Digital, Volume 6, Issue 1 (March 2026) – 23 articles

Text-to-video (T2V) generation has recently emerged as a transformative technology within the field of generative AI, enabling the creation of realistic, temporally coherent videos based on natural language descriptions. This paradigm provides significant added value in many domains such as creative media, human-computer interaction, immersive learning, and simulation. Despite its growing importance, systematic discussion of T2V is still limited compared with adjacent modalities such as text-to-image and image-to-video. To alleviate the scarcity of discussions in the T2V field, this paper provides a systematic review of works published from 2024 onward, consolidating fragmented contributions across the field. We survey and categorize the selected literature into three principal areas—namely, T2V methods, datasets, and evaluation practices—and further subdivide each area into subcategories that reflect recurring themes and methodological patterns in the literature. Emphasis is then placed on identifying key research opportunities and open challenges that need further investigation. Full article

The integration of blockchain technology into Cyber–Physical Systems (CPS) offers decentralized resilience against data manipulation. This also introduces stochastic consensus latencies that threaten real-time control stability. We present a Stochastic-Aware Blockchain Predictive Control (SAB-PC) framework, which models blockchain-induced jitter as a state-dependent Markovian process, and embeds it within a Markovian Jump Linear System (MJLS) formulation. Using mode-dependent Linear Matrix Inequalities (LMIs), we derive Mean Square Stability (MSS) conditions, which capture the interaction between decentralized consensus dynamics and closed-loop control behavior. The framework is validated on the Tennessee Eastman Process (TEP) benchmark, using a calibrated stochastic delay model that reflects realistic blockchain congestion patterns. Our results show that standard blockchain-mediated control architectures become unstable under Practical Byzantine Fault Tolerance (PBFT)-induced quadratic latency growth, whereas SAB-PC maintains stable operation across decentralized networks up to 60 validator nodes. The predictive Safety Runway effectively masks long-tail delay distributions, ensuring real-time feasibility and preserving safe Reactor Pressure trajectories. Under coordinated False Data Injection (FDI) attacks, SAB-PC limits pressure deviations to only 1.2 kPa despite an 8.0 kPa adversarial bias, demonstrating cryptographic and control-theoretic resilience. Full article

This study presents a systematic bibliometric evaluation of artificial intelligence methodologies applied to the preservation and interpretation of Asia Minor’s cultural heritage. Publication trends demonstrate notable continuity, with foundational works sustaining their citation impact over a span of twenty-five years, thereby underscoring enduring scholarly engagement. Network analyses of keyword co-occurrence delineate a conceptual core organized around immersive visualization, exemplified by terms such as cultural heritages, virtual reality, and photogrammetry, while temporal mappings reveal the recent integration of machine learning and deep learning paradigms. Collectively, these findings chart an intellectual landscape in which three-dimensional reconstruction constitutes the foundational axis of research, now progressively enriched by data-driven algorithmic approaches. This synthesis offers a concise yet comprehensive portrait of evolving methodological trajectories and emerging computational frontiers in AI-driven heritage scholarship. Full article

Over the last few years, deep neural networks have achieved outstanding results in computer vision, and have been widely integrated into mobile robot obstacle avoidance systems, where perception-driven classification supports navigation decisions. Most existing approaches rely on either color images (RGB) or depth images (D) as the primary source of information, which limits their ability to jointly exploit appearance and geometric cues. This paper proposes a deep learning-based classification approach that simultaneously exploits RGB and depth information for mobile robot obstacle avoidance. The method adopts an early-stage fusion strategy in which RGB images are first converted into the CMYK color space, after which the K (black) channel is replaced by a normalized depth map to form a four-channel CMYD representation. This representation preserves chromatic information while embedding geometric structure in an intensity-consistent channel and is used as input to a convolutional neural network (CNN). The proposed method is evaluated using locally acquired data under different training options and hyperparameter settings. Experimental results show that, when using the baseline CNN architecture, the proposed fusion strategy achieves an overall classification accuracy of 93.3%, outperforming depth-only inputs (86.5%) and RGB-only images (92.9%). When the refined CNN architecture is employed, classification accuracy is further improved across all tested input representations, reaching approximately 93.9% for RGB images, 91.0% for depth-only inputs, 94.6% for the CMYK color space, and 96.2% for the proposed CMYD fusion. These results demonstrate that combining appearance and depth information through CMYD fusion is beneficial regardless of the network variant, while the refined CNN architecture further enhances the effectiveness of the fused representation for robust obstacle avoidance. Full article

