Authors - Thaw Thaw May Oo, Khaing Khaing Wai Abstract - Modern maritime industry depends largely on digital communications and access control systems for their operation and security maintenance. On the other hand, digital communication and access control systems make maritime industry more vulnerable to cybersecurity attacks, such as unauthorized access, data leaks, and insiders' malicious actions. Centralized security measures become inefficient against modern and advanced cyber threats. In that regard, this paper presents a Smart Digital Lock System using Zero Trust Architecture and AES Encryption. The suggested approach assumes the implementation of zero trust policy in terms of continuous user identity validation requiring tight access control, including strict user authentication and monitoring. Multifactor authentication and real-time monitoring are the key characteristics of the suggested system, especially considering such potential high-risk zones as ships and ports. Communication of authorized parties will be performed using the AES encryption to protect the information's privacy and integrity. As a result, the presented system will be assessed from three perspectives: authentication accuracy, data protection effectiveness, and response latency.
Authors - Ei Marlar Win, Amy Tun, Khant Kyawt Kyawt Theint Abstract - Electrocardiogram (ECG) signal analysis plays an important role in the early detection and diagnosis of cardiovascular diseases. Manual interpretation of ECG recordings is time-consuming and highly dependent on clinical expertise, creating a need for automated and accurate classification systems. This study presents an automated ECG classification model using signal preprocessing, heartbeat segmentation, wavelet, feature extraction, and deep learning. ECG signals are preprocessed to remove noise using filtering and normalization methods. Features are extracted heartbeat segments-based windows around each R peak and classified into five different arrhythmias N (Normal), V (Ventricular), S (Supraventricular), F (Fusion) and Q (Unknown/noisy /unclassified) using wavelet Convolutional Neural Network (CNN) Self Attention model. Experiments on MIT-BIH ECG dataset and analyze the model performance evaluation across a single-lead ECG, multi lead ECG, lead fusion and feature fusion techniques by wavelet attention. The results indicate that the proposed approach yields high classification performance and effectively distinguishes heartbeats abnormalities. Class weighting techniques were applied to address the issue of imbalanced class labels in the ECG dataset. The lead fusion approach achieved classification accuracies of 0.98. Single lead, multi lead and feature fusion experimental approaches were evaluated, resulting in classification accuracies of 0.97, 0.98, and 0.97, respectively. The class-weighting method combined with lead fusion feature extraction obtained an accuracy of 0.95. Furthermore, class weight additional techniques achieved accuracies of 0.91, 0.92 and 0.92, demonstrating variations in model performance across different methodologies. This automated system can support clinicians to assist in the early diagnosis of heart abnormalities and improve healthcare efficiency.
Authors - Caroline Sutiono, Ronald Gunawan, Silvina Chandra, Maria Pia Adiati Abstract - Online to Offline applications (O2O) have transformed a service style to a new level, since consumers increasingly rely on digital technology to access daily food and beverage products and services based on their needs and preferences. Prior to their arrival, the customer browses the menu, place the order and finish the payment and afterwards the product will be collected at the store. The application required to provide details menu information, options and preference as well as payment details. To use of O2O applications requires customers to have sufficient digital literacy to navigate the ap-plication, place orders, and complete upfront payments. Meanwhile, outlet staff must be able to accurately interpret and process each order specification to ensure service accuracy. Therefore, this study is examining the relationship between O2O application usage, service efficiency, and customer experience in F&B retail businesses. This research uses a quantitative research method, with a survey approach with 160 eligible respondents and analyzed thru SEM PLS. This result emphasizes the importance of user experience and service design into interaction in O2O application usage experience, where customers prioritize applications that are intuitive, convenient, and aligned with their needs. Therefore, the effectiveness of O2O applications is influenced not only by operational efficiency but also by how well the technology supports user-friendly and meaningful user experiences.
Authors - Su Thet Oo, Ah Nge Htwe, Nilar Aye Abstract - The automatic detection of AI-generated art images is essential for distinguishing authentic human creations from artificial ones. This process is critical for authenticity verification, provenance control, misinformation management, and digital forensics. With the rapid evolution of deep learning content generation, the existing detection approaches within artistic imagery remain an underexplored domain characterized by artworks that differ widely in style and often contain non-standard, complex, or distorted visual patterns. The proposed model is an empirical study of a fine-tuned CNN-based generative art detection to classify real and human-created art accurately by learning discriminative visual features such as texture, structure, and statistical patterns, adapting a pre-trained CNN model and also finetuning architecture layers and defining the spatial dimension, which is used to determine the level of detail captured in feature extraction and classification. In our system, utilizing a balanced dataset consisting of real and AI-generated art images, the system was trained and evaluated, where a base VGG16 net in traditional architecture and this architecture of pre-trained and fine-tuned VGG16 with hyperparameter tuning of task-specific input representation and data augmentation, layer optimization strategies using the same balanced dataset, with results benchmarked against a strong baseline.
Authors - Ferdinand V. Dalisay, Gerli Ryza DS. Reyes Abstract - On-the-Job Training (OJT) in Philippine higher education institutions (HEIs) stands at a decisive inflection point. Historically constrained by misaligned curricula, weak industry-academe partnerships, and inadequate quality assurance mechanisms, the OJT system is now confronted simultaneously with the disruptive potential of artificial intelligence (AI), the transformative power of data analytics, and the imperatives of broader digital transformation. This systematic literature review synthesizes 35 peer-reviewed studies and policy documents published between 2020 and 2026 to examine how these three technological forces are reshaping and should further reshape the design, implementation, supervision, and evaluation of OJT programs across Philippine colleges and universities. Guided by the TIBS 2026 conference tracks on AI and Intelligent Systems, Data Analytics and Business Intelligence, and Digital Transformation and Technology Strategy, the review constructs a crosscutting analytical framework that interrogates the current state of Philippine OJT against the backdrop of these technological paradigms. Four thematic clusters are identified: (1) AI-mediated supervision, mentoring, and competency scaffolding; (2) data-driven OJT quality assurance and outcome analytics; (3) digital platform ecosystems and virtual work-integrated learning; and (4) strategic alignment between OJT curricula and the emerging digital economy. Findings reveal that while Philippine HEIs have begun to engage with digital tools in OJT administration, deep integration of AI and analytics into OJT pedagogy and governance remains nascent. The review concludes with a multi-stakeholder digital transformation roadmap for the Philippine OJT system, offering implications for CHED policymakers, HEI administrators, industry partners, and technology developers.
Authors - Janssen Emmanuel Jahja, Anderes Gui Abstract - The rapid development of e-commerce also raises the need for new innovations such as Virtual Try-On (VTO) to address the physical limitations of online product evaluation. Nevertheless, the interaction of functional and psychological factors of VTO is poorly understood as influencing its adoption, while their influence on purchase decisions also remains limited. This study investigates these factors with respect to online purchasing intentions. Incorporating an extended Technology Acceptance Model (TAM) with consumer behavior theories, the conceptual model assesses Perceived Ease of Use, Perceived Usefulness, Perceived Enjoyment, Attitude, Personal Innovativeness in IT, and Self-Efficacy. Using a quantitative approach, information was gathered from consumers who shop on e-commerce sites and analyzed using Structural Equation Modeling (SEM). The results show that the hypotheses suggested are well supported. This study contributes theoretically by extending digital retail literature and offers managerial implications for designing VTO features that not only improve the shopping experience but also yield higher sales conversions.
