights • Classroom practice demonstrates eight integrated instructional roles performed by homeroom teachers.• Interactive strategies using media, contextual explanation, and rewards encourage student participation.• Student engagement increases when learning activities involve active interaction and contextual tasks. Keywords Teacher Roles; Learning Interest; Elementary Education; Classroom Learning; Student Engagement

General Background: Hydrogen generated through electrochemical water splitting is a promising clean energy carrier due to its high energy density and carbon-free combustion. Specific Background: The hydrogen evolution reaction (HER) requires efficient cathode catalysts, but platinum-group metals are limited by scarcity and cost, encouraging the exploration of nickel-based electrocatalysts. Knowledge Gap: The relationship between synthesis strategy, nanostructure formation, and HER electrocatalytic behavior of advanced nickel heterostructures and phosphosulfide systems remains insufficiently clarified. Aims: This study evaluates Ni/Ni(OH)₂ heterostructures and nickel phosphosulfide (Ni-P-S) nanocatalysts by examining their preparation, structural characteristics, and HER performance in alkaline media. Results: Structural analysis using XRD, XPS, and TEM confirmed successful nanostructure formation, including core–shell Ni/Ni(OH)₂ structures and porous nanosheet Ni-P-S networks. Electrochemical measurements showed that Ni-P-S required an overpotential of 78 mV at 10 mA cm⁻² and maintained stability for more than 50 hours in 1 M KOH, while Ni/Ni(OH)₂ exhibited higher overpotential values. Novelty: The study provides a comparative evaluation linking synthesis routes, nanostructure morphology, and HER electrocatalytic behavior of two nickel-based catalysts. Implications: These findings support the development of scalable non-precious metal catalysts for alkaline water electrolysis and sustainable hydrogen production technologies. Highlights:• Porous Nanosheet Architecture Provides High Electrochemically Active Surface Area.• Charge Transfer Resistance Decreases at the Catalyst and Electrolyte Interface.• Long-Term Operation Exceeding Fifty Hours Demonstrates Durability in Alkaline Medium. Keywords: Nickel Based Nanocatalysts, Hydrogen Evolution Reaction, Alkaline Water Splitting, Electrocatalysis, Nanostructured Catalysts

General Background: Digital transformation has become a strategic approach for organizations seeking improved operational systems and competitive positioning through the integration of advanced technologies, digital strategies, and data-driven management. Specific Background: In higher education institutions, digital transformation involves technological infrastructure, data and analytics, digital strategy, security, and organizational culture to support institutional performance and competitive advantage dimensions such as quality, service excellence, and customer responsiveness. Knowledge Gap: Despite increasing global attention to digital transformation in universities, empirical studies examining its relationship with sustainable competitive advantage in educational institutions remain limited, particularly within the context of Iraqi higher education. Aims: This study aims to analyze the relationship between digital transformation dimensions and competitive advantage in the performance of educational institutions through a field study conducted in several colleges of the Holy University of Karbala. Results: The findings indicate a statistically significant relationship between digital transformation and competitive advantage dimensions. Digital strategy and data analytics show significant relationships with quality and service excellence, while data and analytics demonstrate a significant relationship with customer responsiveness. Overall results show high levels of digital transformation (mean = 4.00) and competitive advantage (mean = 4.17), with an overall mean of 4.09 indicating strong consensus regarding the role of digital technologies in institutional performance. Novelty: This study provides empirical evidence linking multiple digital transformation dimensions with competitive advantage indicators within university environments. Implications: The results highlight the importance of investing in digital infrastructure, adopting comprehensive digital strategies, and strengthening data-driven decision-making to support institutional performance and long-term competitiveness in higher education. Highlights: Strong institutional agreement regarding digital technology adoption in university operations Data analytics shows a significant relationship with responsiveness to user needs Strategic digital planning demonstrates statistical association with academic service quality Keywords: Digital Transformation; Competitive Advantage; Educational Institution Performance; Data And Analytics; Digital Strategy

General Background: Rapid population growth in Indonesia increases housing demand and pressures residential project schedules. Specific Background: Residential projects require coordination among stakeholders, with the owner’s representative responsible for key project approvals. Knowledge Gap: The role of the owner’s representative in project time performance remains insufficiently studied. Aims: This study examines owner representative decision factors related to schedule performance using SEM-PLS, risk matrix, and SWOT analysis. Results: The model shows strong validity and reliability, with Site, Materials, and Labor identified as the main delay risks. Novelty: The study links owner decision-making with schedule deviation risks through an integrated analytical framework. Implications: Proactive decisions, early site validation, and improved material and labor management are essential to maintain schedule stability in residential construction projects. Highlights: Site conditions, material availability, and workforce factors form the dominant moderate-level delay risks. Measurement model demonstrates strong construct validity and reliability through high AVE and composite reliability values. Strategic positioning in the diversification zone supports Strength–Threats mitigation to control schedule deviation. Keywords: Website Development, Pesantren Profile, Digital Transformation, Usability, Waterfall.