This study examines how a voice-recognition project may support vocational secondary students’ AI literacy. In this applied scenario, students used Arduino hardware and an AI tools platform to collect data, train models, and deploy a basic voice-recognition device, linking introductory AI concepts with practical engineering applications. A mixed-methods design combined pre–post self-report assessment using the AI Literacy Questionnaire (AILQ) with post semi-structured interviews. Emerging gains were associated with the maker-learning pathway, particularly in the affective, behavioral, and cognitive AI literacy domains, whereas ethical outcomes were limited within this intervention window. Qualitative insights provided complementary interpretive context, suggesting that learning through making was experienced as more engaging and personally relevant, while hands-on linked with emerging understanding of AI model behavior and limitations. Overall, the study extends AI-literacy research to a vocational classroom setting, where evidence remains limited. It also highlights a domain-level AI literacy analysis for identifying which components strengthen through making and which may require more explicit instructional scaffolding in this specific vocational context. The exploratory nature of the study offers evidence that maker activities can provide a feasible approach for engaging vocational learners with multidimensional AI literacy. Full article

Traditional methods to learn soft-tissue surgical procedures rely on cadaver labs or patient-based learning, which are costly and geographically limited, and raise ethical questions. Virtual reality (VR) with haptic feedback offers a scalable alternative, but most current platforms emphasize bone-based rather than soft-tissue procedures learned by feel. We developed a VR+haptic simulation for preoperative training of retropubic midurethral sling (MUS) surgery. This study examines the usability of this platform with thirteen expert urogynecologic surgeons and subsequently makes improvements (e.g., in haptics) to evaluate the platform with twelve trainees based on the NASA Task Load Index for workload and a UTAUT-informed usability survey. Objective performance scores were recorded as participants completed up to four levels of increasing realism and difficulty, starting with a transparent body and a reference surgical trajectory. Trainees reported high usability, immersion, and engagement. Experts rated the platform as valuable for sling training and skill assessment. NASA-TLX results indicated low physical and temporal demand, low mental demand and frustration, and moderate effort. These findings suggest that SurgicalEd VR is acceptable and has appropriate workload characteristics for surgical education. Future studies could examine how using VR+ haptic training improves intraoperative performance. Full article

The proliferation of digital health education content (DHEC) offers a transformative opportunity for medical training worldwide. While students in high-income countries routinely integrate these tools, their use and impact in low-resource settings such as Mozambique remain poorly understood. Exploring this topic offers interesting possibilities at the intersection of global health equity, digital literacy, and pedagogical innovation. This study assessed how Mozambican medical students engage with online health videos, examining the types of content they search for, preferred platforms, perceived benefits, and attitudes toward integrating these materials into medical training. A quantitative cross-sectional survey was administered to 151 second-year medical students at the Catholic University of Mozambique and Alberto Chipande University. A structured online questionnaire, comprising multiple-choice, Likert-scale, and open-ended questions, was used. Data were analyzed using descriptive statistics, cross-tabulation, chi-square test, and Cramer’s V effect size. All students (100%) reported searching for online health videos. They primarily do so via YouTube (92.1%) and use mobile phones (98.7%). Students mainly searched topics related to basic biomedical sciences (60%). They reported that video enhances their learning (86.8%), academic work (11.3%), and other skills (1.9%). Mean scores for utility (4.06), self-reported knowledge gain (4.05), and interest in continuing use (4.30) reflected positive perceptions. Furthermore, an overwhelming majority (91.4%) supported the institutional production of educational videos, whereas 8.6% disagreed, citing videos as a tool that diverts students’ focus from reading and a preference for traditional classes. No statistically significant gender-based differences were observed in usefulness, learning levels, or core interest in continuing to search for online videos (p > 0.05). Online health videos are widely used and positively perceived by Mozambican medical students as a supplementary learning tool. The findings highlight the need for institutions to create curriculum-aligned video libraries and strengthen students’ digital literacy, an affordable strategy for enhancing medical education in low-resource contexts. Full article