Authors - Jayanthi J, Krishna Kanwar, Divansh Tarun Mittal, Akash Kumar, Srikanta Pradhan, Arun Kumar K Abstract - The problem of financial distress faced by the sandwich generation-who are held responsible for both the elderly parents and dependent children simultaneously, but not accommodated by available tools-motivates this research. In this work, we developed a portfolio intelligence system named WealthBridge that leverages an AI framework, which includes Random Forest model for risk profiling and an LSTM network for market regime detection. While the model accurately classify investors (with 95% accuracy) and market regimes, it forecasts market trends using time series of various features. A fusion engine then provides recommendation for allocation to different portfolio asset classes and investment in particular stock. It is accessible through the deployment of a Streamlit dashboard, making it an efficient tool for data-driven financial planning. The accuracy was assessed with robust performance of models that caters to financial services of the Indian middle income sandwich generation.
Authors - Marybell Materum, Daniel Dasig Jr, Lucila Magalong, Emelyn Libunao, Shirley Padua, Sonia Pascua, Rizza Gerente and Sharon Sanchez Abstract - Broadband infrastructure has become a critical enabler of digital trans formation, technological competitiveness, and economic sustainability across OECD economies. This study proposes a hybrid technological intelligence framework integrating descriptive analytics, temporal trend modeling, compara tive broadband evaluation, and predictive business interpretation using OECD broadband subscription datasets. The dataset comprised 11,324 broadband obser vations covering fixed, mobile, and fiber-optic technologies across multiple countries and annual periods. A quantitative explanatory research design was em ployed using statistical preprocessing, longitudinal analysis, and machine learn ing-oriented analytical procedures to identify broadband growth dynamics and digital infrastructure disparities. Results revealed substantial asymmetry in broadband adoption patterns, with the United States, Japan, Korea, France, and the United Kingdom demonstrating dominant subscription trajectories and accel erated digital infrastructure expansion. Fiber-optic and mobile broadband tech nologies exhibited the highest growth rates, particularly after 2018, reflecting in tensified digital transformation and remote connectivity demands. The findings demonstrate that broadband intelligence analytics can support strategic business forecasting, digital competitiveness evaluation, telecommunications planning, and evidence-based policy formulation within Industry 4.0 and smart governance ecosystems.
Authors - Govind Kumar, Amresh Kumar, Ajeet Singh Abstract - The process of selecting the right Indian city to live in is an extremely crucial one, which can have a huge impact on One’s life, safety, work and happiness every day. However, the tools available today, The kind of websites that tell about a property, or simple map applications, aren’t smart enough. They Do not know what each member of a neighbourhood really wants. This paper introduces Neighbor- Fit, an innovative AI-driven solution that suggests neighborhoods. Based on the actual need of the user. The system has three new ideas, the first of which is: A composite neighborhood suitability score (CNSS) as a six-part score that perates safety, facilities in the area, travel time, cost of living, green areas, and community life; (2) a smart algorithm called Preference-Adaptive Cascade Hybrid (PACH) which alters its style of recommendation according to the amount of recommendation it already has knows about the user; and (3) an explanation system based on LIME which explains to the user in simple words why a neighborhood was suggested. Tests done on 250 PIN codes In three major cities of India, namely, Delhi, Mumbai and Bengaluru, Preci- shows across. sion@10 of 87.3%, Recall@10 of 84.1%, and F1-Score of 85.7% — better than all There were five methods of comparison (p ¡ 0.05). The system reacts in an average of 340ms time even for 50 users using simultaneously.
Authors - John Julius M. Orillana, Loyd S. Echalar Abstract - Mud crab fattening supports aquaculture, local food supply, and income for small-scale farming communities. In container-based culture systems, farmers face two common problems. They need to keep water quality stable. They need to track crab growth on time. Manual monitoring takes time, changes from one checking period to another, and slows response when water conditions shift. These problems affect crab health, survival, and growth. This study developed CRABSMART, a smart container-based fattening system for mud crabs, Scylla serrata, with integrated water quality monitoring and growth prediction. The system tracks temperature, pH, dissolved oxygen, and salinity through sensors linked to a microcontroller platform. The platform sends the data to a web-based dashboard for real-time display, historical monitoring, and system status tracking. The study also includes a growth prediction component. This component estimates growth trends from recorded water quality conditions and culture duration. The study used a developmental research approach for design, integration, and implementation of the prototype. Functional assessment examined sensor operation, data transmission, dashboard performance, and integration of the prediction component. CRABSMART supports faster decisions, reduces manual monitoring, and improves daily management in mud crab fattening. The system provides a practical approach for smart aquaculture, especially in container-based mud crab production.
Authors - Sarita Thummar, Amit Thakkar, Gayatri Patel, Vaishali Koria, Yug Mordiya Abstract - Leukemia is a malignancy that afflicts blood and bone marrow and requires a precise diagnosis and care to be effective. False diagnosis and diagnosis at a late stage result into death. Diagnostic capabilities have been greatly improved by recent developments in Artificial Intelligence (AI), especially machine learning and deep learning. However, many AI models, also known as black boxes, are opaque and thus restricted to use in a clinical scenario where interpretability and transparency is important. This paper will look at the application of Explainable AI (XAI) to diagnose leukemia, with a particular focus on how it can be used to provide clear and intelligible explanations of AI-driven decisions. The experimental results prove that the given ensemble model can be useful in classifying the subtypes of leukemia. Explainable AI methods like SHAP and LIME also enable more trust since the insights obtained are transparent and clinically relevant. This demonstrates the possibility of interpretable models being applicable to practice to aid clinical diagnosis. By using XAI techniques on trained model, the potential of XAI to bridge the gap between high-performance AI and clinical applicability is demonstrated. Despite its potential, XAI is faced with several challenges to address, including the need to integrate it into existing clinical workflows, technical complexity, and issues of data protection. At the end of the paper, the importance of developing domain-specific XAI methods and collaborative structures to succeed is outlined.
Authors - Carolina Ditan, Daniel Dasig Jr, Sushil Kumar Singh, Isagani Valenzuela II, Catherine Catalan, Bablu Khumar Dhar, Jewelyn Ciocon and Maricris Ediza Abstract - The increasing complexity of sustainable governance ecosystems re quires advanced analytical models capable of integrating multidimensional soci oeconomic, environmental, governance, and technological indicators into inter pretable strategic intelligence systems. This study proposes a Federated Techno logical Intelligence Framework (FTIF) utilizing the World Bank Sustainable and Social Governance Database (WB_SSGD) to analyze governance resilience, en vironmental sustainability, institutional effectiveness, and digital transformation patterns across multiple countries. The study integrates explainable artificial in telligence (XAI), federated analytics, ensemble machine learning, and nonlinear predictive modeling to identify strategic relationships among governance indica tors, energy transition variables, democratic participation metrics, and environ mental sustainability indicators. The methodology combines Random Forest Re gression, Gradient Boosting Machines, Long Short-Term Memory (LSTM) tem poral learning, SHAP explainability mechanisms, and panel-based econometric validation. Findings reveal that governance effectiveness, access to civil justice, corruption control, democratic participation, and carbon intensity significantly influence sustainable development trajectories. The hybrid architecture achieved high predictive reliability with strong convergence stability and reduced predic tion variance across heterogeneous country clusters. The SHAP-based explaina bility analysis further demonstrates that institutional quality variables contribute more significantly to sustainability outcomes than isolated economic indicators. The proposed framework contributes to technological intelligence literature by introducing a scalable and interpretable governance analytics architecture for strategic policymaking and digital sustainability planning. The study offers prac tical implications for governments, higher education institutions, business strate gists, and international development organizations pursuing evidence-based gov ernance transformation.