General Background: Cyberbullying detection in Indonesian social media has become increasingly important due to rapid digital communication growth and complex informal language usage. Specific Background: Automated identification remains challenging because Indonesian online discourse frequently contains slang, ambiguity, sarcasm, and class imbalance, limiting the capability of conventional statistical and earlier deep learning approaches. Knowledge Gap: Prior studies have emphasized traditional classifiers and encoder-based Transformers such as IndoBERT, while generative text-to-text architectures like T5 and their comparison with hybrid feature fusion strategies remain underexplored in Indonesian-language corpora. Aims: This study systematically compares three modeling scenarios—T5 Base, Hybrid (T5 + TF-IDF), and Enhanced (T5 + TF-IDF + sentiment)—to evaluate their performance in detecting cyberbullying from 20,000 Indonesian social media comments with naturally imbalanced distribution. Results: Experimental findings show that T5 Base achieves the highest test Accuracy (0.8325) and Macro F1-Score (0.8329), while Hybrid and Enhanced models yield slightly lower yet competitive performance. The results indicate that contextual semantic representations learned by T5 sufficiently capture explicit and implicit abusive expressions, and additional statistical and sentiment features do not yield superior classification outcomes. Novelty: This research provides empirical evidence that a standalone text-to-text Transformer architecture can outperform hybrid feature fusion strategies in Indonesian cyberbullying detection under limited training data conditions. Implications: The findings support the adoption of end-to-end Transformer-based models for scalable, robust, and linguistically adaptive monitoring systems in low-resource social media environments. Highlights: The standalone text-to-text architecture produced the strongest test-set metrics among all evaluated scenarios. Integration of statistical weighting and sentiment signals did not surpass the semantic-only configuration. Stable generalization was maintained despite limited training allocation and naturally imbalanced data distribution. Keywords: Text Classification, Social Media Analysis, Transformer Models, Indonesian Language, Cyberbullying Detection.

General Background: Early childhood education requires learning media that not only support cognitive development but also stimulate multiple intelligences, particularly interpersonal intelligence, which is essential for children’s social growth. Specific Background: Science learning activities in early childhood settings are often limited to teacher-centered approaches and lack instructional media that encourage active social interaction among children. Knowledge Gap: There is limited availability of science-based instructional media specifically designed to stimulate interpersonal intelligence through collaborative experimental activities for children aged 5–6 years. Aims: This study aims to develop a science experiment book as an instructional medium to stimulate multiple intelligences, particularly interpersonal intelligence, among children aged 5–6 years at TK Idhata Beka. Results: Using a Research and Development approach involving needs analysis, product design, expert validation, and limited field trials, the findings show that the developed science experiment book was categorized as very feasible in terms of media and content quality, and its implementation led to improvements in children’s interpersonal abilities, including cooperation, communication, and peer interaction during science-based experimental activities, indicating that the science experiment book has strong potential as an effective learning medium for supporting the development of interpersonal intelligence in early childhood education. Highlights: Expert appraisal classified the developed learning resource as very valid in content and media aspects. Group-based experimental sessions increased children’s peer dialogue and shared task completion. Structured hands-on activities supported observable progress across social interaction indicators. Keywords: Science Experiment Book, Multiple Intelligences, Interpersonal Intelligence, Early Childhood.

General Background: Air pollution has become a critical global issue affecting environmental sustainability and public health, creating a strong demand for accurate air quality prediction systems. Specific Background: Traditional statistical models and conventional machine learning techniques often struggle to capture the nonlinear and multivariate characteristics of environmental data, particularly when dealing with complex temporal dependencies. Knowledge Gap: Many existing forecasting approaches focus primarily on either short-term sequential learning or long-range temporal modeling, which limits their ability to represent both bidirectional temporal patterns and long-term dependencies in multivariate air quality datasets. Aims: This study proposes a hybrid deep learning framework integrating Transformer, Bidirectional Long Short-Term Memory (BiLSTM), and an Attention mechanism for accurate multivariate air quality prediction. Results: Experiments conducted on the UCI Air Quality dataset demonstrate that the proposed model achieves superior predictive performance with RMSE of 0.0799, MAE of 0.0589, and R² of 0.9621, outperforming baseline models such as standalone Transformer and BiLSTM architectures. Novelty: The proposed framework combines global temporal dependency modeling from Transformer encoders with bidirectional sequence learning from BiLSTM and adaptive temporal weighting through the attention mechanism. Implications: The framework provides a reliable computational approach for environmental monitoring systems, supporting intelligent air quality forecasting, early warning mechanisms, and data-driven environmental decision-making. Highlights Hybrid architecture captures both long-range temporal dependencies and bidirectional sequence relationships in environmental data. Multivariate forecasting shows strong predictive consistency across several pollutants and meteorological variables. Experimental evaluation reports very low prediction errors and strong statistical correlation with observed measurements. Keywords: Air Quality Prediction, Multivariate Time Series, Hybrid Deep Learning, Transformer BiLSTM Model, Environmental Monitoring