Large Language Models (LLMs) have successfully performed calculation-based tasks, generated diverse data visualizations, and executed chemometric analyses. This study systematically evaluated the performance of Microsoft M365 Copilot (GPT-5) across 35 representative questions spanning five domains: (1) chemical equilibrium, pH, titration, and buffer calculations; (2) data visualization, including histograms, box plots, correlation plots, and heatmaps; (3) analysis of periodic table properties using principal component analysis (PCA); (4) image interpretation and generation in classroom contexts; and (5) machine learning applications using Partial Least Squares Discriminant Analysis (PLS-DA). All questions were assessed without the use of additional prompting. Across two independent user accounts, identical question sets were administered twice per month between October and December 2025. Copilot consistently produced accurate, step-by-step solutions for equilibrium and acid–base problems, generated high-quality visualizations directly from uploaded datasets, and correctly constructed PCA score and loading plots with appropriate data standardization. Collectively, these findings demonstrate that Copilot offers substantial value for both research-oriented tasks and chemistry education. Full article

Early identification of reading difficulties enables timely classroom intervention; however, teachers often have limited time and restricted access to specialist assessment. This study explores a brief, teacher-friendly screening approach based on short oral reading recordings to support classroom decision-making. Oral reading samples were collected from 77 Greek primary school pupils (Grades 3–6) during a standardized reading task. Recordings were segmented into 7 s excerpts, converted into spectrogram images, and analyzed using a deep learning model to classify each excerpt as indicative of reading difficulties or not. To reflect realistic school implementation, model development followed an 80/20 participant-level split, with validation conducted on pupils not included in the training set. At the selected operating threshold, the model achieved approximately 84% overall accuracy and a balanced accuracy of 0.85. For practical applicability, a pupil-level indicator—representing the proportion of excerpts flagged as difficult—showed a strong association with expert judgments (r ≈ 0.74). These findings suggest that brief oral reading recordings can provide teachers with an interpretable screening signal to inform monitoring, prioritization, and early classroom support while underscoring the need for further validation under routine school conditions. Full article

Underground mines’ complex environments with dim lighting and high dust and humidity hamper feature extraction and reduce detection accuracy. To address this, we propose a low-light image enhancement-based target detection algorithm. Firstly, LIENet enhances low-light image quality and brightness via a dual-gamma curve and non-reference loss function-guided iterations. Secondly, the hierarchical feature extraction (HFE) method with a dual-branch structure captures long-term and local correlations, focusing on critical corner regions. Finally, HFE is combined with a feature pyramid structure for comprehensive feature representation through a top-down global adjustment. Our method, validated on a self-built dataset, outperforms other algorithms with an mAP@0.5 of 96.96% and mAP@0.5:0.95 of 71.1%, proving excellent low-light detection performance in mines. Full article