Authors - Alyssa C. Vicente, Cedirick Santiago, Elmer M. Alino, Ma. Yvonne Czarina C.Angcaya, Benedict G. Bautista, St. Joseph M. Lumbog Abstract - This systematic review investigates the application of machine learning (ML) and deep learning (DL) in the early detection of Autism Spectrum Disorder (ASD), a neurodevelopmental condition characterized by social and communication deficits. Adhering to the 24-step framework by Muka et al. and PRISMA 2020 guidelines, the methodology involved a rigorous search of four academic databases—IEEE Xplore, Scopus, PubMed, and ACM Digital Library— identifying 67 records. Ultimately, 10 peer-reviewed studies published between 2020 and 2024 were analyzed based on their use of real-world datasets and quantitative metrics. Results indicate that ML models, particularly Convolutional Neural Networks (CNNs) and ensemble classifiers, achieve high predictive performance with accuracies between 80% and 94%. The findings highlight that behavioral data from home videos and eye-tracking scan paths serve as effective indicators for remote, scalable screening. However, the review identifies significant gaps, including small, homogeneous datasets and a lack of model interpretability. To advance the field, future research must focus on Explainable AI (XAI), multimodal fusion, and the development of large-scale, multicultural, open-access datasets to ensure clinical trust and global generalizability.
Authors - Takumi Kato Abstract - According to Processing Fluency Theory, the more fluently people can process an object, the more positive their aesthetic response becomes, making symmetrical designs more desirable. Furthermore, symmetry is also expected in the context of ethical products, as simplicity is effective in fostering an impression of environmental and health considerations. However, symmetry is a highly symbolic and essential design. Based on Construal Level Theory, people prefer essential objects when they feel a greater psychological distance from them, and prefer objects when they feel a greater psychological distance. Through this theoretical lens, the evaluation of essential symmetrical designs may differ depending on the psychological distance from the product. This study posed the research question: "Do people who feel a greater psychological distance from the product rate products with symmetrical designs more highly than those who feel a greater psychological distance?" Focusing on detached houses, a randomized controlled trial was conducted with 1,000 Japanese people aged 20-60. The results showed that in detached house designs, symmetrical designs were significantly more favorably received than asymmetrical designs in terms of living intention, healthy impression, and environmental impression. However, these effects were more pronounced in people living in apartments than in those currently living in detached houses. Therefore, it can be inferred that symmetry is more effective for luxury goods than for inexpensive goods, for gifts to others than for personal use, and for goods that will be useful in the future than for goods that will be useful immediately.
Authors - Jemima Achiah, Benjamin Ghansah, Stephen Opoku Oppong, Charles Buabeng Andoh, Joseph Kwabena Essibu, Christopher Yarkwah Abstract - The integration of digital literacy within basic education has become increasingly important in preparing learners with the competencies required for participation in twenty-first-century society. This study investigates how basic school teachers in Ghana foster learners’ dig-ital literacy competencies within the context of the Standards-Based Curriculum. Specifically, the study examines the instructional strategies employed by teachers, the contextual challenges influencing implementation, and the extent to which these practices shape learner engagement and digital skill acquisition. An embedded mixed-methods research design was adopted, com-bining qualitative and quantitative approaches to provide a comprehensive understanding of classroom practices and learner experiences. Qualitative data were collected through semi-struc-tured interviews with six teachers and observations of school digital infrastructure, while quan-titative data were obtained from 122 learners across three public basic schools in Komenda, Ghana. The findings revealed that teachers predominantly employed learner-centered pedagogi-cal approaches, including hands-on instruction, collaborative learning activities, and the integra-tion of learner-owned digital devices to facilitate practical engagement. Despite persistent chal-lenges relating to inadequate infrastructure, limited access to digital resources, and insufficient professional development opportunities, these instructional practices contributed positively to learners’ motivation, confidence, and practical ICT competencies. The study contributes to the limited empirical literature on teacher-driven digital literacy development within Ghanaian basic education and highlights the critical need for sustained teacher capacity building, improved dig-ital infrastructure, and supportive policy interventions to strengthen effective digital literacy in-tegration in resource-constrained educational contexts.
Authors - Shivam Kumar, Dinesh Kumar Saini Abstract - KG-RAG (Knowledge Graph-Retrieval Augmented Generation) is an advanced AI framework that combines structural knowledge graphs with LLMs to make them smarter, more accurate, robust, and less prone to hallucination. However, existing KG-RAG pipelines are often tightly coupled with specific domains. In addition, most of the systems lack proper schema validation and have limited support for temporal knowledge. GenericKG is a modular framework designed to decouple knowledge ingestion, validation, storage and retrieval across domains. The framework includes an agentic ingestion pipeline with schema-driven knowledge graph construction, supported by multi-level validation (L1-L3) to ensure structural, semantic and temporal consistency. Temporal attributes and semantic embeddings are integrated at framework level, enabling time-aware querying and hybrid retrieval without domain-specific reengineering. This paper is evaluated on three benchmarks: the BC5CDR biomedical corpus (87.92% entity F1 with 100% precision), the WebNLG crossdomain dataset (85.6% entity F1 across 15+ relation types on 100 records), and HotpotQA multi-hop question answering (58.0% accuracy on bridge and comparison questions). A raw-LLM baseline without schema guidance scores 0% on all metrics, confirming the importance of the schemadriven pipeline. This framework is implemented in TypeScript and it will be released as open source.
Authors - Abraham Gezehei, Thomas Hanne, Rolf Dornberger Abstract - This study benchmarks twelve recurrent neural network (RNN) architectures for univariate macroeconomic time-series forecasting, covering LSTM and GRU baselines, width/depth scaling, bidirectional encoders, an attention-like pooling variant, convolutional–recurrent hybrids, and strong regularization. Following the Libra benchmarking philosophy and the multi-metric evaluation advocated by Prater et al., we compare all configurations under identical protocols on 100 series from the Libra Economics collection. A bidirectional GRU yields the best RNN accuracy (sMAPE 41.0, MASE 0.0447), improving over a comparable 2-layer GRU baseline (sMAPE 41.9) at higher wall-clock runtime. Most architectural additions and capacity increases do not improve performance over the simple GRU baseline (e.g., deeper/wider models, pooling-based attention, CNN–RNN hybrids, and heavy dropout). The results suggest that short input windows (dynamically sized at 10% of series length, minimum 10 steps) limit the benefits of architectural complexity in this setting. Classical statistical methods (sNaive, ETS, Theta) outperform all neural models by a wide margin while requiring substantially less computation. For these low-frequency macroeconomic series, shallow GRU variants—especially bidirectional encoders—are the strongest RNN option, but classical baselines remain the practical choice.