General Background: Accurate differentiation between melanoma and basal cell carcinoma (BCC) is essential due to their distinct biological characteristics and clinical management. Specific Background: Raman spectroscopy enables label-free biochemical profiling of tissues by detecting molecular vibrations within the 600–1800 cm⁻¹ fingerprint region. Knowledge Gap: However, systematic discrimination between melanoma and BCC using fresh ex vivo Raman spectra remains limited. Aims: This exploratory study assessed the capability of Raman spectral fingerprints to distinguish melanoma from BCC using standardized preprocessing and statistical analysis. Results: Analysis of 40 spectra (20 melanoma, 20 BCC) acquired at 790 nm identified over 1000 statistically significant Raman shifts (FDR < 0.05), grouped into key biochemical bands related to aromatic amino acids, amide structures, and lipid vibrations. Major peaks at 748–755, 1000–1005, 1440–1455, and 1655–1665 cm⁻¹ showed large effect sizes. Principal component analysis demonstrated clear class separation, with PC1 explaining 61.5% of total variance. Novelty: The study defines distinct Raman spectral biomarkers differentiating melanoma and BCC through integrated statistical and multivariate approaches. Implications: These findings support Raman spectroscopy as a rapid molecular profiling tool for skin cancer subtyping and a basis for future clinical translation. Highlights:• Over 1000 Significant Raman Shifts Clustered Into Major Biochemical Bands Distinguishing Tumour Types.• Aromatic Amino Acids, Amide Structures, and Lipid Vibrations Exhibited Large Effect Sizes Between Groups.• Multivariate Modelling Showed Distinct Clustering With Dominant Variance Captured by the First Principal Component. Keywords: Raman Spectroscopy, Melanoma, Basal Cell Carcinoma, Skin Cancer Diagnostics, Spectral Biomarkers

General Background: Global warming driven by carbon dioxide emissions has become a central challenge to economic systems, particularly in developing and oil-dependent countries. Specific Background: Iraq, as a rentier economy heavily reliant on oil production, faces rising CO₂ emissions alongside fluctuating gross domestic product growth, especially during the period 1980–2016 marked by wars, sanctions, and structural instability. Knowledge Gap: Despite increasing environmental deterioration, limited econometric evidence exists on the dynamic relationship between global warming and economic growth in Iraq. Aims: This study analyzes the short-run and long-run interactions between CO₂ emissions and GDP using annual World Bank data and the Vector Autoregression model. Results: Unit root tests confirm stationarity at first difference, while the Engle–Granger test indicates no long-term cointegration between the variables. VAR estimates reveal weak but positive short-run interactions, where economic growth slightly increases emissions and past emissions strongly determine current emission levels, with model explanatory power reaching 96% and 89% respectively. Novelty: The study provides a structured econometric assessment of environmental and economic dynamics in Iraq using VAR modeling for the full 1980–2016 period. Implications: Findings highlight the short-term mutual dynamics between oil-driven growth and emissions, underscoring the need for diversification strategies and climate-focused economic policies in Iraq. Highlights:• No Long-Run Equilibrium Detected Between Co₂ Emissions and GDP.• Short-Term Dynamics Show Weak Reciprocal Interactions.• Past Emission Levels Strongly Determine Current Emission Trends. Keywords: Global Warming, Carbon Dioxide Emissions, Economic Growth, Vector Autoregression, Iraq

General Background: Communication plays a central role in shaping organizational relationships and cultural interactions within tourism institutions. In service-based sectors such as tourism, communication practices not only transmit information but also construct shared meanings that influence organizational commitment and employee behavior. Specific Background: Tourism organizations often face challenges such as high employee turnover and unstable workforce commitment, which are closely associated with the level of organizational loyalty among employees. Knowledge Gap: Despite the importance of communication processes, many tourism organizations still rely on traditional administrative approaches while paying limited attention to the anthropological dimensions of communication that shape social interaction and organizational culture. Aims: This study examines the role of communication anthropology in explaining organizational loyalty within tourism companies operating in Baghdad. Results: Based on a descriptive analytical approach and a field survey of 95 respondents, the findings indicate that communication anthropology dimensions, including cultural structure of meaning, interactive communicative acts, and reproduction of meaning, are statistically associated with higher levels of organizational loyalty among employees. Novelty: The study introduces communication anthropology as an analytical framework for understanding loyalty within tourism organizations by emphasizing cultural symbols, interaction patterns, and meaning construction. Implications: The findings suggest that tourism administrations should integrate communication-based cultural practices and strengthen semi-formal organizational interaction to support stronger employee commitment and organizational stability. Keywords: Communication Anthropology, Organizational Loyalty, Tourism Organizations, Cultural Communication, Employee Commitment Key Findings Highlights Cultural meaning structures shape employee attachment and belonging in tourism organizations. Communicative interaction patterns contribute to stronger workplace relationships. Meaning reproduction processes support stability within tourism work teams.