Compared with conventional financing approaches, supply chain financing demonstrates superior adaptability in risk management, greater cost-effectiveness in financial control, and enhanced efficiency in approval processes, owing to its deep integration with industrial chains. This investigation explores the intrinsic relationship between digital innovation and corporate supply chain financing. To ensure the rigor and reliability of the research conclusions, we adopt an empirical research method based on the OLS econometric regression model to systematically examine the relationship between digital innovation and supply chain financing. Our findings reveal that digital innovation positively influences corporate operations and information disclosure quality, thereby facilitating supply chain financing acquisition. Specifically, digital innovation enhances both Tobin’s Q and information transparency, which consequently improves firms’ access to supply chain financing. Furthermore, we observe pronounced heterogeneity in digital innovation’s impact on supply chain financing accessibility, with more pronounced effects observed in state-owned enterprises, mature firms, and regions with less developed legal frameworks. From the perspective of theoretical contributions, this study enriches the application scenario of signal transmission theory. We verify that operational improvement driven by digital innovation can serve as an effective signal to alleviate information asymmetry in supply chain financing. Meanwhile, we supplement the research on information asymmetry theory by providing a digital solution to mitigate information frictions between supply chain partners. In terms of practical contributions, we provide actionable insights for firms. Specifically, our findings guide firms to leverage digital innovation to improve supply chain financing accessibility. Additionally, these findings offer references for supply chain stakeholders and relevant authorities to optimize financing support mechanisms. Full article

The article presents a unified fractal approach to processing and analyzing ship trajectories based on AIS and ECDIS data. A comprehensive algorithmic pipeline is proposed, which provides time normalization, coordinate transformation, calculation of dynamic motion characteristics, and application of fractal analysis in sliding windows. This approach allows for the stable calculation of key parameters (course, angular velocity, deviation from the route) and detection of local changes in movement complexity that are not recorded by classical methods. The fractal indicators used (Higuchi, Katz, Petrosyan, DFA dimensions) demonstrate high reproducibility and resistance to typical navigation data shortcomings. The proposed framework is primarily intended for onboard and post-voyage analysis, supporting navigational performance assessment, trajectory reconstruction, and detailed investigation of vessel motion dynamics based on the records from AIS and ECDIS. Full article

This paper proposes an efficient and automated smart healthcare communication framework that integrates a two-level filtering scheme with a multi-objective Genetic Algorithm (GA) to enhance the reliability, timeliness, and energy efficiency of Internet of Medical Things (IoMT) systems. In the first stage, physiological signals collected from heterogeneous sensors (e.g., blood pressure, glucose level, ECG, patient movement, and ambient temperature) were pre-processed using an adaptive least-mean-square (LMS) filter to suppress noise and motion artifacts, thereby improving signal quality prior to analysis. In the second stage, a GA-based optimization engine selects optimal routing paths and transmission parameters by jointly considering end-to-end delay, Signal-to-Noise Ratio (SNR), energy consumption, and packet loss ratio (PLR). The two-level filtering strategy, i.e., LMS, ensures that only denoised and high-priority records are forwarded for more processing, enabling timely delivery for supporting the downstream clinical network by optimizing the communication. The proposed mechanism is evaluated via extensive simulations involving 30–100 devices and multiple generations and is benchmarked against two existing smart healthcare schemes. The results demonstrate that the integrated GA and filtering approach significantly reduces end-to-end delay by 10%, as well as communication latency and energy consumption, while improving the packet delivery ratio by approximately 15%, as well as throughput, SNR, and overall Quality of Service (QoS) by up to 98%. These findings indicate that the proposed framework provides a scalable and intelligent communication backbone for early disease detection, continuous monitoring, and timely intervention in smart healthcare environments. Full article