Authors - Alfito Athar Rayyansyah, Abdurrahman Faris Indriya Himawan, Galuh Sudarawerti Abstract - Governance challenges remain a major concern in large-scale procurement activities, particularly regarding transparency, accountability, and operational effectiveness. This study investigates the role of Big Data Analytics (BDA) and Information Processing Capability (IPC) in enhancing governance outcomes within the e-procurement environment of PT PLN Indonesia Power. Specifically, the study examines how these capabilities contribute to transparency and accountability and how they affect both financial and non-financial procurement performance. A quantitative research design was employed, and data were gathered from employees engaged in procurement-related activities. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The findings reveal that all proposed hypotheses are statistically supported. BDA emerged as the primary factor driving transparency and accountability, which subsequently improves procurement performance, particularly non-financial outcomes. The findings reveal that IPC serves as a key enabler in maximizing the value of BDA while increasing the ability of e-procurement systems to support data-driven analysis. These findings offer practical implications for state-owned enterprises by emphasizing the importance of integrating analytical capabilities and information-processing resources to strengthen governance quality and improve procurement effectiveness in digital environments.
Authors - Ichwan Masnadi, Renza Fahlevi, Elda Nurmalinda Abstract - The purpose of this study is to analyze how Perceived usefulness of smart lighting can affect Revisit Intention through the mediation of Green Hotel Image. This study was conducted on hotel guests who stayed at hotels that implemented smart lighting technology in Jakarta. This study uses quantitative methods by sending questionnaires online via Google Forms to 150 hotel guests who have previously stayed at hotels with smart lighting technology implemented. The data was then processed using SEM-PLS (Structural Equation Modeling–Partial Least Square) through SmartPLS 3 software. The results showed that Perceived usefulness of smart lighting had a positive and significant impact on Green Hotel Image. Green Hotel Image also had a positive and significant effect on Revisit Intention. Perceived usefulness of smart lighting had no effect on Revisit Intention. Furthermore, results from the analysis showed that Green Hotel Image fully mediated the effect of Perceived usefulness of smart lighting on Revisit Intention. In conclusion, guests are not inclined to revisit hotels that implement smart technology such as smart lighting. Smart technology indirectly fulfills its role by increasing the hotel’s green (environment-friendly and sustainability focused) image which leads to customer revisit intention. This study contributes to the SOR Theory by showing how Perceived usefulness of smart lighting is the Stimulus factor, Green Hotel Image is the Organism factor, and Revisit Intention is the Response factor. Hotel managers can benefit from this study by properly branding their hotels’ sustainability to leverage their use of smart technology in order to compete with other hotels.
Authors - Luong Vinh Quoc Danh, Truong Minh Nhan, Nguyen Tan Dat, Nguyen Vinh Thanh, Do Chi Tam, Le Tan My, Nguyen Chanh Nghiem Abstract - Accurate avocado ripeness assessment is essential for ensuring product quality and effective postharvest management, yet conventional evaluation methods remain largely destructive, time-consuming, and limited to representative samples. This paper presents a non-destructive ripeness assessment method combining microwave sensing with feedforward neural network (FNN) classification. A custom-designed open-ended coaxial probe connected to a vector network analyzer was employed to measure the complex reflection coefficient S11 of avocado samples over a frequency range of 1.1–3.1 GHz. Variations in the dielectric properties of avocado flesh during ripening produce corresponding and measurable changes in the S11 characteristics, from which magnitude, phase, and frequency features were extracted and used as inputs to the FNN classifier. The proposed system achieved an overall classification accuracy of 87% in discriminating among three ripeness stages – unripe, ripe, and overripe – thereby demonstrating its viability as a rapid, costeffective, and non-destructive alternative to conventional destructive ripeness assessment methods.
Authors - Christian Vasta, Rolf Dornberger, Thomas Hanne Abstract - The Inventory Routing Problem (IRP) is a critical challenge in logistics, combining vehicle routing with inventory management under a unified objective. Recent research in computational intelligence has advanced the use of metaheuristics for tackling such combinatorial problems. Among these, Simulated Annealing (SA) remains underexplored for IRP compared to more commonly applied methods. In this study, we address this gap by implementing a custom SA algorithm to solve a deterministic five-day IRP. The goal is to minimize total transportation costs while satisfying daily customer demand using a single-vehicle fleet with fixed capacity. The algorithm's performance is evaluated with 20 independent runs and compared to a modified Tabu Search benchmark using the same deterministic instance. Our results show that Simulated Annealing performs competitively, producing high-quality solutions, with moderate variation observed across different cooling schedules and repeated runs. Although it shows greater sensitivity to initial parameters and stochastic behavior, its exploratory nature allows it to overcome local optima more effectively than Tabu Search in some cases. The outcomes suggest that SA is a viable alternative for IRP under deterministic conditions, particularly when flexibility in parameter tuning is prioritized.
Authors - Febin Koshy Jacob, Indranil Bose, Sarika D Tavhare, Sandhya Anilkumar Abstract - Modern automotive Electronic Control Unit (ECU) systems demand robust and accurate validation frameworks to address increasing system complexity while minimizing manual test effort and development cost. This paper novels an automated Hardware-in-the-Loop (HIL) testing framework for validation of automotive systems, with a primary focus on automated waveform pattern analysis method. The framework integrates a dSPACE real-time interface with a hardware test bench and algorithm developed using a MATLAB-based simulation model of the Body Control Module (BCM) to generate and analyze input signals. Python-based automation scripts are utilized for test execution control, synchronized data acquisition, and automated result analysis, ensuring repeatable and scalable testing across multiple application domains. The core contribution is a reference-driven waveform comparison methodology, where signals captured from the Device Under Test (DUT) are evaluated against predefined golden reference waveforms. The approach quantifies Root Mean Square Error (RMSE) percentage and timing deviations across individual channels, enabling precise detection of mismatches in waveform sequences. The framework is demonstrated through automotive tail lamp animation pattern validation, where output sequences are compared against reference waveforms for accuracy and robust assessment. Additionally, the solution is extendable to electric vehicle subsystems such as Battery Management Systems (BMS), Traction Motor Control Units (TMCU), and Off-Board Chargers (OFBC), supporting both dynamic and steady-state validation such as torque-speed curve, Battery profile testing, Sensor accuracy etc. The implementation achieves approximately 45.8% automation of test cases and reduces overall validation time by about 41.2%, resulting in improved repeatability, reduced manual intervention, and faster development cycles, ultimately enabling faster time-to-customer and providing a scalable and efficient solution for modern automotive and electric vehicle system validation.
Authors - Apolinar P. Datu, Jeferson C Mojica, Pamela Daphne R. Busog, Kelvin M. Custodio, Desiree Anne D. Mendoza, Kristel Shane C. Paminter, Rose Ann T. Genova, Keno A. Villavicencio Abstract - This study explores how on-the-job training (OJT) helps student interns improve their ability to work with others. It focuses on how real workplace exposure strengthens teamwork, communication, and adaptability. Data were collected from 150 interns from different academic programs using a survey that examined their experiences during training. The findings show that most interns felt a noticeable improvement in their collaborative skills. Many were actively involved in meetings, team activities, and workplace discussions, which gave them valuable opportunities to interact and contribute. These experiences not only helped them communicate more confidently but also made them more comfortable working as part of a team. The results also indicate that supportive work environments—those that encourage communication and teamwork—play an important role in helping interns grow. In addition, OJT helped boost their confidence, sense of responsibility, and readiness for future employment. Overall, the study highlights the importance of OJT as a bridge between academic learning and real-world practice. It reinforces the idea that hands-on experience is essential in preparing students for a workplace that values collaboration and adaptability.
Authors - Bhavya Balakrishnan, Srinivasa HP Abstract - The massive deployment of heterogeneous, resource- con-strained and always-on devices underlying the Internet of Things (IoT) has introduced complex cybersecurity challenges. The rapid growth of the Internet of Things (IoT) due to the large-scale deployment of heterogeneous, resource-constrained and always-on devices has resulted in complex cybersecurity challenges. The physical and digital components in the IoT systems are tightly bound which increases the attack sur-face and makes them highly prone to threats of malware infections, data theft, unauthorized access and distributed denial of service. Traditional security mechanisms and rule-based intrusion detection systems cannot manage the dynamic, large-volume and evolving IoT traffic. The solutions provided by machine learning have been widely concerned due to its capability of learning data patterns and finding abnormal and malicious activities. However, existing machine learning models have serious constraints such as lack of labelled information, extreme class imbalance, and inability to generalize to new and never-seen attacks. In recent years, Generative Adversarial Networks (GANs) have emerged as a promising paradigm to improve the cybersecurity of IoT through artificial generation of realistic synthetic data, adversarial sample enhancement, alleviating data imbalance and modelling adversarial attack-defense dynamics. GAN based models have showed great gains in intrusion detection, anomaly detection and malware analysis in the IoT networks . However, modern studies are still divided on this issue due to variations in GAN architectures, datasets, evaluation procedures, and experimental procedures. In addition, most of the researches have been more concentrated on offline benchmark databases, with less focus on checking through realistic IoT testbeds, which could be more precise in capturing the actual deployment conditions.
Authors - Ferry Setyadi Atmadja, Sabo Hermawan, Eka Dewi Utari, Suciati Putri Nurjanah, Siti Dwi Hastuti Abstract - The exorbitant costs associated with professional Content Management Systems (CMS) have precipitated a severe theory to practice gap in digital archive education. This infrastructural barrier disproportionately disadvantages institutions with constrained budgets, fundamentally threatening the inclusive education mandates of Sustainable Development Goal (SDG) 4. To bridge this ped-agogical divide, this study developed and validated a zero-license educational framework utilizing Microsoft Excel's Visual Basic for Applications (VBA) to simulate a professional electronic records environment. Employing an R&D methodology (ADDIE model) with a cohort of 40 undergraduate students, the proposed framework circumvented hardware and financial constraints by operating offline on low-specification devices. Results indicated high expert validation (4.35/5.0) and a statistically significant enhancement in students' practical archival skills, evidenced by a moderate to high Normalized Gain (N-Gain) of 0.61. Furthermore, the system demonstrated exceptional usability with a System Usability Scale (SUS) score of 76.5. These findings provide empirical evidence that strategic, low-cost technological interventions can effectively democratize digital archive learning, offering a highly scalable solution for marginalized educational ecosystems in developing regions.
Authors - Rayyan Naufal Anandito, Muhammad Fedylopa Ginting, Trias Septyoari Putranto Abstract - The rise of Automated Biometric Boarding Systems (ABBS) for public transportation, driven by the potential to enrich convenience while integrating artificial intelligence into their activities has not been without the desire among policymakers and business leaders to get a better grasp on how biometry could be integrated in mandatory adoption contexts. Abstract This study aims to investigate passenger acceptance and continuance intention of AI-based face recognition boarding system in PT Kereta Api Indonesia (KAI) Gambir Railway Station 2023. Based on an integrated framework of Technology Acceptance Model (TAM) and Expectation-Confirmation Model (ECM), complemented with Trust and Perceived Privacy Risk, this study explores the pathways through which affective factors and institutional factors influence long-term behavioral intentions in a compulsory acceptance context. Data from cross-sectional, quantitative. 150 purposively sampled passengers were analyzed by PLS-SEM using SmartPLS 4.0. This is the first time that these findings challenge many of the assumptions about technology adoption and provide relevant policy recommendations for transport authorities based on a framework for AI governance aligned with Indonesia's Personal Data Protection Law (UU No. 27/2022).
Authors - Ridhi Sharma, Ashok Kumar Abstract - This manuscript discovers the role of information theoretic measures for feature selection while dealing with high dimensional data sets. The study uses entropy, mutual information and divergence measures to address the issues of classification and high computational complexity of real data set which is affect by redundant and irrelevant features, by analyzing the dependency patterns and feature relevance in complex data set. Under different data conditions, the proposed approach for feature selection, in comparison to traditional methods, handles the non-linear relationships and noisy attributes effectively in terms of relevance, classification and interpretation. In-formation theoretic methods provide more precise feature selection and pattern identification results in the data sets. Despite the challenges of computational cost and scalability, the study shows that information theoretic measures can perform better in feature selection and decision making of the data mining.
Authors - Nishi Doshi, Shrey Shah Abstract - Hospitals run more machine learning on GPUs while the carbon footprint of grid electricity rises and falls through the day. Using a computer simulation, we compare 13 scheduling rules on mixed GPU hardware, with synthetic patient-style jobs, urgency tiers, and time-ofday carbon traces. We do not study patient outcomes; every percentage we report is a simulator queue number, not a clinical finding. We ask whether running non-urgent jobs overnight is almost as good as a richer rule that mixes urgency and carbon (CUCA at weight 0.45, written CUCA 0.45). The comparison keeps carbon reduction secondary to clinical priority and deadline compliance, so each policy is judged on both average kg CO2e and missed-deadline behavior. CarbonGreedy and CarbonShift are carbon-first stress tests that demonstrate how poorly wrong vendor presets can disrupt clinical priorities, and are not meant for production. Numbers are averages over many test settings, with wide run-to-run spread and no statistical adjustment, so headline ratios are exploratory. On an eight-GPU baseline, the overnight rule closes about 78% of the carbon gap between urgency-only and CUCA 0.45 while missing fewer urgent deadlines than either. CarbonShift lets about 46% of the most urgent jobs miss their deadline; this is simulated queueing, not bedside harm. At 48 jobs per hour, the carbon footprints almost tie, yet the overnight rule still misses fewer urgent deadlines. A geography test, where regions share one daily carbon shape with only timezone shifts, trims under one percentage point of average carbon; a twelve-hour routine window saves a little carbon for CUCA 0.45 but raises overall missed deadlines. Overnight batching stays competitive on average modelled carbon; carbon-only rules belong only in stress tests.