This paper investigates the use of large language models (LLMs) as evaluators in multidimensional machine translation (MT) assessment, focusing on the English–Indonesian language pair. Building on established evaluation frameworks, we adopt an MQM-aligned rubric that assesses translation quality along morphosyntactic, semantic, and pragmatic dimensions. Three LLM-based translation systems (Qwen 3 (0.6B), LLaMA 3.2 (3B), and Gemma 3 (1B)) are evaluated using both expert human judgments and an LLM-based evaluator (GPT–5), allowing for a detailed comparison of alignment, bias, and consistency between human and AI-based assessments. In addition, a classroom calibration study is conducted to examine how rubric-guided evaluation supports alignment among novice evaluators. The results indicate that GPT–5 exhibits strong agreement with human evaluators in terms of relative quality ranking, while systematic differences in absolute scoring highlight calibration challenges. Overall, this study provides insights into the role of LLMs as reference-free evaluators for MT and illustrates how multidimensional rubrics can support both research-oriented evaluation and pedagogical applications in a mid-resource language setting. Full article

This article presents a comprehensive bibliometric analysis of the indexed academic literature on the application of distributed ledger technology (DLT) and blockchain in the tourism industry. Using the bibliometrix library within the RStudio environment, key bibliometric indicators were examined in order to characterize the evolution, structure, and thematic focus of this emerging field of research. The systematic literature review, which adhered to PRISMA guidelines, involved retrieving publications from the Web of Science and Scopus databases. A curated dataset of 100 relevant documents was identified and analyzed in terms of annual scientific production, leading journals, influential authors, and highly cited publications. The results indicate that blockchain technology dominates the literature, with a strong emphasis on its potential to enhance trust, transparency, and efficiency in tourism-related processes. In particular, identity management, secure transactions, and disintermediation emerge as central research themes, reflecting blockchain’s capacity to support decentralized, immutable, and privacy-preserving interactions between tourists and service providers. Overall, the findings reveal a rapidly growing and increasingly structured body of knowledge, highlighting emerging research directions and technological challenges for future studies on DLT applications in tourism. Full article

This study reviews the current applications of generative artificial intelligence (GenAI) in architectural design education using the PRISMA framework. It compares these applications across the different design stages, namely the pre-design, concept generation, design development, and design production, to identify the current state of evidence and conceptual discussions reported in the literature. The study also discusses the associated opportunities and challenges in this regard. The findings indicate that there is a growing interest in integrating GenAI into architectural design education, especially in the early design stages. However, one of the most significant gaps in this regard lies in the lack of empirical evidence on the long-term impacts of GenAI on students’ critical thinking and problem-solving skills. Future research is needed to explore the integration of GenAI throughout the entire design process, including design development and refinement. There is also a need to incorporate the relevant ethical guidelines for AI-generated content into academic quality assurance systems and to strengthen institutional preparedness through targeted training and policy development. Full article

Android malware continues to evolve through obfuscation and polymorphism, posing challenges for both signature-based defenses and machine learning models trained on limited and imbalanced datasets. Synthetic data has been proposed as a remedy for scarcity, yet the role of Large Language Models (LLMs) in generating effective malware data for detection tasks remains underexplored. In this study, we fine-tune GPT-4.1-mini to produce structured records for three malware families: BankBot, Locker/SLocker, and Airpush/StopSMS, using the KronoDroid dataset. After addressing generation inconsistencies with prompt engineering and post-processing, we evaluate multiple classifiers under three settings: training with real data only, real-plus-synthetic data, and synthetic data alone. Results show that real-only training achieves near-perfect detection, while augmentation with synthetic data preserves high performance with only minor degradations. In contrast, synthetic-only training produces mixed outcomes, with effectiveness varying across malware families and fine-tuning strategies. These findings suggest that LLM-generated tabular malware feature records can enhance scarce datasets without compromising detection accuracy, but remain insufficient as a standalone training source. Full article