Authors - Abhijit Dnyaneshwar Jadhav, Prashant G. Ahire, Madhuri Hiwale Abstract - In vitro fertilization (IVF) is currently one of the most powerful assisted reproductive technologies for infertility treatment. However, the embryo selection process still represents a bottleneck that greatly influences the rates of implantation and live birth. Traditional methods of embryo evaluation involve embryo morphology grading. But this approach suffers from subjectivity, variability, and heavily depends on the skill and experience of the embryologist. To go beyond the limitations of human assessment, the latest improvements in artificial intelligence (AI), machine learning (ML), and deep learning (DL) have made possible the automated embryo evaluation using pictures, time-lapse morphokinetics, and clinical data. This paper reviews comprehensively the currently available AI-enabled IVF systems while also first introducing the conventional embryo assessment and later presenting the most sophisticated multimodal deep learning frameworks. The paper also discusses some of the major outstanding issues such as the poor performance of models on new datasets, the lack of the shared and agreed upon benchmarks, and the limited explainability of the models. We have also developed a Multimodal Explainable Artificial Intelligence Frame-work for IVF (MEAIF-IVF) to fill in these gaps in which image of the embryo, time-lapse video of the embryo, and clinical patient information are all combined into one deep learning model. This system uses convolutional neural networks and vision transformers for spatial feature extraction, recurrent neural networks for temporal modeling, and attention-based fusion for multimodal integration.
Authors - Peruru Gayathri, Rohini M, Anand R Nair Abstract - Cyber threats are getting more sophisticated and conventional security solutions are not keeping up with detecting cyber-attack. In this research, a hybrid detection and prediction system for TTP (Tactics, Techniques and Procedures) based on deep learning and graph-based is presented. The planned study is based on an analysis of data originating from cyber security systems at large scale, which can be used to detect attack patterns and correlations of attacks. Host logs and threat intelligence data are trained using deep learning models to detect discriminative features, while graph-based models are used to model the structural relationships between users, systems, and attack patterns. Combined these techniques will result in more complex attacks and lateral movement being easier to detect. It also assumes probable attack methods to move to the next level, so that it can predict the attacks and take proactive actions to mitigate attacks in the future. The entire predictive and graph based solution enhances threat visibility and threat response speed, while boosting threat detection accuracy. The system enables the detection of the APTs and real time monitoring them by the Cyber Security analysts. The experimental results show that the highest accurate transformer is able to achieve 95% classification accuracy, and the graph neural network is demonstrated to achieve 78.26% accuracy for predicting next technique. The framework has been shown end-to-end, with the intent of showing it can be utilized as an extra layer of Intelligence on the enterprise security side, with Splunk.
Authors - Vishwa Kumaresh Abstract - A local supplier delay or demand shock in multi-echelon supply chains can make upstream orders volatile long before the full costs appear in planning dashboards. In this study, we propose an interpretable warning-to-action layer for supply-chain digital twins. This layer sits above the replenishment controller: it estimates disruption-regime risk from rolling demand, inventory, backlog, order, and lead-time telemetry, then maps that risk to bounded changes in responsiveness, safety stock, and order caps. We calibrate a gradient-boosted stump classifier that combines standard warning indicators, cross-echelon imbalance measures, and nonlinear stress descriptors. A small mode table converts the resulting probability into five auditable replenishment modes. This method is tested on twelve disruption scenarios grouped into six mechanism classes, using ten baselines and an untouched lockbox of 576 observations. The proposed policy reduces aggregate system expenditure by 15.2% and cross-echelon volatility (bullwhip) by 44.5%, relative to a linear guard that uses the same broad action family. The largest gains occur in lead-time disruptions and backlog cascades. Compound shocks demonstrate marginal performance gains, as existing linear guards effectively capture these dynamics within standard monitoring frameworks.
Authors - Wannakorn Phornprasert, Waraporn Phothirin, Thapanapong Sararat, Wongpanya S. Nuankaew, Pratya Nuankaew Abstract - This study uses Learning Analytics to assess university students’ eye health risks based on social media usage data, focusing on descriptive and diagnostic analyses. Data collected from 44 undergraduates via a self-reported questionnaire with 82 key questions covered general details, social media habits, device and screen environments, symptoms of Computer Vision Syndrome, and Felder–Silverman learning styles. The descriptive analysis revealed Instagram as the most popular platform, frequent nighttime use after 20:00, and many students spend over six hours daily on social media. While most respondents were categorized as low risk, symptoms such as watery eyes, eye pain, light sensitivity, and neck pain were commonly reported. The diagnostic analysis linked risky sitting postures, looking below eye level, prolonged daily usage, and nighttime social media activity to increased eye health risks. These findings support initiatives for digital well-being and learning support in higher education.
Authors - Mariel Leo T. Violeta Abstract - The increasing incidence of academic credential fraud, inefficient verification procedures, and reliance on centralized record management systems present significant challenges for higher education institutions. This study proposes and evaluates a blockchain-based academic credential issuance and verification platform using Hyperledger Fabric to improve the security, authenticity, and efficiency of academic credential management. The platform enables university registrars to issue digital academic credentials, allows students to securely access and share academic records, and provides employers and external entities with a reliable credential verification mechanism. To ensure data integrity while maintaining scalability and privacy, the framework integrates blockchain-based cryptographic hashing with off-chain cloud storage. A quantitative descriptive research design was employed using the Technology Acceptance Model (TAM) as the theoretical framework. Data were collected from 40 registrar personnel at the Polytechnic University of the Philippines through a structured survey instrument measuring Perceived Usefulness and Perceived Ease of Use. Findings revealed that respondents strongly agreed that the platform improves security, credential verification, operational efficiency, accessibility, and flexibility. The results demonstrate that Hyperledger Fabric can provide a secure, tamper-resistant, and efficient infrastructure for managing academic credentials in higher education institutions. The study contributes to the growing adoption of blockchain technology in education by presenting a practical and institution-oriented framework for secure and verifiable digital credential management.
Authors - Bhagyalakshmi S Pai, Jeevanand E S, Radhika P.C, Krupa B Nair, Sreeja Radhakrishnan, Dhanalakshmi Menon Abstract - The present study attempts to empirically investigate how the customers’ awareness relates to the adoption of green banking initiatives of commercial banks in Kerala, India. The study employs data gathered from 540 customers of five banks (SBI, Canara, PNB, ICICI Bank, HDFC Bank, and Axis Bank) by using a structured questionnaire, and builds and validates the structural model for green banking adoption. Customer awareness is considered as a higher order construct which consists of Environmental Awareness and General Awareness. The analysis used descriptive statistics, reliability analysis, Confirmatory Factor Analysis (CFA), two-stage analysis of Structural Equation Modeling (SEM), and Z test and One-Way ANOVA test to determine awareness levels and differences in demographic data. The results show that there exists a high Awareness–Adoption Gap, that is, a superficial awareness of green banking, which is not yet accompanied by a conceptual understanding of it. The study also reveals that adoption of e-banking is mainly for convenience and that practice in key life-stages and occupations have a strong bearing on adoption behaviour.
Authors - Wannakorn Phornprasert, Ratchanin Intham, Thapanapong Sararat, Wongpanya S. Nuankaew, Pratya Nuankaew Abstract - This study explored learning well-being and indicators of academic burnout associated with pseudo depression risk among university students at the University of Phayao. Data collection involved a general information questionnaire, an academic burnout assessment scale, and the DASS-21. Descriptive and diagnostic statistics were applied. Results indicated a moderate level of overall academic burnout, with academic fatigue scoring higher than academic withdrawal. Emotional risk assessment found that 50.0% of students showed mild to severe pseudo depression symptoms. Additionally, scores for academic fatigue, academic withdrawal, and overall burnout were positively linked to depression, anxiety, and stress. These results suggest that descriptive and diagnostic approaches can serve as initial tools for screening and promoting students' learning well-being in Thai higher education.
Authors - Pratya Nuankaew, Panisara Paksasuk, Thanapon Thiradathanapattaradecha, Thapanapong Sararat, Wongpanya S. Nuankaew Abstract - This study analyzes student behavioral data to understand factors influencing secondhand fashion purchases in the digital age. A survey was conducted with 40 University of Phayao students who are experienced in buying secondhand fashion items. Data analysis included descriptive statistics and diagnostic approaches to profile students, their purchasing habits, perceptions, and key factors. Results indicated that all participants had prior secondhand shopping experience, using both physical stores and online platforms as key channels. Product quality received the highest average score of 4.20, followed by a positive attitude toward second-hand fashion at 4.05, frugality at 4.00, and brand reputation and environmental responsibility at 3.85, with sustainable fashion close behind at 3.83. These findings suggest that students’ choices are influenced more by quality, value, personal attitudes, and sustainability awareness than by social media influencers alone. The research provides valuable insights for promoting sustainable fashion, designing platforms, and developing future predictive analytics.
Authors - Pratya Nuankaew, Duangjai Pongsawan, Supan Tongphet, Thapanapong Sararat, Wongpanya S. Nuankaew Abstract - This research aimed to examine the use of student-pet interaction data to enhance understanding of university students' mental well-being. Descriptive and diagnostic data analyses were conducted. The sample comprised 40 students. Data collection was conducted using questionnaires to collect baseline information, characteristics of interaction with pets, and evaluations with the CCAS, PSS-10, and ST-5 instruments. The analysis revealed that the majority of students experienced a high level of attachment and comfort with their pets, with an average CCAS score of 3.57. The average PSS-10 score was 20.48, indicating moderate stress levels, and the mean ST-5 score was 7.43. Diagnostic analysis suggested that the duration of contact with pets, pet type, living conditions, and pet ownership status were potentially associated with students' stress levels. These findings may serve as an initial guideline for developing monitoring and support programs to promote the mental well-being of university students.
Authors - Wannakorn Phornprasert, Papimon Novichai, Thapanapong Sararat, Wongpanya S. Nuankaew, Pratya Nuankaew Abstract - This investigation aimed to analyze the VARK learning style and ergonomic data to identify the risk of office syndrome among university students. A quantitative, cross-sectional approach was employed, utilizing questionnaire data from 40 students. The analysis used descriptive statistics to summarize general characteristics, learning styles, and risk levels, and diagnostic analyses to identify factors associated with office syndrome risk. The most prevalent learning styles identified were Read/Write (30.0%) and Kinesthetic (25.0%). Ergonomic assessments revealed that 42.5% of students were at high risk, while 35.0% were at moderate risk. Factors correlated with risk included excessive phone usage (exceeding 4 hours per day), inappropriate chair height, unsuitable armrests, incorrect screen positioning, and improper keyboard posture. These findings indicate that combining learning preferences with ergonomic data can serve as an initial screening tool for risk assessment and facilitate the development of learning environments tailored to students in the digital era.
Authors - Avni Tyagi, Suman Madan Abstract - Machine Learning Operations (MLOps) has become a paradigm necessary to simplify machine learning systems development, implementation, and operation. Although MLOps focuses on automation, scalability, and fast deployment based on CI/CD practices, issues of security are usually under-explored, making ML pipelines very vulnerable.To examine the main security risks of contemporary ML pipelines, the paper explores the intersections between adversarial machine learning and MLOps and DevSecOps. It determines key attack vectors, such as data poisoning, model tampering, and infrastructure-level exploits, which may impair data integrity, model reliability, and system trustworthiness, through a review of recent literature (2020-2026).It also analyzes mitigation measures like adversarial robustness testing, cryptographic model signing, and continuous monitoring models and looks at new frameworks like SecMLOps and MLSecOps that help to put security in the ML lifecycle.It points out trade-offs between improved security, system performance, and complexity, and the importance of balanced architectures. Results show that adversarial testing and verifying the model with secure artifacts can decrease model failure rates by 3060 percent, and that continuous monitoring can improve the latency of anomaly detection by almost 40 percent.The paper ends with description of future research directions such as standardized benchmarks, enhanced robustness testing, and hardware-aided security of robust AI systems.
Authors - Sachin Ramling Jadhav, Rajveer Nandkar, Srushti Rajput, Rajvardhan Desai, Gunjan Ramteke,Samruddhi Rajput Abstract - Dealing with city problems like cracked roads, trash piles, leaks in pipes, or dark lamp posts keeps urban teams busy. When fixes depend on old paper methods, pieces of info get lost, trust dips, responses drag. A new setup steps in - CCIRS - running through a basic website made with PHP tools. Instead of guessing what comes first, supervisors follow a clear score called PI, shaped by how bad things look, where many reports cluster, plus how long issues wait. Behind the scenes, staff watch live updates, study trends, trace progress using their control view online. Half a year of testing in three city areas of Pune cut response times by 59.0%. Because of this change, meeting service targets got better by nearly half. Old ways of handling issues were clearly outperformed. Math behind sorting locations was built and tested. Ranking urgency used formulas that matched real outcomes well.
Authors - Mrityunjaya Chavannavar, Melita Simoes , Nikhil Shetty , Chirivella Vishal Abstract - Over the years, there is a rapid growth of social media-based financial content. Finfluencers have been emerging as influential sources that provide investment information to young retail investors. This research is inclined towards understanding the influence of finfluencers on numerous behavioural biases that include herd mentality, overconfidence, and FOMO. This study also examines their influence on decision-making when it comes to investments and the overall risk perception in the current digitally enabled investment landscape. There is interplay between social media platforms, financial influencers, and behavioural biases and can be observed among young retail investors in India. Most traditional theories in finance assume that a majority of investors behave rationally while behavioural finance acknowledges the impact of cognitive and emotional biases influence investment decisions. This quantitative study makes use of a descriptive-analytical approach. The primary data used here was gathered with the help of structured online questionnaires distributed to 120 young retail investors. Data analysis was carried out with the help of IBM SPSS Statistics. Tests such as correlation analysis, multiple regression models, and ANOVA with post-hoc Tukey HSD were undertaken. Findings showed that general social media usage frequency had no significant relationship with the four behavioural biases examined. Perceived credibility of finfluencer content demonstrated significant negative relationships with all four biases (overconfidence: β = -0.387, p = 0.001; herding: β = -0.252, p = 0.044; confirmation: β = -0.321, p = 0.006; availability: β = -0.354, p = 0.003). This indicates that high-quality financial influencers may serve a corrective rather than amplifying function. Indiscriminate following of numerous finfluencers positively predicted confirmation bias (β = 0.191, p = 0.025). Investors with over five years of experience revealed significantly lower biases. This study can be used for better investor protection, and financial literacy initiatives and can be embedded in various regulatory frameworks.