A universal approach to managing a person’s digital identity may be the single most important advancement to the Internet since its inception, promising the seamless flow of information, averting cybercrime, eliminating login credentials, and restoring privacy and trust through greater control of one’s identity online. However, this advancement brings significant risks, especially regarding personal privacy. It demands the meticulous development of digital identity infrastructure that balances robust data security measures with ethical handling of sensitive information, thereby safeguarding against misuse and unauthorised access. Currently, a consolidated vision for digital identity implementation remains unresolved, and aligning the different stakeholders’ motives and expectations is a challenging task. This article reviews and analyses the perspectives and expectations of four key stakeholder groups—government, business, academia, and consumers—regarding a digital identity ecosystem, aiming to increase trust in an eventual design framework. Using an online survey stratified across government, business, academia, and consumers, we identify areas of alignment and divergence regarding privacy, trust, usability, and governance expectations. We then encode these stakeholder expectations into a layered conceptual structure and illustrate its use as metadata for context-layered retrieval-augmented generation (RAG) in digital identity scenarios. Full article

Decentralized Autonomous Organizations (DAOs) suffer from critical governance challenges, such as low voter participation, large token holders’ dominance, and inefficient proposal analysis by manual processes. We propose APOLLO (Autonomous Predictive On-Chain Learning Orchestrator), an AI-powered approach that automates the governance lifecycle in order to address these problems. The gemma-3-4b Large Language Model (LLM) in conjunction with Retrieval-Augmented Generation (RAG) powers APOLLO’s multi-agent system, which enhances contextual comprehension of proposals. The system enhances governance by merging real-time on-chain and off-chain data, ensuring adaptive decision-making. Automated proposal writing, logistic regression-based approval probability prediction, and real-time vote outcome analysis with contextual feature-based confidence scores are some of the major advancements. LLM is used to draft proposals and a feedback loop to enrich its knowledge base, reducing whale dominance and voter apathy with a transparent, bias-resistant system. This work demonstrates the revolutionary potential of AI in promoting decentralized governance, paving the way for more effective, inclusive, and dynamic DAO systems. Full article

Cross-modal and immersive technologies offer new opportunities for experiential learning in early childhood, yet few studies examine integrated systems that combine multimedia, mini-games, 3D exploration, virtual reality (VR), and augmented reality (AR) within a unified environment. This article presents the design and implementation of the Solar System Experience (SSE), a cross-modal extended reality (XR) learning suite developed for preschool education and deployable on low-cost hardware. A dual-perspective evaluation captured both preschool teachers’ adoption intentions and preschool learners’ experiential responses. Fifty-four teachers completed an adapted Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) questionnaire, while seventy-two students participated in structured sessions with all SSE components and responded to a 32-item experiential questionnaire. Results show that teachers held positive perceptions of cross-modal XR learning, with Subjective Norm emerging as the strongest predictor of Behavioral Intention. Students reported uniformly high engagement, with AR and the interactive eBook receiving the highest ratings and VR perceived as highly engaging yet accompanied by usability challenges. The findings demonstrate how cross-modal design can support experiential learning in preschool contexts and highlight technological, organizational, and pedagogical factors influencing educator adoption and children’s in situ experience. Implications for designing accessible XR systems for early childhood and directions for future research are discussed. Full article

The rapid diffusion of artificial intelligence (AI) in marketing has reshaped how consumers interact with digital content and evaluate ethical aspects of firms. The present study examines how familiarity with and trust in AI shape consumers’ acceptance of AI-based advertising and, in turn, their ethical purchasing behavior. Data were collected from 505 Greek consumers through an online survey and analyzed using hierarchical and logistic regression models. Reliability and validity tests confirmed the robustness of the measurement instruments. The results show that familiarity with AI technologies significantly enhances trust and ethical confidence toward AI systems. In turn, trust in AI strongly predicts the consumers’ acceptance of AI-driven advertising, while acceptance positively affects ethical consumption intentions. The findings also confirm a mediating relationship, indicating that acceptance of AI-based advertising transmits the effect of AI rust to ethical consumption. By integrating ethical and technological dimensions within a single behavioral model, the study provides a more comprehensive view of how consumers form attitudes toward AI-enabled marketing. Overall, the findings highlight that transparent and responsible AI practices can strengthen brand credibility, foster ethical engagement, and support more sustainable consumer choices. Full article