Authors - April L. Macasieb-Gumnad, Roberto M. Arguelles Abstract - The study focuses on transformational leadership, entrepreneurship, and sustainability in higher education. Using Saint Louis University (Philippines) as a case study, the purpose was to (1) identify the role transformational leadership has in developing (or affecting) the characteristics of an entrepreneurial university, (2) identify how transformational leadership fosters sustainable innovation, and (3) assess the effect entrepreneurial university characteristics have on achieving sustainable outcomes. This quantitative research used three different instruments that were previously validated (HEInnovate Questionnaire; Sustainability Assessment Questionnaire; and Survey of Transformational Leadership) to gather data from a sample of 795 respondents at SLU and analyzed the resulting data using Spearman-rank correlation analysis and simple linear regression. This study provided practical applications to the literature on higher education management through empirical evidence of relationships between types of leadership styles, achievement of SDGs, organizational structures/models/characteristics, and sustainability of innovation in higher educations.The SLU CARES Innovation Framework was proposed to provide actionable insights for academic and administrative leaders seeking to align Catholic educational missions with contemporary demands for innovation and sustainability.
Authors - Adin Nasywa Alifah, Puspita Kencana Sari Abstract - Metaverse gaming platforms like Minecraft and Roblox have evolved into important social spaces for Gen Z globally, including in Indonesia. These platforms are also emerging as high-risk environments for cybersecurity threats with implications for user security behavior and privacy protection. This study applies Protection Motivation Theory (PMT) to examine how self-efficacy and attitudes toward sharing personal information online predict phishing susceptibility among Indonesian Gen Z users. Using PLS-SEM on data from 200 users aged 18–28, results show that self-efficacy reduces phishing susceptibility both directly and indirectly through information-sharing attitudes, indicating partial mediation. These findings provide behavioral intelligence to support cybersecurity strategy, risk governance, and user privacy in emerging metaverse gaming ecosystems, particularly among Gen Z users.
Authors - Darrel A. Cardana, Ethel Zean M. Anosa, Angeline B. Elegio, Jes Maries Mendez, Ivy Corazon Mangaya-ay Abstract - Agri-Aqua Technology Business Incubators (ATBIs) play an important role in promoting innovation, entrepreneurship, technology commercialization, and institutional collaboration within higher education institutions. This study assessed the operational performance and institutional development of the BISU Agri-Aqua Technology Business Incubator (ATBI). Specifically, the study evaluated the accomplishments of the incubator in terms of personnel capacitation, partnership and linkage development, awareness and promotional activities, incubation services, technology incubation initiatives, intellectual property generation, and policy institutionalization. The study also examined the capacitybuilding activities, partnership initiatives, intellectual property outputs, and the problems and strategic solutions encountered during implementation. The study employed a descriptive-evaluative research design utilizing documentary analysis of the official accomplishment report and supporting institutional documents of the BISU ATBI. Frequency counts, percentage analysis, and thematic analysis were utilized in analyzing the collected data. The findings revealed that the BISU ATBI successfully implemented several operational and institutional initiatives. The incubator conducted seventeen (17) trainings and workshops, forged twelve (12) MOUs with incubatees and six (6) institutional partnerships, conducted eight (8) awareness seminars, developed ten (10) business plans, filed ten (10) trademarks and five (5) copyrights, and enrolled seventeen (17) incubatees in the incubation program. However, only two (2) technologies were successfully co-incubated despite the target of ten technologies, indicating challenges in technology commercialization and adoption. The study also identified regulatory hurdles, technology readiness concerns, partnership issues, and low technology adoption as major implementation challenges. Overall, the findings indicate that the BISU ATBI established a strong operational and institutional foundation for technology business incubation, although continuous enhancement of commercialization and technology adoption initiatives remains necessary.
Authors - Nguyen Quoc Cuong, Nguyen Ha, Mai Thi Bich Ngọc Abstract - The proliferation of short-form video platforms has reshaped consumer decision-making, yet how electronic word of mouth (eWOM) attributes influence Generation Z fashion shopping intention in emerging markets remains underexplored. Grounded in the Information Adoption Model (IAM) and Attitude–Intention framework, this study examines the impact of TikTok-based eWOM on fashion shopping intention among Vietnamese Generation Z consumers. Using PLS-SEM analysis of 263 valid survey responses, results reveal that eWOM Information Quality and Credibility significantly predict Attitude toward eWOM, while Information Usefulness is the strongest predictor of eWOM Adoption; Information Quantity exerts a positive but weaker effect. Both Attitude and Adoption significantly influence Fashion Shopping Intention, with Attitude as the dominant predictor, and mediation analysis confirms their roles as key intervening mechanisms. These findings extend the IAM to short-form video and social commerce contexts, demonstrating that Generation Z engages in evaluative, quality-oriented content processing rather than responding passively to volume. Practically, results offer actionable guidance for fashion marketers to prioritize authentic, credible, and informative eWOM strategies on TikTok.
Authors - Rakhmonova Nargiza Rashidovna, Rajapov Shukhrat Zaripbaevich Abstract - The growing volume of international trade is increasing pressure on road border customs posts, making their operational efficiency a key factor in facilitating foreign trade. Chronic congestion, long vehicle queues, and procedural delays at land border crossings hinder logistics efficiency and increase trade costs. Digitalization is increasingly viewed as a strategic solution for modernizing customs administration while ensuring effective control and economic security. This study examines ways to further improve the efficiency of road border customs posts through digitalization, using the case of Uzbekistan. The analysis is based on data from 322 road border customs posts and employs economic and statistical methods, including regression analysis and structural equation modeling (SEM). The model assesses the impact of human resources, infrastructure capacity, and digital inspection technologies—specifically, the number of employees, traffic lanes, inspection and verification complexes (ISC and Z-portal), passenger flows, and reported violations—on daily vehicle traffic volumes. The results consistently show that human resources are the most significant factor in customs post efficiency. An increase in the number of employees has a strong and statistically significant positive effect on daily vehicle flow across all parameters of the model. In contrast, the expansion of physical infrastructure, measured by the number of traffic lanes, shows a negative or weakly significant relationship, indicating that infrastructure alone does not guarantee increased throughput. Digital control systems show a positive but statistically insignificant effect, suggesting incomplete integration into operational processes. The results indicate that to achieve significant efficiency gains, digitalization must be combined with effective human resource management and organizational optimization. Policy measures should prioritize capacity building, intelligent traffic management, and deeper integration of digital systems to reduce congestion, speed up logistics, and improve conditions for foreign trade.
